Tough and Robust Mechanically Interlocked Gel–Elastomer Hybrid Electrode for Soft Strain Gauge

Abstract Soft strain gauges provide a flexible and versatile alternative to traditional rigid and inextensible gauges, overcoming issues such as impedance mismatch, the limited sensing range, and fatigue/fracture. Although several materials and structural designs are used to fabricate soft strain gauges, achieving multi‐functionality for applications remains a significant challenge. Herein, a mechanically interlocked gel–elastomer hybrid material is exploited for soft strain gauge. Such a material design provides exceptional fracture energy of 59.6 kJ m−2 and a fatigue threshold of 3300 J m−2, along with impressive strength and stretchability. The hybrid material electrode possesses excellent sensing performances under both static and dynamic loading conditions. It boasts a tiny detection limit of 0.05% strain, ultrafast time resolution of 0.495 ms, and high linearity. This hybrid material electrode can accurately detect full‐range human‐related frequency vibrations ranging from 0.5 to 1000 Hz, enabling the measurement of physiological parameters. Additionally, the patterned soft strain gauge, created through lithography, demonstrates superior signal–noise rate and electromechanical robustness against deformation. By integrating a multiple‐channel device, an intelligent motion detection system is developed, which can classify six typical human body movements with the assistance of machine learning. This innovation is expected to drive advancements in wearable device technology.


Supplementary Note 1. Characterizations and Measurements
Material Characterizations: In the characterization of the ionic gel and TPU hybrid gel, the Field-Emission Scanning Electron Microscopy (Verios G4, America) instrument was utilized, and it was operated at an acceleration voltage of 5 kV. The FT-IR spectrometer (Vertex 70, Bruker) used in this study was operated in the wavenumber range of 4000-600 cm -1 , which covers a broad range of the infrared region and enables the detection of a wide range of functional groups. For preparing FT-IR samples, the TPU hybrids gel was first cut into small pieces, dried in an oven, and then ground into a fine powder. Subsequently, the hybrid gel powder was mixed with potassium bromide (KBr) and compressed under high pressure to form a thin pellet. Finally, the blank group (without specimen) was calibrated before testing the specimen. The pellet was placed in the sample holder of the FT-IR spectrometer and analyze in accordance with the instrument manufacturer's instructions. Raman spectra were conducted on a Dilor LABRAM-1B multichannel confocal micro-spectrometer with a 532 nm laser excitation. Thermogravimetric analysis (TGA) was performed using a TG-DTA instrument (STA449F5) between 25 to 800 °C at a heating speed of 10 o C min -1 under an argon atmosphere. The hydrophobicity of the membranes was measured with a video water contact angle system (SPCA, HARKE). The ionic conductivity of the gel was investigated by electrochemical impedance spectroscopy, and the ionic conductivity σ was calculated using the following equation: In the formula, L is the thickness of the electrode film, R is the bulk resistance obtained from the EIS plot, and A is the area of an electrode. Fatigue test: Similar single-notch tensile tests were carried out to verify the fatigue resistance of the specimens. The experimental equipment was illustrated in Figure S13. Cyclic stretching tests were performed by using notched samples with various precut crack lengths (c), which are smaller than the width (D) of the sample. All tensile cycles were continuously conducted at a speed rate of 20 mm/s under various strains without any relaxation time.
The energy release rate (G) was calculated by the following equation: Where k was a related function of strain variation empirically determined by 31 k   , c was the crack propagation length, and W was the strain energy density of an unnotched sample of the same dimensions stretched to the same strain ε.
Vibration test system: To establish a measurement system capable of generating vibrations with controllable frequency and measuring the amplitude, the appropriate equipment for the measurement system, such as a signal amplifier (UTEKL YE1311), vibration exciter a (UTEKL JZ-1), accelerometer (Bruel-Kjaer DeltaTron 4520-001), and a data acquisition system (UTEKL 3404FRS-DY) to collect and store the data, were configured. And then, perform test runs and collect data, adjusting the parameters as needed to obtain the desired frequency range and amplitude levels. The collected data were analyzed to determine the frequency of the vibrations under various loading conditions. Machine learning: Convolutional layers were employed to recognize significant patterns and S4 features in the data that were pertinent to the classification task. These extracted features were fed into fully connected layers that perform the actual classification task. The output of the fully connected layers was a probability distribution over the different classes of human motion. The CNN was trained using a labeled dataset of human motion data. Figure S1. Multiple molecular interactions in prepared ionic gel. S5 Figure S2. The photographs show the water contact angle of (a) TPU substrate and (b) oxygen plasma treated TPU film (c) initial smooth TPU surface. (b) TPU after exposed to oxygen plasma assumes rich morphology and becomes functionalized with polar surface groups.

Supplementary figures and tables
To quantify the hydrophilic of the TPU substrate, water contact angle measurements are carried out. The TPU substrate is hydrophobic with an initial contact angle of 112.3° while with the plasma treatment by oxygen, the TPU substrate turns to be highly hydrophilic (25.6°) due to attaching an abundant of functional groups and the droplet could be completely absorbed within 1 s, as illustrated in the Figure S2. The infiltration of the pre-polymerization ionic gel could be greatly enhanced by the hydrophilic characteristic of the TPU, which is critical to the improved adhesion strength between the ionic gel and TPU substrate.
S6 Figure S3. Water contents, ionic conductivities, and stress-strain curves of PVA-P(AAm-co-AA)/CaCl 2 ionic gel with different CaCl 2 contents. The CaCl 2 content is referred to as the weight proportion in the dry samples.
S7 Figure S4. Water retention of the ionic gel with various CaCl 2 content at different time intervals. Figure S5. The photographs show the ionic gel infiltration process.
The infiltration process was recorded of the hydrogel precursor by a CCD camera. Due to improved the hydrophilicity of the TPU substrate, allowing the hydrogel precursor rapidly infiltrate to the whole substrate. The surface morphology was obtained by the ultra depth of field video microscope (Leica DVM6) to investigate the uniformity and consistency. As illustrated in Figure S6a    According to the single-edge notch tension test, the calculated fracture energy of TPU hybrid S11 gel is 51.5 kJ m -2 .   Table S2. Figure S18. Scale bar from red to blue indicates that the stress concentration from high to low. Figure S18 illustrated the FEA models of the ionic gel. We assumed different components were isotropic and uniform in integrated overall structure. The Young's modulus and Poisson's ratio of TPU fiber were respectively 614.57 MPa and 0.35, those of the ionic gel mixture are S17 respectively 10 MPa and 0.4. The structures were discretized using hexahedral elements (C3D8).
The binding constraint was applied in different layers with tensile strain loading. The crack propagation process was simulated by the extended finite element method with sweep mesh refinement. As a result, the ionic gel with crack induces severe stress concentration on the crack tips, while the structure with an TPU enhanced induces a slightly stress concentration on notched region.        Figure S42. Equivalent circuit of a wireless sensor module.
S36 Figure S43. Images of volunteers wearing seven strain gauge sensors during different movements.
S37 Figure S44 Some datasets used in machine learning

Supporting Video
Supporting Video S1. The TPU hybrid gel lifting a 1kg weight.
Supporting Video S2. Single-edge notch tensile test of TPU hybrid gel.
Supporting Video S3. The notched TPU hybrid gel subject to cyclic load of stretch at strain 200%.  Nat. Commun.