DOI:
10.1039/D5MH01126D
(Communication)
Mater. Horiz., 2025, Advance Article
Spider-silk-inspired self-healing conductive elastomer for joint rehabilitation detection and tactile temperature warning
Received
13th June 2025
, Accepted 22nd July 2025
First published on 11th August 2025
Abstract
Flexible and self-healing ion-conductive materials show potential advantages in wearable health monitoring systems based on the internet of things (IoT). However, their practical applications are limited by the directional selectivity and detection range of the sensor. Here, a room-temperature self-healing, ion-conductive polyurethane elastomer that shows excellent mechanical properties, high conductivity, bidirectional sensing and thermoelectric properties was synthesized. Due to the presence of multiple hydrogen bonds, it can effectively dissipate energy through the destruction and recombination of multiple reversible hydrogen bonds, achieving a high mechanical strength (34 MPa) and excellent room-temperature self-healing ability (self-healing efficiency of 91.4%). Significantly, the synergistic monitoring of strain, temperature, and thermoelectricity can be realized based on the existence of the synergistic ion/electron-conductive components of the self-healing ion-conductive elastomer. The strain module enables bi-directional monitoring and high sensitivity (GF ∼ 5.23). Additionally, the temperature module has a resistance temperature coefficient of 2.42, while the thermoelectric module provides a power density of 1.59 mW m−1 K−2. Furthermore, leveraging the photon-thermal-electric coupling effect, a non-contact photoelectric sensor is fabricated, which can realize high-temperature detection/protection. As a wearable electronic device, it shows advantages in joint rehabilitation monitoring systems and prevention/monitoring systems.
New concepts
In this study, inspired by the structure of spiderweb protein, a health monitoring platform based on a self-healing conductive polyurethane elastomer was constructed. Compared with previous studies, the polyurethane elastomer exhibits excellent mechanical strength (34 MPa) and room-temperature self-healing performance (self-healing efficiency of 91.4% at room temperature) through the construction of multi-hydrogen-bond nanodomains. Additionally, the well-designed ionically/electronically conductive elastomer exhibits bidirectional mechanical detection and dual temperature detection capabilities, allowing it to simultaneously decode biomechanical and thermal characteristics. It can be applied to wearable electronic devices to monitor the rehabilitation status of human joints and external environment for real-time rehabilitation assessment and preemptive injury warning. Therefore, the ion/electron-conductive self-healing polyurethane expands ‘sensory fusion’ in wearable electronics, and transforms multiple physical data into operable health insights through a single material.
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1. Introduction
The convergence of artificial intelligence (AI) and internet of things (IoT) technologies has garnered widespread attention for smart wearable devices in the field of healthcare. Self-healing ion-conductive elastomers offer significant advantages in health monitoring (e.g., body temperature, human joint movement),1–4 electronic skin,5–7 and human–computer interaction8–10 due to their excellent mechanical and self-healing properties. Although soft electronic materials have made great strides in health detection, most flexible sensors with non-integrated functions can only detect mechanical deformation or environmental stimuli (such as temperature,11 humidity and photonic signals) due to incompatible sensing mechanisms.12,13 Therefore, the development of stretchable and self-healing electronics that combine sensing of multiple mechanical and environmental stimuli is essential, but often challenging.
Profiting from the rapid development of supermolecular chemistry, ion-conductive elastomers with specific mechanical properties and self-healing ability can be prepared by introducing synergy among multiple dynamic bonds (e.g., hydrogen bonds,14,15 ion interactions,16,17 host–guest interactions18–20) or regulation of microphase separation structures (such as polyurethane–ionic liquid systems)21,22 in polymers. However, due to the contradictory nature of mechanical strength and self-healing ability, self-healing ion-conductive elastomers usually exhibit a combination of high mechanical strength and high healing temperature.23–25 For example, Ye et al.26 prepared a poly(oxime-urethane) elastomer with high tensile strength (22 MPa) and good self-healing ability (self-healing efficiency ∼91.7%) through hydrogen bonding and coordination interactions. The recovery toughness was 75.4 MJ m−3 at 80 °C. These requirements significantly increase energy consumption, which limits practical application in complex scenarios. In addition, in order to achieve the synergy of self-healing with conductivity, continuous ion channels (such as ionic liquid enrichment zones) can be formed through microphase separation, thus ensuring that the conductivity (>10−3 S cm−1) is not disturbed by the self-healing process.27,28 For example, Xu et al.29 prepared a self-healing ionic gel with high conductivity (5.28 mS cm−1) and excellent self-healing ability (self-healing efficiency ∼98.8%) by constructing a phase separation structure. The hard phase region undergoes self-healing via reversible fracture and formation, while the hydrogen bonds between the polymer and IL in the soft phase region facilitate ion migration, leading to high conductivity. Therefore, in order to meet the needs of practical applications, the preparation of ion-conductive elastomers with high mechanical strength and excellent room temperature self-healing ability is an urgent problem to be solved.
The emergence of the internet of things (IoT) has stimulated demand for smart sensors with health monitoring and high-temperature monitoring/early warning ability to pre-empt potential health and safety risks. However, traditional health monitoring (which mainly relies on medical methods such as magnetic resonance imaging and X-ray imaging)30–32 and high-temperature-prevention systems have high monitoring costs and low convenience. Although emerging technologies such as gait analysis methods and wearable physiological monitoring devices show promise, their application is currently constrained by environmental and equipment limitations.33–36 Furthermore, most of the current thermal monitoring platforms remain predominantly confined to biometric thermometry applications (e.g., wearable fever detection) and exhibiting various limitations for simultaneously sensing elevated ambient thermal regimes and preventing injuries. Lin et al.37 prepared a multifunctional sensor by introducing non-covalently modified carbon nanotubes into carboxylated styrene-butadiene rubber. The prepared sensor has an impressive thermal response (0.01636 °C−1), which enables temperature detection in the human body. Lu et al.38 prepared a NiO/CNTF flexible temperature sensor to prevent fires caused by out-of-control electronic equipment. The sensor has a wide operating range (−15 to 60 °C) and high sensitivity (maximum TCR is 20.2% °C−1). Most current temperature sensors for health monitoring can monitor only the object temperature or ambient temperature, but cannot monitor both to achieve better protection for the human body. Therefore, the realization of integrated flexible wearable systems with concurrent environmental sensing and physiological monitoring capabilities is of considerable significance for enabling continuous health surveillance and proactive risk mitigation in daily living scenarios.
Here, a series of room-temperature self-healing ion-conductive polyurethane elastomers with excellent mechanical properties, high ionic conductivity, bidirectional sensing and thermoelectric properties were prepared via the synergistic use of LiTFSI/COOH-CNTs and multiply hydrogen-bonded polyurethane. The existence of multiply hydrogen-bonded hard phase regions and soft phase regions containing a large number of ether bonds provides the basis for high mechanical strength and self-healing. The hard phase region dissipates energy through the fracture and formation of multiple hydrogen bonds, endowing the material with high mechanical strength. At the same time, multiple hydrogen bonds are broken and recombined to achieve self-healing at room temperature. In addition, Li–O bonds are formed between the polyurethane in the soft phase region and the soft segment of LiTFSI, which promotes ion migration through molecular chain movement. Furthermore, the polyurethane molecular chain can also form hydrogen bonds with the COOH-CNTs, which ensures the high conductivity of the ion-conductive elastomer. Interestingly, based on its excellent synergistic ion/electron-conductive components, synergistic monitoring of strain, temperature, and thermoelectricity can be achieved. Thus, the elastomer shows advantages as a wearable electronic device in joint rehabilitation monitoring systems and prevention/monitoring systems.
2. Results and discussion
2.1. Structure and mechanical performance of self-healing polyurethane elastomers
Most organisms have complex repair mechanisms to reduce damage. For example, the structural proteins of spider silk are characterized by repetitive amino acid sequences that organize into rigid β-sheet crystalline domains (imparting mechanical strength) and flexible amorphous regions (providing elasticity). Under external stress, the β-sheet crystalline domains exhibit dynamic hydrogen bond dissociation and reconfiguration, facilitating energy dissipation while maintaining structural integrity (Fig. 1a).39 Inspired by the multi-level hydrogen bond structure of spider silk, a linear polymer was synthesized via two-step polymerization using polytetrahydrofuran ether glycol (PTMG), isophorone diisocyanate (IPDI) and adipic acid dihydrazide (ADH), and then a polyurethane incorporating numerous hydrogen bond donors and acceptors was prepared through crosslinking with 2-amino-2-methyl-1,3-propanediol (AMPD); the final polyurethane was designated as SPU (Fig. 2a and Fig. S1). ADH was chosen as a chain extender because of its hydrazide groups, which react with the isocyanate groups of IPDI to generate acylsemicarbazide (ASCZ) bonds. These ASCZ bonds feature plentiful hydrogen bond donors (–NH–) and acceptors (–C
O, –NH–), facilitating the development of supramolecular networks. Additionally, the successful preparation of SPU was confirmed via Fourier transform infrared (FT-IR) spectroscopy (Fig. S2). The stretching vibration of hydrogen-bonded C
O was significantly enhanced at 1660 cm−1 (Fig. 2b), indicating that the incorporation of ADH produced an ASCZ-rich part (–NH and –C
O groups), which effectively increased the number and strength of hydrogen bond interactions. Furthermore, the characteristic amide I (C
O stretching at 1680 cm−1) and amide II (N–H bending/C–N stretching at 1540 cm−1) bands showed a blue shift and peak broadening, demonstrating that the intermolecular hydrogen bond enhancement (N–H⋯O
C) was generated by the ASCZ structure formed during chain extension.40 Deconvolution analysis of the FT-IR stretching region at 1750–1600 cm−1 resolved the C
O vibration band of SPU4 into seven Gaussian components (Fig. 2c), which were systematically assigned to distinct carbonyl species: free (1728 cm−1) and hydrogen-bonded (1703 cm−1) carbamate groups, non-associated (1687 cm−1) and H-bonded (1665 cm−1) urea linkages, and three amide-related configurations (1648, 1632, 1615 cm−1), confirming the presence of multiple modes of hydrogen-bonding interactions (N–H⋯O
C) within the ASCZ segments that collectively contribute to supramolecular network formation.41 The hierarchical hydrogen-bonding networks formed between ASCZ segments (N–H⋯O
C interactions) fundamentally underpin the enhanced mechanical performance of SPU. The mechanical strength of SPU exhibited progressive enhancement with increasing ADH content. Specifically, when the molar ratio of PTMG to ADH reached 1
:
1, the resulting SPU exhibited a maximum tensile strength of 34 MPa, which represents an obvious advantage compared with previous studies (Fig. 2d and Fig. S11a). This enhancement mechanism is mainly due to the hydrogen bond interactions between the amino group of ADH and the hard segment of SPU, which effectively limit the mobility of the molecular chain under stress conditions. Additionally, the incorporation of the cross-linking agent AMPD significantly improved the mechanical strength of SPU, achieving a tensile strength of 36 MPa. This enhancement was accompanied by a reduction in tensile ductility, suggesting a trade-off between cross-linking density and the flexibility of the material (Fig. 2e, Fig. S3, S5 and S6). This behavior likely stems from AMPD-induced covalent network formation, which restricts polymer chain mobility while increasing structural rigidity. The synergistic interplay between the hierarchical hydrogen-bonding architecture of SPU (comprising 66% reversible N–H⋯O
C interactions) and chemically cross-linked networks endows the material with exceptional damage tolerance. As quantified by puncture resistance tests and trouser tear measurements, the fracture energy of the elastomer was 87.23 kJ m−2 (Fig. 2f and g), which may be attributed to the synergistic energy dissipation mechanism formed by the reversible fracture/recombination of ASCZ hydrogen bonds and the tortuous crack path caused by the chemical crosslinking structure.42
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| Fig. 1 Structure and application of the self-healing polyurethane ion-conductive elastomer. | |
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| Fig. 2 Synthesis and characterization of the self-healing polyurethane SPU. (a) Molecular structure of the polymer SPU. (b) Infrared curves of SPU with different ADH and AMPD contents in the range of 1500–1800 cm−1. (c) Infrared Gaussian distribution curve of the C O group of SPU4. (d) Stress–strain curves of SPU with different ADH contents. (e) Stress–strain curves of SPU with different AMPD contents. (f) Optical photographs of the puncture resistance of the polymer SPU4. (g) Stress–strain curve of demonstrating the tear resistance of the polymer SPU4. | |
2.2. Self-healing performances of the self-healing polyurethane elastomers
Through the synergistic effect of multi-scale hydrogen bond networks and dynamic covalent bonds, a balance between mechanical strength and self-healing ability is achieved. This hierarchical hydrogen bond network is mainly composed of strong hydrogen bonds (N–H⋯O
C) from the acylsemicarbazide (ASCZ) fragments, moderate interactions from the urethane (–O–CO–NH–) and amide (–CONH–) motifs, and weak H-bonds from the IPDI-derived NH-group. The existence of this synergy allows the hydrogen bonds to break and dissipate energy during the stretching process of SPU. After recycling, the hydrogen bonds are recombined to form a new network structure that guarantees its mechanical properties (Fig. 3a). As a result of its doubly cross-linked structure, SPU shows exceptional load-bearing capacity, being capable of supporting weights up to 18
750 times its own mass while maintaining dimensional stability with minimal permanent deformation post-recovery (Fig. 3b). The self-healing ability of SPU was quantitatively studied via tensile testing. As illustrated in Fig. 3c, both the tensile strength and strain of the self-healed SPU demonstrate progressive enhancement with extended healing duration. Following a 48-hour self-healing period, the tensile strength and strain of the material were effectively restored to 32.62 MPa and 512%, respectively, approaching the performance levels of the pristine SPU specimens. This recovery corresponds to a remarkable healing efficiency of 91.4%, calculated as the ratio of healed-to-original tensile strength. To investigate the self-healing mechanism, a high-resolution microscope was utilized for three-dimensional morphological assessments of the SPU surfaces at various recovery stages. Prior to damage, the SPU exhibited distinctive surface roughness, whereas the sheared sample displayed distinct notch marks at the fracture interface. Notably, after 24 hours of recovery at ambient temperature, these gaps show significant attenuation, and the scratches become significantly shallower and eventually disappear. At the same time, it can be observed in the reflection infrared spectrum that the hydrogen bond strength is almost the same as that in the initial state, indicating that the elastomer is almost completely repaired (Fig. 3d and Fig S4). These results indicate that SPU has excellent room-temperature self-healing ability.
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| Fig. 3 Self-healing properties of self-healing polyurethane SPU. (a) Intermolecular interaction and self-healing mechanism of the polymer SPU. (b) Optical photographs of the polymer SPU4 pulling heavy objects. (c) Stress–strain curves of polymer SPU4 after different repair times. (d) Optical images and ultra-depth-of-field images of the repair process of the polymer SPU4. | |
2.3. Synthesis and performance of the ionic–electronic conductive elastomer
An ionically conductive self-healing elastomer (SPU4–LiTFSIm) was engineered by incorporating lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) into supramolecular polyurethane (SPU). The successful introduction of LiTFSI was studied using FT-IR spectroscopy. The characteristic peaks of N–H (3320–3285 cm−1) and the C
O (1730–1715 cm−1) stretching vibration shift were observed (Fig. 4a and b), confirming the coordination of Li+ with the polymer soft segments while preserving the hydrogen-bonding crosslinks essential for mechanical resilience. Additionally, the TFSI− ions of the small molecular structure are embedded in the polyurethane, resulting in the plasticization of the polyurethane and a linear decrease in its mechanical properties. When the content reaches 50%, the mechanical strength decreases by about 80 times (Fig. S7). However, as the ion content increases, the conductivity is significantly enhanced (Fig. S14). Based on the balance between conductivity and mechanical properties, SPU–LiTFSI0.3 was finally selected for further optimization. To solve the inherent problem of the trade-off between conductivity and mechanical properties in the SPU4–LiTFSI0.3 system, COOH-CNTs were introduced into SPU. The mechanical properties of the SPU–LiTFSI–CNT elastomer were gradually enhanced with increasing COOH-CNTs content due to the hydrogen bonding between SPU–LiTFSI–CNT and the amino acid groups and the π–π stacking of TFSI−. The tensile strength was increased to 14 MPa, and the material exhibited ultra-high ductility (ε ≈ 900%) with an ionic conductivity of 4.2 mS cm−1 (Fig. 4c, Fig. S14 and S15). At the same time, it exhibited considerable fatigue resistance, maintaining 92% initial elastic recovery after 5 deformation cycles (0–300% strain) (Fig. 4d and Fig. S8). Subsequently, the surface structure of the SPU4–LiTFSI0.3–CNT0.3 elastomer was characterized using scanning electron microscopy (SEM). It can be observed that the elements C, O, N, F and S were evenly distributed in the polymer, indicating that LiTFSI and COOH-CNTs were evenly distributed in SPU4 (Fig. 4e, Fig. S8 and S9). However, it can also be observed that the COOH-CNTs are deposited at the bottom layer of polyurethane under the action of gravity in the cross-sectional SEM image of the SPU–LiTFSI0.3–CNT0.3 elastomer, while the LiTFSI is uniformly distributed in the SPU elastomer. This uneven distribution provides a basis for the realization of bidirectional sensing (Fig. S10). Furthermore, due to the presence of mobile ions and hydrogen bonds, SPU4–LiTFSI0.3–CNT0.3 can achieve self-healing while maintaining enhanced ionic conductivity through the permeable ion transport pathway. In order to quantitatively evaluate the self-healing ability of the SPU4–LiTFSI0.3–CNT0.3 ionic elastomer, the elastomer strip was integrated into a series circuit. The SPU4–LiTFSI0.3–CNT0.3 ionic elastomer exhibited ultrafast electrical recovery speed (25 °C, 38% RH), and the volume resistivity returned to the baseline level within 500 ms after contact (Fig. 4f). Concurrently, instantaneous LED reactivation was observed (Fig. 4g), confirming the re-establishment of continuous ion-conductive pathways across the healed interface through the reconstruction of Li+ coordination networks and reorientation of COOH-CNTs-assisted electron tunneling junctions (Fig. S17). This rapid dual-channel restoration mechanism to achieve both ionic and electronic continuity, coupled with the tripartite performance characteristics, positions SPU4–LiTFSI0.3–CNT0.3 as a prime candidate for durable flexible electronics.
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| Fig. 4 Characterization of the self-healing polyurethane ion-conductive elastomer. (a) Infrared curves of the ion-conductive elastomers with different LiTFSI contents. (b) Infrared curve of the ionically conductive elastomers with different LiTFSI contents in region 1600–1800 cm−1. (c) Stress–strain curves of ion-conductive elastomers with different COOH-CNTs contents. (d) Cyclic curves of the polymer SPU4–LiTFSI0.3–CNT0.3 under different strains. (e) SEM and EDS spectra of the polymer SPU4–LiTFSI0.3–CNT0.3. (f) Resistance change rate of the polymer SPU4–LiTFSI0.3–CNT0.3 after repair. (g) Optical images of small light bulbs before and after repair of the polymer SPU4–LiTFSI0.3–CNT0.3. | |
2.4. Sensing performance of the ionically and electronically conductive elastomers
The electrical properties of SPU4–LiTFSI0.3–CNT0.3 were further characterized to evaluate its potential use as a flexible sensor. It is worth noting that current common flexible conductive materials often face a trade-off between ionic conductivity and mechanical strength.43 In our study, the addition of solid conductive additives (LiTFSI) and COOH-CNTs was found to improve the Young's modulus and toughness, which may overcome the undesirable contradictions in traditional soft conductors. Therefore, SPU4–LiTFSI0.3–CNT0.3 was fabricated as a strain sensor, and its sensing performance was evaluated. The sensitivity of the SPU4–LiTFSI0.3–CNT0.3 sensor was quantitatively assessed through cyclic tensile testing (0–300% strain). The relative resistance change (ΔR/R0) exhibits a monotonic increase with the applied strain, demonstrating a nonlinear piezoresistive response characteristic of hierarchical conductive networks. This strain-dependent behavior arises from the dynamic interplay between the disruption of the COOH-CNTs-based electron tunneling pathways and the alignment-enhanced ionic conduction through the oriented Li+ transport channels. In the low-strain regime (ε < 50%) governed by reversible microcrack formation in the COOH-CNTs percolation network, the GF is ∼0.67. In the intermediate regime (50–200%), the GF increases to 3.64–6.84, reflecting the synergistic effects of COOH-CNTs reorientation and ion-dipole network distortion. In the high-strain regime (200–300%), the GF stabilizes at 5.23 due to the strain-induced crystallization of the PU soft segments (Fig. 5a and Fig. S17). A higher GF indicates a more sensitive resistance response, which is more conducive to detecting small movements. In light of its excellent tensile properties, the relative real-time resistance (ΔR/R0) of the SPU4–LiTFSI0.3–CNT0.3 sensor was tested under 5–50% strain and bending at different angles (Fig. 5b, c and Fig. S19). It can be seen that the SPU4–LiTFSI0.3–CNT0.3 sensor maintains a regular and stable response under different strains and bending angles, showing excellent stress sensitivity. Subsequently, when loaded onto human joints (fingers, elbows, wrists), the sensor can capture the motions of these joints. Different joint movements give rise to different signals, which can be used to clearly distinguish the movements of the different joints (Fig. 5d–f). Remarkably, the sensor achieves millisecond-scale response time (τ = 163.7 ms) with exceptional cyclic stability (over 103 cycles) (Fig. 5g and h). In addition, although the sensor exhibits some signal variation at different temperatures and humidity values, the strain-sensing signals showed negligible difference when tested at the same strain and frequency under different temperatures and humidity values, demonstrating that the SPU–LiTFSI–CNT sensor can perform accurate and reliable strain sensing at various temperatures (Fig. S18). In addition, the sensor exhibits good biocompatibility (Fig. S23). This tunable strain-response profile, coupled with a broad sensing range (>300% strain), positions the material as a versatile platform for multiscale biomechanical monitoring ranging from subtle pulse waveforms (ε ∼ 1%) to large joint movements (ε > 200%).
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| Fig. 5 Sensing performance of the self-healing polyurethane ion-conductive elastomer SPU4–LiTFSI0.3–CNT0.3 sensor. (a) GF of the SPU4–LiTFSI0.3–CNT0.3 sensor under different strain ranges. (b) Sensing stability of the SPU4–LiTFSI0.3–CNT0.3 sensor under 10–50% strain. (c) Changes in the sensing signal of the SPU4–LiTFSI0.3–CNT0.3 sensor at different bending angles. (d)–(f) SPU4–LiTFSI0.3–CNT0.3 sensor detecting the resistance sensing signal changes associated with finger, wrist, and knee movements, respectively. (g) Bending sensing response time and recovery time of the SPU4–LiTFSI0.3–CNT0.3 sensor. (h) Sensing stability of the SPU4–LiTFSI0.3–CNT0.3 sensor after 1000 bending cycles. | |
2.5. Joint rehabilitation detection performance of ionically and electronically conductive elastomers
It is well known that the wrist connects the hand to the forearm. If the wrist has inflammation or injury symptoms, the movement of the hand will be limited, the grip strength of the hand will be reduced, and the torque of the wrist will be poor.44 Therefore, the recovery of the wrist can be predicted according to the wrist bending torque during rehabilitation. Due to the synergistic effect of the ion/electron-conductive materials, the SPU4–LiTFSI0.3–CNT0.3 sensor exhibits bidirectional responses to bending. When bent upwards, the relative resistance change shows a positive response, while when bent downwards, it shows a negative response (Fig. 6a, Fig. S19 and S20). Based on the bidirectional sensing characteristics of the sensor, a joint detection system was designed based on the four common motion states of the wrist joint (flexion, extension, adduction, and extension; Fig. 6b). According to the relative resistance change, it corresponds to the four motion states of the wrist joint. When the wrist joint is in a healthy state, it shows a standard relative resistance change (Fig. 6c–f). When the wrist joint is under rehabilitation, the rehabilitation state can be detected in real time based on comparison of the observed real-time resistance change to the standard state. On the other hand, in the course of daily exercise, incorrect running may cause knee joint injury, which is likely to have an impact on the daily life of patients. Therefore, early detection of knee joint motion is of great significance for preventing/reducing knee joint injury. Therefore, based on the excellent bidirectional sensing characteristics of SPU4–LiTFSI0.3–CNT0.3, a knee joint detection sensor was prepared. Different ΔR/R0 states are observed under different motion states of the knee joint. For example, in the bending state, the relative resistance change is 50%, while the normal micro-bending state is 30% (Fig. 5g and h). Therefore, it is possible to detect whether the knee joint can be bent normally in order to detect its health status. In view of the joint detection ability shown by the sensor, it may be used in a human joint detection system in the future for joint rehabilitation status detection and joint injury prevention.
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| Fig. 6 Bidirectional sensing properties of the self-healing polyurethane ion-conductive elastomer SPU4–LiTFSI0.3–CNT0.3 sensor. (a) Stability of the bi-directional sensing signal of the SPU4–LiTFSI0.3–CNT0.3 sensor. (b) Upward bending response time of SPU4–LiTFSI0.3–CNT0.3 sensor. (c)–(f) The SPU4–LiTFSI0.3–CNT0.3 sensor is used as a joint detection device to detect the schematic diagram and sensing signal changes of human wrist motion (flexion, extension, extension, and retraction). (g) and (h) The SPU4–LiTFSI0.3–CNT0.3 sensor was used as a joint rehabilitation detection device to detect the schematic diagram and sensing signal changes of the knee jump response. | |
2.6. Temperature-detection performance of polyurethane ion-conductive elastomers
2.6.1. Temperature-sensing performance. The temperature-sensing characteristics of SPU4–LiTFSI0.3–CNT0.3 were studied by observing the change in resistance with the change in external temperature. As shown in Fig. 7a, as the external temperature increased, the resistance of the SPU4–LiTFSI0.3–CNT0.3 sensor was reduced by an order of magnitude. The relative resistance of the SPU4–LiTFSI0.3–CNT0.3 decreased linearly with increasing temperature from 30 °C to 100 °C, showing a typical negative temperature coefficient (NTC) effect. Subsequently, the feasibility of using the conductive fabric as a thermistor was evaluated using the resistance temperature coefficient (TCR), which can be expressed as:
TCR = (1/R0) × (R − R0)/ΔT |
where R0 is the initial resistance of the sample at 30 °C, and R − R0 corresponds to the resistance change with the temperature change (ΔT). As shown in Fig. 7b, the sensor exhibits a negative temperature coefficient effect and a monotonic resistance–temperature relationship in the range of 30–100 °C. ΔR/R0 is close to linear in the range of 30–70 °C (R2 = 0.9959), which ensures the accuracy of the conversion of the temperature change into an electronic signal. In particular, the TCR is 2.43 in the range of 30–70 °C, which is a high value. In addition, the calculated TCR was 0.51 in the range of 70–100 °C. Subsequently, the minimum monitoring limit of the sensor was studied. When the external temperature rises from 36 °C to 38 °C, ΔR/R0 gradually decreases at an interval of 0.1 °C, indicating that the resolution of the sensor can be estimated as 0.1 °C. This high resolution allows the sensor to detect very small temperature changes with a very low detection limit (0.1 °C) to monitor human body temperature (Fig. 7c). In order to further explore the ability of the sensor to monitor human body temperature changes, the change in the ΔR/R0 value of the sensor with increasing temperature from 32 to 40 °C was studied with an increment of 2 °C. It can be observed that as the temperature increases, ΔR/R0 gradually decreases. When the temperature is temporarily stable, the ΔR/R0 ratio is maintained at a certain value, indicating that the sensor can accurately detect temperature changes in real time (Fig. 7d). From heating and cooling, it can be observed that the relative resistance changes sensitively and stably with the change of temperature (Fig. 7e). To explore the stability of the temperature response, the temperature resistance behavior of the sensor was studied after multiple heating–cooling cycles from 30 °C to 60 °C. It can be found that the change in ΔR/R0 was almost the same after multiple heating–cooling cycles, indicating that the sensor had good temperature response stability (Fig. 7f). In light of the fact that COOH-CNTs have photothermal characteristics, the relative resistance changes of the sensor under illumination were explored. Under the action of infrared light, the relative resistance change of the sensor was reduced by 300% within 30 s, because the photothermal effect of COOH-CNTs can increase the overall temperature (Fig. 7g). This enhances the movement of the polymer molecular chain and accelerates the ion migration within the molecule. At the same time, it can be seen from Fig. 7h and i that the relative resistance of the sensor is stable after multiple illumination cycles and is not affected by the illumination distance. These results show that the SPU4–LiTFSI0.3–CNT0.3 sensor has excellent temperature response characteristics. Based on its low-temperature monitoring characteristics, it may be used for temperature monitoring of human inflammatory sites.
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| Fig. 7 Temperature-sensing properties of the self-healing polyurethane ion-conductive elastomer SPU4–LiTFSI0.3–CNT0.3 sensor. (a) Temperature-sensing properties of the SPU4–LiTFSI0.3–CNT0.3 sensor. (b) Temperature response sensitivity of the SPU4–LiTFSI0.3–CNT0.3 sensor. (c) Sensing characteristics of the SPU4–LiTFSI0.3–CNT0.3 sensor for temperature differences of 0.1 °C. (d) Temperature response stability of the SPU4–LiTFSI0.3–CNT0.3 sensor. (e) Temperature response time of the SPU4–LiTFSI0.3–CNT0.3 sensor. (f) Temperature response stability of the SPU4–LiTFSI0.3–CNT0.3 sensor at a temperature difference of 30 °C. (g) Resistance changes of the SPU4–LiTFSI0.3–CNT0.3 sensor under near-infrared light conditions. (h) Cyclic stability of the sensing of the SPU4–LiTFSI0.3–CNT0.3 sensor under switching cycles under near-infrared light. (i) The sensing cycle stability of SPU4–LiTFSI0.3–CNT0.3 sensor under near-infrared illumination at different distances. | |
2.6.2. Thermoelectric performance. Because the conductive elastomer SPU4–LiTFSI0.3–CNT0.3 contains a large number of mobile ions, exposure a temperature gradient can promote ion migration to produce a voltage signal. Therefore, in order to obtain the temperature signal more accurately, a laboratory-built test platform was used to measure the thermal voltage of the conductive elastomer, with a constant-temperature heating table being used to establish the temperature gradient on the side of the conductive elastomer (Fig. 8a and Fig. S22). The two sides of the conductive elastomer are then assembled with wires and connected to a multimeter to measure the voltage. The temperature of the prepared sample was recorded using a thermocouple. The measured voltage of the ion-conductive elastomer was recorded at different temperature gradients. All measurements were performed at room temperature with a relative humidity of about 38%. The conductivity of the ion-conductive elastomer was calculated via electrochemical impedance spectroscopy (Fig. 8b and Fig. S13), and can be expressed as:
where σ is the conductivity of the sample, L is the length of the sample, R is the resistance of the sample, and A is the effective area of the electrode. The Seebeck coefficient (S) of the ion-conductive elastomer is an important index to characterize its thermoelectric properties. The value of S can be obtained from the slope of the voltage–temperature difference curve. The voltage–temperature difference curves of ion-conductive elastomers with different COOH-CNTs contents are shown in Fig. 8c. In order to further study the thermoelectric properties of SPU4–LiTFSI0.3–CNT0.2, the effect of the CNT content on the ion-conductive elastomer was investigated. When the content of COOH-CNTs is 20%, the Seebeck coefficient and conductivity are 0.41 mV K−1 and 1.32 S m−1, respectively (Fig. 8d). Compared with SPU–LiTFSI0.3–CNT0, the ion power factor decreased from 1.59 mW m−1 K−2 to 0.22 mW m−1 K−2 (Fig. 8e). This may be due to the increase in COOH-CNTs content forming a dense conductive network, which limits carrier migration and enhances interface scattering to promote increased conductivity. However, if the conductive network is too dense, the carrier transport tends toward metal behavior (low Seebeck coefficient). Considering the balance between conductivity and the Seebeck coefficient, SPU–LiTFSI0.3–CNT0.3 was finally selected as the material for follow-up research. Based on this, the thermal conductivity of the ion-conductive elastomer with different COOH-CNTs contents were explored, and the thermoelectric figure of merit (ZT) of the ion-conductive elastomer was calculated.
Where S is the Seebeck coefficient, σ is the electrical conductivity, T is the absolute temperature, and κ is the thermal conductivity. When the COOH-CNTs content is 20%, ZT is 0.023 (Fig. 8f and g). In order to determine the sensing ability and stability of the ion-conductive elastomer over a wider temperature range, the thermal voltages of the conductive elastomer samples were measured at temperature gradients of 10, 20, 30 and 40 K (Fig. 8h). The results show that the thermal voltage increases with the increase of the temperature gradient and shows good stability at a given same temperature gradient.
 |
| Fig. 8 Thermoelectric properties of the self-healing polyurethane ion-conductive elastomer SPU4–LiTFSI0.3–CNT0.3 sensor. (a) Thermoelectric mechanism of the SPU4–LiTFSI0.3–CNT0.3 sensor. (b) Electrochemical impedance spectroscopy (EIS) of SPU4 with different COOH-CNTs contents. (c) Voltage–temperature curve of SPU4 with different CNT contents. (d) Comparison of the Seebeck coefficient and electrical conductivity of SPU4 with different CNT contents. (e) PF values of SPU4 with different COOH-CNTs contents. (f) Thermal conductivity of SPU4 with different COOH-CNTs contents. (g) ZT values of SPU4 with different COOH-CNTs contents. (h) Variation of the voltage of the SPU4–LiTFSI0.3–CNT0.3 sensor with temperature difference. | |
2.6.3. Photothermal performance. Due to its high electrical and thermal conductivity, SPU4–LiTFSI0.3–CNT0.3 exhibits efficient absorption of near-infrared (NIR) light. This absorption can be converted into heat. Based on the above theory, to further expand the application scenarios of the SPU4–LiTFSI0.3–CNT0.3 temperature sensor, its photothermal characteristics were studied. An 808-nm near-infrared laser was used to irradiate one end of the conductive elastomer, and the surface temperature increased under the action of the photothermal effect, which promotes the migration of ions to the other side, thereby generating a thermal voltage (Fig. 9a). The infrared laser with a wavelength of 808 nm was used as the light source. From the near-infrared-vis-ultraviolet spectrum in the range of 400–2500 nm, it can be seen that the addition of COOH-CNTs significantly enhances the photothermal effect, and the absorption rate can reach more than 95% (Fig. 9b). In order to further study the photothermal conversion ability of SPU4–LiTFSI0.3–CNT0.3, its photothermal conversion efficiency with different COOH-CNTs contents was investigated. It can be observed that for a given illumination time, with increasing COOH-CNTs content, the photothermal conversion ability gradually increases. When the COOH-CNTs content reaches 30%, the photothermal conversion reaches its maximum. The surface temperature of SPU4–LiTFSI0.3–CNT0.3 rises sharply to 181.4 °C within 120 seconds, and then remains stable. To evaluate the heating effect of the SPU4–LiTFSI0.3–CNT0.3 sensor under different illumination powers, the power was controlled at 1.6 W cm−2 for an irradiation time of 150 s (Fig. 9e). The temperature of pure SPU4–LiTFSI0.3 remained at ∼25 °C over 90 s. With increasing COOH-CNTs content, the temperature of SPU4–LiTFSI0.3–CNT0.3 can reach 155 °C in the same time (Fig. 9d). The temperature of SPU4–LiTFSI0.3–CNT0.3 rapidly rises above 100 °C at 150 s under 0.48 W cm−2 near-infrared light (Fig. 9c). The above experimental results confirm that SPU4–LiTFSI0.3–CNT0.3 has excellent photothermal conversion efficiency.
 |
| Fig. 9 Photoelectric properties of the self-healing polyurethane ion-conductive elastomer SPU4–LiTFSI0.3–CNT0.3 sensor. (a) Photothermoelectric mechanism of the SPU4–LiTFSI0.3–CNT0.3 sensor. (b) UV-Vis-NIR spectra of SPU4 with different COOH-CNTs contents. (c) Photothermal near-infrared spectra of SPU4 with different COOH-CNTs contents. (d) Temperature change of SPU4 with different COOH-CNTs contents under near-infrared illumination. (e) Temperature changes of SPU4 under different near-infrared light powers. | |
2.7. Thermoelectric-based object recognition and high-temperature warning
Due to the excellent sensing properties, temperature sensing and thermoelectric properties of the SPU4–LiTFSI0.3–CNT0.3 ion-conductive elastomer, it can be integrated onto the hand for high temperature monitoring and human–computer interaction. In order to ensure low cost and wide applicability, the prepared SPU4–LiTFSI0.3–CNT0.3 ion-conductive elastomer was curled into a ring, placed onto the finger joint, and connected using copper wire. Copper foil was used as the electrode to obtain a universal touch-monitoring system (Fig. 10a). The temperature-monitoring system identifies the temperature by monitoring the thermal voltage signal generated by the Seebeck effect. In particular, it is worth noting that the sensor produced different voltage signals at different joints due to changes in distance and movement (e.g., “grip”, stretch, proximity). As shown in Fig. 10b–d, when holding a beaker filled with iced, lukewarm, or hot water, different voltage signals are generated at different joints, allowing for accurate sensing of temperature. In addition, due to the excellent photothermal conversion capability of the sensor, the integrated temperature monitoring system can detect a voltage signal before contact to sense the temperature and prevent burns due to high temperatures (Fig. 10e). The sensor demonstrates significant potential for assisting visually impaired individuals in object recognition and manipulation during daily living activities, as well as in high-temperature sensing applications for robotic–computer interaction systems (Fig. 10f).
 |
| Fig. 10 Temperature-recognition system based on SPU4–LiTFSI0.3–CNT0.3. (a) Physical diagram and composition of the SPU4–LiTFSI0.3–CNT0.3 temperature identification system. (b) Optical images and thermal voltage distribution of hot water obtained using the SPU4–LiTFSI0.3–CNT0.3 temperature identification system. (c) Optical photos and thermal voltage distribution of warm water captured by the SPU4–LiTFSI0.3–CNT0.3 temperature-recognition system. (d) Optical photos and thermal voltage distribution of cold water captured by the SPU4–LiTFSI0.3–CNT0.3 temperature-identification system. (e) Optical photos and thermal voltage distribution of high-temperature objects sensed by the SPU4–LiTFSI0.3–CNT0.3 temperature-recognition system. (f) Use of the SPU4–LiTFSI0.3–CNT0.3 temperature-identification system to assist blind people in monitoring the risk of high temperature. | |
3. Conclusion
In this work, a room-temperature self-healing ion-conductive polyurethane elastomer was synthesized by incorporating multiple hydrogen-bond donors and acceptors. This elastomer exhibited superior mechanical properties, high conductivity, bidirectional sensing capabilities, and thermoelectric properties through the synergistic combination of carbon nanotubes (COOH-CNTs) and lithium bis(trifluoromethanesulfonyl)imide (LiTFSI). The presence of multiple hydrogen bonds facilitated energy dissipation through reversible destruction and recombination, leading to high mechanical strength (34 MPa) and exceptional self-healing efficiency at room temperature (91.4%). Moreover, the incorporation of synergistic ion/electron-conductive components in the elastomer enabled the simultaneous monitoring of strain, temperature, and thermoelectricity in a synergistic manner. The strain module exhibited bidirectional monitoring with high sensitivity (GF ∼ 5.23), while the temperature module displayed a resistance temperature coefficient of 2.42. The thermoelectric module demonstrated a power density of 1.59 mW m−1 K−2. Furthermore, based on the excellent photothermal conversion ability of the COOH-CNTs, non-contact sensing could be realized. This sensor technology presents novel opportunities for applications in human health monitoring, human–computer interaction, and high-temperature warning systems.
4. Experimental section
4.1. Materials
Poly(tetrahydrofuran) (PTMG, Mn ∼ 1000), adipic dihydrazide (ADH, ≥98%), N,N-dimethylacetamide (DMAc, ≥99.8%), February dibutyltin silicate (DBTDL, ≥98%), and lithium bis(trifluoromethanesulphonyl)imide (LiTFSI, ≥98%) were supplied by Anhui Sunrise Technology Co., Ltd. Isophorone diisocyanate (IPDI, ≥99%) was bought from Shanghai Macklin Biochemical Co. 2-Amino-2-methyl-1,3-propanediol (AMPD, ≥99%) was purchased from Shanghai Yi'en Chemical Technology Co., Ltd. Carboxylic carbon nanotubes (COOH-CNTs) were purchased from Jiangsu Xianfeng Nano Co., Ltd.
4.2. Preparation of self-healing polyurethane (SPU)
Using SPU4 as a representative example, PTMG (5 g), IPDI (2.53 g) and DBTDL (0.08 g) were added to three flasks in turn, and DMAc (10 mL) was used as a solvent. The reaction was carried out at 80 °C for 2 hours. Then, DMAc (20 mL) was added and stirred for 10 min, followed by ADH (0.71 g) at 80 °C for 2 h. Finally, AMPD (0.088 g) was dissolved in DMAc (10 ml) and injected into the reaction system and mechanically stirred for 2 h, after which an appropriate amount of DMAc was added by observing the viscosity change of the system to obtain the final polyurethane elastomer. The samples were named SPU–ADHx–AMPDy, abbreviated as SPU, where x is the molar ratio of ADH to PTMG, and y is the mass ratio of AMPD in the system.
4.3. Preparation of SPU–LiTFSIm–CNTn
The self-healing polyurethane APU prepared in the previous step was dissolved in THF. After the SPU was completely dissolved, a certain amount of LiTFSI (10%, 20%, 30%) was added. After stirring for 30 min, COOH-CNTs (10%, 20%, 30%) with different polyurethane mass fractions were added, respectively. After stirring for 2 h, it was completely dispersed and introduced into a polytetrafluoroethylene plate to form a film at room temperature.
4.4. Characterization
4.4.1. General characterization. The surface morphology and elemental distribution of the samples were analyzed using scanning electron microscopy (SEM, S4800, Japan) and energy dispersive X-ray spectroscopy (EDS). The self-healing behavior of the sample was characterized using an ultra-depth-of-field microscope. The ultraviolet-visible (UV-vis) absorption spectra of the samples were measured using a UV-vis-NIR spectrometer equipped with an integrating sphere accessory. Fourier transform infrared spectroscopy (FTIR) was employed to analyze the chemical structure of the samples in the wavenumber range of 400 to 4000 cm−1. Tensile tests were carried out using servo-controlled high- and low-temperature tensile testing machines at room temperature. The samples were cut into a dumbbell shape, and a strain rate of 50 mm min−1 was used for both the unidirectional stretching and loading–unloading cycles. The elastic modulus was determined from the initial slope of the stress–strain curves. The toughness was acquired by integrating the area under the stress–strain curves. The fracture toughness of the samples was tested using the standard test method (ASTM D5045). The thermoelectric properties and sensing properties of the samples were measured using a digital source table 2450. An 808-nm infrared laser was used as the light source. An infrared camera was used to record the infrared photos of the sample under 808-nm laser irradiation.
4.4.2. Sensing characterization. In the bending sensing test, the bending angle was calculated by calculating the plane angle and cross-sectional angle of the SPU–LiTFSI–CNT sensor during the bending process. When conducting human research, the SPU–LiTFSI–CNT sensor was installed on a human joint for measurement. The bending ability of the sensor was mainly determined by the bending angle of the joint. The resistance changes of fingers, elbows and wrists at 90° bending were mainly measured.In the joint health detection test, based on the bidirectional bending sensing characteristics of the SPU–LiTFSI–CNT sensor, we installed it on the human wrist joint and detected the four most basic movements of the wrist (flexion, extension, contraction, and extension). In order to distinguish the signals of different actions, a curve was defined. The upper and lower bending signals correspond to the bending, and the two actions of reception and diffusion are realized by integrating the signals. The contraction means that the wrist bends horizontally downward, while extension means that the wrist bends horizontally upward.
Conflicts of interest
There are no conflicts to declare.
Data availability
The data supporting this article have been included as part of the SI. See DOI: https://doi.org/10.1039/d5mh01126d
The data supporting this article have been included as part of the SI.
Acknowledgements
This work was financially supported by the Natural Science Foundation of Shaanxi Province (2024JC-YBMS-412), Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 24JR036).
References
- A. Roy, S. Zenker, S. Jain, R. Afshari, Y. Oz, Y. Zheng and N. Annabi, Adv. Mater., 2024, 36, 2404225 CrossRef CAS
. - X. Chen, Z. Wan, R. Zhang, L. Ma, Z. Yang and X. Xiao, Chem. Eng. J., 2024, 499, 156431 CrossRef CAS
. - J. Li, Z. Fang, D. Wei and Y. Liu, Adv. Healthcare Mater., 2024, 13, 2401532 CrossRef
. - M. Zhong, L. Zhang, X. Liu, Y. Zhou, M. Zhang, Y. Wang, L. Yang and D. Wei, Chem. Eng. J., 2021, 412, 128649 CrossRef
. - J. C. Yang, J. Mun, S. Y. Kwon, S. Park, Z. Bao and S. Park, Adv. Mater., 2019, 31, 1904765 CrossRef PubMed
. - S. Xiang, D. Liu, C. Jiang, W. Zhou, D. Ling, W. Zheng, X. Sun, X. Li, Y. Mao and C. Shan, Adv. Funct. Mater., 2021, 31, 2100940 CrossRef
. - A. Chen, J. Zhang, J. Zhu, Z. Yan, Q. Wu, S. Han, J. Huang and L. Guan, J. Mater. Chem. A, 2023, 11, 4977–4986 RSC
. - X. Li, M. Jiang, Y. Du, X. Ding, C. Xiao, Y. Wang, Y. Yang, Y. Zhuo, K. Zheng, X. Liu, L. Chen, Y. Gong, X. Tian and X. Zhang, Mater. Horiz., 2023, 10, 2945–2957 RSC
. - K. Xue, C. Shao, J. Yu, H. Zhang, B. Wang, W. Ren, Y. Cheng, Z. Jin, F. Zhang, Z. Wang and R. Sun, Adv. Funct. Mater., 2023, 33, 2305879 CrossRef
. - Q. Cao and W. Yuan, Chem. Eng. J., 2024, 494, 153254 CrossRef
. - Z. Ma, Y. Zhang, R. Jiang, L. Shao, J. Cao, H. Guo and G. Zhang, Compos. Sci. Technol., 2024, 248, 110460 CrossRef
. - L. Chen, X. Chang, H. Wang, J. Chen and Y. Zhu, Nano Energy, 2022, 96, 107077 CrossRef CAS
. - T. Chen, Q. Wei, Y. Ma, Y. Tang, L. Ma, S. Deng and B. Xu, Nano Energy, 2024, 127, 109752 CrossRef CAS
. - J. Cao, C. Lu, J. Zhuang, M. Liu, X. Zhang, Y. Yu and Q. Tao, Angew. Chem., Int. Ed., 2017, 56, 8795–8800 CrossRef CAS
. - J. Wu, F. Zeng, Z. Fan, S. Xuan, Z. Hua and G. Liu, Adv. Funct. Mater., 2024, 34, 2410518 CrossRef CAS
. - L. Gao, W. Jiang, X. Zhang, Y. Sun, K. Chen, W. Li, H. Xie and J. Liu, Chem. Eng. J., 2024, 479, 147822 CrossRef CAS
. - W. Zhao, Y. Zheng, A. Huang, M. Jiang, L. Wang, Q. Zhang and W. Jiang, Adv. Mater., 2024, 36, 2402386 CrossRef CAS
. - B. Qin, S. Zhang, P. Sun, B. Tang, Z. Yin, X. Cao, Q. Chen, J.-F. Xu and X. Zhang, Adv. Mater., 2020, 32, 2000096 CrossRef CAS PubMed
. - J. Lai, M. Xie, Q. Zhao, C. Zhang, Z. Wang and H. Xia, Adv. Funct. Mater., 2025, 35, 2415732 CrossRef CAS
. - Y. Zhang, S. Zhang, M. Zhai, B. Wei, B. Lyu and L. Liu, ACS Appl. Polym. Mater., 2024, 6, 8399–8408 CrossRef CAS
. - D. H. Ho, Y. M. Kim, U. J. Kim, K. S. Yu, J. H. Kwon, H. C. Moon and J. H. Cho, Adv. Energy Mater., 2023, 13, 2301133 CrossRef CAS
. - Y. Tian, Y. Wei, M. Wang, J. Wang, X. Li, X. Qin and L. Zhang, Nano Energy, 2025, 139, 110908 CrossRef CAS
. - J. Chen, Y. Gao, L. Shi, W. Yu, Z. Sun, Y. Zhou, S. Liu, H. Mao, D. Zhang, T. Lu, Q. Chen, D. Yu and S. Ding, Nat. Commun., 2022, 13, 4868 CrossRef CAS
. - S. Peng, N. Thirunavukkarasu, J. Chen, X. Zheng, C. Long, X. Huang, Z. Weng, L. Zheng, H. Wang, X. Peng and L. Wu, Chem. Eng. J., 2023, 463, 142312 CrossRef CAS
. - R. Zhou, Y. Jin, W. Zeng, H. Jin, L. Shi, L. Bai and X. Shang, Adv. Funct. Mater., 2023, 33, 2301921 CrossRef CAS
. - T. Ye, J. Tan, T. Wu, F. Zhang, S. Chen and C. Wang, Sci. China:Chem., 2025, 68, 1998–2009 CrossRef CAS
. - X. Xun, Z. Zhang, X. Zhao, B. Zhao, F. Gao, Z. Kang, Q. Liao and Y. Zhang, ACS Nano, 2020, 14, 9066–9072 CrossRef CAS
. - Y. Zhu, Y. He, W. Lu, H. Tian, F. Fei, P. Zhou and J. Wang, J. Mater. Chem. A, 2024, 12, 28716–28730 RSC
. - F. Xu, H. Li and Y. Li, Adv. Mater., 2024, 36, 2412317 CrossRef CAS PubMed
. - J. Yuan, Y. Zhang, C. Wei and R. Zhu, Adv. Sci., 2023, 10, 2303114 CrossRef
. - P. Wang, J. Liu, W. Yu, G. Li, C. Meng and S. Guo, Nano Energy, 2022, 103, 107768 CrossRef CAS
. - L. Liu, J. Li, Z. Tian, X. Hu, H. Wu, X. Chen, L. Zhang and W. Ou-Yang, Nano Energy, 2024, 128, 109817 CrossRef CAS
. - P. Parashar, M. K. Sharma, B. K. Nahak, A. Khan, W.-Z. Hsu, Y.-H. Tseng, J. R. Chowdhury, Y.-H. Huang, J.-C. Liao, F.-C. Kao and Z.-H. Lin, J. Mater. Chem. A, 2025, 13, 13750–13762 RSC
. - S. P. Sreenilayam, I. U. Ahad, V. Nicolosi, V. Acinas Garzon and D. Brabazon, Mater. Today, 2020, 32, 147–177 CrossRef
. - K. Meng, X. Xiao, W. Wei, G. Chen, A. Nashalian, S. Shen, X. Xiao and J. Chen, Adv. Mater., 2022, 34, 2109357 CrossRef CAS PubMed
. - Y. Zhang, X. Zuo, S. Zhang, Z. Ma and G. Zhang, Compos. Sci. Technol., 2024, 256, 110756 CrossRef
. - M. Lin, Z. Zheng, L. Yang, M. Luo, L. Fu, B. Lin and C. Xu, Adv. Mater., 2022, 34, 2107309 CrossRef
. - Y. Lu, H. Zhang, Y. Zhao, H. Liu, Z. Nie, F. Xu, J. Zhu and W. Huang, Adv. Mater., 2024, 36, 2310613 CrossRef
. - X. Zhu, W. Zhang, G. Lu, H. Zhao and L. Wang, ACS Nano, 2022, 16, 16724–16735 CrossRef PubMed
. - N. Yu, B. Cheng, Y. Liu, W. Wu, R. K. Y. Li, Z. Liang, F. Cheng and H. Zhao, Small, 2024, 20, 2405700 CrossRef PubMed
. - B. Li, F. Xu, T. Guan, Y. Li and J. Sun, Adv. Mater., 2023, 35, 2211456 CrossRef
. - X. Zhu, Y. Hao, L.-F. Huang, H. Zhao and L. Wang, J. Mater. Chem. A, 2024, 12, 26158–26169 RSC
. - C. Luo, Y. Chen, Z. Huang, M. Fu, W. Ou, T. Huang and K. Yue, Adv. Funct. Mater., 2023, 33, 2304486 CrossRef
. - Y. Guo, H. Zhang, L. Fang, Z. Wang, W. He, S. Shi, R. Zhang, J. Cheng and P. Wang, Nano Energy, 2024, 123, 109427 CrossRef CAS
.
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