Search Results (847)

This review explores the variety of diamond-based sensing applications, emphasizing their material properties, such as high Young’s modulus, thermal conductivity, wide bandgap, chemical stability, and radiation hardness. These diamond properties give excellent performance in mechanical, pressure, thermal, magnetic, optoelectronic, radiation, biosensing, quantum, and other applications. In vibration sensing, nano/poly/single-crystal diamond resonators operate from MHz to GHz frequencies, with high quality factor via CVD growth, diamond-on-insulator techniques, and ICP etching. Pressure sensing uses boron-doped piezoresistive, as well as capacitive and Fabry–Pérot readouts. Thermal sensing merges NV nanothermometry, single-crystal resonant thermometers, and resistive/diode sensors. Magnetic detection offers FeGa/Ti/diamond heterostructures, complementing NV. Optoelectronic applications utilize DUV photodiodes and color centers. Radiation detectors benefit from diamond’s neutron conversion capability. Biosensing leverages boron-doped diamond and hydrogen-terminated SGFETs, as well as gas targets such as NO₂/NH₃/H₂ via surface transfer doping and Pd Schottky/MIS. Imaging uses AFM/NV probes and boron-doped diamond tips. Persistent challenges, such as grain boundary losses in nanocrystalline diamond, limited diamond-on-insulator bonding yield, high temperature interface degradation, humidity-dependent gas transduction, stabilization of hydrogen termination, near-surface nitrogen-vacancy noise, and the cost of high-quality single-crystal diamond, are being addressed through interface and surface chemistry control, catalytic/dielectric stack engineering, photonic integration, and scalable chemical vapor deposition routes. These advances are enabling integrated, high-reliability diamond sensors for extreme and quantum-enhanced applications. Full article

Listeria monocytogenes (LM) contamination constitutes a paramount global threat to food safety, necessitating the urgent development of advanced, rapid, and non-destructive detection methodologies to ensure food security. This study successfully synthesized Bi₂WO₆ nanoflowers through optimized feed ratios of raw materials and further functionalized them with noble metal Au to construct a high-performance Au-Bi₂WO₆ composite nanomaterial. The composite exhibited high sensing performance toward acetoin, including high sensitivity (R_a/R_g = 36.9@50 ppm), rapid response–recovery kinetics (13/12 s), and excellent selectivity. Through UV-Vis diffuse reflectance spectroscopy (UV-Vis DRS) and X-ray photoelectron spectroscopy (XPS) characterizations, efficient electron exchange between Au and Bi₂WO₆ was confirmed. This electron exchange increased the initial resistance of the material, effectively enhancing the response value toward the target gas. Furthermore, the chemical sensitization effect of Au significantly increased the surface-active oxygen content, promoted gas–solid interfacial reactions, and improved the adsorption capacity for target gases. Compared to conventional turbidimetry, the Au-Bi₂WO₆ nanoflower-based gas sensor demonstrates superior practical potential, offering a novel technological approach for non-destructive and rapid detection of foodborne pathogens. Full article

The transition toward Industry 4.0 requires advanced sensing platforms capable of delivering real-time, high-fidelity data under extreme industrial conditions. Thin-film sensors, leveraging both photonic and functional approaches, are emerging as key enablers of this transformation. By exploiting optical phenomena such as Fabry–Pérot interference, guided-mode resonance, plasmonics, and photonic crystal effects, thin-film photonic devices provide highly sensitive, electromagnetic interference-immune, and remotely interrogated solutions for monitoring temperature, strain, and chemical environments. Complementarily, functional thin films including oxide-based chemiresistors, nanoparticle coatings, and flexible electronic skins extend sensing capabilities to diverse industrial contexts, from hazardous gas detection to structural health monitoring. This review surveys the fundamental optical principles, material platforms, and deposition strategies that underpin thin-film sensors, emphasizing advances in nanostructured oxides, 2D materials, hybrid perovskites, and additive manufacturing methods. Application-focused sections highlight their deployment in temperature and stress monitoring, chemical leakage detection, and industrial safety. Integration into Internet of Things (IoT) networks, cyber-physical systems, and photonic integrated circuits is examined, alongside challenges related to durability, reproducibility, and packaging. Future directions point to AI-driven signal processing, flexible and printable architectures, and autonomous self-calibration. Together, these developments position thin-film sensors as foundational technologies for intelligent, resilient, and adaptive manufacturing in Industry 4.0. Full article

Volatile organic compounds (VOCs) released from automotive interior materials and exchanged with external air seriously compromise cabin air quality and pose health risks to occupants. Electronic noses (E-noses) based on metal oxide semiconductor (MOS) micro-electro-mechanical system (MEMS) sensor arrays provide an efficient, real-time solution for in-vehicle gas monitoring. This review examines the use of SnO₂-, ZnO-, and TiO₂-based MEMS sensor arrays for this purpose. The sensing mechanisms, performance characteristics, and current limitations of these core materials are critically analyzed. Key MEMS fabrication techniques, including magnetron sputtering, chemical vapor deposition, and atomic layer deposition, are presented. Commonly employed pattern recognition algorithms—principal component analysis (PCA), support vector machines (SVM), and artificial neural networks (ANN)—are evaluated in terms of principle and effectiveness. Recent advances in low-power, portable E-nose systems for detecting formaldehyde, benzene, toluene, and other target analytes inside vehicles are highlighted. Future directions, including circuit–algorithm co-optimization, enhanced portability, and neuromorphic computing integration, are discussed. MOS MEMS E-noses effectively overcome the drawbacks of conventional analytical methods and are poised for widespread adoption in automotive air-quality management. Full article

Food fraud, particularly in the olive oil sector, represents a pressing concern within the agri-food industry, with implications for consumer trust and product authenticity. Certified products like Protected Designation of Origin (PDO) Extra Virgin Olive Oil (EVOO) are premium products that undergo strict quality controls, must comply with specific production regulations, and generally have a higher market price. These characteristics make them particularly vulnerable to economically motivated adulteration. In this study, the adulteration of PDO EVOO with Olive Pomace Oil (POO) and Olive Oil (OO) was investigated through a combined analytical approach. A traditional technique, gas chromatography–mass spectrometry (GC-MS) combined with solid-phase microextraction (SPME), was employed alongside an innovative method based on an electronic nose equipped with metal oxide semiconductor (MOX) sensors. GC-MS analysis enabled the identification of characteristic volatile compounds, providing a detailed chemical fingerprint of the different oil samples. Concurrently, the MOX sensor array successfully detected variations in the volatile profiles released by the adulterated oils, demonstrating its potential as a rapid and cost-effective screening tool. The complementary use of both techniques highlighted the reliability of MOX sensors in differentiating authentic PDO EVOO from adulterated samples and underscored their applicability in routine quality control and fraud prevention strategies. Full article

This review summarizes the use of Auger Electron Spectroscopy (AES) for microchemical analysis of two different types of dielectric/(Al,Ga)N-based systems: (i) extrinsic dielectric PECVD SiO₂, ALD Al₂O₃, and ECR-CVD SiN_x films on Al_xGa_1−xN/GaN structures in the context of their application in microelectronic power devices and (ii) intrinsic Al₂O₃ films on AlN epitaxial layers grown by high-temperature oxidation for nanostructured technology of various gas/ion sensors. Particular attention is given to AES depth profiling across complete multilayer cross-sections, combining qualitative analysis of spectral line shape and intensity evolution as well as kinetic energy shifts with quantitative elemental depth distributions. This approach enables identification of chemical states and oxidation-related transformations at dielectric/semiconductor interfaces. Reported results demonstrate that AES provides micro- to nanometer-scale chemical information essential for distinguishing interfacial from the bulk properties. The capabilities and inherent limitations of AES depth profiling, including sputter-induced artifacts are also addressed, highlighting the role of optimized experimental conditions in reliable interface analysis. Full article

Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large volumes of field operating data from flammable and toxic gas sensors have been accumulated, providing a data foundation for leak-pattern studies grounded in real-world scenarios. In this study, 56 leak samples verified by site feedback were selected. Time-aware interpolation and Z-score normalization were used for preprocessing, and time-series features—including standard deviation of first differences, autocorrelation coefficients, and frequency-domain energy—were extracted. Leak patterns were then identified using two unsupervised approaches: K-Means clustering and a 1D-CNN autoencoder. Results show that K-Means effectively distinguishes macro-patterns such as sustained leaks, instantaneous leaks, fluctuating leaks, and interrupted leaks, while the autoencoder demonstrates stronger capability in extracting temporal features, revealing leak evolution and transition characteristics. The two methods are complementary and together provide a viable route to developing an end-to-end model for leak scenario identification and risk discrimination. This work not only verifies the feasibility of conducting leak-pattern recognition using real GDS data but also offers technical guidance for the intelligent upgrading of hazardous chemical monitoring and early-warning systems. Full article

Exhaled breath analysis has gained considerable interest as a noninvasive diagnostic tool capable of detecting volatile organic compounds (VOCs) and inorganic gases that serve as biomarkers for various diseases. Polymer-based gas sensors have garnered significant attention due to their high sensitivity, room-temperature operation, excellent flexibility, and tunable chemical properties. This review comprehensively summarized recent advancements in polymer-based gas sensors for the detection of disease biomarkers in exhaled breath. The gas-sensing mechanism of polymers, along with novel gas-sensitive materials such as conductive polymers, polymer composites, and functionalized polymers was examined in detail. Moreover, key applications in diagnosing diseases, including asthma, chronic kidney disease, lung cancer, and diabetes, were highlighted through detecting specific biomarkers. Furthermore, current challenges related to sensor selectivity, stability, and interference from environmental humidity were discussed, and potential solutions were proposed. Future perspectives were offered on the development of next-generation polymer-based sensors, including the integration of machine learning for data analysis and the design of electronic-nose (e-nose) sensor arrays. Full article

Uniform application of fertilizers and pesticides continues to dominate global agriculture despite significant spatial variability in soil and crop conditions. This mismatch results in avoidable yield gaps, excessive chemical waste, and environmental pressures, including nutrient leaching and greenhouse gas emissions. The integration of Artificial Intelligence (AI) and Remote Sensing (RS) has emerged as a transformative framework for diagnosing this variability and enabling site-specific, climate-responsive management. This systematic synthesis reviews evidence from 2000–2025 to assess how AI–RS technologies optimize agrochemical efficiency. A comprehensive search across Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar were used. Following rigorous screening and quality assessment, 142 studies were selected for detailed analysis. Data extraction focused on sensor platforms (Landsat-8/9, Sentinel-1/2, UAVs), AI approaches (Random Forests, CNNs, Physics-Informed Neural Networks), and operational outcomes. The synthesized data demonstrate that AI–RS systems can predict critical soil attributes, specifically salinity, moisture, and nutrient levels, with 80–97% accuracy in some cases, depending on spectral resolution and algorithm choice. Operational implementations of Variable-Rate Application (VRA) guided by these predictive maps resulted in fertilizer reductions of 15–30%, pesticide use reductions of 20–40%, and improvements in water-use efficiency of 25–40%. In fields with high soil heterogeneity, these precision strategies delivered yield gains of 8–15%. AI–RS technologies have matured from experimental methods into robust tools capable of shifting agrochemical science from reactive, uniform practices to predictive, precise strategies. However, widespread adoption is currently limited by challenges in data standardization, model transferability, and regulatory alignment. Future progress requires the development of interoperable data infrastructures, digital soil twins, and multi-sensor fusion pipelines to position these technologies as central pillars of sustainable agricultural intensification. Full article

Gas sensors are vital tools in areas such as environmental monitoring, industrial safety, and personal healthcare. Among various sensing materials, semiconductor metal oxides (SMOs) are widely studied owing to their high sensitivity, good stability, and notable catalytic activity. To overcome inherent drawbacks of pure SMOs—such as high operating temperatures, limited selectivity, sluggish response/recovery behavior, and inadequate long-term stability—functionalization with noble metals has emerged as a powerful modification strategy. This review systematically outlines the primary mechanisms through which noble metals enhance gas sensing performance and analyzes the key factors influencing sensor behavior. Finally, we discuss the current challenges and future directions in the development of noble metal-modified SMO gas sensors. Full article

Continuous monitoring of seawater nutrients is crucial for marine resource research and conservation, yet it faces challenges due to the constraints of offshore working conditions. We developed a multi-analyte sensor based on flow analysis technology, which integrates wet-chemical colorimetry/fluorometry for the simultaneous in situ determination of nitrite, nitrate, ammonium, silicate, and phosphate in seawater. To mitigate bubble interference, an integrated gas-trapping cavity was designed, and a data-cleaning algorithm based on the interquartile range method was implemented. In June 2025, a sea trial was conducted at two stations in the northern South China Sea, the results of which showed high consistency with laboratory standard methods: the maximum absolute relative errors were 1.79% for nitrite, 5.01% for nitrate, 1.42% for ammonium, 5.93% for phosphate, and 2.95% for silicate. The performance under real marine conditions is demonstrated by relative errors below 6% and linear correlation coefficients exceeding 0.999 for all parameters. This research demonstrates a practical approach for in situ marine observation. Full article

The current research presents the synthesis, characterization, and application of a novel gas sensor based on polypyrrole/strontium titanate (PPy/STO) nanocomposites for the selective detection of CO₂. Utilizing chemical oxidative polymerization, PPy and PPy/STO nanocomposites with varying STO (10–50) wt.% were synthesized and characterized. The structural and morphological analysis confirms the formation of spherical structure and well-dispersed PPy nanoparticles with increasing crystallinity and interaction of STO in PPy chain particle compactness as the STO content increases. The integration of perovskite STO within the conducting polymer matrix enhances the electronic structure, porosity, and surface area of the composite, promoting improved gas sensing performance. Electrical impedance spectroscopy reveals that the composites exhibit a frequency-dependent dielectric response and conduction attributed to charge carrier mobility and interfacial polarization effects. PPy/STO 20% exhibits highest conductivity and dielectric constants of 0.03604 Scm⁻¹ and 1.074 × 10⁴, respectively. Real-time CO₂ sensing experiments conducted at 50 °C demonstrate good sensitivity, stability, and rapid response/recovery characteristics, particularly for the PPy/STO 10% and 40% composites. These findings highlight the potential of PPy/STO nanocomposites as flexible, lightweight, and efficient materials for portable CO₂ gas sensors, addressing the growing needs for environmental and health monitoring. Full article

The long-term reliability of catalytic gas sensors is strongly influenced by changes in the chemical state and cleanliness of the catalyst surface. In this work, we investigate the surface composition and stability of the platinum (Pt) nanoparticle catalytic layer in Gas Metal-Oxide-Semiconductor (GMOS) sensors under varying environmental conditions. Using X-ray Photoelectron Spectroscopy (XPS) and High-Resolution (HR) XPS, we compared fresh, aged samples, thermally treated samples, and samples stored with or without a mechanical filter. The results show that prolonged ambient storage leads to the accumulation of adventitious carbon and nitrogen-containing species, as well as partial oxidation of platinum, which reduces the number of active metallic Pt sites. Thermal treatment at 300 °C for 30 min restores metallic Pt exposure by removing surface contaminants and narrowing the Pt 4f peaks. However, recontamination occurs during subsequent storage, with significant differences depending on surface protection. Sensors equipped with a mechanical filter exhibited obvious Pt metallic peaks in HR-XPS analysis, with lower carbon and nitrogen levels, compared to unprotected samples. These findings demonstrate that while heating refreshes catalytic activity, long-term stability requires complementary filtration to prevent re-adsorption of airborne species. The combined approach of heating and filtration is thus essential to ensure reliable performance of GMOS sensors for indoor and outdoor air quality monitoring. Full article

Monitoring the freshness of perishable foods remains a challenge due to the lack of simple and reliable indicators that can visually reflect chemical and microbial changes. In this study, a colorimetric freshness indicator was developed using bromocresol green (BCG) and bromocresol purple (BCP), two pH-sensitive dyes with complementary transition ranges, to provide a visible and quantitative response corresponding to beef quality during cold storage. Cellulose acetate (CA) films were prepared by incorporating the dyes with different plasticizers—glycerol and polyethylene glycol (PEG 200 and PEG 400)—at varying ratios, resulting in 24 formulations. Based on color stability and sensitivity to trimethylamine (TMA) vapor, two optimized indicators were selected for further packaging tests with beef samples stored at 4 °C. Beef freshness was evaluated by total bacterial count (TBC), total volatile basic nitrogen (TVB-N), and pH, while volatile amines in the headspace were quantified using solid-phase microextraction–gas chromatography–flame ionization detection (SPME–GC–FID). The color difference (ΔE) of the indicators showed strong correlations with TBC and TVB-N, and a threshold of ΔE ≈ 12 provided a practical visual cue corresponding to the microbiological safety limit. The two indicators exhibited complementary functions, with G100-1 acting as an early-warning sensor and G100-2 maintaining contrast at later stages. These findings demonstrate the potential of this dual-indicator system as a simple, non-destructive tool for intelligent packaging applications. Full article

Monitoring nitrogen dioxide (NO₂) in various scenarios is crucial due to its significant environmental impact as a hazardous gas which is emitted by several industrial sectors. This study reports the optimized synthesis of WO₃ flower-like structures using the microwave-assisted hydrothermal method under various experimental conditions, resulting in the optimized sample designated MF-WO₃-K2. Structural, morphological, and chemical characterizations revealed that WO₃ microflowers (MF-WO₃-K2) exhibit a hexagonal crystalline phase, a bandgap of 2.4 eV, and a high specific surface area of 61 m²/g. The gas-sensing performance of WO₃ microflowers was investigated by electrical measurements of six similarly fabricated MF-WO₃-K2 sensors. The MF-WO₃-K2 sensors demonstrated a remarkable sensor signal of 225 for 5 ppm NO₂ at 150 °C and response/recovery times of 14.5/2.4 min, coupled with outstanding selectivity against potential interfering gases such as CO, H₂, C₂H₂, and C₂H₄. Additionally, the sensors achieved a low detection limit of 65 ppb for NO₂ at 150 °C. The exceptional sensing properties of WO₃ microflowers are attributed to the abundance of active sites on the surface, large specific surface area, and the presence of pores in the material that facilitate the diffusion of NO₂ molecules into the structure. Overall, the WO₃ microflowers demonstrate a promising ability to be used as a sensitive layer in high-performance chemiresistive gas sensors due to their high sensor performance and good reproducibility for NO₂ detection. Full article