Biomimetic Wearable Sensors: Emerging Combination of Intelligence and Electronics

Digital Fingerprinting of Complex Liquids Using a Reconfigurable Multi-Sensor System with Foundation Models

Combining chemical sensor arrays with machine learning enables designing intelligent systems to perform complex sensing tasks and unveil properties that are not directly accessible through conventional analytical chemistry. However, personalized and portable sensor systems are typically unsuitable for the generation of extensive data sets, thereby limiting the ability to train large models in the chemical sensing realm. Foundation models have demonstrated unprecedented zero-shot learning capabilities on various data structures and modalities, in particular for language and vision. Transfer learning from such models is explored by providing a framework to create effective data representations for chemical sensors and ultimately describe a novel, generalizable approach for AI-assisted chemical sensing. The translation of signals produced by remarkably simple and portable multi-sensor systems into visual fingerprints of liquid samples under test is demonstrated, and it is illustrated that how a pipeline incorporating pretrained vision models yields> 95 % $>95\%$ average classification accuracy in four unrelated chemical sensing tasks with limited domain-specific training measurements. This approach matches or outperforms expert-curated sensor signal features, thereby providing a generalization of data processing for ultimate ease-of-use and broad applicability to enable interpretation of multi-signal outputs for generic sensing applications.

A semi-automated quantum-mechanical workflow for the generation of molecular monolayers and aggregates

Organic electronics (OE) such as organic light-emitting diodes or organic solar cells represent an important and innovative research area to achieve global goals like environmentally friendly energy production. To accelerate OE material discovery, various computational methods are employed. For the initial generation of structures, a molecular cluster approach is employed. Here, we present a semi-automated workflow for the generation of monolayers and aggregates using the GFNn-xTB methods and composite density functional theory (DFT-3c). Furthermore, we present the novel D11A8MERO dye interaction energy benchmark with high-level coupled cluster reference interaction energies for the assessment of efficient quantum chemical and force-field methods. GFN2-xTB performs similar to low-cost DFT, reaching DFT/mGGA accuracy at two orders of magnitude lower computational cost. As an example application, we investigate the influence of the dye aggregate size on the optical and electrical properties and show that at least four molecules in a cluster model are needed for a qualitatively reasonable description.

Biomechanics on Ultra-Sensitivity of Venus Flytrap's Micronewton Trigger Hairs

Numerous plants evolve ingeniously microcantilever-based hairs to ultra-sensitively detect out-of-plane quasi-static tactile loads, providing a natural blueprint for upgrading the industrial static mode microcantilever sensors, but how do the biological sensory hairs work mechanically? Here, the action potential-producing trigger hairs of carnivorous Venus flytraps (Dionaea muscipula) are investigated in detail from biomechanical perspective. Under tiny mechanical stimulation, the deformable trigger hair, composed of distal stiff lever and proximal flexible podium, will lead to rapid trap closure and prey capture. The multiple features determining the sensitivity such as conical morphology, multi-scale functional structures, kidney-shaped sensory cells, and combined deformation under tiny mechanical stimulation are comprehensively researched. Based on materials mechanics, finite element simulation, and bio-inspired original artificial sensors, it is verified that the omnidirectional ultra-sensitivity of trigger hair is attributed to the stiff-flexible coupling of material, the double stress concentration, the circular distribution of sensory cells, and the positive local buckling. Also, the balance strategy of slender hair between sensitivity and structural stability (i.e., avoiding disastrous collapse) is detailed revealed. The unique basic biomechanical mechanism underlying trigger hairs is essential for significantly enhancing the performance of the traditional industrial static mode microcantilever sensors, and ensure the stability of arbitrary load perception.

Light-Activated Virtual Sensor Array with Machine Learning for Non-Invasive Diagnosis of Coronary Heart Disease

Early non-invasive diagnosis of coronary heart disease (CHD) is critical. However, it is challenging to achieve accurate CHD diagnosis via detecting breath. In this work, heterostructured complexes of black phosphorus (BP) and two-dimensional carbide and nitride (MXene) with high gas sensitivity and photo responsiveness were formulated using a self-assembly strategy. A light-activated virtual sensor array (LAVSA) based on BP/Ti3C2Tx was prepared under photomodulation and further assembled into an instant gas sensing platform (IGSP). In addition, a machine learning (ML) algorithm was introduced to help the IGSP detect and recognize the signals of breath samples to diagnose CHD. Due to the synergistic effect of BP and Ti3C2Tx as well as photo excitation, the synthesized heterostructured complexes exhibited higher performance than pristine Ti3C2Tx, with a response value 26% higher than that of pristine Ti3C2Tx. In addition, with the help of a pattern recognition algorithm, LAVSA successfully detected and identified 15 odor molecules affiliated with alcohols, ketones, aldehydes, esters, and acids. Meanwhile, with the assistance of ML, the IGSP achieved 69.2% accuracy in detecting the breath odor of 45 volunteers from healthy people and CHD patients. In conclusion, an immediate, low-cost, and accurate prototype was designed and fabricated for the noninvasive diagnosis of CHD, which provided a generalized solution for diagnosing other diseases and other more complex application scenarios.

A Highly Sensitive and Stretchable Core-Shell Fiber Sensor for Gesture Recognition and Surface Pressure Distribution Monitoring

This work reports a highly-strain flexible fiber sensor with a core-shell structure utilizes a unique swelling diffusion technique to infiltrate carbon nanotubes (CNTs) into the surface layer of Ecoflex fibers. Compared with traditional blended Ecoflex/CNTs fibers, this manufacturing process ensures that the sensor maintains the mechanical properties (923% strain) of the Ecoflex fiber while also improving sensitivity (gauge factor is up to 3716). By adjusting the penetration time during fabrication, the sensor can be customized for different uses. As an application demonstration, the fiber sensor is integrated into the glove to develop a wearable gesture language recognition system with high sensitivity and precision. Additionally, the authors successfully monitor the pressure distribution on the curved surface of a soccer ball by winding the fiber sensor along the ball's surface.

What Exactly Can Bionic Strategies Achieve for Flexible Sensors?

Flexible sensors have attracted great attention in the field of wearable electronic devices due to their deformability, lightness, and versatility. However, property improvement remains a key challenge. Fortunately, natural organisms exhibit many unique response mechanisms to various stimuli, and the corresponding structures and compositions provide advanced design ideas for the development of flexible sensors. Therefore, this Review highlights recent advances in sensing performance and functional characteristics of flexible sensors from the perspective of bionics for the first time. First, the "twins" of bionics and flexible sensors are introduced. Second, the enhancements in electrical and mechanical performance through bionic strategies are summarized according to the prototypes of humans, plants, and animals. Third, the functional characteristics of bionic strategies for flexible sensors are discussed in detail, including self-healing, color-changing, tangential force, strain redistribution, and interfacial resistance. Finally, we summarize the challenges and development trends of bioinspired flexible sensors. This Review aims to deepen the understanding of bionic strategies and provide innovative ideas and references for the design and manufacture of next-generation flexible sensors.

High-Sensitive Spatiotemporal Distribution Imaging of Compression Stresses Based on Time-Evolutional Responsiveness

Mechanoresponsive materials have been studied to visualize and measure stresses in various fields. However, the high-sensitive and spatiotemporal imaging remain a challenging issue. In particular, the time evolutional responsiveness is not easily integrated in mechanoresponsive materials. In the present study, high-sensitive spatiotemporal imaging of weak compression stresses is achieved by time-evolutional controlled diffusion processes using conjugated polymer, capsule, and sponge. Stimuli-responsive polydiacetylene (PDA) is coated inside a sponge. A mechanoresponsive capsule is set on the top face of the sponge. When compression stresses in the range of 6.67-533 kPa are applied to the device, the blue color of PDA is changed to red by the diffusion of the interior liquid containing a guest polymer flowed out of the disrupted capsule. The applied strength (F/N), time (t/s), and impulse (F·t/N s) are visualized and quantified by the red-color intensity. When a guest metal ion is intercalated in the layered structure of PDA to tune the responsivity, the device visualizes the elapsed time (τ/min) after unloading the stresses. PDA, capsule, and sponge play the important roles to achieve the time evolutional responsiveness for the high-sensitive spatiotemporal distribution imaging through the controlled diffusion processes.

Recent Advances in Tactile Sensory Systems: Mechanisms, Fabrication, and Applications

Flexible electronics is a cutting-edge field that has paved the way for artificial tactile systems that mimic biological functions of sensing mechanical stimuli. These systems have an immense potential to enhance human-machine interactions (HMIs). However, tactile sensing still faces formidable challenges in delivering precise and nuanced feedback, such as achieving a high sensitivity to emulate human touch, coping with environmental variability, and devising algorithms that can effectively interpret tactile data for meaningful interactions in diverse contexts. In this review, we summarize the recent advances of tactile sensory systems, such as piezoresistive, capacitive, piezoelectric, and triboelectric tactile sensors. We also review the state-of-the-art fabrication techniques for artificial tactile sensors. Next, we focus on the potential applications of HMIs, such as intelligent robotics, wearable devices, prosthetics, and medical healthcare. Finally, we conclude with the challenges and future development trends of tactile sensors.

Smart Nanofibrous Hydrogel Wound Dressings for Dynamic Infection Diagnosis and Control: Soft but Functionally Rigid

Chronic wounds, such as burns and diabetic foot ulcers, pose significant challenges to global healthcare systems due to prolonged hospitalization and increased costs attributed to susceptibility to bacterial infections. The conventional use of antibiotic-loaded and metal-impregnated dressings exacerbates concerns related to multidrug resistance and skin argyrosis. In response to these challenges, our research introduces a unique approach utilizing antibiotic-free smart hydrogel wound dressings with integrated infection eradication and diagnostic capabilities. Electrospinning stands out as a method capable of producing hydrogel nanofibrous materials possessing favorable characteristics for treating wounds and detecting infections under conditions utilizing sustainable materials. In this study, innovative dressings are fabricated through electrospinning polycaprolactone (PCL)/gelatin (GEL) hybrid hydrogel nanofibers, incorporating pDA as a cross-linker, εPL as a broad-spectrum antimicrobial agent, and anthocyanin as a pH-responsive probe. The developed dressings demonstrate exceptional antioxidant (>90% radical scavenging) and antimicrobial properties (95-100% killing). The inclusion of polyphenols/flavonoids and εPL leads to absolute bacterial eradication, and in vitro assessments using HaCaT cells indicate increased cell proliferation, decreased reactive oxygen species (ROS) production, and enhanced cell viability (100% Cell viability). The dressings display notable alterations in color that correspond to different wound conditions. Specifically, they exhibit a red/violet hue under healthy wound conditions (pH 4-6.5) and a green/blue color under unhealthy wound conditions (pH > 6.5). These distinctive color changes provide valuable insights into the versatile applications of the dressings in the care and management of wounds. Our findings suggest that these antibiotic-free smart hydrogel wound dressings hold promise as an effective and sustainable solution for chronic wounds, providing simultaneous infection control and diagnostic monitoring. This research contributes to advancing the field of wound care, offering a potential paradigm shift in the development of next-generation wound dressings.