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Smart clothes equipped with flexible sensing systems provide a comfortable means to track health status in real time. Although these sensors are flexible and small, the core signal-processing units still rely on a conventional printed circuit board (PCB), making current health-monitoring devices bulky and inconvenient to wear. In this study, a printed fabric-based hybrid circuit was designed and prepared—with a series of characteristics, such as surface/sectional morphology, electrical properties, and stability—to study its reliability. Furthermore, to verify the function of the fabric-based circuit, simulations and measurements of the circuit, as well as the collection and processing of a normal adult’s electrophysiological signals, were conducted. Under 10,000 stretching and bending cycles with a certain elongation and bending angle, the resistance remained 0.27 Ω/cm and 0.64 Ω/cm, respectively, demonstrating excellent conductivity and reliability. Additionally, the results of the simulation and experiment showed that the circuit can successfully amplify weak electrocardiogram (ECG) signals with a magnification of 1600 times with environmental filtering and 50 Hz of industrial frequency interference. This technology can monitor human electrophysiological signals, such as ECGs, electromyograms (EMGs), and joint motion, providing valuable practical guidance for the unobtrusive monitoring of smart clothes.

Artificial visual neural systems have emerged as promising candidates for overcoming the von Neumann bottleneck via integrating image perception, storage, and computation. Existing photoelectric memristors are limited by the need for specific wavelengths or long input times to maintain stable behavior. Here, we introduce a benzothiophene‐modified covalent organic framework, enhancing the photoelectric response of methyl trinuclear copper for low‐voltage (0.2 V) redox processes. The material enables the modulation of 50 conductive states via light and electrical signals, improving recognition accuracy in low light, dense fog, and high‐frequency motion. The ITO/BTT‐Cu3/ITO device's accuracy increases from 7.1% with 2 states to 87.1% after training. This construction strategy and the synergistic effect of photoelectric interactions offer a new pathway for the development of photoelectric neuromorphic computing elements capable of processing environmental information in situ. Donor‐redox type covalent organic framework (COF) films exhibit photoelectric regulation capability under illumination due to the synergistic effect of electron‐donating groups and light‐induced charge transfer. This characteristic offers the advantages of high efficiency and low power consumption in integrated sensing‐computation‐storage systems for multi‐scenario recognition.

The establishment of a structural health monitoring (SHM) system for the damage and defects of composite structures is of great theoretical and engineering value to ensure their production and operational safety. Advanced machine learning technologies, such as deep learning, have become one of the main driving forces for state monitoring and predictive analysis of these structures. However, it is difficult to obtain sufficient data to train the deep learning model, which may fail to build an accurate and efficient SHM model. To overcome this problem, a new method based on Lamb waves and the diffusion model (DM) is proposed to realize the identification and classification of different defects for carbon-fiber-reinforced polymer (CFRP) structures. In this study, DM is used as the generation model of data enhancement, and the optimized and improved DDPM model is constructed in this experiment. The deep residual neural network (DenseNet) is used to identify and classify the defect features from the Lamb wave signals. Experimental and test results show that the deep learning framework designed in this study based on DenseNet classification and DDPM data enhancement can accurately detect and classify damage signals of common defects in CFRP composite plates.

It is widely accepted that the lightweight design of a grid fin is closely related to its aerodynamic performance and structural integrity, while limited work seeks their balance. This study proposes a lightweight grid fin design method by taking the locally swept-back angle as a variable based on three-dimensional computational fluid dynamics and fluid–thermo–structure coupling analysis for Mach numbers ranging from 0.8 to 5. The effect of the swept-back angle on the relative aerodynamic efficiency profit, weight saving, and structural integrity (with a focus on static strength) was analyzed. The results showed that the locally swept-back configuration maintained structural integrity while enabling simultaneous aerodynamic performance improvement and weight saving across different Mach numbers through swept-back angle adjustment. At Mach 0.8, 1.5, and 2.0, the 20° swept-back configuration achieved a 13.2% weight saving and improved aerodynamic performance. At Mach 0.9, the 15° configuration delivered optimal aerodynamic enhancement with a 10% weight saving. Notably, the 15° configuration demonstrated excellent balance after evaluating all Mach number operating conditions. All these highlight a good attempt for the trade-off design of structures among weight saving, aerodynamic performance, and structural integrity.

With the growing demand for chemical information collection from the environment, miniaturized size and high sensitivity labeled microelectromechanical systems (MEMS) gas molecular sensing devices have emerged as a promising element in the development of machine olfactory. However, prolonged exposure to gas analytes often induces slow chemical transformations on the sensing film surface, leading to reduced chemical activity, performance degradation, and mechanical failures such as membrane cracking or delamination. In the real market, especially under harsh working environments, long‐term stability is a critical quality metric in gas sensor development. Therefore, the pursuit of MEMS gas sensors that offer both high sensitivity and extended lifespan becomes indispensable yet challenging. Thus, this review provides a comprehensive overview of recent studies and achievements in MEMS gas sensors, highlighting efforts aimed at enhancing their working stability. Key areas of focus include advancements in the chemical and physical characteristics of sensing films, as well as improvements in device structures. Furthermore, current limitations, perspectives, and future possibilities for designing and fabricating MEMS gas sensors with long‐term stability are discussed. Recent advances in microelectromechanical systems (MEMS) gas sensors are reviewed, emphasizing strategies to enhance long‐term stability, including chemical modification of sensing films and device structural optimization. Key challenges and future opportunities are also highlighted.

Highlights A skin-interfaced microneedle patch simultaneously and continuously measures six metabolic biomarkers from dermal interstitial fluid—glucose, uric acid, cholesterol, sodium, potassium, and pH. Modular microneedle units assembled on a compliant polystyrene-isoprene-polystyrene substrate offer mechanical robustness and excellent flexibility, enabling seamless adhesion, stable skin-sensor coupling, and user-specific configuration, which delivers durable, conformal wear with high signal fidelity in daily use. An end-to-end personalized health evaluation system: high-dimensional multiplexed signals are processed by an optimized machine-learning pipeline to quantify and predict metabolic responses to daily behaviors, supporting personalized guidance (e.g., postprandial control, electrolyte balance). Given the inherent complexity of metabolic pathways and disease-associated agents, next-generation healthcare necessitates wearable, non-invasive, and customized approaches to continuously monitor a broad spectrum of physiologically relevant biomarkers for personalized health management. Moreover, existing data-based analytical strategies remain inadequate for delivering quantitative and predictive evaluations of health status in real-life settings. Here, we report an electronic multiplexed microneedle-based biosensor patch (eMPatch) that enables real-time, minimally invasive monitoring of key metabolic biomarkers in interstitial fluid, including glucose, uric acid, cholesterol, sodium, potassium, and pH. By integrating modular microneedle (MN) sensors into a skin-interfaced flexible platform, the eMPatch achieves robust mechanical stability and seamless skin conformity, thereby ensuring reliable and continuous sensing within the dermal space. In vivo validation in animal models under metabolic intervention highlights the strong capability of the eMPatch for real-time physiological tracking across diverse daily activities. Implemented with a machine learning algorithm, the eMPatch enables automatic feature extraction and multi-task health assessment, achieving a classification accuracy of 0.996 in distinguishing normal and diet-induced metabolic disorder for health condition identification and an R 2 score of 0.977 for the corresponding degree evaluation. This study highlights the potential of the MN-integrated, machine learning-enhanced biosensing platform toward personalized health management.

In this paper, the selective laser melting (SLM) forming method was combined with the requirement of boiling heat transfer for porous structure to study the forming process and boiling heat transfer characteristics of porous structure of G-surface lattice formed by SLM. 316L stainless steel powder was used to rasterize the G-surface structure. Besides, porosity and surface morphology of G-surface lattice were analyzed. Deionized water was used for boiling heat transfer experiment to study the effect of technological parameters on boiling heat transfer enhancement. On this basis, the bubble escape modes in different stages of boiling heat transfer of G-surface lattice were considered, and the delay of the critical heat flux caused by G-surface lattice was explained. This study provides a useful reference for the design of lattice heat cooling structure.

In this paper, the selective laser melting (SLM) forming method was combined with the requirement of boiling heat transfer for porous structure to study the forming process and boiling heat transfer characteristics of porous structure of G-surface lattice formed by SLM. 316L stainless steel powder was used to rasterize the G-surface structure. Besides, porosity and surface morphology of G-surface lattice were analyzed. Deionized water was used for boiling heat transfer experiment to study the effect of technological parameters on boiling heat transfer enhancement. On this basis, the bubble escape modes in different stages of boiling heat transfer of G-surface lattice were considered, and the delay of the critical heat flux caused by G-surface lattice was explained. This study provides a useful reference for the design of lattice heat cooling structure.

Accurate and convenient detection of human blood glucose levels is an effective method for early diagnosis of diabetes and prevention of complications. The flexible and wearable electrochemical glucose sensor with low cost, fast responsiveness, good stability, reliability, and high sensitivity has attracted much attention in monitoring glucose concentration. The preparation of a conductive layer with catalytic activity on a flexible substrate is the key to making a wearable glucose sensor. Here, graphene composite materials sintered with copper oxide (CuO) nanoparticles are successfully prepared on a polyimide film by laser direct writing method and fabricated a flexible non‐enzymatic glucose sensor using laser‐engraved graphene (LEG) as a conductive electrode. The CuO/LEG sensor exhibits a high sensitivity of 619.43 μA mm−1 cm−2 in 0–3 mm glucose and 462.96 μA mm−1 cm−2 in 0–8 mm glucose. In addition, the CuO/LEG sensor shows good reproducibility, high anti‐interference capability, and long‐term stability. It also presents good bending stability, which can maintain 82.40% initial current after 100 times bending. Moreover, the CuO/LEG sensor has an obvious step‐ampere response in the detection of sweat samples, indicating the great potential of wearable sweat sensors. A high‐performance copper oxide (CuO)/laser‐engraved graphene (LEG) based non‐enzymatic glucose sensor is prepared by laser direct writing method. The CuO/LEG sensor exhibits a high sensitivity of 619.43 μA mm−1 cm−2 in 0–3 mm glucose and 462.96 μA mm−1 cm−2 in 0–8 mm glucose. Moreover, the CuO/LEG sensor also shows good selectivity, reproducibility, long‐term stability, and bending stability.

Advanced manufacturing technologies are driving chemical sensing materials to the atomic scale. Single-atom chemical sensing materials, produced through atomic-scale manufacturing (ASM) methods, exhibit outstanding sensing performance and novel mechanisms owing to their unique physiochemical properties. The stability of single-atom site bonding and the appropriate coordination environment are crucial to the chemical sensing capabilities of single-atom catalysts (SACs). ASM methods are emerging in controlling the properties of individual atomic sites and thereby regulating their performance. Despite the immense potential of ASM methods in chemical sensing, they remain in early stages. This review systematically summarizes the recent advancements of atomic-level sensing from the perspective of ASM. Besides, we highlight the methods used to produce atomic-scale single-atom catalysts and discuss their manufacturing mechanism, chemical sensing mechanisms, and applications in chemical sensing. Moreover, the challenges and prospects of single-atom chemical sensing materials are presented from the view of atomic manufacturing.