MOF-Composites for adsorption and degradation of contaminants in wastewater

Javier Aguila-Rosas ab, Francisco J. Cano a, Alan Nagaya a, Carlos T. Quirino-Barreda b, Ma. de Jesús Martínez Ortiz c, Ariel Guzmán Vargas *c, Ilich A. Ibarra *a and Enrique Lima *a
aLaboratorio de Fisicoquímica y Reactividad de Superficies (LaFReS), Instituto de Investigaciones en Materiales, Universidad Nacional Autónoma de México, Circuito Exterior s/n, CU, Del. Coyoacán, 04510, Ciudad de México, Mexico. E-mail: lima@iim.unam.mx; Fax: +52-55-5622-4595
bLaboratorio de Farmacia Molecular y liberación controlada, Universidad Autónoma Metropolitana-Xochimilco, Calzada del Hueso 1100, Col. Villa Quietud, C.P. 04960, CDMX, Mexico
cESIQIE-IPN, Departamento de Ingeniería Química, Laboratorio de Investigacion en Materiales Porosos, Catalisis Ambiental y Quimica Fina, México, DF 07738, UPALM Edif.7 P.B. Zacatenco, Mexico

Received 19th May 2025 , Accepted 3rd July 2025

First published on 11th July 2025


Abstract

MOF-composites are porous materials with a large surface area and functionalized with other types of materials (metallic nanoparticles, oxides, zeolites, quantum dots, etc.), affording optimal and chemically stable structures for wastewater remediation. They are primarily used for the selective adsorption of contaminants such as heavy metals, dyes, pharmaceuticals, and pesticides. Furthermore, they can act as photocatalysts for the degradation of organic compounds under ultraviolet and visible light, as well as serve as supports in advanced oxidation processes. Their adjustable structure allows them to be designed according to the type of contaminant, and they are currently reusable, which favours their sustainability. Due to their efficiency and versatility, they represent an innovative and exciting alternative for the treatment of contaminated water.


1. Introduction

Growing concerns about water pollution have driven the development of innovative technologies for wastewater remediation.1,2 Among these technologies, metal–organic frameworks (MOFs) have emerged as promising materials due to their high surface area, tuneable porosity, and versatile functionalization.3 A MOF composite is formed by integrating functional materials such as polymers, nanoparticles, or biomolecules into its metal–organic structure to improve its stability, selectivity, and adsorbent or catalytic capacity.4 These materials offer effective solutions for the adsorption and degradation of various contaminants in wastewater, including pharmaceuticals, dyes, and heavy metals. Furthermore, their application in biosensors for contaminant detection and their potential for industrial production open new perspectives in water treatment.

MOF composites have established themselves as an innovative and versatile solution for wastewater treatment, standing out for their ability to adsorb and degrade a wide range of persistent and toxic pollutants.5 In the case of pharmaceuticals, such as antibiotics, analgesics, and hormones, these materials offer efficient adsorption thanks to their porous structure and high affinity for organic compounds. Similarly, functionalized MOFs have demonstrated remarkable capture efficacy against contamination by synthetic dyes such as methylene blue and methyl orange.6,7 Furthermore, their architecture allows them to trap highly hazardous heavy metals, such as Pb2+, Cd2+, Hg2+, and As3+, with high capacity and selectivity.8,9 Beyond adsorption, these materials also participate in advanced oxidation processes, generating radicals such as sulfate (SO4˙) and hydroxyl (˙OH), which break down complex organic pollutants into less harmful byproducts.10 In parallel, they have been used in the development of biosensors for the selective and ultrasensitive detection of pollutants, taking advantage of their fluorescent and conductive properties to identify metal ions such as Cd2+ and Hg2+,11 which reinforces their value as a comprehensive tool for environmental monitoring and remediation.

In this paper, we present a review focused on the most recent advances in the use of compounds metal–organic frameworks (MOF-composites) for the adsorption and degradation of contaminants in wastewater. This emerging technology has demonstrated great potential for the efficient removal of various contaminants, which we will address, as well as for advanced chemical processes for contaminant detection using biosensors. Our objective is to provide an updated and critical overview of their applicability, efficacy, and potential for industrial scaling, highlighting their relevance for more sustainable and effective water treatment.

2. Main contaminants in wastewater

Wastewater, generated by domestic, industrial, and agricultural activities, contains a complex mix of pollutants that pose significant risks to human health, aquatic ecosystems, and the environment in general12,13 (Fig. 1). Among the main pollutants are pharmaceuticals, considered emerging due to their persistence and adverse effects, such as endocrine disruption in aquatic organisms and the generation of bacterial resistance; antibiotics, analgesics, hormones, and antidepressants have been detected in water bodies even at very low concentrations.14 Synthetic dyes, used in the textile, leather, and food industries, are also persistent and toxic, and their resistance to biodegradation makes conventional treatment difficult.15 Heavy metals such as lead, mercury, cadmium, and arsenic, from mining and the chemical industry, are highly toxic even in small quantities, accumulating in living beings and entering the food chain, requiring specific processes such as adsorption or bioremediation for their removal.16 Likewise, the excessive presence of nutrients such as nitrogen and phosphorus, derived primarily from agricultural activity, causes eutrophication, generating dead zones due to the loss of dissolved oxygen.17 Furthermore, industrial wastewater also includes a wide variety of toxic chemicals such as solvents, pesticides, and organochlorine compounds from the petrochemical, pharmaceutical, and agrochemical sectors, which are difficult to treat and have long-lasting effects.18 Finally, industries such as nuclear and mining can discharge radioactive substances into aquatic environments, posing serious threats to both the environment and public health by contaminating drinking water sources and affecting nearby communities.19 All of this underscores the need to update and strengthen wastewater treatment and remediation strategies through more comprehensive approaches and advanced technologies.
image file: d5cc02843d-f1.tif
Fig. 1 Main pollutants and materials used for water remediation.

The use of micro and nanotechnology in adsorbent materials such as graphene and zeolites has improved the capture of specific contaminants, including pharmaceuticals and heavy metals. However, a technology called MOF is also being incorporated for real-time monitoring and intelligent control systems that optimize treatment based on water conditions. This technology, combined with the combination of other materials, not only improves the quality of treated water but also contributes to sustainable development, allowing its reuse in irrigation and industry in some cases.

3. Characteristics of MOF-composites: advantages and opportunities

MOF composites represent a versatile and powerful platform for a wide range of scientific and industrial applications. Their capacity for custom structural design, high porosity, and functionalization make them unique materials with clear advantages over conventional technologies. As technical barriers are overcome, these materials are expected to play a key role in sustainable development, energy transition, and environmental protection.5

Materials of MOFs combined with other compounds, whether organic, inorganic, or biological, to create materials with improved or completely new properties. They leverage the porosity, chemical functionality, and ordered structure of MOFs, along with the complementary properties of the second component, such as conductivity, thermal stability, or catalytic activity.20,21

MOF composites are typically synthesized through diverse methodologies such as solvothermal, microwave-assisted, sonochemical, and mechanochemical routes,11,22,23 the latter offering the additional benefit of solvent-free fabrication.24 These techniques afford precise control over morphology, crystallinity, and pore architecture. Furthermore, post-synthetic modification (PSM)—including ligand exchange, metal node substitution, or surface functionalization—enables fine-tuning of the frameworks’ physicochemical attributes, improving hydrolytic stability, affinity, and selectivity for specific targets.25 Collectively, the integration of controlled synthesis and PSM facilitates the rational design of advanced MOF-composites tailored for adsorption and degradation of pollutants in complex aqueous environments.26

A major limitation of pristine MOFs in water treatment is their vulnerability to hydrolytic degradation, which often results in the collapse of the framework and loss of active sites.27 To mitigate this issue, various strategies have been explored, including the design of intrinsically stable frameworks, the formation of composites with hydrophobic materials (e.g., polymers or carbon derivatives), and surface hydrophobization via PSM.28 These adaptations have proven critical for maintaining performance under continuous flow conditions and real-world wastewater matrices.

One of the most studied types is MOF–polymer composites, in which MOFs are incorporated into or coated with polymer matrices. This combination improves the processability of the materials, enabling the manufacture of flexible membranes, films, and coatings.29 Furthermore, polymers can provide greater mechanical strength and stability in humid or corrosive environments, expanding the use of MOFs in water contaminant separation systems and chemical sensors.30

Another important type is composites with metallic nanoparticles, where MOFs act as matrices that encapsulate or support metals such as gold, silver, platinum, or palladium.31 This allows for high dispersion of the nanoparticles and improves catalytic activity, as well as electron transfer. Thanks to this synergy, these materials are highly effective in heterogeneous catalysis and the electrochemical detection of contaminants present in water.32

Composites between MOFs and metal oxides, such as TiO2, ZnO, or Fe3O4, have also been developed. These oxides provide thermal stability and, in many cases, photocatalytic capacity, opening up opportunities for the degradation of contaminants immersed in water and the development of optical sensors.33

An area of accelerated growth is the combination of MOFs with carbon materials, including graphene, carbon nanotubes, or activated carbon. These composites significantly improve the electrical conductivity of MOFs and enable their use in energy storage devices, such as supercapacitors or rechargeable batteries.34 Furthermore, the high specific surface area of carbon complements the porosity of MOFs, creating systems with high adsorption capacity for residues present in wastewater.35

Another interesting type of composite includes MOFs integrated with semiconductors36 or photoactive materials such as CdS, ZnS,37 or perovskites,38 and with new materials such as quantum dots39 and Mxenes.40 These systems improve light absorption efficiency and the separation of charges generated by irradiation, enabling their use in solar photocatalysis and in energy conversion devices, such as solar cells and visible-light reactors for environmental purification.41

Finally, mechanochemical methods—such as ball milling—have emerged as a sustainable and scalable route for the synthesis of MOF-based composites. These solvent-free approaches not only reduce environmental impact but also facilitate the direct integration of secondary phases like metal oxides or porous supports, thereby simplifying composite fabrication at industrial scale.24

At a general level, functional composites with MOFs offer significant advantages. They allow combining the properties of MOFs with those of other materials, generating synergies that result in improvements in stability, selectivity, catalytic efficiency, and conductivity.20,21 Furthermore, these composites are more versatile in terms of processing methods, which is key for industrial and technological applications. This combination of advantages has led to the exploration of MOF-based composites in fields as diverse as the environment and wastewater treatment.

4. MOF-composites as adsorption materials

Among the diverse technologies developed for water purification—such as chemical oxidation, membrane filtration, solvent extraction, and photocatalysis—adsorption remains one of the most efficient and sustainable approaches. This is largely attributable to its operational simplicity, low energy requirements, and broad applicability across a wide range of pollutants.42,43 In this context, MOF-composites have emerged as a promising class of next-generation adsorbents, offering solutions to the inherent limitations of pristine MOFs while substantially extending their functionality in complex aqueous systems.

The development of MOF-composites involves the deliberate integration of MOF structures with complementary materials that impart enhanced physicochemical properties, including improved structural stability, expanded surface reactivity, and tailored active sites for adsorption.44,45 This strategic synergy confers superior hydrolytic stability, broadens the spectrum of adsorbable contaminants and activates multiple adsorption mechanisms. Consequently, MOF-composites demonstrate elevated performance in removing a variety of emerging contaminants, such as pharmaceuticals, dyes, heavy metals, and persistent organic pollutants.46,47

A wide range of functionalization strategies has been explored, each leveraging the intrinsic advantages of the incorporated phase (Fig. 2).48 Carbon-based materials (e.g., graphene oxide, carbon nanotubes) introduce high surface area, chemical robustness, and π-electron-rich domains, enhancing interactions such as π–π stacking and hydrogen bonding.49 Zeolites, with their crystalline microporous structure and ion-exchange capacity, improve selectivity and provide additional binding sites, especially for ionic species.50 Natural clays, such as montmorillonite and bentonite, offer layered morphologies and abundant functional groups that enhance dispersibility and interfacial interactions.51 Mesoporous silica materials serve as structurally rigid scaffolds, promoting hierarchical porosity and facilitating molecular diffusion.52 In particular, silica-based composites incorporating mesoporous matrices such as SBA-15 or MCM-41 impart mechanical stability and facilitate the formation of hierarchically porous MOF architectures. These supports improve diffusion pathways, accelerate adsorption kinetics, and reinforce framework integrity.53 Notably, systems such as UiO-66@SBA-15 have demonstrated enhanced Cr(VI) removal efficiency and reusability, attributed to stabilised active sites and synergistic interfacial interactions.54


image file: d5cc02843d-f2.tif
Fig. 2 Functionalization strategies for improving pollutant removal via adsorption.

Additionally, MOF-on-MOF heterostructures—formed via epitaxial growth or modular self-assembly—allow the coexistence of distinct porosities, metal nodes, and surface chemistries within a single system, thus unlocking multifunctional performance and tuneable adsorption characteristics.48,55

The exceptional adsorption performance of MOF-composites arises from the convergence of four principal attributes: (i) high surface area and porosity, enhancing adsorbate accessibility; (ii) hierarchical pore networks that facilitate diffusion kinetics; (iii) surface chemical heterogeneity enabling multiple interaction modes (electrostatic, coordinative, hydrogen bonding, π–π); and (iv) the simultaneous operation of diverse adsorption mechanisms, including physisorption, chemisorption, redox-driven interactions, and catalytic enhancement.56

Overall, MOF composite represent a transformative class of adsorbents, uniting the structural tunability and modular design of MOFs with the functional diversity of auxiliary materials. Their capacity to integrate multiple active sites, maintain structural integrity, and interact with chemically diverse pollutants positions them as leading candidates for advanced, adaptive, and scalable water treatment technologies.

4.1 Mechanisms of adsorption

The remarkable adsorption behaviour of a MOF-composite arises from the convergence of multiple physicochemical mechanisms, which may operate independently or synergistically. These mechanisms are profoundly influenced by the intrinsic characteristics of the adsorbent—such as surface chemistry, porosity, and functional composition—as well as by external environmental factors, including pH, ionic strength, and temperature gradients.57 Principal adsorption processes include electrostatic interactions, π–π stacking, hydrogen bonding, pore-filling, and van der Waals or hydrophobic forces (Fig. 3), with their relative contributions varying according to both the nature of the contaminant and the surrounding solution environment.
image file: d5cc02843d-f3.tif
Fig. 3 Various possible mechanisms for the removal of pollutants using MOFs as adsorbents.

Electrostatic interactions often dominate the adsorption behaviour of MOF composites, particularly in the uptake of charged species. The strength and direction of these interactions are governed by the ionization state of the adsorbate and the surface charge of the material—both of which are highly pH-dependent.58,59 For example, Yu et al. (2017) reported enhanced Pb2+ uptake onto functionalized MOFs at near-neutral pH due to deprotonation of carboxylate groups, whereas uptake was significantly inhibited under acidic conditions due to proton competition.60 A similar pH-dependent trend was observed in the adsorption of Congo Red (CR) by Ce-MOF-4, where adsorption efficiency was maximized at low pH via attractive electrostatic interactions but declined under basic conditions due to charge repulsion. Nevertheless, residual adsorption at high pH suggested the concurrent involvement of non-electrostatic mechanisms, such as π–π stacking and hydrogen bonding. The adsorption of Malachite Green (MG) on Ce-MOF-4 further illustrates this complexity: at acidic pH, MG exists predominantly in a neutral form, reducing the relevance of electrostatic attraction and allowing π–π stacking to prevail. In contrast, under basic conditions, the negatively charged surface of the MOF restored electrostatic attraction, thereby enhancing adsorption performance.61 These findings underscore the dynamic mechanistic switching in MOF composites, governed by both pH and contaminant speciation. Liu et al. (2023a) substantiated this behaviour using a MIL-101-NH2(Fe)/biochar composite, where tetracycline adsorption remained effective across a wide pH range, attributed to the system's ability to adapt its surface charge and binding profile under varying electrostatic regimes.62

π–π interactions are particularly significant in the adsorption of aromatic compounds, offering selective recognition through electron-rich surfaces. In the M-HAF-2 series, Zhu et al. (2022) demonstrated that the alignment of aromatic linkers enhanced both the structural integrity of the framework and its affinity for aromatic contaminants.63 This concept was extended by Bruno et al. (2021), who developed a Bio-MOF where π–π stacking not only governed adsorption but also played a central role in framework assembly.64 More recently, Kopcsik et al. (2025) provided compelling evidence of π–π selectivity by differentiating aromatic solvents based on electronic structure, thereby highlighting the potential of ligand engineering to optimize MOF affinity for specific aromatic targets.65

Hydrogen bonding introduces an additional layer of molecular recognition, particularly for polar adsorbates, and often complements other interactions. Liu et al. (2024) reported a Cu-MOF/Ti3C2Tx system featuring reversible quadruple hydrogen bonding, which conferred superior mechanical stability and adsorption performance under dynamic aqueous conditions.66 Although hydrogen-bonded organic frameworks (HOFs) have been more extensively studied for gas-phase separations, their well-defined bonding motifs, and structural adaptability—exemplified by Lin et al. (2024)—offer valuable insights into designing MOFs with enhanced water compatibility and binding specificity.67

Pore-filling and steric confinement mechanisms become particularly relevant when targeting large, weakly interacting molecules. Hierarchical and mesoporous architectures (HP-MOFs) mitigate the diffusion limitations typical of microporous systems by introducing larger channels and facilitating rapid molecular transport. Xiong et al. (2022) and Feng et al. (2020) demonstrated that such structures significantly improve the accessibility of adsorption sites and enhance kinetic performance.68,69 Arslan et al. (2024) further exemplified this through the rapid adsorption of naproxen—achieved within five minutes—using a micro–mesoporous carbonaceous material, where pore-filling was identified as the dominant uptake mechanism.70 Similarly, Attallah et al. (2023) employed spectroscopic techniques to confirm sequential pore occupation during water adsorption, offering direct evidence of diffusion-limited regimes in hierarchically porous MOFs.71

Although generally weaker, van der Waals and hydrophobic interactions can play a pivotal role in capturing non-polar species, especially in scenarios where electrostatic interactions are suppressed. These interactions are significantly enhanced by the incorporation of hydrophobic moieties—such as reduced graphene or alkyl-functionalized ligands—into the MOF matrix. Such modifications facilitate the partitioning of apolar molecules by displacing water molecules and reducing system free energy.72,73 In complex water matrices, particularly under neutral pH or in the presence of competing ions, these hydrophobic domains can substantially improve both selectivity and overall adsorption efficiency.

In summary, adsorption in MOF composites is governed by a continuum of coexisting and interdependent mechanisms, whose dominance can be finely tuned through careful material design, environmental adjustment, and contaminant profiling. Rather than relying on a singular interaction type, these systems leverage synergistic processes—including electrostatics, π–π stacking, hydrogen bonding, pore confinement, and hydrophobic effects—to achieve high adsorption capacity, selectivity, and resilience across a diverse range of water treatment applications.

4.1.1. Kinetic behaviour and rate-limiting processes. The kinetics of adsorption in MOF composites reflect the inherent multiscale complexity of their hierarchical architectures and surface heterogeneity. These processes are governed by a sequence of mass transfer and surface interaction events, with the rate-limiting step ultimately determining the overall adsorption velocity.74 In systems, kinetic behaviour is further modulated by structural features such as interconnected porosity, functionalized domains, and the coexistence of crystalline and amorphous phases—attributes that influence both external mass transfer and internal diffusion regimes.

Adsorption typically proceeds through four main stages (Fig. 4): (i) mass transport from the bulk solution to the outer surface of the adsorbent (film diffusion); (ii) traversal of the liquid boundary layer (external diffusion); (iii) intraparticle diffusion through the pore network; and (iv) attachment onto active sites via physisorption or chemisorption. In the context of MOF composites, the controlling step is frequently attributed to intraparticle diffusion or surface interaction, although their relative dominance varies depending on contaminant properties, material morphology, and solution chemistry.75,76


image file: d5cc02843d-f4.tif
Fig. 4 Schematic representation of adsorption mass transfer steps. Adaptation from Juela D. et al. 2021.77

Kinetic modeling remains a key analytical approach for elucidating these dynamic processes. Among the most extensively employed models are the pseudo-first-order (PFO) and pseudo-second-order (PSO) equations. PFO is generally linked to physisorption governed by external diffusion, whereas PSO is often interpreted as reflective of chemisorption-controlled mechanisms (Fig. 5).76 Despite their empirical nature and lack of mechanistic specificity, these models remain popular due to their simplicity and statistical robustness. In particular, the PSO model is overwhelmingly favored in studies involving MOF composites, frequently offering superior correlation with experimental data across diverse conditions.


image file: d5cc02843d-f5.tif
Fig. 5 Physical interpretation of the PFO and PSO models. Reproduced from ref. 76 with permission from Elsevier, copyright 2020.

This prevalence was critically assessed by Emmanuel et al. (2020), who found that approximately 87% of adsorption studies employed the PSO model, with nearly 12% omitting PFO analysis altogether.78 While this trend suggests a dominant chemisorption mechanism, it also raises methodological concerns regarding the indiscriminate application of PSO in the absence of corroborating mechanistic evidence. Curve-fitting parameters alone are insufficient to confirm rate-determining steps unless supported by additional analyses, such as temperature-dependent kinetics, isosteric enthalpy calculations, or spectroscopic evidence of bond formation.

To gain further insight into diffusion-limited regimes, the intraparticle diffusion model proposed by Weber and Morris is often utilized. By plotting qtversus t1/2, this model reveals sequential linear regions that correspond to distinct kinetic regimes—from initial boundary layer diffusion to progressive pore occupation and eventual equilibrium (Fig. 6).79 However, despite its diagnostic utility, the model remains underutilized in the context of MOF composites.


image file: d5cc02843d-f6.tif
Fig. 6 Multilinear adsorption phases in the Weber–Morris model.

Selected studies highlight both its potential and limitations. Zhu et al. (2022) applied the model to Cu(I)-tpp@ZIF-8, revealing a biphasic adsorption process comprising initial surface uptake followed by slower intraparticle diffusion.80 In contrast, Gao et al. (2023) observed non-zero intercepts in Cr(VI) adsorption using UiO-66@GO and UiO-66@SBA-15 system, suggesting that internal diffusion was not the sole rate-limiting factor, but operated in conjunction with film diffusion and functional group-mediated surface interactions.81 Similarly, Ghassa et al. (2023) identified dual kinetic phases in dibenzothiophene removal by MIL-53(Cr) composites, with the latter stage—linked to desulphurization—being influenced by more than just pore diffusion constraints.82

These findings underscore the interpretative value of the Weber–Morris model, while also highlighting key methodological challenges. The absence of standardized criteria for identifying kinetic phases, coupled with limited integration with complementary techniques—such as in situ spectroscopy, transient adsorption monitoring, or molecular diffusion simulations—has hindered its widespread adoption. To transcend empirical curve fitting, future research must adopt a multi-technique framework that holistically captures both transport and interfacial phenomena in MOF composites.

4.1.2. Equilibrium isotherms and surface heterogeneity. Equilibrium isotherms serve as fundamental tools for characterizing the adsorption capacity and surface behaviour of MOF composites. By describing the relationship between the equilibrium concentration of a contaminant in solution (Ce) and the quantity adsorbed per unit mass of adsorbent (qe) at a constant temperature, isotherms reveal not only the maximum adsorption potential but also the physicochemical nature of the adsorption process.83 Crucially, they provide insights into surface heterogeneity, site accessibility, and the thermodynamic characteristics of adsorbate–adsorbent interactions, distinguishing between physisorption and chemisorption mechanisms.84

The intrinsic structural complexity of MOF composite—often comprising crystalline frameworks interlaced with amorphous phases or functionalized components—results in significant surface heterogeneity. This necessitates the use of multiple isotherm models, each capturing distinct assumptions regarding site distribution, adsorption energetics, and interaction mechanisms.

Table 1 summarizes the most frequently employed isotherm models in MOF-composite studies. The Langmuir model, based on the assumption of monolayer adsorption at energetically equivalent and localized sites, is typically suited to systems where chemisorption dominates. In contrast, the Freundlich model assumes adsorption occurs on a heterogeneous surface with a non-uniform distribution of adsorption heat, rendering it appropriate for materials with pronounced textural and chemical diversity. Bridging these two regimes, the Sips and Redlich–Peterson models accommodate both surface heterogeneity and site saturation, offering flexibility in describing functionalized materials with mixed surface properties. Other models, such as Temkin, Dubinin–Radushkevich (D–R), and Henry's law, provide deeper insights into adsorption energetics, pore-filling phenomena, or behavior in dilute systems.

Table 1 Overview of isotherm models used to describe equilibrium adsorption
Model Equation Assumptions Key parameters Ref.
Langmuir image file: d5cc02843d-t1.tif Homogeneous surface, monolayer adsorption, no adsorbate–adsorbate interaction. Q max, KL 85
Freundlich image file: d5cc02843d-t2.tif Heterogeneous surface, multilayer adsorption, energy distribution. K F, 1/n 86
Sips (Langmuir–Freundlich) image file: d5cc02843d-t3.tif Hybrid behaviour; suitable for materials with mixed surface properties. Q maxKS, n 87
Redlich–Peterson image file: d5cc02843d-t4.tif An empirical model combining Langmuir and Freundlich features. K R, aR, g 88
Temkin image file: d5cc02843d-t5.tif Adsorption heat decreases linearly with coverage. b, KTe 86
Henry Q e = KHCe Linear adsorption at low concentrations (dilute systems). K H 89
Dubinin–Radushkevich q e = qS[thin space (1/6-em)]exp(−KDRε2) Micropore filling; physical adsorption; Gaussian energy distribution. Q max, qS 87


The appropriateness of each model depends on both the structural features of the functionalized material and the chemical nature of the target contaminant. As outlined in Table 2, composites incorporating carbonaceous components—such as graphene oxide, biochar, or polysaccharide-derived functional groups—tend to conform to the Freundlich or multi-site models. This reflects their chemically diverse, π-electron-rich, and defect-laden surfaces, which support a wide array of interactions including π–π stacking, hydrogen bonding, and electrostatic attraction.90,91

Table 2 Summary of MOF composite-based adsorbents for the removal of various organic and inorganic pollutants from aqueous media
MOF composite Surface area Target pollutants Adsorption capacity Kinetic model Isotherm model Remarks Ref.
Cu-BTC nanoparticles on sulphated-macroalgae biomass Not reported Methylene blue (MB), Methyl orange (MO) 42 (algal), 73 (Cu-BTC), 162 mg g−1 (Cu-BTC@Algal) PSO Langmuir Fast and selective removal of cationic dyes; enhanced performance vs. Cu-BTC alone; 97% MB and 68% MO removal in 10 min 91
Chromium-based MIL-101 MOF on nanoclay 4959 m2 g−1 Various organic dyes Superior to existing materials; high and selective dye removal PSO Not reported Reusable for multiple regeneration cycles; excellent porosity; promising for wastewater treatment 92
Zr-, Zn-, and Co-based MOFs on COFs (three variants) 748.2 (COF-1), 898.4 (COF-2), 551.8 (COF-3) m2 g−1 UO22+ (all), MB (only COF-1) UO22+: 399.6 (COF-1), 538.3 (COF-2), 424.5 mg g−1; MB: 142.7 mg g−1 (COF-1 only) PSO Langmuir (MB and UO22+) Mechanisms include electrostatic interactions and surface complexation; tested in simulated seawater; up to 8 regeneration cycles effective. 93
TMU-10 MOF functionalized with graphene oxide (GO) Not reported Phenol 212.76 mg g−1 PSO Langmuir Rapid and high-capacity phenol removal from environmental water; superior to pristine TMU-10 94
NH2-UiO-101(Zr) MOF embedded in oxalyl-functionalized cotton Not reported MB, RhB (dyes); diazinon, chlorpyrifos (pesticides) Dyes: up to 187.03 mg g−1; pesticides: up to 464.69 mg g−1 PSO Langmuir Adsorption capacity increased 2.3–2.5× for dyes and 3.1–3.3× for pesticides vs. cotton; slight reduction after 5 regeneration cycles. 95
Fe–Al BDC-based MOF (powder and sand-fixed hybrid) Not reported Rhodamine B (RhB) Batch: 48.59 mg g−1; column: 113.05 mg g−1 (qexp); 114.94 mg g−1 (Thomas) PSO Freundlich (batch); Thomas/Yoon-Nelson/BDST (column) Column adsorption efficient at 5 mL min−1, 20 mg L−1, 13 cm; viable for upscaling; cost-effective water filter system in poor communities 96
2D–3D hierarchical MOF@MOF (Zr-BTB on PCN-134) Not reported Bisphenols (BPA, etc.) 135.1–628.9 mg g−1 (depending on BP type) PSO Langmuir Enrichment factors: 310–374; detection limit: 0.02–0.03 ng mL−1; recoveries: 72.8–108%; mechanisms: H-bonding and π–π stacking 97
MIL-101(Cr)-based magnetic mesoporous composite 892.5 m2 g−1 MB, Orange G (OG), asphaltenes 395.26 (MB), 198.02 (OG), 109.17 (asphaltenes) mg g−1 PSO Langmuir (dyes), Freundlich (asphaltenes) Chemisorption for dyes (endothermic), physisorption for asphaltenes 98
Aminated chromium MOF (MIL-101-NH2) and triazine-based COF High BET; mesoporous Acid blue 9 256 mg g−1 PSO Langmuir Effective monolayer chemisorption at pH 2, 35 °C; high affinity; optimised for textile wastewater treatment 99
ZIF-8 and multiwalled carbon nanotube (MWCNT) composite Not reported Phosphate, acetaminophen (AAP), triclosan (TCS) Phosphate: 188.5 mg g−1; >92–100% removal in real wastewater (0.98–5.93 mg L−1) Not explicitly stated Not explicitly stated Excellent simultaneous removal; tolerance to EOC interference; high efficiency in complex water matrices; validated with modelling 100
MIL-100 core wrapped with ZIF-8 (core–shell MOF@MOF) High (not specified) TC, RhB, MB, MO, Sudan III, AMX TC: 1288 mg g−1; RhB: 1181 mg g−1 PSO Freundlich Thermodynamically favourable; endothermic (TC), exothermic (RhB); mechanisms: electrostatic, H-bonding, π–π stacking, coordination; high selectivity and reusability 101
Zr-based UiO-66-NDC MOF intercalated with graphene oxide Not reported Pb(II) (Lead ions) 254.45 mg g−1 (Langmuir, 298 K) PSO Temkin (best fit); also fitted to Langmuir & Freundlich Chemisorption (exothermic and spontaneous); high reusability (4+ cycles); superior structural stability vs. GO alone 90
Activated carbon incorporated in MIL-53(Cr) MOF Enhanced vs. pristine MIL-53 (exact value not given) Dibenzothiophene (DBT) Up to 95.88% DBT removal (e.g., 2.5% AC@MIL-53(Cr); 1000–2000 ppm S) PSO Temkin Chemisorption confirmed; optimal performance at 2.5% AC loading; enhanced porosity and surface chemistry enable high DBT uptake. 82
Lanthanum/palladium-based MOF Not reported Triclosan (TCS) 610.85 mg g−1 PSO Langmuir Chemisorption mechanism; endothermic and spontaneous (ΔH°, ΔG°); excellent regeneration (5 cycles); removal via π–π stacking, pore filling, H-bonding, electrostatics 102


Conversely, materials based on crystalline MOFs integrated with inorganic supports—such as MIL-101 immobilized on nano clays or Fe–Al BDC frameworks in fixed-bed columns—frequently exhibit behaviour consistent with the Langmuir model. This suggests a dominant monolayer adsorption process occurring on structurally uniform and energetically equivalent active sites.93,96 In these systems, strong agreement with the Langmuir model often parallels the application of pseudo-second order (PSO) kinetics, reinforcing the interpretation of chemisorption as both the rate-limiting and capacity-defining mechanism.102

These trends highlight the critical importance of selecting and interpreting isotherm models with care in MOF composite research. While high correlation coefficients (R2 values) offer a preliminary assessment of model suitability, they must be contextualized with supporting structural, spectroscopic, and kinetic evidence to derive meaningful mechanistic conclusions. As the field advances, the design of high-performance MOF composites will increasingly depend not only on achieving superior adsorption capacities but also on developing a nuanced understanding of how structural hierarchy, chemical composition, and surface heterogeneity govern adsorption phenomena under environmentally relevant conditions.

4.2 Adsorption of contaminants in wastewater by MOF-composite

4.2.1 Adsorption of dyes. Synthetic dyes have long served as benchmark contaminants in adsorption research owing to their well-defined molecular structures, pronounced optical activity, and environmental persistence.75 Their extensive use across textile, pharmaceutical, and cosmetic industries, coupled with their resistance to biodegradation, renders them both ideal model compounds and priority targets for wastewater remediation.103 Accordingly, dye adsorption has become a critical platform for evaluating the structure–function relationships, mechanistic pathways, and operational robustness of MOF composites.

MOF composites has demonstrated exceptional versatility in dye removal, enabled by their modular architectures, hierarchical porosity, and chemically diverse surfaces. For instance, a MIL-100(Fe)/graphene aerogel composite exhibited a high adsorption capacity for methylene blue (MB, 333.3 mg g−1) and concurrently catalysed oxidative degradation via Fenton-like reactions—demonstrating a dual-function mechanism involving physisorption, chemisorption, and redox activity.104 Similarly, Anil Kumar et al. (2024) reported impressive removal of MB and methyl orange (MO) (up to 631 mg g−1) using Fe/Al-MOFs functionalized with graphene oxide and Moringa oleifera extract, showcasing the potential of combining bio-derived extracts with carbonaceous platforms to produce multifunctional, partially recyclable, and eco-compatible sorbents.105

The incorporation of covalent organic frameworks (COFs) into MOF matrices has emerged as a strategy to enhance structural stability and target selectivity. For example, Firoozi et al. (2020) synthesized a MOF-5/COF composite capable of selectively adsorbing cationic dyes via π–π stacking and hydrogen bonding. Although the adsorption capacity was moderate (∼18 mg g−1), the material remained structurally stable under alkaline conditions that typically degrade conventional MOFs.106

Pore architecture plays a pivotal role in adsorption efficacy. Santoso et al. (2021) demonstrated that acetic acid-mediated synthesis of ZIF-8 yielded mesoporous crystals with higher MB uptake than DMF-synthesized analogues with greater surface area, underscoring the importance of pore accessibility and diffusion over surface area alone.107

Multifunctional materials supported on magnetic or fibrous scaffolds offer additional advantages in recovery and regeneration. For example, cellulose-based composites embedded with HKUST-1 and Fe3O4 facilitated magnetic separation alongside catalytic–adsorptive dye removal. However, distinguishing between the contributions of physical adsorption and catalytic degradation remains a methodological challenge.108

Ligand engineering also influences adsorption performance. MIL-101–NH2 showed exceptional uptake of linear anionic dyes such as Congo Red (up to 2967.1 mg g−1), primarily via hydrogen bonding and electrostatic attraction involving terminal –NH2 groups. Nonetheless, the adsorption of bulkier molecules was hindered by steric effects, highlighting the need for adsorbent–adsorbate geometric compatibility.109

The choice of metal nodes is another determinant of performance. TMU-8, a cadmium-based MOF, effectively removed Reactive Black 5 following Langmuir and PSO models, yet concerns over Cd2+ toxicity limit its practical relevance.110 In contrast, Zn-based 2D MOFs outperformed Ni and Cu analogues in removing Alizarin Yellow GG, with adsorption behaviour modulated by ionic strength and metal–ligand coordination dynamics.111

Computational tools have provided a deeper mechanistic understanding. Dadashi Firouzjaei et al. (2020) employed molecular dynamics and DFT calculations to reveal strong π–π and electrostatic interactions (up to −323 kcal mol−1) in GO–Cu-MOF system, corroborating experimental observations of high MB uptake.112

Selectivity in multicomponent systems is increasingly explored. Al Sharabati et al. (2020) demonstrated that MIL-53(Al) adsorbed both MO and MG in binary systems, with a slight preference for MO. The material followed PSO kinetics and Freundlich isotherms, maintaining performance over multiple regeneration cycles, thus validating its applicability in real wastewater contexts.113

Membrane-based applications have also gained traction. Al-UiO-66 embedded in PVDF membranes achieved >99% removal of small dyes in binary mixtures and retained >90% efficiency across ten reuse cycles. Adsorption was governed by size exclusion and electrostatic interactions, although selectivity between similarly sized or charged dyes remains a key limitation.114

MOF–COF material has shown consistent dye uptake across classes (e.g., crystal violet, MB), with capacities between 102–114 mg g−1 and stability over wide pH ranges.115 More complex systems, such as MIL-101(Cr)/Fe3O4/MCM-41/montmorillonite quaternary, exhibited multifunctional behaviour: chemisorption governed dye removal (MB: 395.3 mg g−1), while physisorption dominated for weakly interactive hydrocarbons such as asphaltenes.98 However, their intricate synthesis presents challenges for scalability and cost-effectiveness.

Table 3 summarizes key characteristics of recent MOF composites for dye adsorption, including capacities, target compounds, kinetic and isotherm models, and recyclability. A critical review of these systems reveals a pronounced dominance of pseudo-second-order kinetics, consistent with chemisorption-driven mechanisms. Most isotherms conform to Langmuir or Freundlich models, reflecting either monolayer adsorption on uniform sites or heterogeneous surface interactions. Several materials exhibit dual-model behaviour, suggesting coexisting adsorption regimes.

Table 3 Comparative summary of MOF composites for organic dye adsorption
MOF composite Target dye(s) Optimal conditions Adsorption capacity Isotherm model Kinetic model Reusability Ref.
AlGC (Al-MOF/GO) Methyl orange (MO), methylene blue (MB) Taguchi-optimized: contact time, dosage, dye concentration, T MO: 577 ± 37 mg g−1 Freundlich/Langmuir PSO >65% after 3 cycles 105
MB: 336 ± 13 mg g−1
FeGC (Fe-MOF/GO) Methyl orange (MO), methylene blue (MB) Taguchi-optimized: contact time, dosage, dye concentration, T MO: 631 ± 42 mg g−1 Freundlich/Langmuir PSO >65% after 3 cycles
MB: 387± 7 mg g−1
MG-HA (MIL-100(Fe)/graphene aerogel) Methylene blue (MB) Adsorption + H2O2 degradation; saturation capacity; 5 cycles tested 333.33 mg g−1 (MB); complete removal with catalytic synergy Not specified Adsorption: PSO 93.4% retained after 5 cycles 104
Catalysis: PFO
M5C (MOF-5/COF) AO, RB pH = 9.5; contact time optimized AO: 17.95 mg g−1 Langmuir PSO Not reported 106
RB: 16.18 mg g−1
ZIF-8 (AA) MB Mesoporous: 2.76 nm pores, 500 m2 g−1, 65 nm particles Not quantified here (but ≫ ZIF-8 DMF) Langmuir PSO Not reported 107
MIL-101-NH2-1 (BDC/NH2-BDC) CR, MO, AC (anionic dyes) pH not specified; comparative across 3 samples CR: 2967.1 Not specified Not specified Not reported 109
MO: 461.7
AC: 259.8 mg g−1
TMU-8 (Cd-MOF) Reactive black 5 (RB5) pH, sorbent dose, time, salt concentration optimized q max = 79.36 mg g−1 (Langmuir) Langmuir > Freundlich PSO Not reported 110
GO–Cu-MOF MB pH ácido; 65 °C favorable Cu–MOF: 106–142 mg g−1 Not specified PSO Not reported 112
GO–Cu–MOF: 173–262 mg g−1
MIL-53(Al) MG (cationic), MO (anionic) pH & T optimized; studied in single and binary dye systems >95% removal efficiency (both dyes) Freundlich PSO 4 cycles without capacity loss 113
Cu-BDC Alizarin yellow GG (anionic) Ionic strength promoted adsorption Not specified Freundlich PFO & PSO Calcination/EtOH 111
Zn-BDC Alizarin yellow GG (anionic) Ionic strength suppressive (gentle) Not specified Langmuir PSO Calcination/EtOH
Ni-BDC Alizarin Yellow GG (anionic) Ionic strength is strongly suppressive Not specified Langmuir PSO Calcination/EtOH
Al-UiO-66-MMM (PVDF) MG, MO, MY, RhB, JGB, RB5 pH = 5–6; 30 °C; binary dye systems tested MG: 157.98 mg g−1 (Langmuir) Langmuir PSO + Intraparticle diffusion >90% removal after 10 cycles 114
>99% removal (MO, MY, MG)
NH2-MIL-88(Fe)/COF MB, MO, CV pH = 7; optimized time, concentration MB: 114 mg g−1 Langmuir PSO High after 5 cycles 115
MO: 106 mg g−1
CV: 102 mg g−1
OG: moderate Freundlich (asphaltenes)
RB: 16.18 mg g−1
Co-MOF@CNT MB (cationic, mixed with MO) 25 °C, pH = 7, mixed dye solution MB: ∼98% removal efficiency Not specified PSO 4 cycles (MB removal) 116
Zn-MOF NPC@MIL-101 MB (cationic) pH = 8; 50 mg L−1 MB; 30 min contact time (RSM optimized) 95.7% removal Langmuir PSO Not specified 117
CS/MOF-235 (chitosan-MOF) MO > MB Not detailed; comparative with CS & MOF-235 2857–2326 mg g−1 (MO) vs. MB: lower Not specified Intraparticle + film diffusion + chemisorption Not reported 118
MLa-MOF (magnetic La-MOF) Brilliant Blue FCF pH = 3; Box-Behnken optimized 342 mg g−1 Langmuir PSO >6 cycles; PXRD stable 119


Despite notable progress, several research gaps persist. Many studies lack thermodynamic or competitive adsorption analyses, and performance under real wastewater conditions remains underexplored. Issues such as long-term operational stability, fouling resistance, and regeneration under practical conditions are often neglected. Moving forward advances in scalable, green synthesis routes and the integration of advanced characterization techniques—such as in situ spectroscopy and multicomponent analysis—will be essential to bridge the gap between laboratory efficacy and field-level implementation.

4.2 Adsorption of drugs

Pharmaceutical residues have emerged as high priority micropollutants due to their environmental persistence, low biodegradability, and potential to induce antimicrobial resistance.120 Their structural diversity—including ionizable functional groups, varying hydrophobicity, and molecular complexity—renders their removal particularly challenging for conventional treatment systems, especially at trace concentrations.121 MOF composites, with their tuneable porosity and chemically versatile surfaces, offer a promising platform to overcome these limitations through targeted interactions and enhanced adsorption dynamics.

Functionalization with carbonaceous materials, biopolymers, or functional polymers enables the fine-tuning of MOF performance by improving surface functionality, dispersion, and interaction diversity. These modifications create multivalent domains capable of engaging in π–π stacking, hydrogen bonding, electrostatic attraction, and van der Waals interactions.122 Such systems broaden the adsorption spectrum while improving mechanical robustness and reusability.

A prominent example is the UiO-66@Cr-MIL-101 composite developed by Sharafinia et al. (2023), which achieved 99.5% amoxicillin removal within 20 minutes at pH 6. The system followed Langmuir and pseudo-second order (PSO) models, indicative of homogeneous chemisorption on well-defined active sites. The functionalized structure facilitated enhanced site accessibility and mass transfer.123 Similarly, Wang et al. (2023) reported a MOF-199/CNT composite with a capacity of 40.8 mg g−1 for ibuprofen and demonstrated effective drug capture from urine samples—highlighting the translational potential of MOF composite in real biological matrices.124

Sustainable synthesis approaches have also yielded promising results. Rivadeneira-Mendoza et al. (2023) fabricated a hydrochar@MIL-53(Al) functionalized from corn cob waste, achieving complete removal of naproxen and ketorolac at concentrations up to 150 ppm. The material retained ∼90% of its capacity over five cycles, and the adsorption was best described by Extended Freundlich and Sips models, suggesting cooperative multilayer binding on heterogeneous surfaces.125

Mechanistic insights have been deepened through the integration of modelling and experimentation. Quintero-Álvarez et al. (2023) applied statistical physics models to the uptake of naproxen, diclofenac, and acetaminophen on MIL-100(Fe) and MIL-101(Fe), revealing multimolecular, exothermic adsorption driven by hydrogen bonding, electrostatics, and van der Waals forces. The higher mesoporosity and Fe–O density of MIL-101(Fe) accounted for its superior performance, offering guidance for rational material design.126

Functionalization strategies continue to play a central role. Zhang et al. (2023) synthesized MIL-101-ED via ethylenediamine grafting, achieving selective removal of antipsychotic drugs while excluding >90% of proteins. This restricted-access behaviour suggests the potential for drug capture in biofluids and pre-treatment applications.127 Likewise, MIL-101(Cr)/activated carbon doped with urea showed enhanced sulfacetamide uptake through Langmuir-type chemisorption, while maintaining high regeneration efficiency.128

Computational studies have improved the understanding of structural limitations. Yaicel et al. (2023) demonstrated that standard ZIF-8 models failed to predict the uptake of larger molecules such as caffeine and 5-fluorouracil. Only defective models featuring missing linkers and exposed metal sites aligned with experimental results, underscoring the need for realistic frameworks in simulation workflows.129 Surface chemistry modulation has also expanded application versatility. Farrando-Pérez et al. (2023) evaluated UiO-66 derivatives functionalized with –H, –NH2, and –NO2 groups for brimonidine tartrate removal. The –NO2 derivative exhibited the highest adsorption (∼680 mg cm−3), though concerns regarding biocompatibility at high loading levels highlight the importance of balancing efficiency with environmental and toxicological safety.130

Post-synthetic activation represents an alternative performance-enhancement strategy. Tajahmadi et al. (2023) improved quercetin uptake to 370 mg g−1 by ethanol activation of Gd-MOF, achieving spontaneous thermodynamics without altering the structure of the material.131 Similarly, Yadav et al. (2023) utilized PEG-coating on MIL-100(Fe) to encapsulate norfloxacin, reaching 20 wt% loading and achieving sustained release over 60 hours—demonstrating dual utility for environmental remediation and drug delivery applications.132

Translational insights have also emerged from biomedical systems. Bikiaris et al. (2023) designed a dual-phase Fe-BTC/PEG–PCL composite capable of loading 40 wt% paclitaxel and achieving complete release within four days. Although intended for therapeutic use, the composite's tuneable interface and controlled release behaviour provide valuable cues for pollutant desorption and diffusion control in environmental systems.133

Table 4 summarizes key findings from recent MOF composites applied to pharmaceutical adsorption, detailing adsorption capacities, target drugs, kinetic and isotherm models, and regeneration performance. Across studies, PSO kinetics and Langmuir or Freundlich isotherms dominate, consistent with chemisorption mechanisms involving electrostatic, hydrogen bonding, and π–π interactions. The increasing application of advanced models—such as Sips and statistical physics approaches—reflects the growing recognition of surface heterogeneity and cooperative binding effects in these systems.

Table 4 Comparative overview of MOF composites for the adsorption of drugs from aqueous media
MOF composite Target drug Optimal conditions Max. removal (%) Isotherm model Kinetic model Ref.
UIO-66@Cr-MIL-101 Amoxicillin (AMX) 20 wt% Cr-MIL-101, AMX 80 mg L−1, 20 min contact time, pH = 6 99.50% Langmuir PSO 123
MOF-199/CNTs Ibuprofen, ketoprofen, naproxen Room temperature; tested in urine samples; reusable without deactivation Ibuprofen: 40.8 mg g−1 Not reported Not reported 124
Recovery: 89.7–97.5% (urine samples)
HC@MIL-53(Al) Ketorolac (KTC), naproxen (NPX) 150 ppm (each); binary system; temperatures 298–333 K 100% removal (KTC and NPX at ≤150 ppm) Extended Freundlich (best fit for KTC) PSO 125
Adsorption under extreme conditions ∼90% retained after 5 cycles Also: ext. Langmuir, sips
MIL-101(Fe), MIL-100(Fe) Naproxen (NAP), diclofenac (DFC), acetaminophen (APAP) T = 30–40 °C; pH = 7; multimolecular adsorption MIL-101(Fe): NAP = 2.19 mmol g−1 Statistical physics models (multimolecular) Not reported 126
DFC = 1.71 mmol g−1
(lower for APAP)
Fe3O4@SiO2@UiO-67-COOH Antipsychotic drugs (multiple) Monolayer chemisorption: π–π, H-bond, electrostatic; automated extraction (96 samples in 9 min); pH ∼ physiological Recovery: 95.7–112.3% Langmuir PSO 136
LOD: 0.06–0.9 ng mL−1 (UPLC-MS/MS)
Protein exclusion: 98.9–99.8%
ZIF-8 (ZIF-8P vs. ZIF-8S models) 5-Fluorouracil (5FU), Caffeine (CAF) ZIF-8S enables adsorption at the surface only; methanol/water solvents ZIF-8S matches CAF adsorption & release experiments; 5FU: qualitative match Not applicable (simulated) Not applicable (simulated) 129
MIL-101-ED Antipsychotic drugs (e.g., risperidone) pH = 3; electrostatic + π–π + H-bonding; reusable 4×; protein exclusion 91.9–94% Adsorption efficiency: 80.1–101.4%; Not specified Not specified 127
Recovery: 83.2–110.8%;
LOD < 5.55 ng mL−1
UiO-66, UiO-66-NH2, UiO-66-NO2 Brimonidine tartrate T = 25 °C; pH ∼ physiological; 24 h saturation; release up to 25 days UiO-66[thin space (1/6-em)]:[thin space (1/6-em)]200 mg g−1/460 mg cm3 Langmuir Not explicitly modelled (24 h equilibrium) 130
UiO-66-NH2: 190 mg g−1/610 mg cm3
UiO-66-NO2: 180 mg g−1/680 mg cm3
Urea-MIL-101(Cr)@AC Sulfacetamide (SA) Adsorption equilibrium in ∼60 min; H-bonding with –SO2 and –NH2 groups High adsorption efficiency; excellent reusability Langmuir PSO 128
Activated Gd-MOF (3d-E) Quercetin (Qt) T < 40 °C; optimal at pH ∼neutral; activation via EtOH exchange 370 mg g−1 (highest for 3d-E) Temkin > Freundlich > Langmuir > D–R Not specified 131
MIL-100(Fe), PEG@MIL-100(Fe) Norfloxacin (NFX) ∼20% loading; 60 h to saturation; PEG coating slows release 20 wt% (15% in 8 h; 100% in 60 h); sustained release confirmed Not specified Not specified 132
Fe-BTC, mPEG-PCL@Fe-BTC Paclitaxel (PTX) ∼40% loading; full release in 4 days; nanoparticles ∼143 nm (solid-in-oil-in-water method) 100% release in 4 days Not specified (desorption) Bimodal exponential model 133
∼40% drug loading
{H3PW12O40}/MIL-88A/B/NFO Tetracycline (TC), ciprofloxacin (CIP) 100% removal: TC in 14 min, CIP in 20 min; max Q: TC = 1428.57, CIP = 344.82 mg g−1 TC: 1428.57 mg g−1 Freundlich PSO 137
CIP: 344.82 mg g−1
HKUST-1/ZIF-8 Cefixime (CFX), lamotrigine (LTG) 25 °C, pH 7, 240 min; magnetic separation; stable 5 cycles CFX = 110 mg g−1 (vs 38 HKUST-1) Langmuir PSO 138
LTG = 139 mg g−1 (vs. 101 ZIF-8)
Hierarchical Zn-MOF Antipsychotics (APD) Optimized structures; adsorption energy minimum; electronic stability; surface mapping Not applicable (computational) Not applicable Not applicable 139
IRMOF-16, HKUST-1, ZIF-8 Phenazopyridine (PHP) pH-dependent adsorption in aqueous solution; π–π, H-bond, electrostatic ∼100% (IRMOF-16), ∼60% (HKUST-1), ∼40% (ZIF-8) Not applicable Not applicable 140
E ads: −343.4 kJ mol−1 (MD), −1.53 eV (DFT)
MIL-53(Co-Fe)@MIL-53(Ni)/TiO2 Imatinib (IMB) pH, contact time: 75 min; adsorption + visible light photodegradation Freundlich KF = 25.498 mg g−1 Freundlich PSO 141
Intensity n = 2.09


Despite these advances, critical challenges persist. Thermodynamic profiling, multicomponent competition, and evaluation under real wastewater conditions are often absent. Additionally, long-term stability, fouling resistance, and cost-effective regeneration strategies remain underexplored. Future work should prioritize the integration of in situ characterization techniques, realistic environmental matrices, and scalable, green synthesis protocols to bridge the gap between laboratory feasibility and field deployment in pharmaceutical pollution control.

4.3 Adsorption of metals

The remediation of heavy metal ions from wastewater presents unique physicochemical challenges distinct from those posed by organic contaminants such as dyes and pharmaceuticals. Metal ions exhibit diverse behaviours arising from their variable oxidation states, hydration shells, coordination geometries, and redox activity.134 As such, effective removal requires adsorbents with high selectivity, robust binding affinities, and structural stability across a broad range of aqueous environments.135 MOF composites, with their modular design, tuneable surface chemistry, and multifunctional architectures, have emerged as compelling candidates to meet these multifaceted demands.

Despite the growing body of research on MOF composites for the photocatalytic degradation of metal ions, adsorption-specific investigations remain comparatively underdeveloped. Many studies report equilibrium and removal data without in-depth analysis of the underlying mechanisms, thereby limiting the rational design of ion-selective or competitive adsorption systems.

A notable example is the carboxyl-functionalized UiO-66 functionalized with 1,2,4,5-benzenetetracarboxylic acid, which demonstrated preferential uptake of Pb2+ (100 mg g−1) over Cd2+ (37 mg g−1). This selectivity was attributed to electrostatic interactions influenced by hydration thermodynamics. The ζ-potential (−7.7 mV at pH 7) promoted cation adsorption, and although minor efficiency loss occurred over cycles, the system highlighted the effectiveness of ligand engineering for enhancing metal specificity.142 Further advances were reported by Wang et al. (2021), who developed a Ni0.6Fe2.4O4-UiO-66-PEI capable of removing Pb2+ and Cr(VI) with capacities of 273.2 and 428.6 mg g−1, respectively. XPS and DFT analyses revealed distinct mechanistic pathways: amine-mediated chelation for Pb2+ and redox-assisted adsorption for Cr(VI). The composite was magnetically recoverable and retained over 90% efficiency after five regeneration cycles, exemplifying the utility of multifunctional design.143

Magnetically responsive MOF composites have also demonstrated efficient multi-metal capture. For instance, magnetite@Fe-BTC removed Cu2+, Pb2+, Hg2+, and As(III) with capacities ranging from 55 to 155 mg g−1, following the selectivity trend Cu2+ > Pb2+ > Hg2+ > As3+. While most metals adhered to Langmuir-PSO behaviour, Hg2+ adsorption was better represented by the Temkin model, suggesting heterogeneous binding energies.144

Redox-active systems have proven particularly effective in Cr(VI) remediation. TMPA@MOF-801 aerogels achieved 350.6 mg g−1 uptake through a synergistic mechanism involving electrostatic attraction, ion exchange, and in situ reduction to Cr(III). The material maintained ∼88% removal efficiency over six cycles and exhibited high tolerance to competing ions, expanding the functional scope of MOFs beyond traditional adsorption paradigms. Comparable performance was achieved using nanocellulose–MOF-801 aerogels, which achieved 93.9% Cr(VI) removal in continuous flow columns, treating over 4 L of solution with less than 1 g of adsorbent—highlighting their practical potential.145

The selective recovery of noble metals has also gained attention. Shi et al. (2025) developed UiO-66-(OH)2–FA composites embedded in flexible filtration textiles, achieving >99.99% Au3+ removal (190.98 mg g−1) while preserving mechanical integrity and reusability. This approach addresses the scalability limitations of powdered MOF systems by enabling integration into robust, application-ready formats.146 For alkali and alkaline earth metals, WP@PSS@Cu-MOF selectively adsorbed Li+ (9.69 mg g−1) in the presence of Na+, K+, Ca2+, and Mg2+. The mechanism involved a combination of steric exclusion and sulfonate–Li+ coordination. Although capacities were moderate, the material demonstrated promising selectivity and maintained ∼88% efficiency over five cycles under brine-like conditions, offering the potential for lithium recovery from complex matrices.147

Multicomponent systems have demonstrated high capacities and complex mechanisms. The Co–Al–LDH@Fe2O3/3DPCNF exhibited uptake capacities of 400.4 mg g−1 for Cr(VI) and 426.8 mg g−1 for Pb2+, through mechanisms including surface complexation, isomorphic substitution, redox processes, and metal precipitation. PSO kinetics and Sips isotherms described the behavior, indicating heterogeneous and cooperative adsorption dynamics.148 MOF–COF materials have also shown synergistic adsorption behavior. The UiO-66-NH2@LZU1 composite removed U(VI) at 180.4 mg g−1, outperforming the individual components. This enhancement was ascribed to the complementary coordination environments created by nitrogen- and oxygen-rich sites.149

Among the highest reported capacities, Zr–MOF–NAC achieved Hg2+ uptake of 644 mg g−1via S–Hg and N–Hg coordination, supplemented by electrostatic and film diffusion contributions. Adsorption followed Temkin isotherms and Elovich kinetics, and the material retained 95% of its capacity after four cycles—highlighting its robustness and regeneration efficiency.151

Table 5 presents a comparative overview of recent MOF composites for metal adsorption, detailing adsorption capacities, target ions, kinetic/isotherm models, dominant mechanisms, and recyclability. Collectively, these studies reveal that metal ion adsorption involves far greater structural and mechanistic complexity than the adsorption of organic pollutants. While Langmuir and PSO models remain commonly applied descriptors, they often fail to fully capture the nuanced interplay of redox activity, hydration energetics, speciation, and competitive binding phenomena.

Table 5 Comparative summary of functionalized MOFs for metals
Modified MOF Target metal(s) Optimal conditions Adsorption capacity (mg g−1) Isotherm model Kinetic model Mechanism/selectivity Reusability Ref.
UiO-66 with free –COOH groups Pb2+, Cd2+ pH ≈ 7; [metal]↑ favored interaction Pb2+: 100 Langmuir PSO Ionic interactions; Pb2+ > Cd2+ due to the smaller hydrated radius and lower hydration energy Slight decrease after 3 cycles (washing only) 142
Cd2+: 37
Ni0.6Fe2.4O4-UiO-66-PEI Pb2+, Cr4+ Pb2+: pH 4; Cr(VI): pH 3; teq = 60/30 min Pb2+: 273.2 Hill model PSO Pb2+: chelation with –NH/–NH2; Cr(VI): electrostatic + redox + chelation mechanisms Good after 5 cycles 143
Cr4+:428.6
Magnetite@Fe-BTC Cu2+,Pb2+, As3+, Hg2+ Not specified Cu2+: 55 LangmuirCu2+, As3+, Pb2+ PSO Selectivity: Cu2+ > Pb2+ > Hg2+ > As3+ in mixed solutions Not reported 144
As3+: 57
Pb2+: 147
Hg2+: 155 Temkin (Hg2+)
TMPA@MOF-801 aerogel Cr4+,Cu2+,Pb2+, Zn2+, Cd2+ pH = 2 for Cr(VI); continuous treatment Cr4+: 350.64 Langmuir PSO Cr(VI): electrostatic attraction, redox and ion exchange; selectivity over Cu2+, Pb2+, Zn2+, Cd2+ 6 cycles (∼12% efficiency loss) 145
CF-UiO-66-(OH)2-FA (MCFC) Au3+ pH = 3–4; 25 °C; filtration-friendly format Au3+: 190.98 Langmuir PSO Monolayer chemisorption; spontaneous and exothermic; highly selective for Au3+ from multi-ion mixtures Excellent after 4 cycles 146
Efficiency: >99.99%
WP@PSS@Cu-MOF Li+ pH not reported; brine conditions Li+: 9.69 Not reported PSO Li+: size-sieving (Cu-MOF) + selective sulfonate coordination (PSS); highly selective over Na+, K+, Ca2+, Mg2+ 88.3% capacity after 5 cycles 147
PFO
Fe3O4@C-GO-MOF Pb2+ pH = 6; 10 mg adsorbent; contact time = 60 min Pb2+: 344.83 Langmuir PSO Pb2+ adsorption via amino (UiO-66-NH2), carboxyl and hydroxyl (GO); highly selective over coexisting ions 5 cycles with good recovery 150
Co–Al-LDH@Fe2O3/3DPCNF Cr4+, Pb2+ pH not specified; 5 mg in 100 mL; 60 min Cr4+: 400.40 Sips Multi-step: diffusion + surface complexation Rapid uptake (320–380 mg g−1 at 60 min); mechanisms: surface complexation, isomorphic substitution, e transfer, precipitation 10 cycles with excellent stability 148
Pb2+: 426.76
UiO-66-NH2@LZU1 U6+ Not specified; rapid uptake U(VI): 180.4 Langmuir PSO Synergistic effect of N (COF) and O (MOF) functional groups + electrostatic interaction; multilayer porous structure Not reported 149
(vs. UiO-66-NH2: 108.8)
(vs. LZU1: 65.8)
Zr-MOF-NAC Hg2+ pH = 5.0 Hg2+: 644 Temkin Elovich Chemisorption (dominant); S–Hg and N–Hg coordination; electrostatic interaction; film diffusion as rate-limiting step 5% loss after 4 cycles 151


To advance the rational design of MOF composites for metal remediation, future studies should prioritize multicomponent adsorption systems, in situ spectroscopic analyses, and defect engineering strategies aimed at enhancing selectivity and affinity. Techniques such as solid-state NMR, XPS, and DFT simulations will be instrumental in elucidating metal-specific interactions at the molecular level.152 Such mechanistic insights will underpin the development of structurally robust, regenerable, and highly selective MOF composites capable of efficiently addressing the complex challenge of heavy metal pollution in real-world environments.

5. MOF-composite for degradation of contaminants in wastewater

When adsorption of contaminants is not feasible, their conversion to smaller and harmless products is preferred. Advanced oxidation processes (AOPs) are a special class of wastewater treatment technologies aiming at the conversion of the contaminants to CO2, water, and inorganic substances (by mineralization).153 Their potential to degrade stable and bio-refractory compounds under ambient conditions makes them prominent in the field of water purification research. The denomination AOP comprises a large variety of different technologies, all of which involve the generation of hydroxyl radicals (˙OH), which rapidly attack most organic species.154,155 The AOPs are divided in two groups (Fig. 7) non-photomechanical and photomechamical.155–157 The MOF composites are used as heterogeneous photocatalyst, in this type of photocatalyst, the catalyst and the reaction system are in a different phase.155,158 The process consists of five elementary steps: migration of the analyte from the liquid bulk phase to the photocatalyst surface; adsorption; surface reaction; desorption of the products; transfer of the products to the liquid bulk phase.153,155
image file: d5cc02843d-f7.tif
Fig. 7 Intervention of MOF-composites in the classification of advanced oxidation processes.

The Fig. 8 schematizes the photocatalytic mechanism, in this process the MOF can be used as semiconductor material due to the ligands, or metal ion/metal oxide clusters. It starts when a photon with energy, hν matches or exceeds the band gap energy, hνEg. Conduction electrons, eCB, are promoted from the valence band into the conduction band (CB), leaving a hole, hVB+, behind. The resulting e–h+ pairs within the semiconductor particle can be separated, diffused to the surface of the semiconductor and thus participate in the oxidation and reduction reactions of the organic and inorganic compounds or are subjected for the recombination process by decreasing the quantum yield of the reaction. The electrons in conduction band can react with oxygen, O2 to yield super oxide anion, then in reaction with water can form hydrogen peroxide ˙O2 and then give a hydroxyl radical, ˙OH. The hole can either oxidize a compound directly or react with electron donors like water to form OH radicals, which in turn react with pollutants. The resulting ˙OH radicals are very strong oxidizing agents.155,159


image file: d5cc02843d-f8.tif
Fig. 8 Diagram of a photoexcitation process using a MOF-composite. Reproduced and adapted from ref. 159 Bratovčić 2019.

In degradation processes the MOF is used as a semiconductor material, due to the organic linker ligand and metal ion. The organic ligands can donate many unpaired electrons to the metal ions to fill up the vacant orbital shells. So, metal ions can easily accept these unpaired electrons to form MOF materials. Is common the use of aromatic carboxylic acid ligand, that exhibits an intense UV-absorption peak near 250 nm due to the excess electron density. A feasible strategy is to design a suitable band gap by changing the functional groups on the aromatic ligands, such as NH2 or hydroxyl groups, introducing some dye chromophores or regulating metal ion/metal oxide clusters.155,160,161 The AOPs not only depend on irradiation wavelength and nature and morphology of the photocatalyst, in addition, it depends on irradiation intensity, concentration of analyte, photocatalyst concentration, solution pH, temperature of the reaction.153

To improve the photocatalyst performance in the removal of various types of organic and chemical contaminants in wastewater, the MOF composites emerge as a suitable alternative. The Table 6 summarize recent MOF composites used with this finality. Different types of materials are coupled with MOFs. MOFs coupled with metal, metal-containing nanoparticles,155,162,163 polymers,164–167 graphene oxides,168 MXenes,169 implantation of quantum dots,170 Layered double hydroxides,171 COFs,172,173 due to the combination of different materials and MOFs can reduce the recombination rate of photogenerated electron and hole pairs and simplifies the interfacial charge transfers. Another major advantage of doped incorporated MOF is tuneable pore size, adsorption capacities, dispersibility and interfacial compatibility.

Table 6 MOF-composite for degradation of contaminant
MOF-composite Contaminant Conditions Efficiency (%) Reaction time (min) Ref.
Alginate@CuIMOF Congo red Xe-light 99 60 180
AlginateCuIIMOF 74
Au@PCN 224 Methyl orange Visible light 90 5 175
Methylene blue 90 5
Co@ZIF-8 Methylene blue Visible light 97 180 176
Fe3O4/MCC/Co@ZIF-8
GO@MOF-5 Methylene blue Sunlight 92 360 186
GO@Cu-TCPP MOF Methylene blue Visible light 99.5 10 187
Congo red 94.8 30
Ni/Fe-LDH @ MIL-177LT Acid red 18 Visible light 99 60 188
Ti3C2Tx@UiO-66 Methylene blue UV-Visible light 74 60 189
Direct red 31 50.6
ZNCQDs-Ni/Co-LDH@MOF-5 Methylene blue Sunlight 96.2 80 190
Rose Bengal 95.8 70
Malachite green 99.6 50
ZnO@HKUST-1 Rose Bengal Sunlight 97.4 45 178
ZnOQD@MOF-801 Acid Blue 25 UV light 42.7 60 191
Zr-MOF@TzDa-COF Potassium butyl xanthate Visible light 100 40 192
Potassium amyl xanthane 100
Ethyl zanthane 92.61
Sodium isopropyl xanthane 80.32
CQDs@MOF-808 Acid blue 41 Visible light 86.62 120 193
Bi2MoO6/NH2-UiO-66(Zr) Tetracycline Visible light 81.72 90 185
Chitosan/g-C3N4@Ni-MOF Tetracycline Visible light 96 70 183
Chitosan/MnO2@MOF-801 Rhodamine B Sunlight 95 40 179
Cu@ZIF-7 Rhodamine B Visible light + H2O2 100 30 174
MIL-68-NH2@COF-V Tetracycline Sunlight 96.5 15 194
Rodamine 6G 97.6 25
Phenol 95.3 40
Mg/Al-LDH@UiO-66 Diclofenac Sunlight ∼100 20 195
Polyimide/NH2-UiO-66 Sulfonamides Visible light 96.1 60 181
S,N-CQDs@MOF-808 Rhodamine B Visible light 89 150 196
Sunlight 95
Ti3C2Tx/Pd@MIL-101(Fe) Ofloxacin Visible light ∼100 30 197
Ti3C2Tx@PCN-224 Tetracycline Sunlight 91.2 60 198
Rhodamine B 97.4
W-Zr-MOF-NH2@TpTt-COF Tetracycline Xe-light 87 40 199
SNP-TiO2@Cu-MOF Karenia mikimotoi Sunlight 93.75 360 177
g-C3N4 @Cu-MOF Microcystin Visible light 40–54 120 184
Polypyrrole/NH2-UiO-66 Cr(VI) Visible light 99 150 182


Li et al.174 synthesised Cu dopped ZIF-7 with a molar ratio 1[thin space (1/6-em)]:[thin space (1/6-em)]1 of Cu2+ to Zn2+. Under visible light irradiation, Cu@ZIF-7 serves as a Fenton-like catalyst. With the addition of 9.8 mmol H2O2 and exposure to visible light for 30 min, 10 mg of Cu@ZIF-7 can completely decompose RhB solution. Han et al.175 designed Au doped PCN 224 as bi-functional wastewater treatment agents to absorb and decompose organics molecules efficiently under light irradiation. This MOF composites performs better in absorbing organic compounds consisted of S contained heterocyclic ring (such as methylene blue). Mehrehjedy et al.176 synthetised a Co doped ZIF-8 using visible light for degradation methylene blue achievement 97% of efficiency.

Hu et al.177 prepared SNP-TiO2 by loading non-noble metal modified on the surface of Cu-MOF to photocatalyst Karenia mikimotoi under visible light using concentration of 100 mg L−1 and reaction times 6 h, the visible the reported efficiency was 93.75%. Roy et al.178 incorporated ZnO with Cu-MOF for degradation of Rose Bengal exhibit an efficiency of 97.4% in 45 min under natural sunlight with catalyst dosage of 320 mg L−1, showed that the degradation followed first-order kinetics with a rate constant of 0.077869 min−1. The addition metal oxide not only improved the photocatalytic quality, Mehrehjedy et al.176 additionally doped the Co@ZIF-8 with Fe3O4 and microcrystalline cellulose (MCC) to enhance the reusability of the MOF composite through the application of an external magnetic field, and without the modification of the efficiency.

Ishfaq et al.179 made nanocomposites of chitosan and MnO2 with MOF-801 for degradation of Rh B. Li et al.180 integrated Cu-MOFs into an alginate substrate to offer environmentally friendly, sustainable, facile separation, and high-performance MOF-based hydrogel photocatalysis platforms for Cr(IV) and Congo red dye combined. Wang et al.181 and Wang et al.182 used NH2-UiO-66 composites for the photo-catalytic degradation of sulphonamides and Cr(IV) respectively using polyimide and p-type polypyrrole. Wani et al.,183 integrate Ni-MOF in chitosan and g-C3N4 for degradation of tetracycline and antibacterial activities, using polymers and nanoparticles with MOFs.

Wang et al.184 prepared nanocomposites to inactivate Microsystis aeruginosa and degradate microcystin under visible light irradiation, doping with g-C3N4 a Cu-MOF. Wang et al.185 synthetized a Bi2MoO6/NH2-UiO-66(Zr) for photocatalytic degradation under visible light conditions.

The efficiency for photocatalytic degradation of tetracycline reaches 81.72% in 90 min. Bouider et al.186 and Zao et al.187 incorporate MOF with graphene oxide (GO) for degradation of methylene blue, obtained a degradation of 92% in 360 min under sunlight radiation and 94.8% in 30 min under visible light radiation.

Far et al.189 grown the UiO-66 on Ti3C2Tx MXene nanosheets enhanced the degradation of methylene blue and direct red 31 dyes efficiency to 78% and 56% within 60 min of UV-visible irradiation. Li et al.198 using solvothermal synthesis for create a composite Ti3C2Tx MXene and PCN-224 for degradation of tetracycline and Rhodamine B obtained 91.2% and 97.4% efficiency under sunlight irradiation during 60 min. Eslaminejad et al.197 combined palladium nano particles with Ti3C2Tx MXene for the degradation of ofloxacin achievement almost 100% of efficiency under visible light during 30 min.

Rahmani et al.196 modified MOF-808 with sulphured and nitrogen co-doped carbon quantum dots (S,N-CQDs) using a simple solvothermal technique, MOF-808 was not photo-active in visible light region because of its large band gap energy (3.85 eV), the prepared composites exhibited photocatalytic activity in Rhodamine B degradation up to 89% under visible light and 95% under sunlight irradiation in optimal conditions. Ghasemzadeh & Akhbari studied the degradation of acid blue 41 with two different modified MOFs with quantum dots, with ZnO quantum dots doped MOF-801 obtained an efficiency of 42.7% using UV light191 and carbon quantum dots doped MOF-808 obtained an efficiency of 86.62% under visible light.193

Bhuyan & Ahmaruzzaman190 loaded Ni/Co-LDH@ MOF-5 composite with metal-oxide quantum dots (ZNCQD) was via ultrasonication, towards the degradation of methylene blue, rose Bengal and malachite green (MG). The maximum percentage degradation was 96.2%, 95.8%, and 99.6%, respectively. Wang et al.195 constructed UiO-66 MOF with layered Mg/Al-LDH in a hydrothermal synthesis strategy for the highly efficient photodegradation of diclofenac reached 100% mineralization using solar energy. Pirkarami et al.188 decorated MIL-177-LT with Ni/Fe LDH for degradation of the Acid Red 18 from water.

Zhang et al.192 prepared Zr-MOF@TzDa-COF for degradation of Potassium butyl xanthate, Potassium amyl xanthane, Ethyl zanthane and Sodium isopropyl xanthane using visible light, obtained of 100%, 100%, 92.61% and 80.32% respectively. Li et al.199 fabricated hollow heterojunction of W-Zr-MOF-NH2@TpTt-COF synthesized through acid etching and hydrothermal methods for tetracycline degradation, it showed 87% degradation of tetracycline under 300 W Xenon lamp irradiation. Zhou et al.194 customized MIL-68@COF-V which realize the efficient degradation, up to 95%, of tetracycline within 15 min, rhodamine 6G within 25 min, and phenol within 40 min using sunlight. Finally, it is worth noting that Gan et al. investigated the removal of the organophosphate herbicide glyphosate (GP) from water using a Zr-based MOF (mCB-MOF-2), which combines high adsorption capacity (11.4 mmol g−1) with selective photodegradation. This MOF converts 69% of GP into non-toxic products such as sarcosine, while effectively preventing the formation of aminomethylphosphonic acid (AMPA).200 This research stands out, since in recent years various material modification strategies have been described to improve the photocatalytic degradation performance of pesticides201 and and it has also been investigated that the metal center of MOFs gives it properties for the adsorption and photodegradation of herbicides.202

6. MOF-composites based biosensors of wastewater pollutants

Biosensors are devices for detecting the specific analyte that combines a biological element with the transducer or the detector element (Fig. 9).203,204 The fundamental function of a sensor is to convert a signal into information that can be measured, displayed, and processed directly.205,206 The sensitive biological species can be microorganisms, tissue, cells, receptors, enzymes, antibodies, nucleic acids, etc., which are biologically derived materials or bio-mimetic components that bind with the transducer.203,205,207,208 Biosensors usually divided in two types, electrochemical sensors, the sensing strategy utilizes changes in various electrical parameters, techniques such as cyclic voltammetry (CV), chronoamerometry, square wave voltammetry (SWV), electrochemical impedance spectroscopy (EIS) and differential pulse voltammetry (DPV) are employed. The other type are the optical sensors with techniques as fluorescence, surface plasmon resonance (SPR), surface enhanced Raman scattering (SERS) and optical fibers.204,208–210
image file: d5cc02843d-f9.tif
Fig. 9 Schematic diagram of a biosensor based on a MOF composite.

The rapid industrialization results a water pollution to causes ecological imbalance and threatens human health. The detection of contaminants such as pesticides, antibiotics, nitro-explosives, heavy metals, etc.206 The design and development of effective and sensitive analytical strategies for precise pretreatment and determination of different water contaminants in various aqueous solutions are critical. More interestingly, selectivity and sensitivity are essential to accurately determine the concentration of water contaminants because they are usually present at low concentrations.204,211

The realization of MOF composites provides a platform to strength MOFs in the biosensor's realm. MOFs properties including high pore volume, large surface area, and good thermal stability. Besides, MOFs can conjugate biomolecules through electrostatic interaction, π–π stacking, coordination bonding, chemical bonding, etc. Therefore, MOF composites-based biosensors with superior properties are expected to be sensitive in the detection.203,204,212 The main advantages of using MOFs for sensing materials stem from their distinct and highly modifiable physical, chemical, and structural (and thus functional) properties, such as regular porosity and adjustable pore size, multivariate structures with multiple metal centres and diverse organic linkers. Post-synthetic modifications (PSM) can also be utilized to functionalize MOFs (external surface and interior pore space) and provide new functions. Selectivity is determined by pore and aperture sizes, and MOF surface chemistry, such as specific (bio)chemical interactions of the analyte with functional groups or opens metal sites (OMS). The rate of analyte diffusion to the interaction site, is related to MOF particle size and pore diameters, nanosized MOFs, which have faster diffusion rates and consequently faster analyte responses than micrometer-sized MOFs, are often preferred to ensure rapid analyte uptake and equilibration with better sensitivity.203,213

The Table 7 summarizes important parameter of recent MOF composite to detected wastewaters pollutants. Nanoparticles formed common composites with MOFs to create biosensors. Liang et al.214 characterize a Fe-MOF@YAU-101/GCE sensor that can efficiently and synchronously identify Hg2+, Pb2+ and Cd2+ limits of detection (LOD) are 1.33 × 10−8 M, 6.67 × 10−10 M and 3.33 × 10−10 M respectively. The detection limits are superior to the permissible limits set by the National Environmental Protection Agency, using DVP sensing method. Menon et al.215 grown MOF-5 in situ over the tannic acid-capped gold nanoparticles (AuNPs) of plasmonic U-bent fiber optic sensor (U-FOS) probes. The Pb2+ ion binding to MOF-5 was detected and quantified as an increase in the plasmonic absorption of the light by AuNPs due to significant refractive index changes at the AuNP surface. Bodkhe et al.216 fabricated selective and sensitive Hg2+ion electrochemical sensor using a solvothermally synthesised composite of silver nanoparticles (AgNPs) and zinc benzene dicarboxylate (ZnBDC) MOF. The electrochemical sensor was fabricated by drop-casting the Ag@ZnBDC composite onto a glassy carbon electrode. Remarkably, the sensor exclusively and highly selectively responded to Hg2+ions among Cd2+, Cr3+, Cu2+, Fe3+, and Pb2+ ions at a concentration of 1 μM. Saravanakumar et al.217 fabricated Cu-MOF@Pd-500 modified glassy carbon (GC) electrode was for the sensing of Bisphenol A (BPA).

Table 7 MOF-composites for contaminant sensing
MOF-composite Detection method Analyte Role of MOF Linear dynamic range Limit of detection Ref.
Ag@ZnBDC-MOF CV and DVP Hg2+ Sensing 1–10 nM 4.16 nM 216
AuNPs/Zr-MOF@graphene DVP Sudan I Electron mobility and surface area 0.1–800 μM 0.1 μM 228
Sunset Yellow 0.1–1000 μM 0.1 μM
BNCDs/Tb-MOF@GR5 DNAzyme Fluorescence Pb2+ Fluorescence tag 2–1000 nM 0.96 ppb 219
BPA aptamer@Ni-MOF Fluorescence Bisphenol A Protecting 15–40 μM 0.34 μM 223
Ce-doped Ti3C2Tx@ MOF-199 DVP L-Tryptophan Sensing 0.5–156.5 μM 0.18 μM 234
CDs/Eu@NH2-UiO-66 Fluorescence Hg2+ Sensing 76.67 nM 218
Pb2+ 5.12 nM
Chitosan@cotton@Zr-MOF Fluorescence 2,4-D Sensing 10.9 nM 232
NTX 50.8 nM
CNTs@Ce-MOF EIS Nitrite Sensing 0.65–3.25 μM 87.65 mA mM−1 cm2 225
3.25–7000 μM 0.35 mA mM−1 cm2
Cu-MOF@Pd-500 CV Bisphenol A Sensing 1–22 μM 0.025 μM 217
DVP 1–150 μM 0.06 μM
Eu-MOF@PHEMA Fluorescence Fe3+ Sensing 2–120 ppm 0.16 ppm 233
Fe-MOF@YAU-101/GCE DVP Cd2+ Sensing 0.002–30 μM 3.33 × 10−10 M 214
Hg2+ 0.04–34 μM 1.33 × 10−8 M
Pb2+ 6.67 × 10−10 M
FeSx@MOF-808/Ti3C2Tx SWV As3+ Sensing 0.05–100 ng mL−1 0.02 ng mL−1 235
g-CNQDs @NH2-UiO-66 Fluorescence Hg2+ Sensing 2.4 nmol L−1 220
GOx-cDNA@Ce-MOF PGM-based Ofloxacin Loading platform 50 pg mL−1– 500 ng mL−1 40 pg mL−1 222
KB-CNTs@UiO-66 CV and DVP Methyl parathion Selective recognition and enrichment capability. 0.005–12 μM 1.53 nM 226
MOF-5@Tannic acid-AUNPs Fiber optic Pb2+ Plasmonic absorption 0.5 ppb–50 ppm 0.5 ppb 215
NH2-UiO-66@TpTt-COF Fluorescence Tetracycline Attenuating ACQ 0–160 μmol L−1 7.4 nmol L−1 236
NH2-UiO-66@TpTt-COF MIP-PEC Dibutyl phthalate Sensing 0.1 nmol L−1–100 μmol L−1 3.0 × 10−11 mol L−1 237
PVA/PAA/MOF NiCoZn-LDH@ GO TF-μSPE-HPLC-UV Caffeine Sensing 0.3–1000 ng mL−1 0.1 ng mL−1 230
Tramadol 0.5–1000 ng mL−1 0.15 ng mL−1
Codeine 0.5–1000 ng mL−1 0.15 ng mL−1
Hydrocodone 0.5–1000 ng mL−1 0.15 ng mL−1
Naloxone 0.5–1000 ng mL−1 0.1 ng mL−1
Noscapine 0.3–1000 ng mL−1 0.1 ng mL−1
Celecoxib 0.3–1000 ng mL−1 0.1 ng mL−1
SMD aptamer/Ru@Zn-oxalate ECL Sulfadimethoxine ECL sensor 100 fM–500 nM 27.3 fM 224
Sn-MOF-650@rGO CV Catechol Sensing 0.20–28 μ M 33 nM 229
Tp-COF@Cu-BDC EIS 2-Aminophenol Sensing 2–250 μM 0.597 μM 238
ZIF-67@Co/NC-MWCNT SWV Cd2+ Surface area and active sites 0.12–2.5 μM 4.5 nM 227
Pb2+ 4.5 nM
Zn-LMOF@PMMA Luminesce DCN Sensing <20 μM 231
Ornidazole <50 μM


Other technologies to create sensing MOFs are the addition of dots. Yan et al.218 prepared Eu doped NH2-UiO-66 MOF complexed with S-containing carbon dots (CDs) exhibited dual-emission fluorescence. The MOF composite could bind specifically to Hg2+and Pb2+ with enhanced sensitivity. Jain et al.219 designed a fluorescence-based biosensor for the selective and sensitive detection of Pb2+ with boron and nitrogen carbon dots (BNCDs)-doped carboxyl functionalized-terbium metal–organic framework (COOH-Tb MOF) as a fluorescent tag and quencher-modified catalytic NH3-GR5 DNAzyme as a bioreceptor molecule. Sonowal et al.220 synthesized NH2-UiO-66 using in situ solvothermal synthetic technique for fluorescence sensing of Hg2+ in water. The well-distributed graphitic carbon nitride quantum dots on the MOF improve Hg2+ sensing activity in water owing to their great electronic and optical properties.

The use of aptamer, synthetic of ssDNA, ssRNA or peptides that bind to some target with high specificity and affinity,221 in MOFs for selectivity is wide extended. Wang et al.222 developed a personal glucose meter (PGM)-based aptasensor utilizing a porous spherical cerium-based metal–organic framework (Ce-MOF) as a loading platform for glucose oxidase (GOx) and an aptamer, oligonucleotide sequences (cDNA), for the sensing of ofloxacin, a widely used antibiotic, is of particular concern due to its potential for contamination in milk and surface water. Zhang et al.223 fabricated a Ni-MOF that carrier aptamers of Bisphenol A (BPA), that immobilizing the aptamers inside its array channels under Mg2+ regulation. Due to the in-hole fixation strategy of aptamers, Ni-MOF played protection aptamers not only the activity of BPA aptamer@Ni-MOF composite was maintained more than 50% in complex environments such as pH 3.0–11.0, 30 70 °C and organic solvents, but also the aptamer was protected from nuclease hydrolysis under physiological conditions. Wang et al.224 constructed an electrochemiluminescence (ECL) aptamer sensor based on Ru@Zn-oxalate MOF composites is for sensitive detection of sulfadimethoxine (SDM). The prepared Ru@Zn-oxalate MOF composites with the three-dimensional structure provide good ECL performance. The use of the aptamer improves the selectivity of the sensor. Thus, high-sensitivity detection of SDM specificity is realized through the specific affinity between SDM and its aptamer.

Carbon derivates as Carbon nanotubes (CNT) are used to enhanced conductivity or controlled pore size. Wang et al.225 report a 2D Ce-MOF nanosheets grown on carbon nanotube substrates. The construction of layered MOF nanosheet materials is based on CNTs as the backbone and relies on the low ligand–metal ratios which is controlled by the loaded organic ligands on the surface of CNTs.

The synthesized layered CNTs@Ce-MOF nanosheet composite material uses Ce as the centre ion and electrocatalytic active material for nitrite sensing. Guo et al.226 fabricated an electrochemical sensor for methyl parathion electrochemical sensor from UiO-66 and Ketjen black-CNTs. For the KB-CNTs@UiO-66 composite, Zr-based UiO-66 with Zr–OH groups possessed good affinity for phosphate groups on methyl parathion molecules, which enhanced the selective recognition and enrichment capability. The KB-CNTs possessed three-dimensional interconnected carbon conductive network with synergistic point-line carbon conductive contact, which showed excellent electrical conductivity. Zhao et al.227 using a ZIF-67 derived cobalt/nitrogen-doped carbon (NC) composite polyhedrons linked with multi-walled carbon nanotubes (MWCNTs), for the simultaneous monitoring of Cd2+ and Pb2+. Nanoporous ZIF-67 was first in situ grown on the MWCNTs, followed by carbonization, endowing the composite with good electrical conductivity and a large specific surface area, providing more active sites for subsequent metal ion attachment.

Graphene composites can amplify the electron transfer rate in electrochemical biosensors. Sun et al.228 modified with Au nanoparticles the composite Zr-MOF@Graphene as the electrode modification material for detecting sunset yellow and Sudan I. The prepared AuNPs/Zr-MOF@Graphene composites presented electrochemical an improving accumulation efficiency, electrocatalytic activity and promoting charge transfer. Liu et al.229 presents the development of Sn-MOF-650@rGO, created by carbonizing reduced graphene oxide (rGO) on the surface of Sn-MOF after in situ encapsulation. The Sn-MOF-650@rGO modified glassy carbon electrode was successfully constructed for the electrochemical detection of catechol. Karazmi et al.230 combined polymers, LDH and graphene oxide to synthetised polyvinyl alcohol (PVA)/poly acrylic acid (PAA)/MOF NiCoZn-LDH@graphene oxide (GO) electrospun nanofiber for multiple drugs. The combination of MOF NiCoZn-LDH@GO with a highly porous structure and rich functional groups in the PVA/PAA substrate casing significantly improves the absorption properties of the nanofibers.

The composites MOF and polymers was used to improve usage properties. Gao et al.231 prepared a Zn-luminescent MOF-based sensor with poly(methyl methacrylate) polymer, the polymer matrix provided the chemical protection for MOF particles. The as fabricated Zn-LMOF@PMMA exhibited strong blue emission under ultraviolet light irradiation, which can act as luminescent probe for detecting antibiotics and pesticides and exhibited visual, real-time detection of antibiotics ornidazole (ODZ) and pesticides 2,6-dichloro-4-nitrobenzenamine (DCN). Ghost et al.232 designed a formamide functionalized Zr-MOF, was used for the selective and fast fluorescence sensing anti-microbial drug nitroxoline (NTX) and herbicide 2,4-dichlorophenoxyaceticacid (2,4-D). For rapid and on-site naked eye detection, a cheap, handy, reusable, and biocompatible, they fabricated a chitosan@cotton@MOF composite. The composite was capable to change its colour up to nanomolar-level concentrations. Rozenberga et al.233 immobilize Eu-MOF, EnMTA, in a cross-linked poly(2 hydroxyethyl methacrylate) (PHEMA) matrix resulted in a significant enhancement in the ligand stability, as well as an increase in the Fe3+ sensing range (2–120 ppm). Compared to native EuMTA powder (0.16–4 ppm). The PHEMA matrix also functioned to protect the embedded MOF, improving the stability of the sensor under acidic conditions, while enhancing Fe3+ ion selectivity compared to a EuMTA powder suspension.

The Ti3C2Tx Mxene improves sensitivity of MOFs sensors. Zhang et al.234 sensed L-Tryptophan using a Ce-doped Ti3C2Tx@MOF-199 nanocomposite. Density functional theory (DFT) calculation suggested that the doping of Ce can effectively enhance the electronic structure of MOF-199 and improve its adsorption capacity for L-Trp. Additionally, the integration of Ce-doped MOF-199 with Ti3C2Tx MXene substantially improves the conductivity of MOFs, resulting in enhanced electrocatalytic activity of the nanocomposite material. Chen et al.235 introduces a FeSx@MOF-808/Ti3C2Tx for detection of As3+. The composite enhances the sensitivity and selectivity detection. The sensor demonstrated exceptional sensitivity with a detection limit of 0.02 ng mL−1 and a broad linear response range of 0.05–100 ng mL−1, surpassing World Health Organization guidelines. And notably exhibited minimal interference from common heavy metals.

The heterostructure of MOF@COF improve photoactivity and interaction between sensor and analyte. Li et al.236 developed a fluorescence sensor based on dual-emissions NH2-UiO-66@TpTt-COF composite by an interfacial growth method for detecting tetracycline. The import of NH2-UiO-66 attenuates the aggregation-caused quenching (ACQ) of TpTt-COF. Tetracycline is recognized by the MOF composite through π–π stacking and interacts. The MOF-composite has a detection limit of 7.4 nmol L−1 and is applied for the detection of tetracycline in soil and river water. The same MOF composite was used for Yang et al.237 for determination of dibutyl phthalate (DBP). However, for improve the sensing performance, molecularly imprinted polymer (MIP) was developed by sol–gel polymerization method as the recognition component photoelectrochemical sensor (PEC). The NH2-UiO-66/TpPa-1-COF was synthesized using a simple one-step solvothermal method, which improved photocurrent response owing to heterojunction formation, favourable energy-band configuration and strong light absorption capacity. Song et al.238 employed Cu-MOF material was employed as a substrate, and its conductivity and catalytic properties were then enhanced by incorporating Tp-BD-DBd COF within the pores. Tp-COF@Cu-BDC introduced not only extensive π-conjugated systems but also the mutual electron-donating amino and carboxylate electron-with drawing groups that may help improve the adsorption capability of 2-aminophenol (AP).

The binary MOF composite (2) i.e., the sensor exhibited excellent limit of detection (LOD) value of 2.4 nmol L−1 for Hg2 +. The sensor also exhibited excellent performance for mercury(II) detection in real water samples. The characterizations of the synthesized materials were done using various spectroscopic techniques and the fluorescence sensing mechanism was studied.

7. Current perspectives and prospects for MOF-composites in water contaminant applications

Currently, the systematic investigation of MOF composites for the adsorption of dyes, pharmaceuticals, and metal ions has firmly established them as versatile platforms for aqueous pollutant remediation. Their consistently superior performance arises from a unique combination of properties: high densities of chemically accessible active sites, hierarchical and tunable porosity, and synergistic multivalent binding mechanisms—including electrostatic interactions, π–π stacking, hydrogen bonding, coordination chemistry, and, when applicable, redox-mediated processes.

Nevertheless, despite their significant potential, MOF composites still face several challenges. Key aspects such as scalable synthesis, long-term stability under real-world conditions, and cost-effective production must be optimized to enable wider industrial implementation. In addition, further advancements are required in processing techniques that facilitate their integration into solid matrices or membranes—an essential step for many water treatment applications. Nevertheless, recent developments in green synthesis and post-synthetic modification methods offer a promising outlook. The integration of artificial intelligence is expected to accelerate the design of highly efficient MOFs tailored to specific environmental targets.

A comparative evaluation of recent adsorption studies (Table 8) reveals that MOF composites consistently outperform conventional materials across a wide range of contaminants, including dyes, pharmaceuticals, and heavy metals. For cationic dyes such as methylene blue, composites such as functionalized Fe-MOF/GO (387.0 mg g−1) and Al-MOF/GO (336.0 mg g−1) exhibit significantly higher adsorption capacities than traditional carbonaceous, clay-based, or biopolymeric composites, which seldom exceed 200 mg g−1.105,239 This superiority is even more pronounced for Rhodamine B, where a ZIF–MIL-4 composite achieved an exceptional capacity of 1181.0 mg g−1, orders of magnitude greater than that of conventional polymer–inorganic composites.101

Table 8 Comparison of adsorption capacities of MOF-based and other composites for various water pollutants
Pollutant type Pollutant Category Material Adsorption capacity (mg g−1) Ref.
Cationic dye MB MOF composite Functionalized Fe-MOF/GO (FeGC) 387.0 105
Functionalized Al-MOF/GO (AlGC) 336.0 105
GO–Cu–MOF composite 262.0 112
Other composites C-M-SD/MMT-Fe3O4 Hydrogel 224.5 242
ZnO@orange peel (ZnO@OP) 205.4 243
ZnO@activated carbon (ZnO@AC) 180.0 243
Porous carbon/Na-P1 Zeolite composite (CZ-10) 175.9 244
ZnO nanoparticles 175.7 243
Clay/chitosan composite (SRC/Cs) 97.1 239
Magnetic walnut–chitosan composite (m-WCH) 85.5 245
GO/ZnTiO3/TiO2 (GO/ZTO/TO) 78.0 246
KOH/chitosan co-modified biochar (CHKBC) 62.0 247
Alginate–sepiolite biocomposite (ALG-Sep(1.5)) 55.5 248
RhB MOF composite MOF-5/COF composite (M5C) 16.2 106
FeAl(BDC) MOF 48.6 96
UiO-66/MXene composite 285.0 249
ZIF-MIL-4 composite 1181.0 101
Other composites Cu-BTC@Alg/Fe3O4 composite 3.7 250
PANI/TiO2/CuO composite ∼0.934 251
Anionic dye AY MOF composite Ni-BDC 657.9 111
Cu-BDC 709.2 111
Zn-BDC 714.3 111
Other composites LDH (Mg–Al NO3) 26.3 252
Q-UiO-66 (defect-engineered) 398.2 253
Biogenic ZnO NPs 5.3 254
MO MOF composite MIL-101-NH 2 -1 461.7 109
Other composites CHI/C-TiO2-50 196.6 255
PGZrP composite 36.5 256
Drugs Quercetin MOF composite Activated Gd-MOF (3d-E) 370.0 131
Other composites SiO2@MIP (molecularly imprinted polymer) 35.7 240
Cefixime (CFX) MOF composite HKUST-1/ZIF-8 110.0 138
Other Composites Fe3O4@MWCNT-CdS composite 105.3 257
SGCAN (sol–gel carbon aerogel modified with Ni) 52.0 258
Ibuprofen MOF composite MOF-199/CNTs 40.8 124
Other composites Alg/AC/CMC composite beads (hydrogel) 39.6 259
Alg/AC/CMC composite beads (reswelled) 34.0 259
Alg/AC beads (reswelled) 8.6 259
Metals Pb2+ MOF composite UiO-66 with free –COOH groups 100.0 142
Other composites Wool–alginate–gum (GA + XG) hydrogel composite 85.2 260
Li+ MOF composite WP@PSS@Cu-MOF 9.7 147
Other composites LDH-Si-BX (lithium/aluminium LDH-SiO2 loaded bauxite composite) 1.7 261
U(VI) MOF composite UiO-66-NH 2 @LZU1 180.4 149
Other composites nZVI/clay composite 88.9 241


Similar trends are observed for anionic dyes, pharmaceutical residues, and metal ions. MOF composites such as Ni-BDC, Cu-BDC, and Zn-BDC surpass 700 mg g−1 in the adsorption of Alizarin Yellow, far exceeding the performance of layered double hydroxides and biogenic oxide nanoparticles.111 In the case of pharmaceutical adsorption, Gd-MOF-based composite and HKUST-1/ZIF-8 composites outperform sol–gel-derived carbon aerogels and molecularly imprinted polymers, achieving capacities an order of magnitude higher.131,138,240 For metal ion capture, UiO-66-NH2@LZU1 demonstrates a U(VI) uptake of 180.4 mg g−1, substantially superior to conventional clay or bio-based composites.149,241 Nevertheless, each pollutant class imposes distinct physicochemical demands. Dyes have long served as model compounds for probing the interplay between pore architecture and functional site accessibility, emphasizing the role of molecular size, charge, and diffusion pathways. In contrast, pharmaceutical adsorption introduces complexities related to dynamic speciation, co-solute interference, and biofluid-like matrices, requiring materials capable of balancing strong uptake with controlled release. The adsorption of metal ions presents an additional layer of mechanistic complexity—arising from redox sensitivity, multivalent speciation, and competitive binding in polymetallic environments—which necessitates a highly tailored material design and more precise mechanistic elucidation. Moreover, the modularity of MOF frameworks allows precise functionalization strategies to target specific pollutants, providing fine control over selectivity, adsorption kinetics, and capacity even under complex, competitive conditions. By contrast, traditional composites, although advantageous in terms of cost and ease of production, are limited by lower surface areas, poorly defined active site distributions, and reduced chemical resilience under challenging environmental conditions, restricting their efficiency and versatility in practical applications.

While considerable strides have been made in the synthesis, characterization, and benchmarking of MOF composites, several critical gaps remain. Adsorption studies on metal ions remain largely empirical, often lacking integration with advanced spectroscopic, thermodynamic, or computational techniques to verify proposed mechanisms. Across all contaminant classes, the continued reliance on static batch experiments under idealized conditions limits the predictive relevance of laboratory findings, underscoring the urgent need for in situ characterization and performance validation under realistic, multicomponent, and flow-based scenarios. To overcome these limitations, future research must adopt an integrated, mechanism-based approach to materials development.

Furthermore, advanced analytical methods capable of capturing in situ adsorption dynamics, such as time-resolved spectroscopy, solid-state NMR, and ambient TEM, will be essential to resolve mechanistic ambiguities. On the other hand, designing scalable synthesis routes, ensuring long-term material stability, and validating performance under real-world water treatment conditions are all imperative to unlock the full potential of MOF composites.

Finally, MOF composites are clearly promising for the degradation of water pollutants (Table 9), as are the uses of pure MOFs or individual materials such as polymers, oxides, nanoparticles, MXenes, or quantum dots. This is because MOF composites overcome critical limitations of each material when used separately, providing unique synergies that enhance processes such as photocatalysis, peroxide activation, and Fenton-type reactions. While pure MOFs offer high porosity and adsorption capacity, they typically exhibit low stability in aqueous media and limited catalytic activity. On the other hand, materials such as metal oxides, QDs, or MXenes can be good conductors or photocatalysts, but lack the porous structure and molecular selectivity of MOFs. When combined in composites, functional integration is achieved, enabling, for example, improved electron–hole charge separation, accelerated generation of reactive oxygen species (ROS), enhanced structural and hydrolytic stability, and facilitated material reuse. Composites such as MOF@TiO2, MOF@MXene, or MOF@g-C3N4 have demonstrated superior efficiency in the degradation of emerging contaminants such as pharmaceuticals, dyes, and pesticides, even under visible light. Furthermore, some MOF composites enable magnetic separation or even simultaneous detection of the contaminant. Therefore, MOF composites offer an optimal balance of adsorption, reactivity, stability, and processability, positioning them as one of the most advanced and versatile technologies for treating contaminated water in the near future.

Table 9 Degradation efficiency of water pollutants by MOF compounds compared to MOF
Degradation efficiency (%)
Water contaminant MOF-composite MOF
Tetracycline Bi2MoO6/NH2-UiO-66(Zr)185 Chitosan/g-C3N4 @Ni-MOF183 MIL-68-NH2@COF-V194 Ti3C2Tx@PCN-224198 W-Zr-MOF-NH2@TpTt-COF199 MIL 88-A262
81.72 96 96.5 91.2 87 100
Methylene blue Au@PCN 224175 Fe3O4/MCC/Co@ZIF-8176 GO@MOF-5186 Ti3C2Tx@UiO-66189 ZNCQDs-Ni/Co-LDH@MOF-5191 ZIF-8263
90 97 92 74 96.2 83.2
Rhodamine B Cu@ZIF-7174 Chitosan/MnO2@MOF-801179 Ti3C2Tx@PCN-224198 S,N-CQDs@MOF-808196 ZIF-67264
95 100 97.4 89 and 95 90


8. Conclusions

MOF-composites represent a promising technology for the adsorption, degradation, and sensing of contaminants in wastewater. Their high adsorption capacity, catalytic properties, and versatility by functional modification make them ideal candidates to address current environmental challenges. With continued advances in their development and production, MOF-composites have the potential to transform wastewater treatment, contributing to environmental protection and public health.

We describe how the combination of other materials provides further expanded the potential for metal–organic frameworks (MOFs), as they have demonstrated to be promising tools in environmental remediation applications, such as wastewater, thanks to their inherent properties and structural versatility. In the case of adsorption, these combinations favour greater capacity and selectivity, to trap contaminants at low concentrations. For degradation, especially in photocatalysis, MOF-composites act as active platforms capable of accelerating the decomposition of contaminants under visible or ultraviolet light. Regarding sensing, the composites enable the precise and early detection of specific contaminants due to their high sensitivity and rapid response. Together, these advances consolidate MOF-composite as a promising technology for advanced wastewater monitoring and remediation.

Author contributions

J. Aguila-Rosas, Francisco J. Cano and Alan Nagayaa wrote the paper with input from all authors. T. Quirino-Barreda and Ma. De Jesús Martínez were involved in managing information and commented on the manuscript. J. Aguila-Rosas, Ariel Guzmán, I. Ibarra and E. Lima designed and directed the project.

Conflicts of interest

There are no conflicts to declare.

Data availability

All the data supporting this article have been included in the main text.

Acknowledgements

This work was supported by the SECIHTI Mexico (CBF2023-2024-227).

References

  1. C. Xia, X. Li, Y. Wu, S. Suharti, Y. Unpaprom and A. Pugazhendhi, Environ. Res., 2023, 222, 115318 CrossRef CAS PubMed .
  2. J. R. Dominguez, J. García and S. Alvarez, Environ. Sci. Pollut. Res., 2021, 28, 18725–18726 CrossRef PubMed .
  3. L. Wang, D. Hou, Y. Cao, Y. S. Ok, F. M. Tack, J. Rinklebe and D. O’Connor, Environ. Int., 2020, 134, 105281 CrossRef CAS PubMed .
  4. A. H. Mashhadzadeh, A. Taghizadeh, M. Taghizadeh, M. T. Munir, S. Habibzadeh, A. Salmankhani and M. R. Saeb, J. Compos. Sci., 2020, 4, 75 CrossRef CAS .
  5. J. Darabdhara and M. Ahmaruzzaman, Chemosphere, 2022, 304, 135261 CrossRef CAS PubMed .
  6. I. Ahmed and S. H. Jhung, Mater. Today, 2014, 17, 136–146 CrossRef CAS .
  7. S. Kashif, S. Akram, M. Murtaza, A. Amjad, S. S. A. Shah and A. Waseem, Diam. Relat. Mater., 2023, 136, 110023 CrossRef CAS .
  8. S. Essalmi, S. Lotfi, A. BaQais, M. Saadi, M. Arab and H. Ait Ahsaine, RSC Adv., 2024, 14, 9365 RSC .
  9. K. G. Liu, F. Bigdeli, Z. Sharifzadeh, S. Gholizadeh and A. Morsali, J. Cleaner Prod., 2023, 404, 136709 CrossRef CAS .
  10. X. Han, G. Li, W. Su, X. Xu, M. Yu, G. Wu and W. Xing, Colloids Surf., A, 2024, 702, 135147 CrossRef CAS .
  11. R. Kumar, M. S. Shafique, S. O. M. Chapa and M. J. Madou, Sensors, 2025, 25, 2473 CrossRef CAS PubMed .
  12. A. Kharkova, V. Arlyapov, A. Medvedeva, R. Lepikash, P. Melnikov and A. Reshetilov, Sensors, 2022, 22, 8522 CrossRef CAS PubMed .
  13. S. Wang, Biomolecules, 2021, 11, 399 CrossRef CAS PubMed .
  14. N. Nassiri Koopaei and M. Abdollahi, Daru, J. Pharm. Sci., 2017, 25, 9 CrossRef PubMed .
  15. S. Singh, E. Arputharaj, H. U. Dahms, A. K. Patel and Y. L. Huang, Bioresour. Technol., 2022, 351, 127018 CrossRef CAS PubMed .
  16. R. R. Mandal, Z. Bashir, J. R. Mandal and D. Raj, Environ. Monit. Assess., 2024, 196, 1–21 CrossRef PubMed .
  17. E. Beltrán-Flores, M. Sarrà and P. Blánquez, Chemosphere, 2024, 352, 141283 CrossRef PubMed .
  18. Y. T. Hung, H. A. Aziz, S. F. Ramli, H. H. Paul, C. R. Huhnke and B. M. Adesanmi, Water Environ. Res., 2020, 92, 1504–1509 CrossRef CAS PubMed .
  19. S. Sharma, V. Sharma, A. Mittal, D. K. Das, S. Sethi, S. Yadav, B. Vallamkonda and V. K. Vashistha, Water Environ. Res., 2024, 96, e11106 CrossRef CAS PubMed .
  20. Y. Peng, J. Xu, J. Xu, J. Ma, Y. Bai, S. Cao, S. Zhang and H. Pang, Adv. Colloid Interface Sci., 2022, 307, 102732 CrossRef CAS PubMed .
  21. Y. Xue, S. Zheng, H. Xue and H. Pang, J. Mater. Chem. A, 2019, 7, 7301–7327 RSC .
  22. N. Couzon, M. Ferreira, S. Duval, A. El-Achari, C. Campagne, T. Loiseau and C. Volkringer, ACS Appl. Mater. Interfaces, 2022, 14, 21497–21508 CrossRef CAS PubMed .
  23. H. Rastin, D. Dell’Angelo, A. Sayede, M. Badawi and S. Habibzadeh, Environ. Res., 2025, 282, 122087 CrossRef CAS PubMed .
  24. W. Wang, M. Chai, M. Y. Bin Zulkifli, K. Xu, Y. Chen, L. Wang, V. Chen and J. Hou, Mol. Syst. Des. Eng., 2023, 8, 560–579 RSC .
  25. T. Chen and D. Zhao, Coord. Chem. Rev., 2023, 491, 215259 CrossRef CAS .
  26. Q. Zhang, H. Yang, T. Zhou, X. Chen, W. Li and H. Pang, Adv. Sci., 2022, 9, 2204141 CrossRef CAS PubMed .
  27. S. Singh, N. Sivaram, B. Nath, N. A. Khan, J. Singh and P. C. Ramamurthy, npj Clean Water, 2024, 7, 1–22 CrossRef .
  28. S. Shahzadi, M. Akhtar, M. Arshad, M. H. Ijaz and M. R. S. A. Janjua, RSC Adv., 2024, 14, 27575–27607 RSC .
  29. G. W. Peterson, D. T. Lee, H. F. Barton, T. H. Epps and G. N. Parsons, Nat. Rev. Mater., 2021, 6, 605–621 CrossRef CAS .
  30. M. Kalaj, K. C. Bentz, S. Ayala, J. M. Palomba, K. S. Barcus, Y. Katayama and S. M. Cohen, Chem. Rev., 2020, 120, 8267–8302 CrossRef CAS PubMed .
  31. J. Yu, C. Mu, B. Yan, X. Qin, C. Shen, H. Xue and H. Pang, Mater. Horiz., 2017, 4, 557–569 RSC .
  32. W. Xiang, Y. Zhang, H. Lin and C. J. Liu, Molecules, 2017, 22, 2103 CrossRef PubMed .
  33. S. Subudhi, S. P. Tripathy and K. Parida, Inorg. Chem. Front., 2021, 8, 1619–1636 RSC .
  34. X. W. Liu, T. J. Sun, J. L. Hu and S. D. Wang, J. Mater. Chem. A, 2016, 4, 3584–3616 RSC .
  35. C. Wang, J. Kim, J. Tang, M. Kim, H. Lim, V. Malgras, J. You, Q. Xu, J. Li and Y. Yamauchi, Chem, 2020, 6, 19–40 CAS .
  36. Z. Du, E3S Web Conf., 2023, 385, 04034 CrossRef CAS .
  37. J. Meng, M. He, F. Li, T. Li, Z. Huang and W. Cao, Inorg. Chim. Acta, 2023, 557, 121701 CrossRef CAS .
  38. J. Khor, J. Lee, S. H. W. Kok and L. L. Tan, Mater. Today Chem., 2024, 42, 102405 CrossRef CAS .
  39. W. Lv, Y. Song, H. Pei and Z. Mo, J. Ind. Eng. Chem., 2023, 128, 17–54 CrossRef CAS .
  40. A. E. Mathew, S. Jose, A. M. Babu and A. Varghese, Mater. Today Chem., 2024, 36, 101927 CrossRef CAS .
  41. W. Wang, B. Ibarlucea, C. Huang, R. Dong, M. Al Aiti, S. Huang and G. Cuniberti, Nanoscale Horiz., 2024, 9, 1432–1474 RSC .
  42. Z. Y. Lu, Y. L. Ma, J. T. Zhang, N. S. Fan, B. C. Huang and R. C. Jin, J. Water Process Eng., 2020, 38, 101681 CrossRef .
  43. F. J. Cano, O. Reyes-Vallejo, A. Ashok, M. D. L. L. Olvera, S. Velumani and A. Kassiba, Ceram. Int., 2023, 49, 21185–21205 CrossRef CAS .
  44. M. Bazargan, F. Ghaemi, A. Amiri and M. Mirzaei, Coord. Chem. Rev., 2021, 445, 214107 CrossRef CAS .
  45. A. Kumar and R. Kataria, Sci. Total Environ., 2024, 926, 172129 CrossRef CAS PubMed .
  46. V. Virender, V. Pandey, G. Singh, P. K. Sharma, P. Bhatia, A. A. Solovev and B. Mohan, Top. Curr. Chem., 2024, 383, 1–36 CrossRef PubMed .
  47. U. Farwa, Z. A. Sandhu, A. Kiran, M. A. Raza, S. Ashraf, H. Gulzarab, M. Fiaz, A. Malik and A. G. Al-Sehemi, RSC Adv., 2024, 14, 37164–37195 RSC .
  48. S. K. Gebremariam, L. F. Dumée, P. L. Llewellyn, Y. F. AlWahedi and G. N. Karanikolos, J. Environ. Chem. Eng., 2023, 11, 109291 CrossRef CAS .
  49. V. I. Isaeva, M. D. Vedenyapina, A. Y. Kurmysheva, D. Weichgrebe, R. R. Nair, N. P. T. Nguyen and L. M. Kustov, Molecules, 2021, 26, 6628 CrossRef CAS PubMed .
  50. M. Mancinelli and A. Martucci, Sustainable Chem., 2025, 6, 9 CrossRef CAS .
  51. D. Ewis, M. M. Ba-Abbad, A. Benamor and M. H. El-Naas, Appl. Clay Sci., 2022, 229, 106686 CrossRef CAS .
  52. A. Galarneau, A. Sachse, B. Said, C. H. Pelisson, P. Boscaro, N. Brun, L. Courtheoux, N. Olivi-Tran, B. Coasne and F. Fajula, C. R. Chim, 2016, 19, 231–247 CrossRef CAS .
  53. N. Yuan, X. Zhang and L. Wang, Coord. Chem. Rev., 2020, 421, 213442 CrossRef CAS .
  54. N. Gao, Q. Guan and Z. Kong, RSC Adv., 2023, 13, 15041–15054 RSC .
  55. C. Liu, J. Wang, J. Wan and C. Yu, Coord. Chem. Rev., 2021, 432, 213743 CrossRef CAS .
  56. X. Guo, L. Wang, L. Wang, Q. Huang, L. Bu and Q. Wang, Front. Chem., 2023, 11, 1116524 CrossRef CAS PubMed .
  57. S. Satyam and S. Patra, Heliyon, 2024, 10, e29573 CrossRef CAS PubMed .
  58. M. S. Akhtar, S. Ali and W. Zaman, Molecules, 2024, 29, 4317 CrossRef CAS PubMed .
  59. M. Seiler, S. Stock and A. Drews, J. Biotechnol., 2024, 382, 28–36 CrossRef CAS PubMed .
  60. C. Yu, Z. Shao and H. Hou, Chem. Sci., 2017, 8, 7611 RSC .
  61. H. Molavi and M. S. Salimi, Sci. Rep., 2025, 15, 1–17 CrossRef PubMed .
  62. D. Liu, W. Gu, L. Zhou, J. Lei, L. Wang, J. Zhang and Y. Liu, Sep. Purif. Technol., 2023, 304, 122217 CrossRef CAS .
  63. J. Zhu, L. Samperisi, M. Kalaj, J. A. Chiong, J. B. Bailey, Z. Zhang, C. J. Yu, R. E. Sikma, X. Zou, S. M. Cohen, Z. Huang and F. A. Tezcan, Dalton Trans., 2022, 51, 1927–1935 RSC .
  64. R. Bruno, T. F. Mastropietro, G. De Munno, D. Armentano, E. Pardo, J. Ferrando Soria and J. Janczak, Molecules, 2021, 26, 4594 CrossRef CAS PubMed .
  65. E. Kopcsik, Z. Mucsi, R. Schiwert, L. Vanyorek, B. Viskolcz and M. Nagy, Sci. Rep., 2025, 15, 629 CrossRef CAS PubMed .
  66. F. Liu, P. Ye, Q. Cheng, D. Zhang, Y. Nie, X. Shen, M. Zhu, H. Xu and S. Li, Inorg. Chem., 2024, 63, 14630–14640 CrossRef CAS PubMed .
  67. R. B. Lin, Y. He, P. Li, H. Wang, W. Zhou and B. Chen, Chem. Soc. Rev., 2019, 48, 1362–1389 RSC .
  68. Q. Xiong, Y. Chen, D. Yang, K. Wang, Y. Wang, J. Yang, L. Li and J. Li, Mater. Chem. Front, 2022, 6, 2944–2967 RSC .
  69. L. Feng, K. Y. Wang, X. L. Lv, T. H. Yan and H. C. Zhou, Natl. Sci. Rev., 2020, 7, 1743–1758 CrossRef CAS PubMed .
  70. Y. Arslan, F. Tomul, N. K. Kınaytürk, N. T. Dong, D. Trak, B. Kabak and H. N. Tran, Water Environ. Res., 2024, 96, e10966 CrossRef CAS PubMed .
  71. A. G. Attallah, V. Bon, K. Maity, E. Hirschmann, M. Butterling, A. Wagner and S. Kaskel, ACS Appl. Mater. Interfaces, 2023, 15, 48264–48276 CrossRef CAS PubMed .
  72. M. Makowski and M. Bogunia, J. Phys. Chem. B, 2020, 124, 10326–10336 CrossRef PubMed .
  73. P. Pourhakkak, A. Taghizadeh, M. Taghizadeh, M. Ghaedi and S. Haghdoust, Interface Sci. Technol., 2021, 33, 1–70 Search PubMed .
  74. Z. M. Shakor, N. Parsafard and E. Al-Shafei, J. Mater. Sci., 2025, 2025, 1–27 Search PubMed .
  75. F. J. Cano, R. Sánchez−Albores, A. Ashok, J. Escorcia−García, A. Cruz−Salomón, O. Reyes-Vallejo, P. J. Sebastian and S. Velumani, J. Mater. Sci.: Mater. Electron., 2025, 36, 663 CrossRef CAS .
  76. J. Wang and X. Guo, J. Hazard. Mater., 2020, 390, 122156 CrossRef CAS PubMed .
  77. D. Juela, M. Vera, C. Cruzat, X. Alvarez and E. Vanegas, Sustainable Environ. Res., 2021, 31, 1–14 CrossRef .
  78. E. D. Revellame, D. L. Fortela, W. Sharp, R. Hernandez and M. E. Zappi, Clean. Eng. Technol., 2020, 1, 100032 CrossRef .
  79. J. Wang and X. Guo, Chemosphere, 2022, 309, 136732 CrossRef CAS PubMed .
  80. K. Zhu, J. Wu, R. Fan, Y. Cao, H. Lu, B. Wang and Y. Yang, Chem. Eng. J., 2022, 427, 131483 CrossRef CAS .
  81. N. Gao, Q. Guan and Z. Kong, RSC Adv., 2023, 13, 15041–15054 RSC .
  82. M. Ghassa, Z. Hajjar, F. Khorasheh, S. Soltanali and S. Tayyebi, Energy Fuels, 2023, 37, 13769–13784 CrossRef CAS .
  83. O. Sahu and N. Singh, The Impact and Prospects of Green Chemistry for Textile Technology, 2019, pp. 367–416 Search PubMed .
  84. V. C. Cojocaru, I. N. Cristea, I. A. Paris, I. A. Ionescu and F. L. Chiriac, Sustainability, 2024, 16, 7743 CrossRef CAS .
  85. A. Waheed, N. Baig, N. Ullah and W. Falath, J. Environ. Manage., 2021, 287, 112360 CrossRef CAS PubMed .
  86. S. Kalam, S. A. Abu-Khamsin, M. S. Kamal and S. Patil, ACS Omega, 2021, 6, 32342–32348 CrossRef CAS PubMed .
  87. S. Azizian and S. Eris, Interface Sci. Technol., 2021, 33, 445–509 Search PubMed .
  88. H. N. Tran, N. P. Thanh Trung, E. C. Lima, J. C. Bollinger, N. D. Dat, H. P. Chao and R. S. Juang, J. Chem. Technol. Biotechnol., 2023, 98, 462–472 CrossRef CAS .
  89. D. Mohan, A. K. Chaubey, M. Patel, C. Navarathna, T. E. Mlsna and C. U. Pittman, Sustainable Biochar for Water and Wastewater Treatment, 2022, pp. 153–203 Search PubMed .
  90. S. Singh, U. Basavaraju, T. S. K. Naik, S. K. Behera, N. A. Khan, J. Singh and P. C. Ramamurthy, Environ. Res., 2023, 216, 114750 CrossRef CAS PubMed .
  91. R. M. Abdelhameed, E. Alzahrani, A. A. Shaltout and R. M. Moghazy, Int. J. Biol. Macromol., 2020, 165, 2984–2993 CrossRef CAS PubMed .
  92. H. S. Far, M. Hasanzadeh, M. Najafi and R. Rahimi, ChemistrySelect, 2022, 7(5), e202104191 CrossRef CAS .
  93. S. Koppula, P. Jagasia and S. B. M. Surya, Mater. Today Commun., 2023, 34, 105336 CrossRef CAS .
  94. M. Karamipour, S. Fathi and M. Safari, Int. J. Environ. Anal. Chem., 2023, 103, 3853–3864 CrossRef CAS .
  95. R. M. Abdelhameed and H. E. Emam, Sustainable Mater. Technol., 2022, 31, e00366 CrossRef CAS .
  96. H. Singh, S. Raj, R. K. S. Rathour and J. Bhattacharya, Environ. Sci. Pollut. Res., 2022, 29, 56249–56264 CrossRef CAS PubMed .
  97. P. Qin, S. Zhu, M. Mu, Y. Gao, Z. Cai and M. Lu, Chin. Chem. Lett., 2023, 34, 108620 CrossRef CAS .
  98. B. Elhadj-Daouadji, F. Zaoui, M. A. Zorgani, S. Abubakar, L. A. Siddig, A. S. Abdelhamid, M. Bhardwaj, M. Hachemaoui, M. Guezzoul, A. Kumar, B. Bounaceur, F. Lebsir and N. Saleh, Fuel, 2025, 381, 133284 CrossRef CAS .
  99. M. Dinari and F. Jamshidian, Polymer, 2021, 215, 123383 CrossRef CAS .
  100. Y. Wang, Z. Gao, Y. Shang, Z. Qi, W. Zhao and Y. Peng, Chem. Eng. J., 2021, 417, 128063 CrossRef CAS .
  101. Y. Zhong, X. Mu and U. K. Cheang, Nanoscale Adv., 2022, 4, 1431–1444 RSC .
  102. A. Hossan, J. Mol. Liq., 2023, 382, 122065 CrossRef .
  103. H. Ben Slama, A. C. Bouket, Z. Pourhassan, F. N. Alenezi, A. Silini, H. Cherif-Silini, T. Oszako, L. Luptakova, P. Golińska and L. Belbahri, Appl. Sci., 2021, 11, 6255 CrossRef .
  104. Y. Wan, J. Wang, F. Huang, Y. Xue, N. Cai, J. Liu, W. Chen and F. Yu, RSC Adv., 2018, 8, 34552 RSC .
  105. K. Anil Kumar, A. Bisoi, M. Yeshwanth, N. Shobham, M. Jujaru, J. Panwar and S. Gupta, Environ. Sci., 2024, 10, 1938–1963 Search PubMed .
  106. M. Firoozi, Z. Rafiee and K. Dashtian, ACS Omega, 2020, 5, 9420 CrossRef CAS PubMed .
  107. E. Santoso, R. Ediati, Z. Istiqomah, D. O. Sulistiono, R. E. Nugraha, Y. Kusumawati, H. Bahruji and D. Prasetyoko, Microporous Mesoporous Mater., 2021, 310, 110620 CrossRef CAS .
  108. H. Lu, L. Zhang, B. Wang, Y. Long, M. Zhang, J. Ma, A. Khan, S. P. Chowdhury, X. Zhou and Y. Ni, Cellulose, 2019, 26, 4909–4920 CrossRef CAS .
  109. W. Zhang, R. Z. Zhang, Y. Yin and J. M. Yang, J. Mol. Liq., 2020, 302, 112616 CrossRef CAS .
  110. M. Hazrati and M. Safari, Environ. Prog. Sustainable Energy, 2020, 39, e13411 CrossRef CAS .
  111. Y. Liu, Y. Liu, R. Qu, C. Ji and C. Sun, Colloids Surf., A, 2020, 586, 124259 CrossRef CAS .
  112. M. Dadashi Firouzjaei, F. Akbari Afkhami, M. Rabbani Esfahani, C. H. Turner and S. Nejati, J. Water Process Eng., 2020, 34, 101180 CrossRef .
  113. M. Al Sharabati and R. Sabouni, Polyhedron, 2020, 190, 114762 CrossRef CAS .
  114. P. Liu, J. Lyu and P. Bai, Sep. Purif. Technol., 2025, 354, 128672 CrossRef CAS .
  115. B. Ma, W. Hu, Y. Zhou, Y. Li, Y. Zhang and M. Zhou, Inorg. Chem. Commun., 2025, 172, 113475 CrossRef CAS .
  116. K. M. Qasem, S. Khan, S. Chinnam, H. A. Saleh, I. Mantasha, M. Zeeshan and M. Shahid, Mater. Chem. Phys., 2022, 291, 126748 CrossRef CAS .
  117. S. Sharafinia and A. Rashidi, Arabian J. Chem., 2023, 16, 105288 CrossRef CAS .
  118. T. Saeed, A. Naeem, I. U. Din, M. Farooq, I. W. Khan, M. Hamayun and T. Malik, J. Hazard. Mater., 2022, 427, 127902 CrossRef CAS PubMed .
  119. S. H. Alrefaee, M. M. Aljohani, I. S. Alatawi, A. A. Sari, K. S. Alrashdi, A. T. Mogharbel, M. A. A. Alanazi and N. M. El-Metwaly, J. Mol. Liq., 2024, 401, 124648 CrossRef CAS .
  120. J. Sharma, M. Joshi, A. Bhatnagar, A. K. Chaurasia and S. Nigam, Environ. Res., 2022, 215, 114219 CrossRef CAS PubMed .
  121. M. Usman, O. Monfort, S. Gowrisankaran, B. H. Hameed, K. Hanna and M. Al-Abri, J. Water Process Eng., 2023, 52, 103566 CrossRef .
  122. S. Lal, P. Singh, A. Singhal, S. Kumar, A. P. S. Gahlot, N. Gandhi and P. Kumari, RSC Adv., 2024, 14, 3413–3446 RSC .
  123. S. Sharafinia, A. Rashidi, F. Tabarkhoon, F. Dehghan, F. Tabarkhoon and M. Bazmi, Sci. Rep., 2023, 13, 1–19 CrossRef PubMed .
  124. X. Wang, H. Zhu, X. Shi, X. Qiu, W. Lu and H. Guo, J. Nanosci. Nanotechnol., 2019, 19, 627–633 CrossRef CAS PubMed .
  125. B. F. Rivadeneira-Mendoza, L. S. Quiroz-Fernández, F. F. da Silva, R. Luque, A. M. Balu and J. M. Rodríguez-Díaz, Environ. Sci.: Nano, 2024, 11, 1543–1558 RSC .
  126. F. G. Quintero-Álvarez, C. K. Rojas-Mayorga, D. I. Mendoza-Castillo, I. A. Aguayo-Villarreal and A. Bonilla-Petriciolet, Adsorpt. Sci. Technol., 2022, 2022, 4482263 CrossRef .
  127. Z. Zhang, W. Han, T. Lou, L. Ma, W. Zhou, Z. Xu, M. Chen, L. Wen, Y. Cheng and L. Ding, ACS Appl. Nano Mater., 2022, 5, 17325–17334 CrossRef CAS .
  128. Q. Zhou and G. Liu, Ind. Eng. Chem. Res., 2020, 59, 12056–12064 CrossRef CAS .
  129. Y. G. Proenza and R. L. Longo, J. Chem. Inf. Model., 2020, 60, 644–652 CrossRef CAS PubMed .
  130. J. Farrando-Pérez, G. Martinez-Navarrete, J. Gandara-Loe, S. Reljic, A. Garcia-Ripoll, E. Fernandez and J. Silvestre-Albero, Inorg. Chem., 2022, 61, 18861–18872 CrossRef PubMed .
  131. S. Tajahmadi, A. Shamloo, A. Shojaei and M. Sharifzadeh, ACS Omega, 2022, 7, 41177 CrossRef CAS PubMed .
  132. P. Yadav, S. Kumari, A. Yadav, P. Bhardwaj, M. Maruthi, A. Chakraborty and P. Kanoo, ACS Omega, 2023, 8, 28367–28375 CrossRef CAS PubMed .
  133. N. D. Bikiaris, N. M. Ainali, E. Christodoulou, M. Kostoglou, T. Kehagias, E. Papasouli, E. N. Koukaras and S. G. Nanaki, Nanomaterials, 2020, 10, 2490 CrossRef CAS PubMed .
  134. S. Essalmi, S. Lotfi, A. BaQais, M. Saadi, M. Arab and H. Ait Ahsaine, RSC Adv., 2024, 14, 9365 RSC .
  135. F. Zadehahmadi, N. T. Eden, H. Mahdavi, K. Konstas, J. I. Mardel, M. Shaibani, P. C. Banerjee and M. R. Hill, Environ. Sci., 2023, 9, 1305–1330 CAS .
  136. Z. Zhang, W. Han, J. Qing, T. Meng, W. Zhou, Z. Xu and L. Ding, J. Hazard. Mater., 2024, 465, 133189 CrossRef CAS PubMed .
  137. F. Fatahi, S. Farhadi, A. Zabardasti and F. Mahmoudi, Inorg. Chem. Commun., 2024, 162, 112231 CrossRef CAS .
  138. N. Emami, M. Farhadian, A. R. Solaimany Nazar and S. Tangestaninejad, Int. J. Environ. Sci. Technol., 2023, 20, 1645–1672 CrossRef CAS .
  139. A. A. Issa, M. D. Kamel and D. S. El-Sayed, J. Mol. Model., 2024, 30, 1–11 CrossRef PubMed .
  140. M. Khedri, R. Maleki, S. G. Khiavi, M. Ghasemi, E. Ghasemy, A. M. Jahromi and A. Razmjou, Appl. Mater. Today, 2021, 25, 101196 CrossRef .
  141. M. Ghorbani, A. Saghafi, M. Pakseresht, A. Shams, M. Keshavarzi and S. Asghari, Sep. Purif. Technol., 2024, 336, 126227 CrossRef CAS .
  142. M. N. Nimbalkar and B. R. Bhat, J. Environ. Chem. Eng., 2021, 9, 106216 CrossRef CAS .
  143. C. Wang, C. Xiong, Y. He, C. Yang, X. Li, J. Zheng and S. Wang, Chem. Eng. J., 2021, 415, 128923 CrossRef CAS .
  144. A. A. Castañeda-Ramírez, E. Rojas-García, R. López-Medina, D. C. García-Martínez, J. Nicolás- Antúnez and A. M. Maubert-Franco, Catal. Today, 2022, 394–396, 94–102 CrossRef .
  145. J. Zhao, J. He, L. Liu, S. Shi, H. Guo, L. Xie, X. Chai, K. Xu, G. Du and L. Zhang, Sep. Purif. Technol., 2023, 327, 124942 CrossRef CAS .
  146. L. Shi, F. Xiang, K. Liu, W. Li and Z. Li, Sep. Purif. Technol., 2025, 357, 129921 CrossRef CAS .
  147. W. Bian, J. Chen, Y. Chen, W. Xu and J. Jia, Cellulose, 2021, 28, 3041–3054 CrossRef CAS .
  148. M. B. Poudel, G. P. Awasthi and H. J. Kim, Chem. Eng. J., 2021, 417, 129312 CrossRef CAS .
  149. L. Liu, B. Zhao, D. Wu, X. Wang, W. Yao, Z. Ma and S. Yu, Chemosphere, 2023, 341, 140086 CrossRef CAS PubMed .
  150. Y. Wang, K. Lin, Y. Liu and X. Deng, J. Solid State Chem., 2022, 313, 123300 CrossRef CAS .
  151. Lin, B. Zeng, X. Liu, J. Li, B. Zhang and L. Zhang, J. Cleaner Prod., 2022, 381, 134766 CrossRef CAS .
  152. Y. Fu, Y. Yao, A. C. Forse, J. Li, K. Mochizuki, J. R. Long, J. A. Reimer, G. De Paëpe and X. Kong, Nat. Commun., 2023, 14, 1–9 CAS .
  153. V. Russo, M. Hmoudah, F. Broccoli, M. R. Iesce, O. S. Jung and M. Di Serio, Front. Chem. Eng., 2020, 2, 581487 CrossRef .
  154. F. Ahmadijokani, A. Ghaffarkhah, H. Molavi, S. Dutta, Y. Lu, S. Wuttke, M. Kamkar, O. J. Rojas and M. Arjmand, Adv. Funct. Mater., 2024, 34, 2305527 CrossRef CAS .
  155. G. Ramalingam, R. Pachaiappan, P. S. Kumar, S. Dharani, S. Rajendran, D.-V. N. Vo and T. K. A. Hoang, Chemosphere, 2022, 288, 132448 CrossRef CAS PubMed .
  156. M. Priyadarshini, I. Das, M. M. Ghangrekar and L. Blaney, J. Environ. Manage., 2022, 316, 115295 CrossRef CAS PubMed .
  157. A. Giwa, A. Yusuf, H. A. Balogun, N. S. Sambudi, M. R. Bilad, I. Adeyemi, S. Chakraborty and S. Curcio, Process Saf. Environ. Prot., 2021, 146, 220–256 CrossRef CAS .
  158. M. J. F. Calvete, G. Piccirillo, C. S. Vinagreiro and M. M. Pereira, Coord. Chem. Rev., 2019, 395, 63–85 CrossRef CAS .
  159. A. Bratovčić, Technol. Acta, 2019, 11, 17–23 Search PubMed .
  160. Y. Pan, R. Abazari, J. Yao and J. Gao, J. Phys. Energy, 2021, 3, 032010 CrossRef CAS .
  161. S. Li, S. Yang, G. Liang, M. Yan, C. Wei and Y. Lu, RSC Adv., 2023, 13, 5273–5282 RSC .
  162. J. D. Sosa, T. F. Bennett, K. J. Nelms, B. M. Liu, R. C. Tovar and Y. Liu, Crystals, 2018, 8, 325 CrossRef .
  163. M. B. Chabalala, B. M. Mothudi and B. Ntsendwana, J. Photochem. Photobiol., A, 2024, 447, 115244 CrossRef CAS .
  164. D. Wang and T. Li, Acc. Chem. Res., 2023, 56, 462–474 CrossRef CAS PubMed .
  165. M. Kalaj, K. C. Bentz, S. Ayala Jr, J. M. Palomba, K. S. Barcus, Y. Katayama and S. M. Cohen, Chem. Rev., 2020, 120, 8267–8302 CrossRef CAS PubMed .
  166. R. Freund, S. Canossa, S. M. Cohen, W. Yan, H. Deng, V. Guillerm, M. Eddaoudi, D. G. Madden, D. Fairen-Jimenez, H. Lyu and L. K. Macreadie, Angew. Chem., Int. Ed., 2021, 60, 23946–23974 CrossRef CAS PubMed .
  167. J. Lee, J. Lee, J. Y. Kim and M. Kim, Chem. Soc. Rev., 2023, 52, 6379–6416 RSC .
  168. X. Zhang, S. Zhang, Y. Tang, X. Huang and H. Pang, Composites, Part B, 2022, 230, 109532 CrossRef CAS .
  169. A. Fattah-alhosseini, Z. Sangarimotlagh, M. Karbasi and M. Kaseem, Nano-Struct. Nano-Objects, 2024, 38, 101192 CrossRef CAS .
  170. W. Lv, Y. Song, H. Pei and Z. Mo, J. Ind. Eng. Chem., 2023, 128, 17–54 CrossRef CAS .
  171. X. Hu, W. Zheng, M. Wu, L. Chen and S. Chen, Sustainable Mater. Technol., 2023, 37, e00691 CrossRef CAS .
  172. Y. Shan, L. Chen, H. Pang and Q. Xu, Small Struct., 2021, 2, 2000078 CrossRef CAS .
  173. Z. Chen, Y. Li, Y. Cai, S. Wang, B. Hu, B. Li, X. Ding, L. Zhuang and X. Wang, Carbon Res., 2023, 2, 8 CrossRef CAS .
  174. Y. Li, W. Bi, H. Yang, Y. Yue, S. Liu and G. Hou, Environ. Technol., 2025, 46, 1099–1111 CrossRef CAS PubMed .
  175. D. Han, J. Niu, Y. Yang, C. Huang, W. Tan and X. Zhang, Chemosphere, 2024, 346, 140665 CrossRef CAS PubMed .
  176. A. Mehrehjedy, P. Kumar, Z. Ahmad, P. Jankoski, A. S. Kshirsagar, J. D. Azoulay, X. He, M. K. Gangishetty, T. D. Clemons, X. Gu, W. Miao and S. Guo, ACS Omega, 2024, 9, 49239–49248 CrossRef CAS PubMed .
  177. L. Hu, J. Chen, Y. Wei, M. Wang, Y. Xu, C. Wang, P. Gao, Y. Liu, C. Liu, Y. Song and N. Ding, J. Hazard. Mater., 2023, 442, 130059 CrossRef CAS PubMed .
  178. S. Roy, J. Darabdhara and M. Ahmaruzzaman, Environ. Sci. Pollut. Res., 2023, 30, 95673–95691 CrossRef CAS PubMed .
  179. M. Ishfaq, S. A. Khan, M. A. Nazir, S. Ali, M. Younas, M. Mansha, S. S. A. Shah, M. Arshad and A. ur Rehman, J. Mol. Struct., 2024, 1301, 137384 CrossRef CAS .
  180. Q. Li, C. Zhao, S. Jia, Q. Chen, X. Li, M. She, H. Liu, P. Liu, Y. Wang and J. Li, Chin. Chem. Lett., 2024, 109936 Search PubMed .
  181. J. Wang, M. Yuan, C. Li, B. Zhang, J. Zhu, X. Hao, H. Lu and Y. Ma, J. Colloid Interface Sci., 2022, 612, 536–549 CrossRef CAS PubMed .
  182. Q. Wang, S. Zheng, W. Ma, J. Qian, L. Huang, H. Deng, Q. Zhou, S. Zheng, S. Li, H. Du and Q. Li, Appl. Catal., B, 2024, 344, 123669 CrossRef CAS .
  183. M. Y. Wani, N. Bashir, S. Ahmad, M. Rehman, S. A. Shah and S. U. R. Beig, Environ. Res., 2025, 267, 120659 CrossRef CAS PubMed .
  184. Z. Wang, Y. Xu, C. Wang, L. Yue, T. Liu, Q. Lan, X. Cao and B. Xing, Sep. Purif. Technol., 2023, 313, 123515 CrossRef CAS .
  185. Q. Wang, W. Zhou, C. Li, S. Kawi and Y. Li, J. Alloys Compd., 2025, 1010, 177589 CrossRef CAS .
  186. B. Bouider, S. Haffad, B. S. Bouakaz, M. Berd, S. Ouhnia and A. Habi, J. Inorg. Organomet. Polym. Mater., 2023, 33, 4001–4011 CrossRef CAS .
  187. C. Zhao, X. Yang, B. Zhao, Z. Zhang, W. Guo, A. Shen, M. Ye and W. Wang, J. Membr. Sci., 2024, 695, 122499 CrossRef CAS .
  188. A. Pirkarami, M. H. Shahabifard, A. Javanmard, S. Ashrafian and N. Yousefi-Limaee, J. Water Process Eng., 2024, 65, 105822 CrossRef .
  189. H. S. Far, M. Najafi, E. Moradi, M. Atighi, M. Rabbani and M. Hasanzadeh, J. Mol. Struct., 2024, 1312, 138627 CrossRef CAS .
  190. A. Bhuyan and M. Ahmaruzzaman, J. Alloys Compd., 2024, 972, 172781 CrossRef CAS .
  191. R. Ghasemzadeh and K. Akhbari, J. Photochem. Photobiol., A, 2024, 448, 115306 CrossRef CAS .
  192. H.-X. Zhang, S.-H. Ma, H.-X. Wang, S.-C. Li, H.-Y. Shen, D.-M. Kong, F.-Y. Wang and L.-N. Zhu, J. Environ. Chem. Eng., 2024, 12, 111899 CrossRef CAS .
  193. R. Ghasemzadeh and K. Akhbari, J. Photochem. Photobiol., A, 2025, 458, 115984 CrossRef CAS .
  194. S. Zhou, Y. Kuang, H. Yang, L. Gan, X. Feng, C. Mao, L. Chen, J. Zheng and G. Ouyang, Angew. Chem., Int. Ed., 2024, 136, e202412279 CrossRef .
  195. J. H. Wang, F. Kong, B. F. Liu, S. N. Zhuo, N. Q. Ren and H. Y. Ren, Environ. Sci.: Nano, 2024, 11, 3286–3293 RSC .
  196. M. Rahmani, A. Abbasi and M.-S. Hosseini, Appl. Surf. Sci., 2024, 648, 159014 CrossRef CAS .
  197. S. Eslaminejad, R. Rahimi and M. Fayazi, J. Ind. Eng. Chem., 2025, 141, 94–103 CrossRef CAS .
  198. S. Li, J. Li, W. Ren, Y. Xu and Q. Liu, J. Colloid Interface Sci., 2025, 691, 137462 CrossRef CAS PubMed .
  199. C. Li, L. Bao, Y. Shi, Z. Tian, Y. Ji, M. Cui, Z. Zhao and X. Wang, Appl. Surf. Sci., 2024, 662, 160132 CrossRef CAS .
  200. L. Gan, M. T. Nord, J. M. Lessard, N. Q. Tufts, A. Chidambaram, M. E. Light, H. Huang, E. Solano, J. Fraile, F. Suárez-García, C. Viñas, F. Teixidor, K. C. Stylianou and J. G. Planas, J. Am. Chem. Soc., 2023, 145, 13730–13741 CrossRef CAS PubMed .
  201. J. Zhu, M. Liao, C. Zhao, M. Liu, A. Han, C. Zhu, Y. Sun, M. Zhao, S. Ye and H. Cao, Nano Res., 2023, 16, 6402–6443 CrossRef CAS .
  202. N. C. Chiu, J. M. Lessard, E. N. Musa, L. S. Lancaster, C. Wheeler, T. D. Krueger, C. Chen, T. C. Gallagher, M. T. Nord, H. Huang, P. H. Y. Cheong, C. Fang and K. C. Stylianou, Nat. Commun., 2024, 15, 1–12 Search PubMed .
  203. B. Mohanty, S. Kumari, P. Yadav, P. Kanoo and A. Chakraborty, Coord. Chem. Rev., 2024, 519, 216102 CrossRef CAS .
  204. M. Liang, Y. Liu, S. Lu, Y. Wang, C. Gao, K. Fan and H. Liu, Trends Anal. Chem., 2024, 117800 CrossRef CAS .
  205. J. Aguila-Rosas, D. Ramos, C. T. Quirino-Barreda, J. A. Flores-Aguilar, J. L. Obeso, A. Guzmán-Vargas, I. A. Ibarra and E. Lima, Chem. Commun., 2023, 59, 11753–11766 RSC .
  206. Y. Wen, T. Qin and Y. Zhou, Langmuir, 2024, 40, 5026–5039 CrossRef CAS PubMed .
  207. A. Goel, A. Rastogi, S. Pandey, S. Kulshrestha and S. Goel, Mater. Today Proc., 2023 DOI:10.1016/j.matpr.2023.03.363 .
  208. M. Barreiros dos Santos, L. Rodriguez-Lorenzo, R. Queirós and B. Espiña, Fundamentals of Biosensors and Detection Methods, in Microfluidics and Biosensors in Cancer Research, ed. D. Caballero, S. C. Kundu and R. L. Reis, Advances in Experimental Medicine and Biology, Springer Cham, 2022, vol. 1379, pp. 3–29 Search PubMed .
  209. A. K. Brooks, S. Chakravarty and V. K. Yadavalli, in Microfluidics and Biosensors in Cancer Research: Applications in Cancer Modeling and Theranostics, ed. D. Caballero, S. C. Kundu and R. L. Reis, Springer International Publishing, Cham, 2022, pp. 275–306 Search PubMed .
  210. W. Zhang, X. Li, X. Ding, K. Hua, A. Sun, X. Hu, Z. Nie, Y. Zhang, J. Wang, R. Li and S. Liu, RSC Adv., 2023, 13, 10800–10817 RSC .
  211. F. Ahmadijokani, A. Ghaffarkhah, H. Molavi, S. Dutta, Y. Lu, S. Wuttke, M. Kamkar, O. J. Rojas and M. Arjmand, Adv. Funct. Mater., 2024, 34, 2305527 CrossRef CAS .
  212. W. Chen, Y. Tan, H. Zheng, Z. Wang, Z. Qu and C. Wu, Microchem. J., 2024, 111441 CrossRef CAS .
  213. S. Carrasco, Biosensors, 2018, 8, 92 CrossRef CAS PubMed .
  214. Q. Liang, X. Wang, C. Zhang, D. Zhu, S.-L. Wang, S.-Y. Tian, T. Long, E.-L. Yue, J.-J. Wang and X.-Y. Hou, Talanta, 2023, 259, 124491 CrossRef CAS PubMed .
  215. S. Menon, S. Dutta, N. Madaboosi and V. V. R. Sai, Environ. Sci.: Nano, 2024, 11, 4007–4019 RSC .
  216. G. A. Bodkhe, S. Subramanian, D. K. Gaikwad, M.-L. Tsai, T. Hianik, M. Kim and M. D. Shirsat, J. Phys. Chem. Solids, 2024, 193, 112142 CrossRef CAS .
  217. V. Saravanakumar, V. Rajagopal, K. Narayanan, N. Nesakumar, M. Kathiresan, V. Suryanarayanan and S. Anandan, J. Alloys Compd., 2023, 963, 171216 CrossRef CAS .
  218. D. Yan, F. Yuan, Z. Chen, J. Zhang and S. Song, Chem. Eng. J., 2024, 499, 156102 CrossRef CAS .
  219. S. Jain, N. Dilbaghi, N. K. Singhal, A. Kaushik, K.-H. Kim and S. Kumar, Chem. Eng. J., 2023, 457, 141375 CrossRef CAS .
  220. K. Sonowal and L. Saikia, J. Environ. Sci., 2023, 126, 531–544 CrossRef CAS PubMed .
  221. A. Giannetti and S. Tombelli, Sens. Actuators Rep., 2021, 3, 100030 CrossRef .
  222. Q. Wang, B. He, Y. Liu, L. Wu, W. Zhao, D. Xie, W. Ren and Y. Xu, Anal. Chim. Acta, 2025, 1351, 343880 CrossRef CAS PubMed .
  223. X. Zhang, J. Zhang, D. Yan, Z. Chen, S. Song and F. Yuan, ChemistrySelect, 2024, 9, e202400942 CrossRef CAS .
  224. J. Wang, X. Xu, L. Zheng, Q. Guo and G. Nie, Microchim. Acta, 2023, 190, 131 CrossRef CAS PubMed .
  225. S. Wang, Y. Xue, Z. Yu, F. Huang and Y. Jin, Mater. Today Chem., 2023, 30, 101490 CrossRef CAS .
  226. M. Guo, F. Li, Q. Ran, G. Zhu, Y. Liu, J. Han, G. Wang and H. Zhao, Microchem. J., 2023, 190, 108709 CrossRef CAS .
  227. J. Zhao, Y. Long, C. He, H. Yang, S. Zhao, X. Luo, D. Huo and C. Hou, ACS Sustainable Chem. Eng., 2023, 11, 2160–2171 CrossRef CAS .
  228. R. Sun, R. Lv, Y. Li, T. Du, L. Chen, Y. Zhang, X. Zhang, L. Zhang, H. Ma, H. Sun and Y. Qi, Food Control, 2023, 145, 109491 CrossRef CAS .
  229. Z. Liu, J. Li, Y. Li, Y. Wang, K. Deng, Y. Xie, P. Zhao and J. Fei, Talanta, 2024, 279, 126602 CrossRef CAS PubMed .
  230. F. Kharazmi, F. S. Hosseini and H. Ebrahimzadeh, Talanta, 2024, 267, 125241 CrossRef CAS PubMed .
  231. X. Gao, M. Liu, M. Lei, Y. Kong, X. Xu and Q. Zhang, Talanta, 2024, 277, 126303 CrossRef CAS PubMed .
  232. S. Ghosh, D. Mal, S. Mukherjee and S. Biswas, ACS Sustainable Chem. Eng., 2023, 11, 13179–13186 CrossRef CAS .
  233. L. Rozenberga, W. Bloch, T. A. Gillam, D. G. Lancaster, W. Skinner, M. Krasowska, A. Blencowe and D. A. Beattie, ACS Appl. Polym Mater., 2024, 6, 3544–3553 CrossRef CAS .
  234. L. Zhang, M. Liang, C. Li, F. Li, J. Xin, J. Lang, S. Li and D. Zhang, Anal. Chem., 2025, 97, 7203–7211 CrossRef CAS PubMed .
  235. R. Chen, X. Jia, X. Huang, Z. Mao, H. Zhang, H. Zhou, S. Ren and Z. Gao, Chem. Eng. J., 2025, 511, 162082 CrossRef CAS .
  236. C. Li, X. Xu, J. Xing, F. Wang, Y. Shi, X. Zhao, J. Liu, Y. Yang and Z. Zhao, Appl. Surf. Sci., 2023, 616, 156455 CrossRef CAS .
  237. Y. Yang, H. Wei, X. Wang, D. Sun, L. Yu, B. Bai, X. Jing, S. Qin and H. Qian, Biosens Bioelectron., 2023, 223, 115017 CrossRef CAS PubMed .
  238. Z. Song, Q. Huang, S. Zhang, S. Luo, M. Su, X. Yang, S. Wang and L. Wang, Microchem. J., 2024, 203, 110920 CrossRef CAS .
  239. M. Bellaj, K. Aziz, M. El Achaby, M. El Haddad, L. Gebrati, T. A. Kurniawan, Z. Chen, P.-S. Yap and F. Aziz, Chem. Eng. Sci., 2024, 285, 119615 CrossRef CAS .
  240. K. Zhi, Z. Li, H. Luo, Y. Ding, F. Chen, Y. Tan and H. Liu, Polymers, 2023, 15, 905 CrossRef CAS PubMed .
  241. Z. Ci, Y. Yue, J. Xiao, X. Huang and Y. Sun, J. Colloid Interface Sci., 2023, 630, 395–403 CrossRef CAS PubMed .
  242. C. Cai, L. Gao and Y. Xiong, J. Water Process Eng., 2024, 63, 105420 CrossRef .
  243. W. Ahlawat, N. Dilbaghi, R. Kumar, N. K. Singhal, A. Kaushik and S. Kumar, J. Environ. Chem. Eng., 2023, 11, 110268 CrossRef CAS .
  244. B. Tang, Z. Sun, X. Men, K. Dong, J. Wang, L. Kong, Y. Bai and F. Guo, J. Environ. Chem. Eng., 2024, 12(3), 112819 CrossRef CAS .
  245. Ş. Parlayici and A. Aras, Polym. Bull., 2024, 81, 6603–6640 CrossRef .
  246. X. Jaramillo-Fierro and G. Cuenca, Int. J. Mol. Sci., 2024, 25(8), 4367 CrossRef CAS PubMed .
  247. X. Su, X. Wang, Z. Ge, Z. Bao, L. Lin, Y. Chen, W. Dai, Y. Sun, H. Yuan, W. Yang and J. Meng, Chem. Eng. J., 2024, 486, 150387 CrossRef CAS .
  248. K. Chinoune, A. Mekki, B. Boukoussa, A. Mokhtar, A. Sardi, M. Hachemaoui, J. Iqbal, I. Ismail, M. Abboud and W. A. Aboneama, Inorg. Chem. Commun., 2024, 165, 112558 CrossRef CAS .
  249. K. Kashif, S. Akram, M. Murtaza, A. Amjad, S. S. A. Shah and A. Waseem, Diam. Relat. Mater., 2023, 136, 110023 CrossRef .
  250. M. Hemdan, A. H. Ragab, N. F. Gumaah and M. F. Mubarak, Int. J. Biol. Macromol., 2024, 274, 133498 CrossRef CAS PubMed .
  251. A. Varghese, S. Devi and D. Pinheiro, Mater. Today Commun., 2023, 35, 105739 CrossRef CAS .
  252. K. J. Al-Salihi and W. R. Alfatlawi, IOP Conf. Ser.: Mater. Sci. Eng., 2021, 1094, 012175 CAS .
  253. P.-S. Ghaemmaghami, J. Zolgharnein, M. Y. Masoomi and S. D. Farahani, Inorg. Chem. Commun., 2024, 168, 112881 CrossRef CAS .
  254. A. A. Mashentseva, N. A. Aimanova, N. Parmanbek, B. S. Temirgaziyev, M. Barsbay and M. V. Zdorovets, Nanomaterials, 2022, 12(19), 3293 CrossRef CAS PubMed .
  255. A. S. Abdulhameed, A. H. Jawad, M. Ridwan, T. Khadiran, L. D. Wilson and Z. M. Yaseen, J. Polym. Environ., 2022, 30, 4619–4636 CrossRef CAS .
  256. V. C. Valsalakumar, Y. Sreevalli, A. PK, A. S. Joseph, S. Ubaid and S. Vasudevan, J. Environ. Manage., 2024, 368, 122068 CrossRef CAS PubMed .
  257. H. Sereshti, E. Beyrak-Abadi, M. Esmaeili Bidhendi, I. Ahmad, S. Shahabuddin, H. Rashidi Nodeh, N. Sridewi and W. N. Wan Ibrahim, Nanomaterials, 2022, 12, 3576 CrossRef CAS PubMed .
  258. S. Hosseinpoor, A. Sheikhmohammadi, H. Rasoulzadeh, M. Saadani, S. M. Ghasemi, M. R. Alipour, M. Hadei and S. M. Aghaei Zarch, Chemosphere, 2024, 353, 141547 CrossRef CAS PubMed .
  259. J. W. Lee, J. Han, Y. K. Choi, S. Park and S. H. Lee, Int. J. Biol. Macromol., 2023, 249, 126053 CrossRef CAS PubMed .
  260. F. Azam, S. Ali, F. Ahmad, S. Ahmad, A. Rasheed, Y. Nawab, M. S. Zafar, M. A. Fareed and M. Shahwan, Carbohydr. Polym. Technol. Appl., 2024, 8, 100601 CAS .
  261. C. Qian, M. Zheng, Y. Zhang, E. Xing and B. Gui, Front. Chem., 2023, 11, 1265290 CrossRef CAS PubMed .
  262. Y. Zhang, J. Zhou, X. Chen, L. Wang and W. Cai, Chem. Eng. J., 2019, 369, 745–757 CrossRef CAS .
  263. H. P. Jing, C. C. Wang, Y. W. Zhang, P. Wang and R. Li, RSC Adv., 2014, 4, 54454–54462 RSC .
  264. K. Y. A. Lin and H. A. Chang, J. Taiwan Inst. Chem. Eng., 2015, 53, 40–45 CrossRef CAS .

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