Hydrogen (H2) is recognized as a clean energy carrier with potential applications in various sectors; however, its colorless and high flammability necessitate sensitive and reliable monitoring under ambient conditions. Chemiresistive semiconducting metal oxide sensors are attractive for their simple architecture and high sensitivity, but their performance often drops at room temperature and they still face selectivity challenges, which limit their practical deployment. Here, we address the challenge of room-temperature operation in chemiresistive semiconducting metal oxides-based sensors by engineering magnesium (Mg)-doped porous ZnFe2O4 microspheres (ZFM) and integrating them with platinum-decorated carbon nanotubes (CP). This composite design is expected to enhance room-temperature H2 sensing by increasing defect-mediated adsorption on the ZFM surface, facilitating efficient charge transport through the CNT network and accelerating reaction kinetics via Pt-assisted H2 dissociation. A two-step optimization strategy was adopted to establish composition–microstructure–property correlations: CP loading (0.1, 0.25, 0.5, 0.75 and 1 wt%) was first varied at an intermediate Mg (10 wt%) to investigate the effect of CNT/Pt network evolution, and then the optimal CP level (0.5 wt%) was kept constant while the Mg content (5–15 wt%) was varied. All the prepared composites were comprehensively characterized using XRD, FESEM, EDX, TEM, FTIR, UV–Vis, BET, and XPS to identify their phase composition, morphology, porosity, and surface chemical states. The sensing layers were fabricated by drop-casting onto epoxy-glass electrodes and tested under ambient conditions toward different H2 concentrations. The optimized ZFM10-CP0.5 sensing layer showed the best performance with a response of 2.2 at 10000 ppm H2 with fast response/recovery (11/90.9 s) and preferential response toward H2 relative to the tested organic interferents methanol, ethanol, ethylene glycol, and DMF, while maintaining approximately 90% of its H2 response at 70% relative humidity. XPS, BET, and FESEM collectively suggest that Mg doping increases the contribution of defect-related surface oxygen species, while 0.5 wt% CP provides a more continuous conductive network and accessible porous interface, consistent with the improved H2 response of ZFM10–CP0.5. These results identify an optimized ZFM–CP composition for heater-free room-temperature H2 sensing under the investigated conditions and highlight concurrent tuning of Mg content and CP loading as an effective strategy within this composite.
A hybrid composite based on Urushibara nickel-A and reduced graphene oxide (U-Ni-A/rGO) was developed for nonenzymatic glucose sensing in alkaline media. The composite combines skeletal nickel with a conductive rGO framework, giving an rGO-supported Ni interface with improved dispersion and lower interfacial resistance than U-Ni-A under identical conditions. The electrochemical response was examined by cyclic voltammetry (CV) and chronoamperometry (CA). CV was used to probe concentration-dependent behavior across 0.01–6000 μM, yielding a concentration-specific CV-derived slope of 5,666,475 μA mM−1 cm−2 in the 0.01–0.1 μM window. Quantitative sensing performance was evaluated by CA at 480 mV vs SCE, giving a linear response from 1 to 6000 μM with a sensitivity of 1000 μA mM−1 cm−2 and a response time of less than 2 s. The hybrid sensor also exhibited cycling durability and short-term shelf stability under the tested protocol, and strong selectivity toward glucose, with minimal interference from uric acid, ascorbic acid, dopamine, major cations, and various structurally related sugars under the tested 10:1 glucose-to-interferent screening conditions. These results support U-Ni-A/rGO as a potential platform for nonenzymatic glucose sensing.
Glucose-6-phosphate dehydrogenase (G6PD) is vital for maintaining cellular redox balance through the generation of NADPH in the pentose phosphate pathway. Given the global prevalence of G6PD deficiency and its association with hemolytic anemia, neonatal jaundice, and drug-induced oxidative complications, rapid and reliable assessment of enzyme activity is clinically important. Because NADPH production directly reflects G6PD function, sensitive quantification of NADPH provides an effective strategy for deficiency screening. To address this clinical need, a deployable electrochemical biosensor was developed using a screen-printed graphene electrode modified with a graphene nanoplatelet–WS2 (GNPl–WS2) nanocomposite. The synergistic nanohybrid interface enhances conductivity, increases electroactive surface area, and accelerates electron-transfer kinetics, leading to amplified anodic signals for NADPH oxidation. The platform was systematically optimized and thoroughly characterized to ensure robust analytical performance. Under optimal conditions, the sensor demonstrated a wide linear range from 5 μM to 3.5 mM and a low limit of detection of 1.01 μM. Importantly, the device offers rapid analysis (time-to-result <2 min), portability, and operational simplicity without complex fabrication or electrode regeneration. These features make the proposed deployable sensor a practical and promising tool for point-of-care and field-based evaluation of G6PD activity in biological samples, particularly in blood samples.
Graphene-based hydrogels are promising candidates for motion-sensing applications in assistive technologies. However, conventional systems rely on expensive, non-renewable precursors, which limits scalability and sustainability. In this study, palm kernel shell waste was used as a renewable carbon source to synthesize reduced graphene oxide (RGO), which was subsequently functionalized with silica (RGS) to enhance hydrogel performance. The synthesis of RGO and RGS was optimized to achieve high yield, facile processing, and cost-effectiveness. Incorporation of silica-functionalized RGO improved dispersion, interfacial bonding, and the structural integrity of PVA hydrogels, enhancing mechanical durability and stability. Density Functional Theory (DFT) calculations were performed to elucidate the electronic properties and molecular interactions between PVA and filler supporting the experimental findings. The resulting strain sensor exhibited a high gauge factor (GF ≈ 9.5), fast response, and reliable detection of assistive motions. Furthermore, the device successfully recognized Indonesian Sign System (SIBI) gestures, demonstrating its potential as a sustainable, low-cost, and high-performance platform for assistive communication technologies.
Potatoes, as an important global food crop, currently rely heavily on default parameters or a single search algorithm for model optimization in potato yield prediction research. The performance of gradient boosting models such as XGBoost (eXtreme Gradient Boosting) is highly dependent on hyperparameter configuration, and the grid search method suffers from insufficient spatial exploration, resulting in significant sensitivity to parameters that affect classification results. Therefore, the goal of this study is to enhance the accuracy and stability of yield level classification and to propose an XGBoost model framework with GA (Genetic Algorithm)-PSO (Particle Swarm Optimization) hybrid optimization. This paper constructs an 18-dimensional phenological, hydrothermal, and soil feature set based on measured sensor data from 2015 to 2023 and classifies yields into three levels: low, medium, and high. Subsequently, we use PSO as the backbone to achieve fast parameter convergence and periodically introduce GA genetic crossover and mutation to enhance population diversity, thereby avoiding local optimal traps. The optimization objective function is set to maximize the 5-fold cross-validated macro F1 score. The results show that after GA-PSO optimization, the macro F1 of the model increases to 0.872 ± 0.01, which is 10.66% higher than that of the unoptimized XGBoost (0.788 ± 0.02), and 2.47%, 1.04%, and 1.63% higher than those of grid search (0.851 ± 0.02), single PSO optimization (0.863 ± 0.01), and single GA optimization (0.858 ± 0.01), respectively. The accuracy and Kappa coefficient also reach 0.861 and 0.786, respectively, which are the highest among the compared methods. The SHAP interpretation results further validate that the interaction interval between the temperature suitability index and growing degree days (Idx_T = 0.70–0.85, GDD = 900–1040 °C d) is the key threshold for yield transition. GA-PSO effectively improves model robustness and parameter tuning efficiency through a hybrid mechanism that combines fast convergence and search jumping, providing highly reliable decision support for hierarchical management and water-fertilizer strategy formulation in smart agriculture.
The global trend toward health consciousness has led many people to focus on wellness and nutrition, driving the rapid growth of dietary supplements. Fe3+ is one of the most common ingredients used to support individuals at risk of Fe deficiency. It is also used in the form of fertilizers to improve crop yield. However, the deficiency and excess of iron can be harmful to the human health and plants. Therefore, reliable methods for qualitative and quantitative iron detection must be developed. This study successfully synthesized a new sensor (Rox) using rhodamine B, which serves as a fluorescent dye and an ion-binding site. In addition, an oxime group was incorporated into the structure to improve its hydrophilic properties. Rox exhibited excellent sensitivity and selectivity toward Fe3+ over other competing metal ions and operated through a turn-on fluorescence mechanism. The sensor exhibited a rapid response: it detected Fe3+ within 1 min with a detection limit of 3.54 ppb. Moreover, Fe3+ could be visually detected by the naked eye through a distinct color change from colorless to pink in a CH3CN:H2O (3:7, v/v) solvent system. Rox demonstrated promising performance in detecting Fe3+ in various water samples and living cells. It shows potential for application in portable paper-based qualitative assays. Thus, this study presents a straightforward approach for iron detection in fertilizer samples and pharmaceutical products, enabling safety and quality control and contributing to progress toward the United Nations Sustainable Development Goals.
The ability to map sound fields using airborne acoustic measurement drones opens up new opportunities for both engineering and research applications. In this study, we systematically evaluate the feasibility of drone-based acoustic measurements. Laboratory tests demonstrate that drones are well suited for low-frequency noise measurements, which are challenging for ground-based measurement systems due to the large spatial dimensions of typical noise emitters. We have developed a prototype drone system based on a correlation method between two microphones. The airborne system is validated with a controlled outdoor experiment that uses a self-calibrated low-frequency reference source. The system is capable of measuring radiation patterns and estimating the sound power level of a reference source with a deviation of less than 0.75 dB from its calibrated value following the ISO 3744 procedure. The mapped sound fields further enable source reconstruction and efficient characterization of far-field radiation. One possible application is the acoustic characterization of large energy converters, illustrated here through airborne measurements of noise produced by a wind turbine generator. Airborne acoustic measurements can, therefore, offer capabilities relevant for the commissioning and planning of industrial sites as well as for the validation of atmospheric sound propagation models.
The increasing impact and significance of volatile organic compound (VOC) has led to a growing demand for portable and rapid analytical techniques. Among the available technologies, photoionization detector (PID) has emerged as a widely used solution due to their sensitivity, fast response, and ease of integration into compact systems. However, a consistent limitation reported across the literature is the relatively poor selectivity of PIDs when distinguishing between chemically similar VOCs in complex mixtures. In this review, we provide a detailed examination of the current technologies employed in PID systems, examining photoionizer designs, materials, and their operating principles. Particular attention is given to the limitations these technologies face, as well as the emerging strategies aimed at overcoming existing barriers and enabling the next generation of PID.
Non-invasive wearable electrochemical biosensors have evolved as transformative tools for customized health management, allowing real-time monitoring of physiological and biochemical markers through readily accessible body fluids such as sweat, tears, and interstitial fluid. Compared to conventional diagnostic techniques, these sensors provide a non-invasive, continuous, and user-friendly alternative for tracking health status and disease progression. Advancements in flexible materials, microfluidics, and nanotechnology have improved sensor sensitivity, adaptability, and miniaturization for extended skin contact. Integration with cellphones, cloud computing platforms, and IoT networks enables seamless data transmission and remote monitoring. Additionally, the incorporation of artificial intelligence (AI) and machine learning (ML) enables dynamic data processing, early risk prediction, and tailored intervention. This review underscores the principles of power management solutions, strategies for multiplexed detection, design of electrochemical biosensors, and AI-driven data processing. Applications tracking athletic performance, chronic disease monitoring, mental health assessment, and precision medicine are covered, along with contemporary challenges such as individual variability, environmental interference, and data security. The ongoing convergence of analytics, wireless, and sensing technology is expected to hasten the adoption of smart biosensing systems in precision and preventative healthcare.
Organ: and organoid-on-a-chip (OoC) platforms provide microengineered human tissue models that reproduce key physiological features such as perfusion, mechanical cues, and multicellular interfaces while remaining compatible with established gene expression profiling (GEP) techniques. This review examines how conventional transcriptomic methods, including qPCR, microarrays, and bulk and single-cell RNA sequencing, are integrated with OoC systems and how microphysiological control reshapes the interpretation of gene expression data beyond static culture conditions. Representative applications across major organ systems are synthesized to illustrate how chip design parameters (cell source, architecture, flow, mechanical stimulation, and exposure route) influence transcriptional programs associated with disease phenotypes and drug responses. Rather than presenting OoC-derived gene signatures as stand-alone predictors, we emphasize their value as mechanistic endpoints that link controlled environmental perturbations to pathway-level biological responses. The analysis highlights both advantages, such as time-resolved sampling, improved contextual relevance, and reduced reliance on animal models, and persistent challenges, including device-to-device variability, low-input RNA handling, limited interlaboratory reproducibility, and incomplete standardization. Finally, emerging directions are discussed, including multi-organ integration, patient-specific iPSC-derived models, AI-assisted data analysis, and growing regulatory interest in New Approach Methodologies (NAMs) for safety and efficacy decision support. Together, these developments position OoC-coupled GEP as a promising but still maturing approach for translational research and personalized medicine.
Heavy metal ions present a significant threat to human health and the environment, making their detection critical for public safety and water quality. Organic fluorescent sensors have emerged as a powerful tool for this purpose. These sensors are built from easily synthesized organic molecules incorporated with heteroatoms (N, O, S) that can bind to metal ions. This review focuses on recent progress in using these sensors to detect zinc-group transition metal ions: Zn2+, Cd2+, and Hg2+. A major challenge for this purpose is the difficulty of selective detection due to the similar properties of these ions. We classify organic fluorescent sensors into single-ion and dual/multi-ion systems and provide a critical comparison of their chemical structures, sensing mechanisms, detection limits, and binding ratios. Our analysis offers valuable insights for designing more effective future sensors. We also discuss how integrating organic fluorescent probes with advanced support materials—such as paper strips, MOFs, and nanostructures—along with techniques like chemometric analysis, can significantly improve sensor sensitivity, selectivity, and practicality for real-world environmental applications.
In this study, the development of a green-synthesized surface plasmon resonance (SPR) biosensor based on Fe3O4/Ag nanocomposites (NCs) for bovine serum albumin (BSA) detection through an externally applied magnetic field has been explored. The Fe3O4 nanoparticles (NPs) were synthesized using Moringa oleifera extract and incorporated with Ag NPs. Analysis of structural, optical, and magnetic characterizations confirmed crystalline Fe3O4/Ag NCs with homogeneous Ag distribution, band-gap narrowing, and near-superparamagnetic behavior. For the SPR measurements, resonance angles of 46.41°, 46.52°, and 46.64° for the integrated Fe3O4/Ag NCs in prism/Au configuration were observed depending on 1, 3, and 5 mg/mL BSA concentrations, with a detection limit of 0.40 mg/mL and a R-squared value of 0.99. For the magneto-optic SPR (MOSPR) measurements, an increase in resonance angles of 46.61°, 46.60°, and 46.70° was detected with a magnetic field strength of 40, 50, and 60 Oe, respectively, resulting in full width at half maximum broadening and an increase in detection accuracy in MOSPR compared with conventional SPR, while preserving the same detection limit. The transverse magneto-optical Kerr effect response exhibited a higher sensitivity than conventional SPR, resulting in an approximately twofold enhancement in the MOSPR configuration. The quantitative analysis reveals how the response of green-synthesized Fe3O4/Ag magneto-plasmonic NCs evolves under an applied magnetic field, enabling a magnetically modulated SPR biosensing response for BSA.
This research presents a high-sensitivity fiber optic refractometric sensor integrated with Internet of Things technology for real-time monitoring of carbon dioxide in biogas applications, addressing critical industrial requirements for continuous gas composition analysis while providing remote accessibility and electromagnetic immunity. The sensing mechanism employs polyhexamethylene biguanide (PHMB) functionalized coating applied to de-cladded fiber regions, enhancing selectivity toward carbon dioxide detection through evanescent wave interactions at the core-cladding interface, where carbon dioxide-induced refractive index variations produce measurable optical intensity changes in single-mode fiber architecture. Experimental validation demonstrates robust performance across carbon dioxide concentrations ranging from 34.7% to 93.3%, achieving a high sensitivity of 0.196%/s with response and recovery times of approximately 120 seconds and 180 seconds, respectively, while multiple measurement cycles confirm excellent repeatability and stability without baseline drift or signal degradation. The integrated Internet of Things platform enables seamless data acquisition and web-based visualization through secure interfaces, facilitating real-time monitoring with three-second update intervals and maintaining an average latency of 70 milliseconds for immediate response capabilities. Microstructural analysis confirms chemical stability and adhesion integrity of the PHMB coating following extended carbon dioxide exposure, ensuring long-term operational reliability for continuous monitoring applications. This fiber optic-Internet of Things integration demonstrates significant advantages for industrial gas sensing applications, including compact design, remote monitoring capabilities, and enhanced safety protocols that support process optimization in biogas production facilities while addressing electromagnetic interference concerns common in industrial environments, indicating strong feasibility for commercial deployment in high-sensitivity real-time monitoring systems.
This review presents the evolution of strain sensor technologies from traditional bonded wire models to next-generation flexible designs enabled by advanced materials and additive manufacturing through 3D printing. It highlights breakthroughs in fabrication techniques particularly fused filament fabrication (FFF), direct ink writing (DIW), and vat photopolymerization (VPP) that address the limitations of conventional approaches, including complex multi-step processing and alignment challenges. The incorporation of novel materials such as conductive polymers and hybrid composites is shown to significantly enhance key performance parameters like sensitivity, mechanical durability, and strain sensing range. The convergence of cutting-edge manufacturing, material science, and computational modeling signals a paradigm shift in strain sensing, with broad implications for emerging applications in aerospace, biomedicine, soft robotics, and beyond. This work underscores the importance of continued interdisciplinary collaboration to fully realize the potential of flexible strain sensor technologies in adaptive, high-performance systems.
In this study, we present a comprehensive experimental investigation and theoretical description of the sensing capabilities of the electrochemical reactions of PEDOT/PVA interpenetrated film responding to the electrical, chemical, and thermal ambient conditions. The reaction drives reversible conformational movements or cooperative actuation of the PEDOT chains (the macromolecular electrochemical motors) mimicking biological functionalities that has been explored by few. The PEDOT/PVA hybrid film serves as a model material to simulate the cooperative actuation of sarcomeres in muscles. The consumed reaction energy under cyclic voltametric experimental conditions respond to and sense the ambient conditions: electrical (potential scan rate), chemical (electrolyte concentration) and thermal (temperature). Here, only two connecting wires are required to communicate both the command (current) and sensing signals (charge or energy) between the computer (brain) and the film (muscle). In other words, no additional sensors or connecting wires are required. Based on the reaction rate equation, a theoretical description consistent with the experimental results was developed. If these findings are translated to natural muscles and biological systems, they suggest that at any time the reaction energy in functional cells (such as the sarcomeres in muscles) could generate mechanical, chemical, thermal, and neuronal sensing signals to inform the brain during actuation. This remains an open biological question.
Gold is a valuable noble metal having widespread applications across various fields. However, Au3+ accumulation in the human body and the environment can pose serious health and ecological risks. Therefore, effective methods for Au3+ detection must be developed. Herein, a fluorescence sensor was successfully synthesized, to the best of our knowledge, as the first dicyano-[5]helicene-based sensor (MP) for Au3+ detection. Spectroscopic techniques and single-crystal X-ray analysis were used to confirm the molecular structure and photophysical properties of MP. The sensor exhibited a large Stokes shift of 111 nm and a high fluorescence quantum yield. Upon exposure to Au3+ MP displayed a “turn-off” fluorescence response, which indicates its high selectivity toward Au3+ over other competing metal ions along with excellent sensitivity. The detection limit of MP reached 4.2 ppb, which is lower than the guideline value for Au3+ toxicity in freshwater environments. The sensing mechanism for Au3+ detection was proposed to rely on the alkynophilicity of Au3+, activating the triple bond and inducing hydration of the alkyne moiety. This mechanism was supported by Fourier transform infrared spectroscopy, 1H nuclear magnetic resonance spectroscopy, high-resolution mass spectrometry, and molecular modeling. This sensor demonstrated high potential for qualitative fluorometric assays of Au3+ levels in diverse real samples, such as environmental water, drinking water, tap water, fertilizer solutions, cosmetic products, and human neuroblastoma cells. In addition, it could be applied for the quantitative detection of gold nanorods and further developed into a paper-based test strip for onsite Au3+ screening.
Neuromorphic computing systems could greatly benefit from electronic devices that exhibit analog resistive switching and dynamic adaptation similar to those of biological neurons, particularly for implementing sensory functions such as nociception. Here, we present a graphene oxide–cobalt oxide (GO–CoO) memristor that exhibits analog resistive switching with intrinsic current decay. Successive current–voltage sweeps with varying cutoff voltages demonstrate multilevel, finely tunable resistance states over a broad range without any compliance current. Charge–flux analysis confirms that the device operates as a true memristor, and cumulative conductance buildup under sequential positive and negative biases indicates robust, polarity-independent switching. Moreover, tuning the voltage sweep rate modulates the inertia of charge carriers that governs the switching kinetics. The GO–CoO architecture establishes a conductive network wherein oxygen vacancy dynamics within CoO particles and conductive rGO formation collectively govern analog resistive switching, providing the essential tunability and stability required to emulate synaptic plasticity. Under optimized pulsed stimulation, the memristor faithfully emulates key nociceptive neural responses, including a threshold response, peripheral sensitization (manifested as hyperalgesia and allodynia), central sensitization (temporal summation and facilitation), suprathreshold response, and post-stimulus recovery. The device also exhibits both short-term and long-term synaptic plasticity. These results pave the way for simple, cost-effective memristors capable of emulating neural synaptic functions and pain perception.
Nitrogen-vacancy color centers in diamond have proven themselves as a good, sensitive element for the measurement of magnetic fields. While the mainstream of magnetometers based on NV centers uses so-called optically detected magnetic resonance, there has recently been a suggestion to use dispersive readout of a dielectric cavity to enhance the sensitivity of magnetometers. Here, we demonstrate that the dispersive readout approach can be significantly improved if a two-channel scheme is considered.
This study presents a V-shaped microelectromechanical systems (MEMS) device designed to harness mode localization and nonlinear dynamic effects for high-performance pressure and/or multifunctional sensing. Three V-shaped devices with different geometric configurations were fabricated and tested to evaluate their pressure-sensing performance and anti-crossing behavior under pressure. The latter is particularly beneficial for sensing applications, as it ensures the device remains unaffected by pressure variations, eliminating the need for an additional packaging system. By exploiting mode coupling between the frequencies of symmetric and anti-symmetric modes, the sensors exhibited significant frequency and amplitude shifts across a pressure range of 0.1–760 Torr. One device demonstrated a sensitivity of up to 508.4 ppm/Torr near ambient pressure, while another achieved an ultra-high sensitivity of 7460 ppm/Torr in the medium-vacuum range and 1205.4 ppm/Torr in the low-vacuum range, showcasing excellent sensitivity and linearity. The third device showed a robustness against pressure variations, with one mode selectively insensitive to pressure but responsive to other stimuli, enabling multimodal sensing capabilities. Moreover, the device has been tested under temperature environmental variation, showing a low sensitivity of 20.4 ppm/0C. Comparative analysis with existing MEMS pressure sensors underscores the proposed design's advantages in structural simplicity, compact size, and high sensitivity, particularly in low-vacuum environments, positioning it as a promising solution for advanced sensing applications in biomedical, environmental, and industrial domains.
Surface acoustic wave (SAW) gas-based sensors have attracted significant attention as an emerging sensing technology due to their unique micro/nano-scale acoustic sensing structures and multi-physical field coupling mechanisms, which feature high sensitivity, rapid response, wide detection range, and lightweight. This paper systematically reviews the SAW gas sensing effect and mechanisms, sensing device design and fabrication, signal acquisition and processing circuits. Potential applications in fields such as renewable energy, aerospace, defense, industrial control, and intelligent manufacturing are also discussed, followed by an outlook on future development trends.
