Intelligent Devices Research Articles

Flexible ZIF-67@PVDF tree-like nanofiber membrane with high piezoelectric output for energy harvesting

Modern demands for efficient, environmentally friendly, and self-powered devices have led to growing interest in piezoelectric polymers in the scientific community. In this paper, a ZIF-67 functionalized polyvinylidene fluoride/ tetrabutylammonium hexafluorophosphate tree-like nanofiber membrane (ZIF-67@PVDF/TBAHP-TLNMs) was developed as piezoelectric nanogenerator for energy harvesting. The ZIF-67@PVDF/TBAHP-TLNMs was prepared by in-situ growth of ZIF-67 on the surface of PVDF TLNMs, which demonstrated remarkable energy harvesting capabilities. The results indicated that the β-phase content of the in-situ growth of ZIF-67 reached 86.94 %, augmented the fiber's rotation radius, and elevated the electromechanical conversion efficiency. Notably, the short circuit current reached 3.87 μA and the output voltage exceeded 9.78 V, which was 10.7 times and 5.9 times higher than the electrical signal output of pure PVDF nanofiber membrane, respectively. Furthermore, the ZIF-67@PVDF/TBAHP-TLNMs exhibited exceptional mechanical properties, with an elastic modulus as high as 1.13 GPa, outperforming other nanofiber membranes and catering to the diverse requirements of wearable devices. During a 500 s test cycle, its layered structure remained intact, without any discernible damage or interlayer separation of the nanofibers. At the same time, the device exhibited high electromechanical conversion efficiency and a sensitive sensing function, which made it have broad application prospects in human motion monitoring and intelligent wearable devices.

AirTac: A Contactless Digital Tactile Receptor for Detecting Material and Roughness via Terahertz Sensing

Tactile sensing is an indispensable capability for humans or intelligent devices to engage in complex physical interactions. Mainstream tactile sensors are contact-based, which show limitation in measuring deformable objects and demanding high maintenance effort. As a complementary solution, a new paradigm of contactless tactile sensing is attracting much interest. While promising, they can only identify coarse-grained single tactile perception properties, either material type or surface roughness. In this paper, we propose airTac, which includes a novel contactless digital tactile receptor model, capable of simultaneously extracting these two basic tactile properties in a fine-grained resolution, by harnessing the massive bandwidth of terahertz(THz) frequency band. airTac is designed based on our key finding: while the impact of material type and surface roughness on THz signal intertwine with each other, they are actually separable, i.e., roughness manifests a certain pattern in distorting relative high-frequency components of the whole THz spectrum. Therefore, we custom-design a bio-inspired deep neural network model to decouple the intertwined THz signal, and distill the tactile perception properties embedded underlying the signal. We prototype airTac using a THz time domain spectroscopy, and perform extensive evaluation over 9 different daily material types with 39 different surface roughness. airTac achieves a material identification accuracy of 97.43% and a roughness classification accuracy of 91.46%, which demonstrates that airTac can identify common materials in daily life and exceed a fingertip spatial resolution of 1mm.

Digitalisation opportunities for livestock welfare monitoring with a focus on heat stress

Rising global temperatures due to climate change pose an increased risk of heat stress (HS) in livestock, which requires accurate prediction methods to improve animal welfare and mitigate heat-related economic losses. In this literature review, we aimed to critically examine the potential of digitalisation in precision livestock farming from the perspective of assessing heat stress in cattle. Particular attention was paid to the possibility of using sensors to record changes in behaviour, physiological parameters, and feed and water consumption as predictors for detecting and preventing heat stress and its consequences in animals. The use of already proven sensors, such as those that record animal behaviour, to assess physiological parameters related to heat stress may be promising, using innovative data processing technologies, which requires additional investigation in future studies. Precision livestock farming (PLF) technologies that enable real-time monitoring of herds under specific conditions, such as disease or stress, play a key role in increasing the efficiency of herd management. Sensors, cameras, microphones, GPS, accelerometers and RFID tags are commonly used to collect data on animal behaviour and physiological parameters, helping to detect stress. Machine learning algorithms applied to sensor data can distinguish between different states of declining cow welfare, helping farmers make quick decisions. In summary, the integration of sensors, artificial intelligence and precision devices can help prevent heat stress in farm animals, improving animal welfare and productivity, while mitigating the negative effects of heat stress caused by climate change.

Regulating the microstructure and hydrophilicity of cellulose nanofibers using taurine and the application in humidity sensor and actuator

Exploring the interrelationships between microstructure, hydrophilicity, humidity response, and material stability through molecular structure design and low-cost preparation, and manufacturing sensing and driving materials with a perfect balance between structures and properties is crucial for biomimetic robots and intelligent devices. Herein, inspired by the capillary phenomenon, the cellulose nanofiber (CNF) was grafted by using taurine to form the “micro cilia” microstructure on its surface (CNF-TAU). By controlling the cilia density and symmetry, the hydrophilicity of the materials was adjusted, and the water was allowed to quickly separate from the inside of the materials, which led to improving the humidity sensitivity and stability of materials. Concretely, compared with CNF, the puncture load, tearing strength, and elongation at break of the CNF-TAU-1.0 were improved by 11.95 times, 2.16 times, and 15.6 %, respectively, and excellent tensile strength was presented. Moreover, the hydrophilicity of CNF-TAU was first weakened and then enhanced with TAU content increasing. At last, the CNF-TAU-1.0 (WCA 55.43°) was selected and blended with single-layer graphene oxide (GO) to prepare CNF-TAU/GO composite films by vacuum filtration. The results indicated that the composite film had excellent mechanical (room and high humidity), humidity responsiveness, and humidity gradient-driven properties. In detail, the CNF-TAU/GO-10 % composite film had the generated stable induced voltage (68.4 mV), the fast response/recovery time (0.3 s/0.9 s), the large max. bending angle (178.4°), and the drive cycle stability (1000 cycles). Moreover, the composite film could be applied to produce biomimetic soft robots, such as human fingers and butterfly-shaped robots.

Bioinspired Microgel-Loaded Smart Membrane Filtration with the Thermo- and Ion-Dual Responsive Water Gate for Selective Lead(II) Separation.

Lead (Pb2+) is a ubiquitous pollutant. Membrane filtration represents one of the most common water treatment methods, but nanofiltration and ultrafiltration require high transmembrane pressure, while microfiltration has larger pore sizes than ions, making them unfavorable for direct ion removal at low cost. Selective and direct separation of Pb2+ via membrane filtration at high efficiency without sacrificing the flux of clean water still remains challenging. Herein, inspired by the Pb2+-tolerable oleander that enriches and prevents Pb2+ in roots from permeating the plant body, a smart Pb2+-adsorptive filtration membrane with a temperature- and ion-tunable water gate was prepared by loading dual-responsive poly(N-isopropylacrylamido-co-acrylamido-benzo-18-crown-6) (PNB-5-20) microgels onto a commercial membrane. The PNB-5-20 microgel exhibits pronounced temperature-responsive swelling/deswelling (hydrodynamic diameter, 650-330 nm) with a volume phase transition temperature (VPTT) at ∼33 °C. Moreover, the microgel shows a high Pb2+-adsorption capacity (qmax, 85.4 mg/g) and good selectivity (distribution coefficient Kd ∼ 1000 mL/g) thanks to its complexation with the crown ether, as well as good Pb2+ responsiveness, having the VPTT positively shifted to 40 °C in the presence of Pb2+ with enhanced swelling behaviors. Functionalized with PNB-5-20, the smart membrane integrates Pb2+ detection, adsorption, and tunable water drainage in a single device. The membrane selectively recognizes Pb2+ in the polluted water with the gates in membrane pores switching from "open" to "closed", intercepting and adsorbing Pb2+ with water permeation reduced. Once purified, the gates can be "re-opened" by increasing the temperature. Construction of such an intelligent membrane filtration device with a tunable water gate and excellent Pb2+ recognition and adsorption performance will greatly simplify the remediation of Pb2+-polluted water.

Analysis of the Pet Companion Robot Market

Pet robot is a kind of intelligent device with artificial intelligence and mechanical technology. These robots provide a solution to the challenges of balancing work responsibilities and caring for pets, while also enhancing the interaction between owners and pets to ensure the health of pets. The pet robot market is a new technology market, through the in-depth study of the pet robot market, it can fill the research gap in this field and provide new research opportunities for the academic circle. Through the research methods of questionnaire survey and literature review, we compared the global market strategies of large, small and medium-sized technology pet robot companies, combined with the quantitative literature review and questionnaire quantitative analysis, and drew the conclusion that the pet robot market has great potential, but the entry barrier is high, and the differentiation strategy is needed.

Analysis of the Pet Companion Robot Market

Pet robot is a kind of intelligent device with artificial intelligence and mechanical technology. These robots provide a solution to the challenges of balancing work responsibilities and caring for pets, while also enhancing the interaction between owners and pets to ensure the health of pets. The pet robot market is a new technology market, through the in-depth study of the pet robot market, it can fill the research gap in this field and provide new research opportunities for the academic circle. Through the research methods of questionnaire survey and literature review, we compared the global market strategies of large, small and medium-sized technology pet robot companies, combined with the quantitative literature review and questionnaire quantitative analysis, and drew the conclusion that the pet robot market has great potential, but the entry barrier is high, and the differentiation strategy is needed.

Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.

This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device. The smart device incorporated seven force sensors and a single inertial measurement unit (IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation. Five selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79ms), and portability (32g). The proposed smart device can detect fatigue patterns with high precision and in real time. The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.

Global voices on atrial fibrillation care in China

The population aging, coupled with high prevalence of physical inactivity, obesity, dyslipidemia, hypertension, and diabetes mellitus, has led to a significant increase in the incidence and prevalence of atrial fibrillation (AF) in China. Managing clinical complexity of AF patients poses significant challenges. Current guidelines advocate for holistic or integrated management using the AF Better Care (ABC) pathway. Compliance with the ABC pathway has demonstrated promising benefit in improving clinical outcomes. The mAFA II Trial explores the potential of mobile Health technology-supported integrated care approach in reducing the risks of rehospitalization and clinical adverse events. However, disparities persist between urban and rural areas, with the likelihood of rural older individuals by themselves using intelligent devices being extremely low. Therefore, the application prospects of the mAFA strategy in rural areas are greatly limited. The ongoing MIRACLE-AF Trial aims to address unique healthcare challenges faced by rural older patients with AF through a novel integrated care model, which is led by village doctors and supported by a digital health platform. In conclusion, innovative integrated care approaches using digital technologies offer promising solutions to enhance AF care across diverse settings in China, catering to the needs of both urban and rural populations.

The future of human and animal digital health platforms

Electronic Health (eHealth) has emerged as a pivotal driver of change in modern healthcare, reshaping the way medical information is collected, processed, and utilized. e-health includes digital solutions aimed at improving healthcare delivery, management, and accessibility. The Internet of Medical Things (IoMT) is specifically focused on establishing connections between medical devices and sensors to gather and transmit health-related data. Its primary objective is to enhance healthcare by facilitating real-time monitoring, employing data analytics, and integrating intelligent medical devices. The IoMT and, more broadly, eHealth are yielding positive outcomes, prompting their expanding application into the animal domain. Recent technological advancements facilitate the integration of health platforms, fostering a connection between human and animal health for improved well-being. This article introduces a conceptual framework that synthesizes the main activities in the medial data acquisition-processing pipeline. The framework has been derived from an analysis of the state of the art in the field of the IoMT in human healthcare. Furthermore, the article explores the application of eHealth concepts in the animal domain. Addressing both human and animal health, the paper summarizes the outstanding issues that need to be addressed for the full integration of these technologies into daily life.

Programming of a Portable Digital Monitoring System-Integrated DNA Aptamer Reversely Regulated Oxidase-Like Nanozyme for Real-Time Dynamic Analysis of Atmospheric Perfluorooctanoic Acid.

Timely and efficient analysis of the fluorinated per- and polyfluoroalkyl substances (PFAS) in an atmospheric environment is critical to environmental pollution traceability, early warnings, and governance. Here, a portable, reliable, and intelligent digital monitoring device for onsite real-time dynamic analysis of atmospheric perfluorooctanoic acid (PFOA) is proposed. The sensing mechanism is attributed to the oxidase-like activity of PtCoNPs@g-C3N4 that is reversely regulated by the surface modification of a PFOA-recognizable DNA aptamer, engineering a PFOA-activated oxidase-like activity of nanozyme (Apt-PtCoNPs@g-C3N4) to combine the nonfluorescence o-phenylenediamine (OPD) as the dual-modality response system. The present PFOA interacts with its DNA aptamer and dissociates from the surface of Apt-PtCoNPs@g-C3N4, restoring the oxidase-like activity of PtCoNPs@g-C3N4 to oxidize OPD into yellow fluorescence 2,3-diphenylaniline (DAP), thereby observing a PFOA-triggered colorimetric as well as fluorescence dual-modality change. Then, a hydrogel kit-programmed Apt-PtCoNPs@g-C3N4 + OPD system is used as the sensitive element to incorporate into this homemade portable device, automatically gathering and processing the PFOA-triggered hydrogel colorimetric and fluorescence image gray values by our self-weaving software, ultimately realizing the onsite real-time dynamic analysis of atmospheric PFOA surrounding a fluorochemical production plant. This work provides a direction and theoretical foundation for designing portable onsite screening devices that cater to other atmospheric contaminants detection requirements.

Lignin-Stabilized Tough, Sticky, and Recyclable Liquid Metal/Protein Organogels for Multifunctional Epidermal Smart Materials.

Biomass-encapsulated liquid metals (LMs) composite gels have aroused tremendous attention as epidermal smart materials due to their biocompatibility and sustainability. However, they can still not simultaneously possess toughness, adhesion, and recoverability. In this work, the tough, sticky, and recyclable protein-encapsulated LMs organogels (GLMx) are fabricated through the micro-interfacial stabilization of LMs by lignin and the following preparation of food-making inspired gels. With the help of lignin modification, the LMs micro-drops demonstrated uniform dispersion in the protein matrix, as well as dense non-covalent interactions (e.g., H─bond and hydrophobic interaction) with amino acid residues in peptide chains, which endowed the GLMx with high conductivity (≈5.4Sm-1), toughness (≈738.2kJm-3), self-adhesiveness (a maximal lap-shear strength of ≈58.3kPa), and recoverability. By tightly adhering onto human skin, the GLMx can act as epidermal sensors to detect drastic (e.g., joint bending) and subtle body movements (e.g., swallowing) and even recognize handwriting and speaking in real-time. Moreover, the organogels can also harvest solar energy and convert it into heat and electricity, which is promising in self-powered intelligent devices. Thus, this work paves a facile way to prepare protein/LMs composite organogels that are suitable for multiple applications like healthcare, human-robot interactions, and solar energy conversion.

Adaptive ubiquitous learning for server deployment and distributed offloading in UAV-enhanced IoV

Through creating an environment rich in computational and communication capabilities, ubiquitous computing gradually integrates it with human activities. Inspired by adaptive ubiquitous learning, various intelligent devices (e.g., roadside units and infrared sensors) deployed in the Internet of Vehicles (IoV) are expected to be critical to mitigating urban traffic congestion and enhancing travel safety. In addition, benefiting from the advantages of high mobility and real-time response, Unmanned Aerial Vehicles (UAVs) embody substantial prospects to assist IoV in efficiently and flexibly handling latency-sensitive, computation-intensive tasks. Nevertheless, due to time-varying demands and heterogeneous computing resources, it is challenging to provide effective service for mobile devices while guaranteeing high-quality data transmission. Therefore, a distributed service offloading system framework in UAV-enhanced IoV is designed. To minimize the service latency, a game theory-based distributed service offloading algorithm, named G-DSO, is proposed to realize adaptive ubiquitous learning for service request distribution. Finally, numerous experiments are implemented based on real-world service requirement datasets. Experimental results demonstrate that the proposed G-DSO approach improves the hit rate by 2.68% to 74.42% compared with four existing service offloading methods, verifying the effectiveness and good scalability of G-DSO.

Bioinspired waterproof and self-healing Photonic-Ionic skin for underwater interactive sensing

Bioinspired photonic-ionic skins (PI-skins) possessing multiple signals considerably promote the development of soft ionoelectronics owing to the fascinating interactive sensing capabilities. Unfortunately, swelling and leakage in aqueous environments are significantly detrimental to the stability and durability of underwater sensing. Herein, a novel PI-skin with waterproof, self-healing, and highly resilient properties is ingeniously fabricated via integrating the photonic array and dynamically covalently crosslinking fluorine-rich ionogel. Benefiting from the strain-modulated nanostructure and ionic conduction, the PI-skin achieves unique dual-signal sensing capability with sensitive mechanochromic performance and synchronous electrical response. Furthermore, the synergistic water repellency of the trifluoromethyl on the skeleton and the long alkyl chain modified hydrophobic ionic liquid (IL) inhibit swelling and leakage, significantly enhancing underwater stability. Notably, the exchangeable hindered urea bonds (HUBs) were judiciously introduced to construct a stable dynamically covalently crosslinking network, achieving efficient self-healing and high resilience simultaneously. The PI-skin with excellent adhesion and durability demonstrates the application potential in dual-signal underwater sensing and wireless information transmission. This work offers innovative design inspirations for the advancement of multifunctional artificial skins, intelligent interactive devices, and high-performance wearable iontronics.

Enhancing electromagnetic shielding property and absorption coefficients via constructing electromagnetic rings in janus composites

Polymer based electromagnetic wave (EMW) shielding materials with high conductivity have excellent electromagnetic shielding properties, but also exhibit high reflection of EMWs, resulting in inevitable secondary electromagnetic pollution. EMW shielding materials with low reflection have extensive potential applications in the next generation of anti-EMW equipment. Herein, a production strategy combining three dimensional (3D) printing technology and solution casting method was proposed to prepare polylactic acid (PLA)@multi-walled carbon nanotubes (MWCNT)/polydimethylsiloxane (PDMS)@carbonyl iron powder (CIP) absorptive electromagnetic shielding Janus composites with a ring-shaped electromagnetic synergistic structure (spiral rings or concentric circular rings). By utilizing PLA and MWCNTs as primary components, a 3D integrated framework that combines a highly conductive reflector and a ring inductance structure can achieve significant EMW shielding performance with low reflection. With PDMS as a matrix and magnetic CIP as functional fillers, a strong magnetic permeability network is provided around the ring inductance frame. The magnetic loss resulting from electromagnetic cooperative ring structure further enhances microwave absorption capacity. The composite materials, benefiting from the synergistic interaction between the electromagnetic rings and the reflector, demonstrate excellent impedance matching and efficient EMW shielding capabilities. Specifically, the PLA@MWCNT/PDMS@CIP Janus composites with ring structure exhibit a notable EMW shielding effectiveness (SE) of 31 dB and a reflection coefficient as minimal as 0.3. Therefore, this study presents a powerful approach to the development of low-reflective materials with high EMW SE, promising potential applications in the next-generation intelligent electronic devices.

A perspective on smart universities as being downsized smart cities: a technological view of internet of thing and big data

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a “smart university” as an extension of the overarching “smart city” framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city.

Wireless sensor network-based machine learning framework for smart cities in intelligent waste management

Environmental safety is one of the key issues that are directly related to a country's prosperity. One of the most fundamental aspects of a sustainable economy is waste management and recycling. Better recycling safety and efficiency may be achieved via the use of intelligent devices rather than manual effort. In this research, we describe a machine learning-based architecture for smart trash collection and sorting using the Internet of Things and wireless sensor networks. The goal of this study was to develop an autonomous method for producing an efficient and intelligent waste parameter monitoring system for a novel waste management system, using the Internet of Things (IoT) and Long Range (LoRa) technologies. Several possibilities are explored, all of which may be applied to the development of the three nodes. The number of trash cans, garbage stench, air quality, weight, smoke levels, and waste categories are all tracked in real-time via the Internet of Things and the Thing Speak Cloud Platform, which can be set up in numerous places. In the end, a fog layer-deployed intelligent waste classification framework consists mostly of four layers: input, feature, classification, and output. Using the Thrash Box dataset, the proposed system develops a categorization method into trash classes such as household, medical, and electronic garbage, in addition to object identification. Traditional machine learning methods, such as the multi-kernel support vector machine (SVM) and the Adaboost ensemble classifier, are employed in the classification layer, while the Resnet-101 deep convolutional neural network model is used in the feature layer. Experiments were conducted to evaluate the suggested method's ability to classify garbage and provide accurate predictions about their respective categories. Compared to other state-of-the-art models, the suggested method's performance was shown to be superior in the presented trials.

Photocontrolled Reversible Solid-Fluid Transitions of Azopolymer Nanocomposites for Intelligent Nanomaterials.

Intelligent polymer nanocomposites are multicomponent and multifunctional materials that show immense potential across diverse applications. However, to exhibit intelligent traits such as adaptability, reconfigurability and dynamic properties, these materials often require a solvent or heating environment to facilitate the mobility of polymer chains and nanoparticles, rendering their applications in everyday settings impractical. Here intelligent azopolymer nanocomposites that function effectively in a solvent-free, room-temperature environment based on photocontrolled reversible solid-fluid transitions via switching flow temperatures (Tfs) are shown. A range of nanocomposites is synthesized through the grafting of Au nanoparticles, Au nanorods, quantum dots, or superparamagnetic nanoparticles with photoresponsive azopolymers. Leveraging the reversible cis-trans photoisomerization of azo groups, the azopolymer nanocomposites transition between solid (Tf above room temperature) and fluid (Tf below room temperature) states. Such photocontrolled reversible solid-fluid transitions empower the rewriting of nanopatterns, correction of nanoscale defects, reconfiguration of complex multiscale structures, and design of intelligent optical devices. These findings highlight Tf-switchable polymer nanocomposites as promising candidates for the development of intelligent nanomaterials operative in solvent-free, room-temperature conditions.

Second-Order Nonlinear Switching and Photoluminescence Properties of Cd-Based Hybrid Perovskite with High-Temperature Phase Transition.

Recently, organic-inorganic hybrid perovskites exhibiting facile structural phase transitions have accumulated significant attention due to their switchable second-order nonlinear optical (NLO) properties, which hold significant promise for next-generation intelligent optoelectronic devices. In this study, we present a novel one-dimensional hexagonal hybrid perovskite, (4-methoxypiperidinium)CdCl3, which undergoes a reversible high-temperature structural phase transition at 389 K. Notably, (4-methoxypiperidinium)CdCl3 demonstrates switchable second-order NLO and dielectric properties, accompanied by symmetry breaking from the centrosymmetric Pnma to noncentrosymmetric Pna21 space group. Variable-temperature structure analyses reveal that this transition is mainly driven by the order-disorder transformation of the 4-methoxypiperidinium cations. Furthermore, it also features a promising photoluminescence performance with blue-light emission and a long lifetime of 25.34 ns. It is anticipated that this study will expand the family of hybrid perovskites exhibiting high-temperature phase transitions and offer valuable guidance for the design of new NLO switching materials with superior optoelectronic properties.

Ultra-fast light repair, ultrasensitive, large strain detection range PDA@RGO/EVA composites fiber flexible strain sensor

The sensor fabricated from high-molecular polymer is soft, lightweight, and biocompatible; however, traditional high-molecular polymers suffer from viscoelasticity and poor cycle stability. To address these issues, it is essential to develop flexible matrix materials that can overcome viscoelasticity, ensuring high sensitivity and long-term use under large strains for next-generation wearable intelligent devices. In this study, ethylene-vinyl acetate (EVA) with a shape memory effect was utilized as the substrate for a flexible sensor，the shape memory effect of EVA can eliminate the residual strain generated by each long time of use to improve its service life. A polydopamine@reduced graphene oxide (PDA@RGO)/EVA fiber flexible strain sensor was fabricated through melt extrusion, swelling ultrasound, and in-situ polymerization techniques. The resulting sensor exhibits high sensitivity (gauge factor=1801.21), a wide strain detection range (strain = 193 %), and excellent stability and durability (2000 cycles). Notably, the PDA@RGO/EVA fiber flexible strain sensor demonstrates exceptional dual thermal and light repair functions, with light repair achieving a repair speed of 5 seconds, 100 % electrical performance repair efficiency, and perfect consistency in relative resistance response before and after repair. Furthermore, the PDA@RGO/EVA strain sensor can monitor finger and wrist movements in real-time with high accuracy, highlighting its significant potential application in the wearable technology field.