Surface-enhanced Raman spectroscopy for size-resolved microplastic detection in real-world samples using thiophenol labeling†

Jayasree Kumar^a, Arunima Jinachandran^a, Mounika Renduchintala^a, Venugopal Rao Soma^bc, Vijayakumar Shanmugam^d, Shaik Imamvali^e, Sreenivasulu Tupakula^e and Rajapandiyan Panneerselvam*^a
aRaman Research Laboratory (RARE Lab), Department of Chemistry, SRM University-AP, Amaravati, Andhra Pradesh 522240, India. E-mail: rajapandiyan.p@srmap.edu.in
^bAdvanced Centre of Research in High Energy Materials (ACRHEM), DRDO Industry Academia – Centre of Excellence (DIA-COE), University of Hyderabad, India
^cSchool of Physics, University of Hyderabad, Hyderabad, 500046, India
^dInstitute of Nano Science and Technology, Mohali, India
^eDepartment of Electronics and Communication Engineering, SRM University-AP, Amaravati, Andhra Pradesh, 522240, India

First published on 15th July 2025

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Environmental significance Plastics in environmental systems are steadily accepted as “perilous materials” because of their major health concerns. However, only a few analytical techniques allow their sensitive detection and quantification. Among them, surface-enhanced Raman spectroscopy is a promising method for detecting plastic particles. However, the variable sizes of plastics in environmental matrices lead to inconsistent SERS signal intensities, posing challenges in achieving reliable detection. This study introduces a toluene dispersion strategy combined with a thiophenol label for SERS detection of plastics. This approach enables rapid, accurate, and highly sensitive identification of microplastics across different sizes and morphologies at trace concentrations. Additionally, it facilitates the separation and quantification of microplastics in environmental samples, offering a significant advancement in addressing the growing issue of microplastic pollution.

1. Introduction

Plastic pollution is a significant and pervasive environmental threat in the contemporary world, attracting widespread research interest.^1,2 According to global forecasts, approximately 1.2 billion metric tons of plastic waste are expected to accumulate in the environment by 2060.³ Over the past five years, there has been intense research on the presence of plastics in lake water,⁴ seawater,⁵ beach sand,⁵ and even food products.⁶ Plastic contamination encompasses a range of sizes, from macroplastics (≥2 cm) and mesoplastics (5 mm–2 cm) to microplastics (1 μm–5 mm) and nanoplastics (<1 μm), which are of particular concern because of their long-term persistence and tendency to bioaccumulate.⁷ Owing to their size and colloidal properties,⁸ microplastics tend to accumulate in the environment due to their large surface area and ability to adsorb organic contaminants,⁹ microbial pathogens,¹⁰ and toxic metal ions.¹¹ As a result, these particles can enter the body through ingestion of food, consumption of water, or inhalation of air, subsequently accumulating in various parts of the human body, including breast milk and the placenta.^12,13 Depending on their concentration and size, they raise substantial scientific and public concern.¹⁴

In recent years, several analytical techniques have been employed to detect and characterize microplastics,¹⁵ including pyrolysis mass spectrometry (Py-MS),¹⁶ mass spectrometry (MS),¹⁷ thermogravimetric analysis combined with Fourier transform infrared spectroscopy and gas chromatography and mass spectrometry (TGA-FTIR-GC-MS),¹⁸ scanning electron microscopy (SEM),¹⁹ transmission electron microscopy (TEM),²⁰ infrared (IR),²¹ and Raman spectroscopy (RS).²² Some of these techniques provide qualitative and quantitative analysis of microplastics in the environment and play a crucial role in their detection. However, most of these techniques have a few limitations. For instance, (i) Py-MS can only analyze a few samples per analysis, with a capacity of just 0.5 mg, making it difficult to detect microplastics in environmental samples.²³ (ii) MS involves highly complex sample preparation methods and tedious processes.¹⁷ (iii) SEM and TEM commonly characterize microplastics but do not provide chemical information.¹⁹ (iv) FTIR and RS are non-destructive techniques that can provide chemical and structural information.²⁴ However, due to their sensitivity limitations, FTIR and conventional Raman spectrometers face challenges in detecting microplastics in the nanometer range.

Among these techniques, SERS is a promising analytical technique because of its sensitivity, simplicity, fast analysis, and portability.^2,25–32 SERS offers a straightforward approach for obtaining chemical information on microplastics with high sensitivity and unique molecular specificity.^33–37 In SERS, Raman signals are significantly enhanced by two primary mechanisms: the electromagnetic enhancement mechanism (EM),³⁸ and the chemical enhancement (CE) mechanism.³⁹ In EM, plasmonic nanoparticles act as active substrates by generating an electromagnetic field from their localized surface plasmon resonance (LSPR) when exposed to incident laser light.⁴⁰ Similarly, CE involves the interaction between the analyte and nanoparticles, which can occur through chemical bonding, surface complex formation, or photo-induced charge transfer.⁴¹ Consequently, SERS has been effectively utilized to detect various types of microplastics of different sizes.^42–48

In 2020, Li et al.,⁴⁹ employed SERS to detect and identify polypropylene, polystyrene, polyethylene, and polyvinyl chloride in suspensions using AgNPs as SERS substrates. However, the microplastics utilized in this study were synthesized through a complex experimental procedure.⁴⁹ In another study, Veres and his co-workers demonstrated the detection of polyethylene particles ranging from 1–4 μm and polystyrene particles of 350 nm in aqueous samples, showing a limit of detection of 6.5 μg mL⁻¹ using gold nanoparticles.⁵⁰ However, this method exhibited variations in SERS signal intensity, which was influenced by the different sizes of microplastics.

The key challenges in the SERS-based detection of microplastics are multifaceted. (i) Most existing studies rely on synthesized Raman-active polymers such as polystyrene or commercially available, water-dispersed microplastics with uniform sizes.^50,51 This is primarily because samples containing microplastics of varying sizes often produce irreproducible SERS signals. However, real-world samples typically consist of heterogeneous micro- or nanoplastics, which can significantly affect signal consistency and intensity. (ii) Some reported methods employ aggregating agents to promote interactions between microplastics and nanoparticles.⁴⁹ While this can enhance SERS activity, it often results in non-uniform aggregation, compromising detection reliability. (iii) Moreover, detecting relatively weak Raman-active polymers such as polypropylene, polyethylene, and polyurethane remains challenging under real-world conditions. To overcome these limitations, it is essential to develop innovative SERS-based approaches capable of reliably detecting diverse microplastics, including Raman-inactive microplastics, in complex environmental samples.

Along this direction, we aimed to develop a novel method for detecting microplastics in real-world samples. For this purpose, a simple evaporation-induced self-assembly (EISA) technique combined with a toluene dispersion strategy for the detection of microplastics in real-world samples was successfully demonstrated for the first time, as shown in Fig. 1. Toluene was used to separate and disperse microplastics from the sample solution to overcome the challenges of microplastic size variation.⁵² Importantly, a portable Raman spectrometer was used to detect different sizes of microplastics in real-world samples without imaging using thiophenol as a reporter molecule. Based on microplastic concentration, the plasmonic efficiency of the AgNPs gradually decreased. These promising results pave the way for the quantitative detection of diverse-sized microplastics in real-world samples, such as salt and lake water.

2. Methods

2.1. Chemicals and materials

Polypropylene (PP) plastics (10–50 μm) were purchased from Nano Chemazone (Vijayawada, India). Polystyrene (PS) plastics (250 μm and 2.1 mm) and polyvinyl chloride (PVC) plastics (1–5 μm) were obtained from Sigma-Aldrich. Whatman grade 1 filter paper (pore size ∼11 μm), trisodium citrate (TSC) dihydrate (Na₃C₆H₅O₇·2H₂O), and malachite green (chloride) (C₂₃H₂₅ClN₂) were purchased from Sisco Research Laboratories Private Limited (India). Silver nitrate (AgNO₃) and rhodamine 6G (C₂₈H₃₁N₂O₃Cl) were obtained from Sigma-Aldrich. Thiophenol (C₆H₅SH) was obtained from the Tokyo Chemical Industry (India). Methanol (MeOH) (99.9%) was obtained from Thermo Fisher Scientific, USA. Ammonium hydroxide of 28–30% was supplied by Acro Chemical Corporation. Toluene was obtained from Avra Synthesis Private Limited. Materials including 3M Scotch double-sided tape, Kangaroo DP-52 6 mm punch, and Blue Star microscope glass slides (3 × 1 inch) with polished edges were provided by Sisco Research Laboratories Private Limited (India). Reagent-grade chemicals were used, and deionized (DI) water with a resistivity of 18.3 MΩ cm from the Millipore Milli-Q system was used to prepare the aqueous solutions.

The collection and handling of actual samples adhered to established procedures to prevent contamination. The samples were obtained from pre-rinsed glassware and maintained at RT (room temperature). Lakewater samples (FF8X+47V) were obtained from a lake in Vijayawada (Andhra Pradesh, India). Commercial rock salt samples were purchased from a local market in Vijayawada.

2.2. Preparation of Ag nanoparticles

AgNPs were prepared by reducing AgNO₃ using TSC as the reducing agent.⁵³ The synthesis process commenced by heating 50 mL of a 1 mM AgNO₃ solution to its boiling point. Thereafter, 1 mL of 1% TSC solution was added dropwise, followed by continued boiling for 20 min. Subsequently, it was cooled to RT by immersion in an ice bath. The resulting AgNPs were concentrated to 80% by centrifugation, reducing the volume from 5 to 1 mL in MeOH, which increased the number of hotspots generated during the EISA process.

The synthesized AgNPs were characterized using UV-visible spectroscopy, which revealed a prominent absorption peak at 435 nm, thus validating the formation of AgNPs.

2.3. SERS substrate fabrication

AgNPs decorated filter paper (AgNPs@filter paper) substrates were prepared using the EISA method.⁵⁴ Filter papers were cut into 6 mm discs using a Kangaroo DP-52 punch-hole machine and treated with 30% aqueous ammonia solution for 30 min to enlarge the spaces between cellulose fibers and enhance the adsorption of AgNPs@filter paper. The treated filter paper was dried and placed in a 96-well plate. Subsequently, 250 μL of MeOH concentrated Ag colloidal solution was deposited onto the filter paper and dried in a hot-air oven (50 °C) for 3 h. Finally, the AgNPs@filter paper was removed from the 96-well plate, cleaned with MeOH, air-dried, and used for the SERS measurements.⁵⁵ 5 μL of 1 mM thiophenol was added three times (15 μL of 1 mM thiophenol) for SERS analysis.

2.4. Preparation of toluene-dispersed microplastics of varied sizes

Microplastic samples were dispersed in toluene to achieve a uniform particle size distribution. Specifically, PS (250 μm and 2.1 mm), PP (10–50 μm), and PVC (1–5 μm) were prepared at a range of concentrations from 0.9 mg mL⁻¹ to 0.001 mg mL⁻¹ (0.0001%/1 μg mL⁻¹). In brief, a stock solution of standard microplastic samples was initially prepared at a concentration of 1 mg mL⁻¹. To achieve this, 5 mg of microplastic powder was placed into a 15 mL glass vial, followed by the addition of 5 mL of toluene. The mixture was then heated on a hot plate at 50 °C for 10 min. Notably, upon the addition of toluene, the micrometer-sized microplastic particles began to disperse. The heating step was essential for preparing the transparent microplastic solution. 50 μL of a toluene-dispersed microplastic solution was mixed with 250 μL of methanolic Ag colloid. As described in the previous section, pretreated filter paper was placed in a 96-well plate, and 300 μL of the Ag colloid–microplastic mixture was aliquoted into all the wells for the EISA method. The reaction was conducted in a hot-air oven (50 °C) for 3 h. After that, the microplastic-incubated filter paper substrates were removed from the well plate, cleaned with MeOH, and dried in air. Further, 5 μL of 1 mM thiophenol was added three times for SERS analysis. In real sample analysis, the collected lake water was first filtered to remove debris, dust, and other macro particles. Subsequently, similar to the analysis of standard samples, various concentrations of PP plastic-spiked lake water and salt samples dispersed in toluene were incubated with AgNPs to promote deposition.

2.5. SERS substrate characterization

Substrate characterization of AgNPs@filter paper substrates was performed using UV-visible spectroscopy (200–1000 nm) – multi-scanner sky spectrophotometer (Thermo Scientific, USA) with a resolution of 1 nm, and SEM images were recorded using a field-emission scanning electron microscope (FESEM) (Thermo Scientific, USA). SEM image clarity was improved using the AI tool Cutout.Pro. These analyses were conducted following the fabrication of the substrates using the EISA process.

2.6. SERS measurements

SERS measurements were carried out using a portable Raman instrument (B&W Tek portable Raman spectrometer – BWS415-785S-001; B&W Tek) coupled with a microscopic setup. A 785 nm diode laser was used as the excitation source, equipped with a numerical aperture (NA) of 0.55 (50× plan objective). The spectral measurements were conducted with 90 mW power, a 3 s integration time, single accumulation, and a spectral range of 200–2000 cm⁻¹. Data analysis included automatic baseline correction. Each sample was measured at three randomly selected spots, with three substrates analyzed for each experimental condition. SERS measurements of the microplastics were performed using the same procedure.

The analytical enhancement factor (AEF) was calculated using the following equation,⁵⁶

	(1)

where I_SERS and I_Raman – represent the SERS and Raman intensities, respectively.

C_SERS and C_Raman – represent the concentrations of analytes used in the SERS and Raman processes, respectively.

2.7. Quantitative analysis of microplastics in regression model fitting

The regression model was evaluated to quantify the microplastic samples accurately. A linear regression model characterized the relationship between the Raman peak intensity at 1066 cm⁻¹ associated with thiophenol in SERS measurements and microplastic concentrations. The coefficient of determination (R² value) was utilized to assess the validation of the model fit, along with the concentration axis altered to ensure the linearization of the models was given in the following equation,

f(x) = ax + b	(2)

where a is the slope of the line and b is the y-intercept.

2.8. Microplastics detection in real-world samples

We conducted experiments using (10–50 μm) PP at concentrations ranging from 0.9 to 0.003 mg mL⁻¹ and 0.09 mg mL⁻¹ of (250 μm and 2.1 mm) PS in lake water and sea salt samples, known for their ubiquitous dispersion of microplastics. The chosen concentration range of 0.9 to 0.003 mg mL⁻¹ falls below the typical limit of detection (LOD) for various real-world samples. The procedure detailed in the previous section was utilized to facilitate the detection of microplastics in the environmental samples. In summary, 50 μL of the toluene-dispersed microplastic solution underwent incubation with 250 μL of methanolic Ag colloid to initiate the EISA process. Subsequently, the microplastics@AgNPs were washed with MeOH, and 5 μL of 1 mM thiophenol was added three times for SERS analysis. The concentration of the added microplastics was quantified based on predefined linear regression models. The recovery efficiency was computed using the following equation,

	(3)

where C₁ – represents the concentration of microplastic measured, C₂ – represents the concentration of microplastic introduced (0.9 to 0.003 mg mL⁻¹).

3. Results and discussion

3.1. SERS substrate characterization

In the initial set of experiments, the as-prepared SERS substrate was examined by UV-visible spectroscopy, Raman spectroscopy, and SEM. First, the optical absorption properties of AgNPs were measured using a UV-visible spectrometer. Fig. 2(a) shows that the Ag colloid exhibited a maximum absorption peak at 435 nm, corresponding to a mean particle diameter of ∼40–60 nm. Furthermore, the minor peaks observed between 300–400 nm can be attributed to the transitions of higher-order multipoles in AgNPs.³² Additionally, the surface morphologies of the fabricated substrates were examined using SEM. As depicted in Fig. 2(c) and (d), the surface morphologies of the bare and AgNPs@filter paper were analyzed using SEM at different magnifications, clearly showing the uniform deposition of AgNPs@filter paper.

To date, various methodologies have been proposed for the fabrication of SERS substrates, including the silver mirror reaction,⁵⁷ photochemical reaction,⁵⁸ roughened Ag wire,⁵⁹ and the EISA method.⁶⁰ We chose the EISA method due to its simplicity and the lack of need for costly or complex equipment.⁶¹ By evaporating a colloidal solution of AgNPs in a 96-well plate, we successfully decorated AgNPs onto the filter paper surface, forming numerous hotspots and eliminating the coffee-ring effect.⁶²

Next, the SERS substrate was extensively investigated using Raman spectroscopy. An important consideration in SERS experiments is to ensure that the SERS substrate does not contribute to any Raman signals that might overlap with those of the target analyte. For this purpose, 1 mM of thiophenol was used to probe the surface. To systematically examine this phenomenon, we acquired Raman spectra of the following samples: bare filter paper, AgNPs@filter paper, and thiophenol adsorbed onto the AgNPs@filter paper substrate as illustrated in Fig. 2(b). The prominent peaks of thiophenol molecules are detailed in Table S1.† In particular, the Raman shift at 1066 cm⁻¹ was selected as an indicator of substrate optimization.

3.2. SERS substrate optimization

After characterizing, the AgNPs@filter paper substrates are investigated for their significant enhancement effect. The nanoparticle size and the fabrication process, which ensures high-density hotspots, are critical factors in optimizing SERS performance.⁶³ Here, we synthesized citrate-reduced Ag colloids, achieving high-density hotspots through EISA. The AgNPs exhibited an average size distribution ranging from approximately 40 to 60 nm, as observed in the SEM images in Fig. S1(a).† Generally, AgNPs with a size of approximately 60 nm are known to exhibit optimal SERS performance due to their favorable LSPR characteristics.^64–66 Fig. S1(b)† presents a histogram plot of the particle size distribution, measured from 150 randomly selected AgNPs in the SEM images. The fabrication process was conducted in a 96-well plate to mitigate the coffee ring effect, although the colloid volume used during evaporation significantly influences hotspot formation.⁵⁴

Further, the effect of solvent evaporation and partial dissolution of AgNPs was studied using UV-visible and SERS measurements. The results demonstrated no significant impact on the AgNPs during evaporation with water, MeOH, and a 5:1 methanol-to-toluene mixture, as shown in Fig. S2(a) and (b).†

Consequently, we examined various volumes of MeOH-dispersed colloids, ranging from 50–300 μL, as depicted in Fig. 3(a) and (b). An optimal concentration of 1 mM thiophenol was determined for all optimization analysis, utilizing the characteristic peak at 1066 cm⁻¹. When 50 and 100 μL of Ag colloid were used, the deposition of AgNPs on the filter paper substrate was not uniform, resulting in fewer hotspots and a reduced SERS signal. As the volume increased from 150 to 200 μL, the SERS signal gradually improved, reaching a maximum at 250 μL. However, at 300 μL, the SERS intensity decreased due to the high population density of AgNPs, which hindered effective hotspot formation. Thus, a colloid volume of 250 μL was chosen for its superior enhancement, achieving an optimal distribution of AgNPs and maximizing hotspot generation on the filter paper substrate.

Furthermore, the impact of surface oxidation was investigated in this study. During the EISA process, the Ag colloid was heated for approximately 3 h to create the AgNPs@filter paper substrate. Surface oxidation may occur as the Ag colloid evaporates. To assess this, 250 μL of Ag colloid was evaporated at intervals ranging from 30 to 300 min to determine the onset of surface oxidation. As shown in Fig. S3(a) and (b),† the results indicate that between 30 and 180 min, SERS intensity increases, suggesting successful AgNPs deposition onto the substrate and complete colloid evaporation. After 180 min, a gradual decrease in SERS intensity was observed, likely due to the full evaporation of the Ag colloid and the initiation of surface oxidation. Thus, from 180 to 300 min, the progression of surface oxidation is evidenced by a corresponding decrease in SERS intensity. Therefore, an evaporation time of approximately 3 h (180 min) is used in our experiments to minimize the risk of surface oxidation.

Different thiophenol concentrations were also tested to assess linearity, from 0.75 to 0.075 mM, with a linear regression coefficient R² of 0.9968, as shown in Fig. 3(c) and (d). To assess the uniformity of the SERS substrate, 10 random spots were measured on 5 substrates, resulting in an impressive RSD of 8.22%, calculated from the SERS intensity of 50 spots at 1066 cm⁻¹, as shown in Fig. 3(e) and (f). The results demonstrate the high uniformity and reproducibility of the filter paper substrate fabricated using the EISA method. A low RSD is crucial to indicate the reproducibility of the substrates developed using the EISA method.^62,67 Further, the long-term stability of the substrate was assessed by storing it in a sealed container at 3 °C for 14 days. The corresponding data is presented in Fig. S4(a) and (b).† A gradual decrease in signal intensity up to 19.1% was observed for 14 days.

Moreover, the substrate's enhancement factor (EF) was calculated using the AEF formula provided in eqn (1).⁵⁶ The substrate demonstrated an EF value of 1.04 × 10⁶, likely due to the dense electromagnetic field at the SERS substrate surface.

3.3. Optimization of various Raman labels

After determining the optimal fabrication conditions, we devoted our experiments to detecting microplastics. We expected that surface masking of plasmonic nanoparticles would reduce the LSPR. Briefly, the surface morphological changes in the SEM images of the AgNPs@filter papers incubated with microplastics suggested a plausible detection mechanism. The plasmonic enhancement of the substrates diminished progressively as the microplastic concentration in the colloid increased. This reduction in enhancement was attributed to the formation of a microplastic layer on the plasmonic nanoparticle surfaces. This highlights the surface-masking. The SEM images in Fig. S5(a) and (b),† which depict PS microplastics at various concentrations (0.1 mg mL⁻¹ and 1 mg mL⁻¹), illustrate the variation in microplastic layer accumulation on the substrate depending on the concentration. This reduction in LSPR is directly correlated with the concentration of the microplastic coating on the nanoparticles. The adsorption of Raman labels on the surface indicates this relationship. Raman labels, recognized for their pronounced reactivity and high binding affinity for metal surfaces,⁶⁸ were employed.

Traditional Raman-active molecules, such as dyes and thiol compounds, are commonly used in these studies. To assess the efficacy and interaction of our substrates with different Raman labels, we used 1 mM of thiophenol, malachite green, and rhodamine 6G. With the thiophenol molecule, a negative linear correlation was observed between the microplastic concentration and SERS intensity of the molecule, yielding an R² value of 0.9836, as depicted in Fig. 4(a) and (b), respectively. The assay's LOD was determined to be 0.001 mg mL⁻¹. Consequently, increasing the concentration of the PS microplastics progressively decreases the SERS intensity by reducing or masking the surface of the plasmonic nanoparticles.

Further, we tested two other dye molecules: 1 mM malachite green and rhodamine 6G.^69,70 The Raman peak assignments for all the model molecules are detailed in Table S1.† Notably, like thiophenol, the dye molecules rhodamine 6G (610 cm⁻¹), malachite green (1171 cm⁻¹) exhibited a negative linear correlation with the R² value of 0.9554 and 0.9939 shown in Fig. 4(c) and (f). When comparing the PS concentrations across different label molecules, thiophenol demonstrated the highest peak intensity with a good linear regression coefficient value, attributed to its stronger interaction with the AgNPs.⁷¹ Therefore, thiophenol was chosen as a reporter molecule for microplastic detection in aqueous and real-world samples.^71,72

3.4. SERS detection of different types of microplastics

After successfully developing the detection strategy, the versatility of our method was evaluated for different types of microplastics. Microplastic samples in environmental systems exhibit a wide range of sizes and types, which complicate their detection. To illustrate the application of our detection method, we selected various microplastics, including polystyrene (250 μm), polypropylene (10–50 μm), and polyvinyl chloride (1–5 μm).

Initially, we examined the SERS spectra of toluene-dispersed PS microplastics at concentrations ranging from 0.001 mg mL⁻¹ to 0.9 mg mL⁻¹, using 1 mM thiophenol as a Raman label and the characteristic peak at 1066 cm⁻¹ as the primary evaluation marker, as discussed in the previous section.

Moreover, the method's performance was evaluated in detecting other types and sizes of microplastics, specifically PVC and PP, and the data are presented in Fig. 5(a)–(d). Similar to the results obtained with PS, a negative linear correlation was achieved between the microplastic concentration and SERS signal of thiophenol, with R² values of 0.9931 (PVC) and 0.9916 (PP), respectively. The LOD for these microplastics was also determined to be 0.001 mg mL⁻¹. Therefore, as mentioned earlier in section 3.3, the observed negative correlation can be attributed to the surface masking effect of microplastics, which reduces the plasmonic enhancement on the AgNPs surface and leads to a decrease in the SERS signal of thiophenol. To validate the method for detecting microplastics of the same type but with varying sizes, PS microplastics measuring 250 μm and 2.1 mm were dispersed in toluene. In UV-visible spectroscopy, the peak around ∼282 nm indicates the presence of the π–π* electronic transition in PS.⁷³ The consistent intensity of this peak for PS samples of different sizes dispersed in toluene confirms a uniform dispersion of the microplastic in the solvent, as shown in Fig. S6(a).† Subsequently, SERS measurements were performed on the PS samples of different sizes, revealing consistent results regardless of particle size, as illustrated in Fig. S6(b) and (c).† These findings demonstrate that the toluene dispersion strategy is effective for detecting microplastics of varying sizes.

We further evaluated the efficiency of our strategy by preparing a mixture of three different microplastics – PS, PP, and PVC in equal ratios, with concentrations ranging from 0.003 mg mL⁻¹ to 0.9 mg mL⁻¹, to assess the correlation between microplastic concentration and the SERS intensity of thiophenol. Experimental results indicate that similar to standard microplastic samples, mixed plastic samples also exhibit consistent trends. A negative linear correlation between the concentration of microplastic mixtures and SERS intensity is observed, as shown in Fig. S7(a) and (b).† This highlights the potential of our approach for the quantitative detection of microplastics in real-world samples.

These findings underscore the remarkable sensitivity of the toluene dispersion and EISA method for detecting various sizes and types of microplastics.

3.5. Detection of microplastics in real-world samples

As a proof-of-concept study, we employed toluene dispersion and the EISA method to validate our strategy for detecting microplastics of various sizes in real samples. This approach was tested on PP microplastics ranging from 10 to 50 μm in lake water and salt samples, with concentrations reaching up to 0.003 mg mL⁻¹. Fig. 6(a) and (d) provide a pictorial representation of the SERS spectra with and without the presence of PP samples. Fig. 6(b) and (e) display the SERS spectra at varying concentrations of PP in complex matrices, such as salt and lake water. The SERS measurements showed no additional peaks or interference for samples with a PP concentration of 0 mg mL⁻¹, indicating minimal background interference from these matrices. Furthermore, Fig. 6(c) and (f) present linear calibration plots for different PP concentrations (ranging from 0.003 mg mL⁻¹ to 0.9 mg mL⁻¹) in the same environmental samples.

Similar to the detection in pure PP forms, the linearity graph demonstrates the relationship between PP concentrations in actual samples and the SERS intensity of thiophenol, with R² values of 0.9952 and 0.9950 for the salt and lake water samples, respectively. The quality attributes of the lake water and salt samples dissolved in the water were evaluated, as presented in Table S2.†

We further calculated the recovery rates of PP microplastics at concentrations of 0.009, 0.03, and 0.09 mg mL⁻¹, and different sizes of PS at 0.09 mg mL⁻¹ in real sample systems. The recovery rates were calculated by comparing the actual signals of microplastics detected in real-world samples to the signals obtained from PP (1–50 μm) standard microplastics in a toluene medium, as described in eqn (3). As shown in Table 1, Fig. 7 and S8,† the recovery rates ranged from 90% to 110% for the lake water and salt samples.

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Furthermore, the selectivity of our approach in detecting microplastic was assessed by testing ten different potential coexisting contaminants commonly found in real-world samples such as organic pollutants: perfluorooctane sulfonamide (PFOSA), glyphosate, and thiram,^74–76 inorganic ions: chromium, phosphate, and nitrite,^77,78 inorganic colloids and bio-organisms: clay, algae, and bacteria,^79,80 and an aromatic compound: bisphenol A.⁸¹ As depicted in Fig. S9,† distinct bar diagrams representing the SERS intensity of 1 mM thiophenol at 1066 cm⁻¹ were observed for 1 mg mL⁻¹ concentrations of PP and other contaminants, including 1 mg mL⁻¹ of PFOSA, phosphate, nitrite, thiram, bisphenol A, as well as algae (0.4 OD – optical density), bacteria (0.6 OD), and clay (18.06 TDS). Our results show that the proposed method has high specificity for microplastic detection.

In addition, we performed finite-difference time-domain (FDTD) simulations to investigate the electromagnetic field distribution of AgNPs.⁸² The dielectric function of the AgNPs was fitted based on the particle size measurements obtained from SEM images. We observed that the electric field intensity (|E|) is enhanced as the distance between AgNPs decreases, maintaining a constant particle diameter of 60 nm. The separation between nanoparticles varied from 2 nm to 12 nm. The incident light, with a wavelength range of 300–800 nm, was directed along the z-axis, with polarization parallel to the x-axis.

Mesh sizes of 0.013 nm and 0.034 nm were used in the x and y directions, respectively, to ensure accurate resolution of the nanoparticles. When the gap is less than 2 nm, the maximum |E| drops sharply with increasing gap distance and stabilizes for gaps greater than 2 nm, corresponding to the typical structure of the nanoparticles. Fig. S10(a) and (b)† show the electric field distribution of the AgNPs at interparticle distances of 2 nm and 10 nm, respectively. Therefore, the FDTD simulation data indicate that a hotspot distance of ∼2 nm yields the highest field enhancement.

These findings underscore the significant potential of the toluene dispersion method combined with filter paper-based SERS substrates for detecting microplastics in different sizes.

3.6. Comprehensive assessment of our approach

To highlight the advantages of our strategy, we performed a comparative analysis with current Raman methods for microplastic detection, focusing on the crucial factor LOD, substrate fabrication methodology, instrument, and detection in real-world samples. Previous studies have primarily targeted the enhancement of LOD using sensitive SERS substrates.^83–85 However, this endeavor encounters two major challenges: variations in Raman signals based on microplastic size, and difficulty in detecting non-traditional microplastics. Additionally, many studies have employed pre-treatment processes like vacuum filtration and intricate substrate fabrication methods.^86,87

In contrast, the EISA and toluene dispersion strategy presents a highly sensitive and facile method with a high LOD value compared to existing literature, as demonstrated in Table 2. Our strategy utilizes toluene to separate microplastics and disperses different sizes of microplastics for uniform and sensitive detection. This approach mitigated the issue of size variability and achieved a notable threshold of 0.001 mg mL⁻¹ for PVC, PP, and PS microplastics.

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4. Conclusions

This study introduces a novel SERS-based method for detecting microplastics of varied sizes in real-world samples. More importantly, a toluene dispersion strategy in combination with the EISA method was proposed for the first time to prepare SERS substrate arrays that could indirectly detect microplastics at concentrations as low as 0.001 mg mL⁻¹. Our results show that this method can be successfully applied to the detection of a variety of microplastic materials, such as PS (250 μm and 2.1 mm), PP (10–50 μm), and PVC (1–5 μm). Compared to other SERS-based methods, the established protocol is rapid and efficient for detecting microplastics of different sizes using a portable Raman spectrometer. Furthermore, we demonstrated proof-of-concept studies of salt and lake water samples, which showed remarkable sensitivity. This advancement opens new avenues for monitoring plastic pollution in food and environmental samples, not only for Raman-active plastics.

Data availability

The data supporting this article have been included as part of the ESI.

Author contributions

Jayasree Kumar: writing – original draft, methodology, investigation, conceptualization. Arunima Jinachandran: writing – review & editing. Mounika Renduchintala: methodology. Venugopal Rao Soma: writing – review & editing, validation. Vijayakumar Shanmugam: writing – review & editing. Shaik Imamvali: software (FDTD simulation), Sreenivasulu Tupakula: software (FDTD simulation), Rajapandiyan Panneerselvam: writing – review & editing, supervision, investigation, funding acquisition, conceptualization.

Conflicts of interest

The authors declare no conflicts of interest.

Acknowledgements

The authors thank SRM University-AP for the SEED funding (SRM/TRUST/AP/Jan/IC/22-23/PUR-00007). SRM Institute of Technology, Chennai, for instrument characterization. V. R. Soma thanks DRDO, India, for financial support through ACRHEM (DIA-COE) and support from the Institute of Eminence project [ref. No. UOH/IOE/RC1/RC1- 20-016] by the University of Hyderabad, India.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5en00211g

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