Search Results (1,837)

Metal oxide nanomaterials tailored at the nanoscale are opening new avenues for advanced electroanalytical sensing devices with enhanced properties, including improved electrocatalytic activity. In this work, a novel Mn₂Mo₃O₈/MnO-MWCNT nanocomposite was employed to modify a screen-printed carbon electrode, enabling the fabrication of an amperometric sensor for H₂O₂ operating at relatively low applied potential due to the catalytic activity of the nanocomposite. Further functionalization of this nanostructured surface with glucose oxidase allowed the construction of an electrochemical glucose biosensor, where the Mn₂Mo₃O₈/MnO-MWCNT material acted as an efficient electrocatalyst for hydrogen peroxide detection. The H₂O₂ sensor exhibited a linear response from 0.06 mM to 3.00 mM, with a sensitivity of (2.22 ± 0.02) µA mM⁻¹ and a detection limit of 22 µM. The glucose biosensor showed a linear response in the range from 0.10 mM to 18.9 mM glucose, with a sensitivity of (0.345 ± 0.005) µA mM⁻¹, and a detection limit of 29 µM. The biosensor displayed excellent selectivity and high stability and was successfully applied to the determination of glucose in lactose-free skimmed milk. Full article

High-sensitivity detection of formaldehyde is critically important for environmental protection and public health. Zinc oxide (ZnO) is a widely used core material for chemiresistive gas sensors; however, its conventional low-index facets suffer from a limited number of active sites, creating a bottleneck for further sensitivity enhancement. To overcome this limitation, this study pioneers the application of the highly reactive ZnO $\left[20\overline{2}1\right]$ high-index polar surface for formaldehyde detection. By leveraging its unique stepped atomic configuration and unprecedented density of coordination-unsaturated active sites, we systematically investigate the formaldehyde adsorption behavior and the underlying sensing mechanism using first-principles calculations based on density functional theory (DFT). The pristine ZnO $\left[20\overline{2}1\right]$ surface exhibits intrinsic metallic character. At a coverage of 1 monolayer (ML), the most stable G1 configuration achieves an adsorption energy of −1.54 eV per CH₂O molecule. Within a 2 × 1 supercell, formaldehyde adopts both associative and dissociative adsorption modes. At a lower coverage, formaldehyde forms a stable bidentate structure through dual C–O and Zn–O bonding interactions. Electronic structure analysis reveals significant orbital hybridization and interfacial charge redistribution upon adsorption. Notably, associative adsorption opens a bandgap of 0.04 eV at the Fermi level, inducing a metal-to-semiconductor transition. In contrast, dissociative adsorption results in pronounced n-type doping, thereby elucidating the microscopic origin of the resistivity decrease observed in ZnO-based sensors. Overall, this work highlights the structural advantages of high-index facets and demonstrates for the first time the superior formaldehyde adsorption capability of the ZnO $\left[20\overline{2}1\right]$ facet, providing robust theoretical guidance for the rational design of next-generation, high-performance gas-sensing materials. Full article

Efficient monitoring of pork freshness is essential to minimize spoilage-related losses in the meat industry. To address the limitations of existing detection technologies, namely high cost, poor timeliness and high environmental sensitivity, this study developed a novel electronic nose system integrating a hybrid sensor array with dynamic gas path control. By combining metal oxide semiconductor (MOS) and electrochemical sensors (e.g., MQ137, MQ136), the system exhibits high sensitivity to the key volatile organic compounds (VOCs) released during pork spoilage, achieving a detection accuracy of over 90% in identifying spoilage stages. Combined with a dual-mode gas circuit design (solenoid valve switching time: 0.85 s), the reliability of the system was further demonstrated. This technology offers an economical and efficient real-time monitoring solution for slaughterhouses and cold chain logistics, providing a new low-cost scientific approach for pork freshness assessment. Full article

Black tea quality is governed by aroma chemistry: terpene alcohols (linalool, geraniol, nerolidol), methyl salicylate, and short-chain aldehydes whose abundance and release kinetics from the polyphenol-rich leaf matrix shape perceived grade. Grade information lies not only in the average headspace concentration but in the temporal shape of volatile organic compound (VOC) release under controlled heating. Conventional electronic noses obscure this signal: they rely on multi-sensor arrays, compress each response into summary statistics, and report accuracy only at the level of individual measurements. Whether a single low-cost metal–oxide–semiconductor (MOS) gas sensor can recover grade-defining aroma chemistry, and whether waveform-level modeling can exploit it, was therefore investigated. A portable electronic nose built around a Bosch BME688 sensor recorded 90 time series, each comprising four directly measured channels (temperature, humidity, pressure, gas sensor resistance) and a derived indoor-air-quality (IAQ) proxy computed from them by the on-chip BSEC library, from 16 commercial Turkish black teas across three quality grades. Two representations were compared on the same data: a feature-based pipeline reducing 25 statistical descriptors to seven principal components for six classifiers (best F1-macro = 0.624, MLP), and a raw-waveform Multi-Scale 1D-CNN with Squeeze–Excitation and temporal self-attention (MS-CNN-Attention). Under product-grouped cross-validation, the deep model reached F1-macro = 0.811 (+30%) and graded 14 of 16 products correctly by majority vote, against 11 of 16 for the MLP, with the largest gain in the medium grade (F1: 0.52 → 0.79), where summary-statistic compression destroys the release-kinetic signal. The contributions are threefold: one programmable MOS sensor operated as a thermal-desorption profiler rather than a sensor array; a direct comparison of feature-based classical learning against raw-waveform deep learning on the same small, non-normally distributed dataset; and a product-level decision-consistency metric suited to batch screening. Pairing a low-cost MOS sensor with waveform-level modeling offers a rapid, non-destructive route to aroma-chemistry-based tea quality screening. Full article

Metal oxide semiconductor (MOS) gas sensors are an important part of electronic nose technology because they are sensitive, cheap, and work well with microfabrication for system integration. But sensor drift makes them less useful for long-term, continuous gas monitoring. Changes in how sensors respond over time make pattern recognition models that were trained at first less accurate. This review looks at new ways to deal with sensor drift, with a focus on transfer learning and deep learning methods that have been developing continuously in the last five years. It emphasizes the shift from conventional recalibration and component correction to sophisticated methodologies, including deep domain adaptation, contrastive representation learning, and attention-based models. The review does not just list these methods; it also analyzes their pros and downsides, especially in situations where there is not much labeled data, drift is hard to anticipate, or the computational resources are limited, which is often the case with edge sensors. Full article

This article introduces the current status of the 0.18-micron CMOS MEMS foundry service platform provided by the Taiwan Semiconductor Research Institute (TSRI), extensively covering the CMOS MEMS components that it has supported in development and fabrication. It also attempts to expand the foundry service scope to the broader categories of capacitive sensors and electrostatic actuators. On the one hand, for fabless MEMS component designers, TSRI currently directly allows the design of two types of components: flow sensors with uniformly perforated membranes and actuators with comb-shaped interdigital electrodes. This service also includes tape-out and wire bonding packaging procedures, following procedures similar to those used by general IC designers. On the other hand, this article specifically presents a clear and feasible approach for MEMS designers equipped with simple wet-etching facilities and a clear and feasible approach to develop further CMOS MEMS components such as capacitive pressure sensors, accelerometers, micro mirrors, and scratch drive actuators with minimal post-processing and chip packaging steps. This work provides a practical CMOS-MEMS design and post-processing guideline for extending the current TSRI foundry platform toward capacitive sensing and electrostatic actuation applications with minimal additional fabrication complexity. Full article

Chromic materials exhibiting reversible changes in optical properties under external stimuli represent an important class of smart materials with applications in smart windows, sensors, and optoelectronic devices. Transition-metal oxides (TMOs) provide a versatile platform for chromic functionality due to their coupled structural, electronic, and optical properties. In this review, the chromic response of selected TMO thin films is analyzed using both microscopic and phenomenological approaches. The microscopic description is based on many-body theory, including Green’s function methods and correlation effects, while the macroscopic optical response is described using Drude–Lorentz and Tauc–Lorentz models within the effective medium approximation. Chromic behavior in TMOs is shown to originate from two principal mechanisms: (i) electronic and structural reconstruction driven by Peierls–Mott metal–insulator phase transitions, leading to thermochromism (notably in VO₂ and V₂O₃), and (ii) formation of localized states driven by small-polaron injection, giving rise to electrochromism, gasochromism, and photochromism. The models are applied to representative systems, including VO₂, WO₃, NiO, and TiO₂, demonstrating the chromic changes in the dielectric function spectra. These results highlight chromism in TMOs as a multiscale phenomenon linking microscopic interactions with macroscopic optical response. Full article

Hydrogen is a promising clean-energy carrier, but its low ignition energy, high diffusivity, and wide flammability range demand reliable leak detection. Chemiresistive sensors based on n-type metal oxide semiconductors are attractive owing to their simple architecture, low cost, large resistance modulation, thermal robustness, and compatibility with miniaturized devices. This review focuses on n-type metal oxide semiconductor nanomaterials for hydrogen sensing, particularly ZnO, SnO₂, In₂O₃, WO₃, TiO₂, and related mixed oxides. The fundamental sensing mechanisms are examined, including oxygen chemisorption, electron-depletion-layer modulation, grain-boundary barrier control, catalytic hydrogen spillover, and hydrogen-induced surface reduction or metallization, together with the way these mechanisms compete and cooperate under different operating conditions. Recent performance-enhancement strategies are organized around morphology and porosity control, noble-metal sensitization, defect and dopant engineering, n–n heterojunctions, molecular sieving, and low-temperature activation. Density functional theory is discussed as a design tool for evaluating adsorption energetics, vacancy formation, work-function shifts, band alignment, and interfacial charge transfer, along with its current limitations for modeling humid surfaces. Finally, key challenges and future directions, including humidity tolerance, standardized reporting, device integration, and emerging materials, are summarized to guide the development of high-performance hydrogen sensors. Full article

Conventional photodetectors and image sensors deliver high-fidelity digital outputs but face a growing data-movement bottleneck: the energy and latency cost of transferring raw pixel streams to off-chip memory and processors increasingly dominates over both sensing and computation in modern machine-vision pipelines. An emerging response is the neuromorphic photodetector, a class of optoelectronic device that converts incident light into an electrical signal while simultaneously storing, modulating, and pre-processing that signal in a manner inspired by biological synapses and retinas. Over the past decade, demonstrations have spanned at least eight material platforms—organic semiconductors, organic–carbon-nanotube hybrids, perovskite and perovskite hybrids, metal oxides (including ultra-wide-bandgap and printable variants), wide-bandgap III-nitrides and 4H-SiC, two-dimensional materials, photo-memristors, and silicon CMOS in-sensor compute architectures—and have been realised through four distinct architectural families: phototransistor synapses, photo-memristors, heterojunction in-sensor compute, and linear photovoltaic neural networks. Here, we provide a quantitative cross-platform benchmark across forty in-scope articles, identify persistent photoconductivity as a near-universal device-physical substrate underlying synaptic functionality, characterise the responsivity–speed–energy trade-off structure observed across platforms, and present a critical assessment of energy-reporting practice in the field. We further identify three best-practice exemplars from three independent material platforms that converge on operating biases of 0.01–0.1 V and energies of 0.07–0.8 fJ per event, and we propose a unified reporting framework to enable meaningful cross-platform benchmarking of next-generation neuromorphic photodetectors. Full article

The use of insect-based protein sources in aquaculture is gaining increasing attention with Hermetia illucens (black soldier fly, BSF) larvae meal representing a promising substitute to fishmeal (FM). This study evaluated the effect of partial dietary inclusion of BSF meal (BSF0, BSF2.5, BSF5, BSF10%) on the volatilome of rainbow trout (Oncorhynchus mykiss) fillets, before and after cooking, using gas chromatography–mass spectrometry (GC–MS) and a metal oxide sensor-(MOX)-based device. Fish were fed diets with increasing BSF inclusion, and both raw and cooked fillets were analyzed to assess changes in volatile organic compounds (VOCs). GC–MS enabled the identification and semi-quantitative analysis of VOC classes, while MOX sensor responses were processed using Linear Discriminant Analysis (LDA) to assess discrimination among dietary treatments. Results showed that BSF inclusion influenced the volatile profile, with clearer separation at higher inclusion levels (BSF5–BSF10%), especially in cooked fillets. Thermal processing enhanced these differences. GC–MS analysis revealed a reduction in aldehydes and ketones and an increase in carboxylic acids with higher BSF inclusion. Key compounds such as hexanal and heptanal decreased, indicating changes in lipid-derived volatile pathways. Overall, the integration of GC–MS and MOX sensors proved effective in detecting diet-induced changes, supporting their application as effective and reliable tools for quality assessment in aquaculture products, with potential implications for sensory quality that should be further confirmed through dedicated sensory studies. Full article

Triethylamine (TEA), a widely used volatile organic compound (VOC), poses severe threats to environmental safety and human health upon accidental leakage, making the development of high-performance TEA detection techniques urgently needed. Herein, we report a Sn-based metal–organic framework (Sn-MOF) constructed from 4,5-dichloroimidazole ligands synthesized via a solvothermal approach. The resulting MOF-derived SnO₂ materials were obtained by calcination at 400–600 °C, yielding SnO₂ with tunable specific surface area and surface defect-site density. Structural and surface characterizations revealed that the materials consist of primary nanoparticles in the range of 10–50 nm, forming aggregated particles of 1–2 µm. The gas sensing performance toward TEA was systematically evaluated. The SnO₂-400 °C sensor exhibited the highest response (S = 85.0) to 100 ppm TEA at 190 °C, with a low detection limit of 1 ppm, superior selectivity, good repeatability, and excellent long-term stability. The observed performance variation was attributed to the combined effects of specific surface area, abundant defect-associated surface sites, and suitable mesoporous structure. This work not only provides a high-performance TEA sensor for industrial and food safety monitoring but also offers a rational strategy for designing MOF-derived metal oxide gas sensors with tailored microstructures and surface defect chemistry. Full article

The explosive demand for flexible wearable and portable devices imposes stringent requirements on the mechanical, energy storage, and sensing properties of functional materials. Although two-dimensional (2D) transition metal carbides and nitrides (MXene) possess high conductivity and pseudocapacitance, their severe self-restacking and intrinsic brittleness restrict their practical applications. Herein, a facile vacuum filtration and hot-pressing densification strategy is proposed to fabricate nacre-inspired MXene-based films. By incorporating one-dimensional (1D) high-aspect-ratio TEMPO-oxidized cellulose nanofibrils (TOCNFs), the self-restacking of MXene is effectively suppressed. The optimal M20F5 composite film exhibits a coordinated electromechanical balance, maintaining an electrical conductivity of 1.07 × 10⁶ S m⁻¹ while enduring 2124 folding cycles. For energy storage, the assembled symmetric supercapacitor delivers a specific capacitance of 828.92 F g⁻¹ at 0.5 mA cm⁻² and maintains an energy density of 13.75 Wh kg⁻¹ at a power density of 9500 W kg⁻¹. Furthermore, acting as a piezoresistive sensor, the film achieves reliable detection, spanning from bimodal gait recognition to subtle physiological pulses. This work establishes a viable material design strategy for next-generation supercapacitors and intelligent wearable systems. Full article

Carbon/inorganic hybrid composites have evolved from filler-reinforced materials into design platforms for coupled electromagnetic, thermal, sensing, environmental, protective and energy-related functions. Their distinctive value lies in the possibility of combining a conductive, polarizable or porous carbon phase with an inorganic phase that contributes dielectric, magnetic, catalytic, ionic, thermally conductive or barrier behavior. This review examines carbon/inorganic hybrid multifunctional composites from the viewpoint of structure–property relationships, with emphasis on interfacial design, percolation, anisotropy, hierarchical architecture, processing and metrology. Selected graphitic composite studies are discussed as case studies for broadband dielectric spectroscopy, microwave shielding, high-frequency contact metrology, thermal diffusivity analysis and impedance-monitored graphene filters; these case studies are integrated with the broader international literature on CNT and graphene polymer composites, MXene films and foams, graphene/metal oxide photocatalysts, boron nitride/carbon thermal networks, biochar–graphene adsorbents, smart coatings, sensors, supercapacitors and water remediation systems. The central argument is that credible multifunctionality requires more than measuring several properties on the same material. It requires simultaneous or service-relevant co-optimization under constraints of thickness, density, processability, aging, humidity, corrosive media, regeneration, toxicity, economic feasibility and scalable fabrication. The review concludes with design rules and reporting recommendations intended to help move the field from impressive property demonstrations toward application-ready hybrid material systems. Full article

Non-invasive blood glucose estimation from exhaled breath has been proposed as a painless alternative to repeated capillary measurements; however, performance evaluation remains challenging in small-sample settings. This study investigates the estimation of blood glucose from human breath using volatile organic compound (VOC) signals acquired with an electronic nose. Responses from three metal-oxide sensor channels sensitive to CO, alcohol, and acetone were collected from 58 individuals, with one measurement per subject, and analyzed using strictly patient-level five-fold cross-validation, in which test folds comprised only real subjects. Two experimental factors were examined. First, model performance was evaluated with and without an additional interpretable alcohol–acetone log-ratio capturing relative variation between compounds. Second, model training was performed using either real data only or fold-wise tabular synthetic augmentation generated via a Gaussian copula fitted exclusively on training subjects, while evaluation remained strictly real-only. Under real-only training, classical machine learning models achieved the lowest prediction errors (approximately 6–7 mg/dL), whereas under synthetic augmentation FTTransformer was the best-performing deep learning model. This findings should be understood as a constrained proof-of-concept analysis rather than as evidence of diagnostic capability or clinical readiness. Full article

Real-time online monitoring of key parameters such as the ammonium nitrogen (NH₄⁺) concentration during biological fermentation is important for the optimization of a biological fermentation process. Traditional offline detection technologies like spectrophotometry need contact sampling, with drawbacks of monitoring lag, risk of contamination, etc. In this work, taking the gentamicin fermentation process as an example, we developed an intelligent electronic nose non-contact monitoring system on the basis of fermentation off-gas signals. We captured the typical signals of off-gas and established a quantitative relationship between the signals and the NH₄⁺ concentration in fermentation broth; the system then realized non-contact real-time monitoring. The whole system consists of a gas-switching module, a sensor array module, a signal-processing module, and an intelligent prediction module. The system adopts a five-phase gas switching strategy to suppress sensor drift in metal–oxide–semiconductor (MOS) sensors and a light neural network for prediction, which improve both prediction speed and accuracy. Experiments were conducted using a 5 L fermenter, and the prediction result was consistent with the offline measured value (coefficient of determination, R² = 0.9871, root mean square error, RMSE = 0.0317 g/L). This technique provides a new method for the non-contact measurement of key fermentation parameters, and it can be expanded to other fermentations. Full article