Subtle Movements Research Articles

Highly stretchable, self-healing, adhesive, 3D-printable and antibacterial double-network hydrogels for multifunctional wearable sensors.

Conductive hydrogels based on sodium alginate (SA) have potential applications in human activity monitoring and personal medical diagnosis due to their good conductivity and flexibility. However, most sensing SA-hydrogels exhibit poor mechanical properties and lack of self-healing, self-adhesive, and antibacterial properties, greatly limiting their practical applications. Therefore, in this paper, a multifunctional double-network PAA-SA hydrogel consisting of poly(acrylic acid) (PAA) and sodium alginate (SA) was prepared by a simple strategy. As a rigid network structure, SA endowed the hydrogel double network structure with excellent mechanical performance. As a wearable sensor, the PAA-SA hydrogel exhibited excellent tensile properties (strain: 1799.2 %), self-healing, high sensitivity (GF = 9.9), reliable repeatability, self-adhesive, 3D printability and antibacterial activity. Additionally, the highly sensitive wearing sensing PAA-SA hydrogel could accurately and real-time monitor various intense or subtle human movements, such as joint bending, face and throat vibration. Moreover, PAA-SA hydrogels were not only used for handwritten recognition of Arabic numerals and English letters, but also for real-time sensing of temperature changes and monitoring of human sweating. The prepared multifunctional wearable sensing hydrogel has the advantages of simple and versatile methods and low cost, making it a promising candidate for applications in different fields such as electronic skin, soft robotics, and medical monitoring.

Subtle bradykinesia features are easier to identify and more prevalent than questionable dystonia in essential tremor.

Essential tremor (ET) is characterized by upper limbs action tremor, sometimes extending to other body parts. However, ET can present with additional neurological features known as "soft signs." These signs of uncertain clinical significance are not sufficient to suggest an alternative neurological diagnosis, and include, among others, questionable dystonia and subtle voluntary movement alterations, i.e., bradykinesia and related features. This study aimed to explore the prevalence and relationship between questionable dystonia and subtle bradykinesia features in ET. Forty ET patients were video-recorded during clinical examination. Five movement disorder experts reviewed the videos to identify soft motor signs, i.e., dystonia and movement alterations during finger-tapping namely, (i) bradykinesia (reduced velocity), (ii) dysrhythmia, and (iii) sequence effect. Inter-rater agreement was quantified using the Fleiss' Kappa index. Data analysis was performed using nonparametric tests. We found a fair inter-rater agreement for upper limb dystonia (Fleiss' K = 0.27). Inter-rater agreement was higher (moderate) for head dystonia (Fleiss' K = 0.49) and finger-tapping assessment (Fleiss' K = 0.45). Upper limb dystonia was identified in 70% of patients, head dystonia in 35%, and finger-tapping alterations (in variable combinations) were observed in 95% of individuals (P < 0.001 by Fisher's exact test), including subtle bradykinesia and related features. No significant concordance or correlation was found between the soft signs. Subtle bradykinesia and related features are the most easily identifiable and frequent soft signs in ET, appearing in a higher percentage of patients than subtle dystonia. These findings provide insights into the clinical and pathophysiological understanding of ET.

Detecting subtle subterranean movement via laser speckle imaging.

A diversity of organisms live within underground environments. However, visualizing subterranean behavior is challenging because of the opacity of most substrates. We demonstrate that laser speckle imaging, a non-invasive technique resolving nanometer-scale movements, facilitates quantifying biological activity in a granular medium. We monitored fire ants (Solenopsis invicta) at different developmental stages, burial depths (1-5 cm) and moisture fractions (0 and 0.1 by volume) in a container of 0.7 mm glass particles. Although the speckle pattern from the backscattered light precludes direct imaging of animal kinematics, analysis of integrated image differences revealed that spiking during ant movement increased with the developmental phase. Greater burial depth and saturation resulted in fewer and lower magnitude spikes. We verified that spiking correlated with movement via quasi-2D experiments. This straightforward method, involving a laser and digital camera, can be applied to laboratory and potentially field situations to gain insight into subterranean organism activities.

ECR Spotlight – Hosain Bagheri

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Hosain Bagheri is an author on ‘ Detecting subtle subterranean movement via laser speckle imaging’, published in JEB. Hosain conducted the research described in this article at Georgia Tech, USA under the supervision of Dr Goldman and Dr Goodisman. Hosain is now a Postdoctoral Research Fellow in the lab of Daniel I. Goldman at Georgia Tech, investigating biological organisms through engineering and physics to deepen our understanding of biology and develop real-world applications for societal benefit.

Amplifying Colossal Thermal Expansion of a Martensitic Molecular Crystal through Interlayer Shear‐Induced Side‐Chain Liberation

AbstractMolecular crystals capable of colossal thermal expansion (TE) are fascinating owing to their substantial and continuous volume changes and reasonably linear responses to temperature. This makes them promising candidates for micromachine applications. Macroscopic motion is driven by subtle yet cooperative movements of molecules that respond to the thermal motions of dynamic functional units. The study of p‐TIPS‐DSB presented here offers a compelling case highlighting the relationship between the degree of dynamicity of functional units and TE behavior. In its α‐phase, the p‐TIPS‐DSB crystal undergoes an irreversible martensitic transition to the β‐phase, accompanied by significant cooperative interlayer shear. This process substantially enhances the mobility of the side‐chains driven by the increased free volume surrounding them. This nearly doubles the volumetric TE coefficient from 255.3 (10) to 444.9 (32) MK−1, particularly in the actuation direction from 175.0 (7) to 291.7 (20) MK−1, enabling about 4.5 % elongation/contraction. As demonstrated here, p‐TIPS‐DSB exhibits a decent force density (&gt;1.4×107 N m−3) and precise motion control capabilities due to its hysteresis‐free and non‐abrupt TE nature. Furthermore, we demonstrated the limited operating distance of colossal TE materials can be amplified by utilizing levers, highlighting the high potential of these materials for use in micromachines.

Living-Skin Detection Based on Spatio-Temporal Analysis of Structured Light Pattern.

Living-skin detection is an important step for imaging photoplethysmography and biometric anti-spoofing. In this paper, we propose a new approach that exploits spatio-temporal characteristics of structured light patterns projected on the skin surface for living-skin detection. We observed that due to the interactions between laser photons and tissues inside a multi-layer skin structure, the frequency-domain sharpness feature of laser spots on skin and non-skin surfaces exhibits clear difference. Additionally, the subtle physiological motion of living-skin causes laser interference, leading to brightness fluctuations of laser spots projected on the skin surface. Based on these two observations, we designed a new living-skin detection algorithm to distinguish skin from non-skin using spatio-temporal features of structured laser spots. Experiments in the dark chamber and Neonatal Intensive Care Unit (NICU) demonstrated that the proposed setup and method performed well, achieving a precision of 85.32%, recall of 83.87%, and F1-score of 83.03% averaged over these two scenes. Compared to the approach that only leverages the property of multilayer skin structure, the hybrid approach obtains an averaged improvement of 8.18% in precision, 3.93% in recall, and 8.64% in F1-score. These results validate the efficacy of using frequency domain sharpness and brightness fluctuations to augment the features of living-skin tissues irradiated by structured light, providing a solid basis for structured light based physiological imaging.

A lightweight and explainable model for driver abnormal behavior recognition

With the advancement of intelligent transportation systems, accurate identification of driver abnormal behavior is crucial for enhancing road safety. However, the limited computing power of vehicular systems poses a challenge for running efficient and explainable behavior recognition models. This paper proposes a lightweight and explainable driver abnormal behavior recognition model based on an improved You Only Look Once version 8 (YOLOv8). Firstly, a Spatial and Channel Reconstruction Convolution (SCConv) module is introduced to optimize the Convolution to Feature (C2f) structure, enhancing the model's feature extraction capabilities while reducing parameter redundancy. Secondly, a Spatial Pyramid Pooling with Fast Large Separable Kernel Attention (SPPF-LSKA) module is designed to better capture image context and integrate global information. Additionally, a Dynamic upsample (Dysample) module is introduced to improve the model's ability to capture subtle driver movements. Lastly, a Lightweight Shared Group Normalization Convolution Detection Head (LSGCDH) is designed to enhance the model's generalization ability, significantly reducing the model's computational load, parameter count, and size. Experimental results demonstrate that our approach has significant advantages for edge device deployment compared to mainstream algorithms. The visualization results effectively corroborate the role of each improved structure, enhancing the explainability of the abnormal behavior recognition model, which is beneficial for deployment in vehicular systems and contributes to improving road traffic safety.

MS-YOLO: A Lightweight and High-Precision YOLO Model for Drowning Detection.

A novel detection model, MS-YOLO, is developed in this paper to improve the efficiency of drowning rescue operations. The model is lightweight, high in precision, and applicable for intelligent hardware platforms. Firstly, the MD-C2F structure is built to capture the subtle movements and posture changes in various aquatic environments, with a light weight achieved by introducing dynamic convolution (DcConv). To make the model perform better in small object detection, the EMA mechanism is incorporated into the MD-C2F. Secondly, the MSI-SPPF module is constructed to improve the performance in identifying the features of different scales and the understanding of complex backgrounds. Finally, the ConCat single-channel fusion is replaced by BiFPN weighted channel fusion to retain more feature information and remove the irrelevant information in drowning features. Relative to the Faster R-CNN, SSD, YOLOv6, YOLOv9, and YOLOv10, the MS-YOLO achieves an average accuracy of 86.4% in detection on a self-built dataset at an ultra-low computational cost of 7.3 GFLOPs.

Post-Transition State Bifurcation Controls Torsional Selectivity in Radical Addition of Allenes.

Post-transition state bifurcation (PTSB) has received wide attention in the field of reaction mechanism research due to its role in producing nonstatistical reaction selectivity, which cannot be solely explained by transition state theory. Particularly, even subtle molecular motions such as bond torsion can precipitate PTSB, thereby significantly complicating the quantitative understanding of dynamic selectivity. In this work, we found that the radical addition of allenes is an elementary transformation that generally exhibits PTSB stereoselectivity, where a single radical addition transition state can lead to both Z- and E-allylic radicals via the post-transition state allylic single bond torsion. Interestingly, dynamic Z/E-selectivity favors the Z-allylic radicals, which contrasts the thermodynamic preference. Based on the dynamics study of twenty-five radical additions of mono-substituted allenes with diverse electronic and steric effects, we have identified that this dynamic stereoselectivity is synergistically controlled by the transition state structure and the differential trends within specific dimensions of the bifurcating reaction coordinates, which also holds true for di-substituted allene substrates. This study refines the mechanism of radical addition of allenes and underscores the importance of the differential trend of the reaction coordinates in controlling dynamic selectivity, offering a deeper insight into PTSB selectivity factors.

Multi-touch microcapsules/silicone-polyacrylate hybrid hydrogels with precise self-healing ability and their self-powered sensing applications in emergency rescue

Hydrogels offer significant potential for use in wearable soft electronic devices. However, their application faces considerable challenges in outdoor emergencies or extreme environments where a power supply is unavailable. In this study, multi-touch microcapsules with phase change properties were prepared by coating nanogold through silica as Pickering particles. Subsequently, the microspheres were integrated into a silicone oil-modified polyacrylate hydrogel with self-healing capabilities, resulting in the fabrication of a hydrogel strain sensor that exhibits precise self-healing and self-powered functionality. The mechanical properties of the hydrogel sensor can be modified by adjusting the polymer composition. Due to the multi-contact structure of nano-gold on the surface of microcapsules and the dynamic boronic ester bond interaction, the hydrogel sensor possesses precision self-healing capability. In addition, the hydrogel sensors demonstrate high sensitivity, which can accurately detect some subtle human motions, such as joint flexion. Importantly, the synthesized composite hydrogel exhibits self-powered performance, which could be helpful for rescue operations in outdoor or extreme environments. This study provides helpful results that can be used in the development of wearable soft electronics based on conductive hydrogels.

Efficacy of high-frequency rTMS in the treatment of gait disorder and cognition in patients with Parkinson's disease based on wearable devices and eye-movement assessments.

Postural instability and gait disorder and cognitive dysfunction are common symptoms of Parkinson's disease (PD). Scale assessment is frequently used in the clinic to evaluate PD, but this technique is limited by its lack of sensitivity to changes in disease progression and its difficulty in capturing subtle movements and changes in cognitive function. It is currently believed that high-frequency repetitive transcranial magnetic stimulation (rTMS) can improve motor and cognitive dysfunction in patients with PD, though it remains controversial. Therefore, it is imperative to monitor and dynamically identify changes in postural instability and gait disorder, as well as those in cognitive dysfunction, in PD to develop targeted interventions. In this study, we observed the effect of high-frequency rTMS on gait disorders and cognitive functions in patients with PD by comparing data from wearable devices and eye-tracking devices before and after treatment. A total of 159 patients with PD were included in this study. A GYENNO MATRIX wearable gait analyzer was used to monitor the objective gait data (including the timed up-and-go, narrow-track, and turning tests), the Eyeknow eye-tracking evaluation system was used to monitor the patient's eye movement cognition data (including the smooth pursuit, pro-saccade, and anti-saccade tests), and gait and cognitive function-related scales, including the Tinetti Balance Scale, Tinetti Gait Scale, Berg Balance Scale, Mini-Mental State Examination, and Montreal Cognitive Assessment (MoCA), were evaluated at the same time before and after high-frequency rTMS treatment. The mean step length, mean stride velocity, stride length, and mean step frequency of patients with PD in the timed up-and-go test all increased compared with those before rTMS treatment, whereas the mean stride time and double support decreased. In the narrow-track test, the mean stride velocity increased and the mean stride time decreased. In the turning test, the turning left duration, turning right duration, mean duration, mean number of steps, and average step duration decreased, while the mean angular velocity increased after rTMS treatment. Compared with those before rTMS treatment, the latency period of patients with PD in overlapping saccades decreased, the completion time of overlapping saccades decreased, and the average saccade speed increased. In the anti-saccade test, the completion time decreased and the average saccade speed increased after rTMS treatment. Compared with those before rTMS treatment, the Tinetti Balance Scale, Tinetti Gait Scale, Berg Balance Scale, Mini-Mental State Examination, and MoCA scores increased, and the MoCA sub-items improved in terms of visual-spatial and executive function, language, abstraction, delayed recall, and orientation after rTMS treatment. High-frequency rTMS may be an effective therapy for improving gait disorders and cognitive functions in patients with PD.

Methodological Variations to Explore Conflicting Results in the Existing Literature of Masking Smile Judgment.

Although a smile can serve as an expression of genuine happiness, it can also be generated to conceal negative emotions. The traces of negative emotion present in these types of smiles can produce micro-expressions, subtle movements of the facial muscles manifested in the upper or lower half of the face. Studies examining the judgment of smiles masking negative emotions have mostly employed dichotomous rating measures, while also assuming that dichotomous categorization of a smile as happy or not is synonymous with judgments of the smile's authenticity. The aim of the two studies was to explore the judgment of enjoyment and masking smiles using unipolar and bipolar continuous rating measures and examine differences in the judgment when instruction varied between judgments of happiness and authenticity. In Experiment 1, participants rated smiles on 7-point scales on perceived happiness and authenticity. In Experiment 2, participants rated the smiles on bipolar 7-point scales between happiness and a negative emotion label. In both studies, similar patterns were observed: faces with traces of fear were rated significantly less happy/authentic and those with traces of anger in the brows were rated significantly happier/more authentic. Regarding varied instruction type, no effect was found for the two instruction types, indicating that participants perceive and judge enjoyment and masking smiles similarly according to these two instructions. Additionally, the use of bipolar scales with dimensions between a negative emotion label and happiness were not consistently effective in influencing the judgement of the masking smile.

Ultrasensitive Ultrasoft Buckled Crack‐Based Sensor for Respiration Measurement and Enhanced Human–Machine Interface

Wearable strain sensors have transformed the real‐time monitoring of health conditions and human–machine interactions. However, recently developed wearable strain sensors exhibit several limitations. For example, when a sensor is designed with high sensitivity to detect strain, it struggles to accurately measuring the deformation of low‐stiffness materials like skin. Additionally, finding the optimal balance between sensitivity, durability, hysteresis, and strain range in sensor design is challenging. To address these challenges, a Buckled, Ultrasoft, Crack‐based, Large strain, EpiDermal (BUCKLED) sensor is developed. This sensor integrates the benefits of soft structure engineering with high sensitivity of crack‐based sensing mechanisms to ensure optimal skin deformation measurements. The BUCKLED sensor exhibits significant improvements in compliance (18 500 mm N−1), stretchability (100%), hysteresis (2%), durability (10 000 cycles with 100% strain), and force sensitivity () owing to its buckled shape, confirming its ability to detect subtle movements with enhanced accuracy. The sensor's high compliance allows it to accurately measure low‐stiffness objects, ensuring reliable performance. Furthermore, the sensor's tunability is demonstrating its effectiveness in applications such as respiratory monitoring, facial expression recognition, and silent speech interfaces. Consequently, the proposed sensor is versatile and holds great potential for a wide range of sensing applications.

Microexpression Recognition Method Based on ADP-DSTN Feature Fusion and Convolutional Block Attention Module

Microexpressions are subtle facial movements that occur within an extremely brief time frame, often revealing suppressed emotions. These expressions hold significant importance across various fields, including security monitoring and human–computer interaction. However, the accuracy of microexpression recognition is severely constrained by the inherent characteristics of these expressions. To address the issue of low detection accuracy regarding the subtle features present in microexpressions’ facial action units, this paper proposes a microexpression action unit detection algorithm, Attention-embedded Dual Path and Shallow Three-stream Networks (ADP-DSTN), that incorporates an attention-embedded dual path and a shallow three-stream network. First, an attention mechanism was embedded after each Bottleneck layer in the foundational Dual Path Networks to extract static features representing subtle texture variations that have significant weights in the action units. Subsequently, a shallow three-stream 3D convolutional neural network was employed to extract optical flow features that were particularly sensitive to temporal and discriminative characteristics specific to microexpression action units. Finally, the acquired static facial feature vectors and optical flow feature vectors were concatenated to form a fused feature vector that encompassed more effective information for recognition. Each facial action unit was then trained individually to address the issue of weak correlations among the facial action units, thereby facilitating the classification of microexpression emotions. The experimental results demonstrated that the proposed method achieved great performance across several microexpression datasets. The unweighted average recall (UAR) values were 80.71%, 89.55%, 44.64%, 80.59%, and 88.32% for the SAMM, CASME II, CAS(ME)3, SMIC, and MEGC2019 datasets, respectively. The unweighted F1 scores (UF1) were 79.32%, 88.30%, 43.03%, 81.12%, and 88.95%, respectively. Furthermore, when compared to the benchmark model, our proposed model achieved better performance with lower computational complexity, characterized by a Floating Point Operations (FLOPs) value of 1087.350 M and a total of 6.356 × 106 model parameters.

The next-generation of metaverse embodiment interaction devices: A self-powered sensing smart monitoring system

Wearable electronic devices collect user data to provide personalized experiences and real time interactions for the metaverse, thereby driving its development. This paper introduces an innovative self-powered sensing smart monitoring system (SSSMS) designed to harvest human low-frequency inertial energy and achieve gait recognition and fall monitoring (GR-FM) capabilities. The SSSMS enhances energy harvesting efficiency through Halbach electromagnetic enhancement mechanisms, achieving self-sustainability. Additionally, the conical roller triboelectric nanosensor (CR-TENS) accurately captures various human gaits, transmitting these states in the form of electrical signals to a computer via OpenBCI in real time. These signals are integrated with a deep learning-driven diagnostic module to achieve GR-FM. The experimental results demonstrate that under optimal external excitation, the output power is 2.27 mW, with a power density of 32 W/m3. After 100,000 cycles, both the EMG and CR-TENS modules still maintain good performance, sufficiently providing stable and continuous charging for the battery and offering a stable signal for signal processing. Both the EMG and CR-TENS modules exhibit robust feature capture capabilities, effectively sensing different excitation frequencies, amplitudes, and gait patterns. The SSSMS based on the GRU deep learning model achieves recognition accuracies of 96.13 %, 96.60 %, and 92.22 % for frequency, amplitude, and gait, respectively. Deployment experiments demonstrate high accuracy and sensitivity in frequency, amplitude, gait recognition, and fall monitoring, highlighting the advantageous design of EMG and CR-TENS for perceiving subtle motion state changes. The SSSMS demonstrates outstanding capabilities in self-sustainability and real time user embodiment information capture, showcasing its potential as a next-generation interaction device for the metaverse.

Counterfactual discriminative micro-expression recognition

Micro-expressions are spontaneous, rapid and subtle facial movements that can hardly be suppressed or fabricated. Micro-expression recognition (MER) is one of the most challenging topics in affective computing. It aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting period. Recently, many deep learning-based MER methods have been developed. However, how to effectively capture subtle temporal variations for robust MER still perplexes us. We propose a counterfactual discriminative micro-expression recognition (CoDER) method to effectively learn the slight temporal variations for video-based MER. To explicitly capture the causality from temporal dynamics hidden in the micro-expression (ME) sequence, we propose ME counterfactual reasoning by comparing the effects of the facts w.r.t. original ME sequences and the counterfactuals w.r.t. counterfactually-revised ME sequences, and then perform causality-aware prediction to encourage the model to learn those latent ME temporal cues. Extensive experiments on four widely-used ME databases demonstrate the effectiveness of CoDER, which results in comparable and superior MER performance compared with that of the state-of-the-art methods. The visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.

Lignin sulfonate induced ultrafast fabrication of polypyrrole-based conductive organohydrogel for high performance flexible strain and temperature sensor

The ultrafast preparation of electrically conductive hydrogels to endow high sensing performance and temperature tolerance remains a critical challenge. Herein, lignosulfonate sodium-templated polypyrrole (LS-PPy) nanofillers were rapidly introduced into polyacrylic acid (PAA) hydrogel through ultrafast free radical polymerization in a glycerol/water binary solvent system. The resultant LS-PPy/PAA electrically conductive organohydrogel possesses satisfactory mechanical performance (strength of 56 kPa at a tensile strain of 800 %), strong adhesion, and a desirable low freezing point (−35 °C). Furthermore, this organohydrogel exhibits high strain sensitivity (gauge factor = 2.65), fast response time (∼160 ms), low signal hysteresis, and excellent cyclic stability (over 1200 cycles). And the wearable LS-PPy/PAA organohydrogel sensor could accurately and real-time monitor various intense or subtle human movements, such as joint bending, facial expression and hand writing. Besides, the developed LS-PPy/PAA temperature sensor can respond to environmental temperature variations over a wide range of −20–100 °C. High resolution of 0.5 °C with remarkable sensitivity (−0.80 %/°C and linearity of R2 = 0.99) and repeatability were achieved within 36.5–40 °C, which makes it suitable for human body temperature monitoring. All these results demonstrate the substantial prospective value of the LS-PPy/PAA hydrogel in wearable sensors and other associated fields.

Unique and Scintillating Dance of Saptha Vidanga Sthalas

The dance, Tandava, or the cosmic dance of Nataraja symbolizes the balance and rhythm of the universe, the rhythm of life, and the perpetual flow of energy in the universe. The Dance with subtle, intricate movements of the cosmos could be interpreted as ‘Micro Dance’. The dance of Saptha vidanga sthalas are graceful movements which are characterized by delicacy, precision, and attentiveness to subtle nuances and rhythms of Nature. Ajapa Natanam of Tyagaraja is a subtle and rhythmic breathing movements which are very minute and imperceptible. Tharanga Natanam of Nãgaikkãronam is a delicate sensitive up and down micro movements of waves in a rhythmic pattern with troughs and crests. Kukuda Natanam of Thirukkãrãyil is a sensitive up and down movement of musical notes and is a dance of distinct rhythm, with sharp notes with gradual intensity and crescendo to lower note. Brunga Natanam of Thirukkolili is the graceful movement of a bee to a hypnotic rhythm with its up and down movement of wings which cannot be measured. Kamala Natanam of Thiruvãymur is a subtle, undulating movement of the lotus by a gentle breeze passes over, reflecting the gentle rhythm of the nature. Hamsapãda Natanam of Vedaranyam is the rhythmic gait of a swan, a distinctive swaying motion embodies a sense of tranquillity and beauty. Unmattha Natanam of Thirunallar is the movement of ecstasy, a movement of intense pleasure and profound spiritual experience and emotions.

Sustainable, temperature-tolerant, dual network conductive pressure sensitive adhesive from cellulose and rosin for wearable sensing

Conductive pressure sensitive adhesives (PSA) used for wearable and smart electronic sensors have attracted a significant amount of attention recently. However, achieving multifunctional conductive PSA with the feature of temperature tolerance and sustainability via a convenient and environment-friendly approach still remains challenge. Herein, a novel cellulose-rosin based poly(esterimide) (PEI) was first prepared by esterification and imidization. Then, the cellulose-rosin based PEI was integrated with polymerizable deep eutectic solvents (PDES, 2-hydroxyethyl acrylate and triethanolamine as hydrogen bond donor, choline chloride as hydrogen bond receptor) and performed UV-induced polymerization for formation of the conductive PSA with dual network (DN). The DN structure and the existence of extensive hydrogen bonds endowed these cellulose-rosin based conductive PSA with excellent adhesion property (shear resistance more than 70 h, tack of 14.6 N and 180° peel strength of 148.1 N/m, are higher than that of some typical commercial PSA), exceptional UV-blocking, solvent-resistance (usable in low polar solvent) and temperature tolerance (perform well between −25 °C to 140 °C). Furthermore, these conductive PSA could be used as wearable sensor to monitor subtle movements and achieve real-time monitoring of interface adhesion states even under extreme environmental conditions. This work provides a green strategy for the next-generation of multifunctional PSA.

Multifunctional and flexible sensor based on PU-supported Au/VTe2/Ti3C2Tx yarns for human motion and NO2 detection

Given the considerable harm that NO2 poses to the environment and human health, developing reliable NO2 detection sensors is crucial. Herein, a flexible, multifunctional sensor was successfully synthesized through hydrothermal and the wet impregnation methods using an elastic PU yarn as the substrate. This sensor, named Au/VTe2/Ti3C2Tx–PU, was characterized via X-ray diffraction, X-ray photoelectron spectroscopy, and scanning electron microscopy to analyze its structure, chemical valence states, and micromorphology. At room temperature (23 °C), the sensor demonstrated an exceptionally high response to NO2 gas, with a response value of 1.72 at 50 ppm as well as low response and recovery time of 33 and 38 s. Additionally, testing of the strain performance of Au/VTe2/Ti3C2Tx–PU showed that this flexible sensor exhibits high strain sensitivity, excellent repeatability, and durability, making it suitable for detecting subtle human movements. Overall, the synthesized sensor is in considerable advantages in terms of both gas-sensing performance and tensile performance, exhibiting broad application prospects in human motion and health monitoring.