Flexible capacitive pressure sensors have broad applications in health monitoring and human-computer interaction. In order to improve the performances of flexible capacitive pressure sensors, researchers have proposed a variety of innovative optimization strategies, including improving the deformation ability of the dielectric layer by constructing microstructured materials, utilizing the significant capacitance changes generated by an electric double layer during deformation, and integrating the advantages of different strategies into one device. This review systematically discusses the latest research progress of flexible capacitive pressure sensors from optimization strategies, and deeply analyzes mechanisms, materials, construction methods, and pressure sensing performances. In addition, we also sort out the typical applications of flexible capacitive pressure sensors and explore their future development directions. This review provides guidance for the development of high-performance flexible capacitive pressure sensors.

Accurate detection of hydrogen sulfide in high-humidity environments is critical for applications ranging from oral pathology via breath analysis to industrial safety monitoring. However, conventional sensors suffer severe signal drift and electrode degradation under humidity above 90%, impeding reliable operation in real-world scenarios. Here, we present a self-powered, humidity-resistant sensor using a primary battery architecture coupled with a densely tangled hydrogel electrolyte. The sensing principle relies on non-faradaic adsorption-induced modulation of the Ag electrode potential. By precisely engineering polymer entanglement density, we create a hydrophobic gel armor that suppresses moisture penetration, confining signal drift to 14.68% at 95% relative humidity. This mechanism enables an ultra-low detection limit of 0.029 ppb, a high specificity of 98.7% against common interferents, and robust mechanical flexibility under 30% strain. The device integrates into portable, Bluetooth-enabled platforms for distributed monitoring across applications including clinical breath analysis, food spoilage detection, and underwater pipeline leak inspection.

Infrared photodetectors exhibit significant applications in the fields, such as the defense military, industry, and consumer electronics. Leveraging the advantages, like adjustable bandgap, high carrier mobility, solution processing ability, and low cost, the InX (X: As, Sb) quantum dots (QDs) emerge as the promising candidate u201Cgreenu201D materials for next-generation infrared photodetectors, compared to Hg- and Pb-based infrared QDs. However, the controllable synthesis and improved photoelectric properties of InX QDs are limited by the strong covalent bonding between indium and heavy pnictogens and the high-density surface defects, resulting in device metrics that are yet to match those of their toxic counterparts. In recent years, the synergistic innovations in the synthesis technique, ligand engineering, and device structure design have led to significant improvements in the performance of InX QDs infrared photodetectors. This review focuses on the advances of InX QDs-based infrared photodetectors. Firstly, various synthetic methods of InX QDs are reviewed. Secondly, the performance optimization strategies (synthesis process optimization, surface passivation, and device structure design) of InX QDs infrared photodetectors are discussed in detail. Finally, the challenges and prospects for future research in InX QDs infrared photodetectors are proposed. Through a comparative analysis of the InAs and InSb QDs systems, this review establishes a clear u201Cmaterial-to-deviceu201D optimization path to unlock the full potential of InX QDs photodetectors and accelerate the development of stable, efficient, and eco-friendly infrared devices.

Low-cost gas sensor arrays are attractive for mixed-gas monitoring, but deployment-oriented modeling remains challenging because mixed-gas responses are nonlinear, cross-sensitive, and strongly dependent on sensor dynamic states. Existing electronic-nose models often rely on handcrafted response descriptors or generic sequential networks, which may either compress transient response information or introduce unnecessary computational cost. This work proposes SDRMixer, a lightweight sensor-specific framework for mixed-gas concentration quantification. SDRMixer uses a parameter-free sparse dynamic response encoding to organize the original sensor response, baseline-referenced excitation, and smoothed response kinetics into a physically meaningful dynamic response field. A compact temporal-feature mixer is then applied over fixed response-stage tokens for simultaneous multi-gas regression. To improve calibration coverage, a response-consistent augmentation strategy is used during model training. The proposed framework is evaluated on a previously reported mixed-gas sensor array dataset containing NO2, NH3, CH4, and CO2 mixtures. Both augmentation-enriched calibration domain benchmarking and original-measurement-based validation are conducted to assess prediction performance, computational efficiency, and stability on measured calibration samples. The results show that SDRMixer provides a good trade-off between accuracy and efficiency compared with generic deep learning architectures and compact gas-sensing baselines. These findings indicate that explicit dynamic response encoding combined with lightweight temporal-feature mixing is an effective modeling strategy for compact mixed-gas quantification within the investigated calibration domain.

Developing high-selectivity and high-sensitivity nitrogen dioxide (NO2) gas sensors, and explaining the complex underlying sensitization mechanisms remain critical challenges. Herein, we propose a dual-sensitization mechanism of synergistic enhancement of sulfur vacancy and heterojunction at composite interface to develop a high-performance NO2 gas sensor at room temperature. A novel sulfur vacancy-rich palladium sulfide (PdS) nanocluster is synthesized via a microwave-assisted method, and it is further in situ decorated on molybdenum disulfide (MoS2) nanoflowers to construct hierarchical heterostructure. The high selectivity of PdS/MoS2 gas sensor is verified via experiment and density functional theory calculation. The PdS/MoS2 composite system shows the highest adsorption energy (−1.89 eV) to NO2 among various interfering gases, which further decreases to −2.11 eV after introducing sulfur vacancies. Meanwhile, the proposed dual-sensitization mechanism is based on “targeted electron-reflux” effect resulting from the synergistic action of sulfur vacancies and the p-p heterojunction, which modulates the interfacial carrier migration behaviors. Furthermore, a mobile NO2 monitoring system is developed for on-site NO2 tracking, which is equipped with the PdS/MoS2 gas sensor, universal serial bus module, and smart phone, enabling real-time monitoring and excessive NO2 concentration warning. This work proposes a research approach for complicated gas sensing mechanism, and provides an avenue for portable and intelligent gas detection.

Field-effect transistor (FET) hydrogen sensors have emerged as promising candidates for hydrogen detection due to their high sensitivity, rapid response, and integration capability. However, the mechanistic understanding of catalytic-gate FET hydrogen sensors remains limited, hindering rational device design and optimization. Here, we present a mechanistic modeling and experimental validation of Pd-catalyzed dual-gate TeSeO FET hydrogen sensors. By integrating density functional theory, device simulations, and experiments, we quantitatively link hydrogen adsorption at the Pd gate to the device's electrical response, revealing that hydrogen adsorption lowers the Pd work function and modulates the effective gate voltage and channel current. The derived closed-form model predicts a characteristic saturating dependence of sensor response on hydrogen partial pressure, in good agreement with experimental data across a wide range of hydrogen concentrations and gate biases. Guided by the established catalytic-gate FET model, the optimized sensor exhibits high sensitivity, rapid and reversible detection, a theoretical low detection limit of 35 ppb, and strong selectivity against common interfering gases. This work provides mechanistic insight and practical guidance for the rational design of high-performance FETbased hydrogen sensors.

A comprehensive understanding and effective suppression of dark current in near-infrared organic photodiodes (NIR-OPDs) are crucial for enhancing their detectability, a topic that remains a persistent challenge in this field. Herein, the origins of dark current in NIR photodetectors from the perspective of carrier dynamics is elucidated. Building on this analysis, an interface engineering-based solution targeting undesirable carrier transport and collection is proposed: a wide-band gap, highly biocompatible anode interfacial layer (D149:CoOx) with bidirectional carrier barriers. Its modestly deeper highest occupied molecular orbital blocks thermally activated holes, while the shallower lowest unoccupied molecular orbital impedes electron injection from external circuits, collectively suppressing the dark current of the NIR-OPD (active layer: PTB7-Th:TQPP2FIC). Compared to conventional PEDOT:PSS, D149:CoOx achieves effective dark current suppression without compromising responsivity (0.17/0.23 A W-1 @ PEDOT:PSS/D149:CoOx-OPD), synergistically enabling a specific detectivity of 1012 Jones at -1 V. Furthermore, featuring a lower dark current of ∼ 3 × 10-9 A cm-2 (20× lower than PEDOT:PSS at ∼ 6 × 10-8 A cm-2), the flexible D149:CoOx NIR-OPD is capable of real-time human heart rate monitoring. This work establishes design principles for low-noise NIR devices while demonstrating significant prospects in wearable NIR optoelectronics.

High-performance, broadband infrared photodetectors operating at room temperature are crucial for modern imaging, sensing, and communication systems. However, their development is hindered by the cryogenic cooling requirements of conventional narrow-gap semiconductors and the stability issues of emerging low-dimensional materials. Herein, a novel material engineering and interface regulation strategy for chalcogenide lead salt films is proposed. A high-performance heterojunction photodetector is constructed by integrating an in situ oxidized co-sputtered Sn-doped PbSe film with a magnetron-sputtered ZnO layer. This device exhibits an ultralow dark current density(similar to 40 pA cm(-)(2)) and an ultra-high signal-to-noise ratio(similar to 10(5)), an exceptional specific detectivity(D*) of similar to 1 & times; 10(10) Jones in the visible-near-infrared range and exceeding 1 & times; 10(8) Jones at 3.5 & micro;m, and a fast response time of hundreds of microseconds. Remarkably, it maintains over 90% of its initial performance after three months of unencapsulated storage in air. This work lays the groundwork for future solution-processed versions, provides a viable pathway toward stable and broadband infrared detection technology.

With the expanding applications of electronic noses in areas such as agriculture, petrochemicals, and environmental monitoring, improving their classification accuracy and gas concentration detection precision is essential. Electronic noses often employ neural networks to process their data, and these neural networks require a substantial number of test samples for training. Therefore, it was necessary to obtain a large number of training samples. Based on the sensor's competitive adsorption and desorption properties of the mixed gas, the chemical reaction between the different gases, the chemical reaction between mixed gases and metal oxides, and the transport characteristics of flow carriers, 14 state variables were determined to be used to construct the sensor dynamic response model of a MOS (metal oxide semiconductor) gas sensor through the liquid neural network. Simulations and experiments demonstrate that the model effectively produces large training datasets from small amounts of test data and achieves higher concentration prediction accuracy compared to existing models.

Performing multi-gas detection in mixed gas environments is a challenging problem in many engineering industries. Currently, gas sensor arrays are typically combined with analytical algorithms to predict the concentrations of mixed gases. However, current artificial neural network models usually require a large number of test samples and complete response data to achieve low relative errors. This study proposes a fast and data-efficient quantitative analysis framework that effectively extracts physically meaningful deep transient features, often overlooked by traditional algorithms, by introducing a selective state-space model isomorphic to adsorption kinetics. This enables rapid and accurate prediction of mixed gas concentrations without waiting for complete sensor response stabilization and using only 69 raw samples. This approach not only alleviates the problem of sample scarcity but also uses only the first 8 s of the sensor response (13% of the full 60-second response period). Experimental results showed that the average relative errors (MRE) for H2, NH3, and NO2 were 3.36%, 2.08%, and 6.41%. The overall error was reduced by up to approximately 60% compared to traditional algorithms. Furthermore, this framework demonstrates excellent transfer learning capabilities, providing an efficient, robust, and scalable solution for real-time mixed gas detection under data-scarce conditions.

Utilizing a single light source in conjunction with a beam-splitting method to distribute infrared radiation to multiple detectors is an effective approach for NDIR multi-gas detection. However, compared with conventional NDIR sensor architectures based on a single source and a single detector, such a configuration inevitably leads to a proportional reduction in the radiant flux received by each detector. To overcome the limitation, novel tapered light-converging structures are proposed to directly converge infrared radiation onto detectors' window regions and increase the efficiency of infrared light utilization. Measurements revealed that the detector output increases by a factor of 1.68 compared to the configuration without tapered light-converging structure. Furthermore, the proposed three-gas monitoring system is capable of accurately detecting carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) gases within concentration ranges of 0-5 %, 0-4 %, and 0-3 %, respectively. The fullscale error is within +/- 3 % FS, and the maximum relative standard deviation (RSD) of repeatability is 1.24 %. This work offers a promising strategy and design for achieving compact and accurate multi-gas detection.

Traditional conductive hydrogels suffer from intrinsic mechanical brittleness (typically <1 MPa) and insufficient fatigue resistance, making them prone to structural failure during dynamic wearable applications and long-term use, which inevitably leads to signal drift. Herein, poly(vinyl alcohol) (PVA) was used as the matrix; sodium carboxymethyl cellulose (CMC) constructed a flexible double-network framework; tannic acid (TA) enabled energy dissipation via dynamic hydrogen bonding; and sodium sulfate (Na2SO4) triggered Hofmeister effect-driven crystallization, thereby yielding an ultratough and fatigue-resistant conductive hydrogel. Benefiting from the hierarchical structure, the hydrogel achieved integrated mechanical-electrical performance. After the optimized 16 h Na2SO4 treatment, it exhibited an ultralow density of 0.962 g/cm(3), a tensile strength of 10.05 MPa (40 & times; conventional PVA), a toughness of 18.92 MJ/m(3), and an ionic conductivity of 3.97 S/m (34.5 & times; conventional PVA). The fabricated sensor showed excellent fatigue-resistant sensing performance, maintaining >91% signal retention after 5000 stretching cycles at 100% strain, with a rapid response time of 167 +/- 9 ms, enabling high-fidelity monitoring of joint motions and physiological pulses. When integrated with machine learning, the system enabled Morse-code communication and achieved a gesture-recognition accuracy exceeding 93%. This study provides a simple and feasible strategy for constructing ultratough, fatigue-resistant conductive hydrogels for next-generation durable bioelectronic devices.

Integrated multimodal biosensing platforms are transforming the landscape of bioanalytical technologies by enabling real-time, high-resolution, and multifunctional detection of physiological and environmental biomarkers. This review summarizes the evolution from traditional single-modal biosensors to advanced multimodal systems that unify diverse sensing modalities with computation and storage functionalities. The introduction on the transduction mechanisms of different biosensors and the representative biomarkers was first provided, highlighting the advantages of multimodal sensing with enhanced sensitivity, specificity, and robustness. The advances in fabrication techniques were then discussed, with particular emphasis on printable strategies that facilitate heterogeneous material integration and micro/nanoscale patterning. Moreover, artificial intelligence-driven data processing for on-device decision-making was discussed. Representative applications were then presented in healthcare monitoring, environment detection, and food safety tracking. Finally, current challenges related to material compatibility, data heterogeneity, device

In recent years, electrochemical pressure (ECP) sensors with self-powered and both dynamic and static pressure detection capabilities have received widespread attention. To improve pressure sensing performances while reducing the thickness of conventional sandwich structure ECP sensors, we propose an ECP sensor with a simple electrode coplanar structure. Specifically, it consists of Cu/Zn foil electrodes and LiCl/polyvinyl alcohol (PVA) modified filter paper. Among them, the Cu/Zn coplanar electrodes are used for redox reactions, the LiCl provides conductive ions, and the PVA is used to provide a humid environment to promote the ionization and conduction of LiCl. The rough surface microstructure of the filter paper is used to enhance the pressure sensing performances of the sensor. The results show that the ECP sensor with an electrode coplanar structure can spontaneously output current in the pressure range of 0.4-100 kPa, with sensitivities of 0.273 kPa-1 (0.6-20 kPa) and 0.036 kPa-1 (20-100 kPa). Specifically, compared to ECP sensors with a sandwich structure, it has a wider response range and higher sensitivity. Through the current response, morphological characterizations, and redox reactions, the pressure sensing mechanism is elucidated. Furthermore, the proposed ECP sensor can be used for respiratory state recognition combined with machine learning. This research provides a new approach for developing a high-performance ECP sensor with a simple electrode coplanar structure.

Addressing the long-standing bottlenecks of high operating temperature and excessive power consumption that plague conventional Schottky junction hydrogen sensors, we herein report a high-performance Schottky junction hydrogen sensor constructed from hydrogensensitive palladium-copper (Pd-Cu) alloy and asymmetric layered molybdenum disulfide $\left(\mathbf{M o S}_{\mathbf{2}}\right)$. Stepped asymmetric $\mathbf{M o S}_{\mathbf{2}}$ films with thicknesses ranging from 8 to 60 nm were prepared via mechanical exfoliation, followed by the deposition of Pd-Cu (8:2) alloy electrodes using directcurrent (DC) magnetron sputtering. We systematically characterized the electrical transport, photoelectric coupling, and room-temperature hydrogen-sensing behaviors of the devices, and unveiled the fundamental sensing mechanism whereby hydrogen adsorption reduces the Schottky barrier height, thus enabling efficient modulation of the photogenerated current. The results demonstrate that the as-fabricated device delivers a response of 655.85% toward 0.4 vol% hydrogen under violet light irradiation, enabling accurate quantitative detection of low-concentration hydrogen over the range of 200 ppm to 0.4 vol%. Meanwhile, the device achieves an ultralow static power consumption as low as 4.8 nW. This proof-of-concept sensor enables fast, low-power, low-concentration $\mathbf{H}_{\mathbf{2}}$ detection at room temperature with no heating requirement, offering a promising device platform for high-sensitivity safety monitoring in hydrogen energy systems.

In recent years, electrochemical humidity sensors have received widespread attention due to their zero-power consumption and self-powered characteristics. However, there are still many issues in improving their humidity sensing performances. Herein, we propose an electrochemical humidity sensor using 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM]BF4) ionic liquid as the humidity sensing material. The results demonstrate that the [BMIM]BF4 electrochemical humidity sensor has wide response range (0%-100% relative humidity (RH)), low detection limit (1% RH), and high detection resolution (1% RH) at room temperature (25 degrees C). In addition, its power generation performance is also acceptable, with a response voltage of 0.67 V and a maximum output power of about 0.192 mu W at 100% RH. Furthermore, the [BMIM]BF4 electrochemical humidity sensor can be used for respiratory rate detection. This work provides an effective reference for developing high-performance electrochemical humidity sensors.

Traditional humidity sensors rely on external power sources, resulting in energy consumption and maintenance costs. This study proposes a Cu/polyaniline (PANI)/Zn (CPZ) self-powered electrochemical humidity sensor based on the proton conduction property of PANI, achieving efficient humidity sensing and power generation performances. The CPZ humidity sensor uses PANI as the humidity sensing material and Cu/Zn as the positive/ negative electrodes. By utilizing the hydrophilicity and proton conductivity of PANI in conjunction with redox reactions, the humidity information is converted into voltage signal. The experimental results show that the CPZ humidity sensor exhibits excellent linear response in the range of 0 - 91.5 % relative humidity (RH) at 25 degrees C, with a sensitivity of 0.011 V/RH. The response and recovery times of the CPZ humidity sensor are 32.6 s and 24.0 s, respectively. The maximum output voltage of the CPZ humidity sensor is 0.857 V at 91.5 % RH, and the corresponding output power is 4.28 mu W. Although the PANI has no ionic conductivity like conventional salt electrolyte, its good proton conductivity makes it achieve good humidity sensing response. In addition, the CPZ humidity sensor shows good application potential in self-powered visual humidity detection, non-contact switch and respiratory rate monitoring.

Hydrogen sensors play an indispensable role in hydrogen detection within the hydrogen energy industry and are widely demanded for applications ranging from hydrogen leakage detection to hydrogen production, transportation, and utilization. In these scenes, the interference of environmental humidity remains a significant challenge for the practical application of hydrogen sensors. In this study, a humidity-resistant fuel cell-type hydrogen sensor is proposed by employing a water-retentive encapsulation layer to form a multi-layer proton exchange membrane (PEM). The continuous water supply mechanism and water molecular adsorption modulation effect of the encapsulated PEM enable the fuel cell-type hydrogen sensor to achieve a stable sensor performance across a wide range of 0 -90 % relative humidity (RH) at room temperature (similar to 25 degrees C), with a relative standard deviation of 8.36 % in sensor response. Besides, the encapsulation layer further enhanced the sensor properties. The sensitivity of the optimal sensor reaches 2.755 & times; 10(-4) & micro;A ppm(-1), which is 2.17 times higher than that of the non-encapsulated sensor (1.27 & times;10(-4) & micro;A ppm(-1)). Finally, a hydrogen leakage alarm system based on the proposed fuel cell-type sensor is built to demonstrate its hydrogen detection capability in various humidity and temperature conditions. This work provides a new strategy for the development of high-reliability fuel cell-type gas sensors, showcasing substantial potential for applications in industrial process monitoring and hydrogen energy safety management.

Humidity sensors, as key electronic devices for monitoring humidity, have a wide range of applications, including environmental and human-related humidity detections. Paper has become a promising material for flexible humidity sensors due to its inherent advantages such as low cost, hydrophilicity, environmental friendliness and flexibility. After years of development, paper-based humidity (PBH) sensors have made significant achievements, but many challenges remain in this field. This review aims to systematically discuss the recent developments in PBH sensors (mainly focusing on the past 5 years). First, we overview the main performance parameters and test methods of humidity sensors. Then, the fabrication techniques, as well as the humidity sensing mechanisms and performances of different types of PBH sensors (resistance, capacitance, impedance, triboelectricity, electrochemical, and ion-gradient), are systematically reviewed and discussed. In addition, the emerging applications of PBH sensors for human-related humidity detection are discussed. Finally, the challenges and future trends of PBH sensors are analyzed from different aspects. We hope that this review will provide important insights and guidance for the development of PBH sensors.