Search Results (494)

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

A silk fibroin/cellulose inverse-opal photonic crystal composite with robust mechanical properties was fabricated by blending a silk fibroin solution with methylcellulose, utilizing a 3D poly(methyl methacrylate) (PMMA) photonic crystal array as a template, via sequential infiltration, curing, and etching processes. Leveraging the intrinsic water sensitivity of both silk fibroin and methylcellulose, the resulting composite exhibits exceptional moisture-sensing capabilities across gaseous, liquid, and solid phases. Specifically, for atmospheric humidity, the film delivers a distinct optical response to a relative humidity variation in merely 5%. In liquid systems, owing to the material’s excellent affinity for low-polarity organic solvents and the disruptive effect of highly polar solvents (e.g., water) on the photonic periodic structure, the structural color of the film can sensitively report trace water contents down to 0.025%. Furthermore, in solid matrices, the composite enables the precise detection of not only free water but also water of crystallization. Full article

Conductive hydrogel strain sensors using poly(3,4-ethylenedioxythiophene) (PEDOT) as fillers are rapidly advancing and are emerging as candidates for monitoring devices such as wearable electronic skin. However, due to limitations such as low elongation and high hysteresis, it is difficult to fully leverage its promising sensor properties in practical applications. In this study, we synthesized flake-like PEDOT particles (FP particles) and used Polyacrylamide (PAM) as the hydrogel matrix to fabricate a conductive hydrogel strain sensor. These particles were obtained by grinding PEDOT particles prepared via a template-free method. After swelling with ethylene glycol (EG) and assembly with polyvinyl alcohol (PVA), the FP particles become porous and contain many hydroxyl groups. This design enables the adsorption of acrylamide (AM) monomers within FP particles, facilitating the in situ polymerization of PAM onto the PEDOT/PVA chains, thereby yielding a dual-network structure with strong entanglements. This gives the sensor high elongation and very low hysteresis. In addition, it offers favorable sensor performance, including high sensitivity, high repeatability, and reliability. This strain sensor can be used in wearable electronic skin applications for facial monitoring and motion detection. 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

Chirality is a cornerstone of biological systems and pharmaceutical activity, driving a critical need for rapid and sensitive enantioselective analytical methods. Covalent organic frameworks (COFs) have emerged as versatile porous materials, and their chiral counterparts, chiral COFs (CCOFs), uniquely combine high surface area, pre-designable pores, and a confined chiral microenvironment, making them exceptional platforms for enantioselective fluorescence sensing. This review systematically summarizes recent advances in the construction and application of CCOFs for enantioselective fluorescence sensing. We first outline the primary synthetic strategies for CCOFs, including direct synthesis, post-synthetic modification, and chiral induction. Subsequently, based on the direction of fluorescence signal change upon analyte binding, we classify the sensing mechanisms into three categories: “turn-off” (quenching via static complexation or photoinduced electron transfer), “turn-on” (enhancement through rigidification or suppression of electron transfer), and ratiometric (self-calibrating dual-emission response). Representative examples for the detection of amino acids, amino alcohols, terpenes, and saccharides are highlighted for each mode. Special emphasis is placed on structure–property relationships, such as the synergistic roles of hydrogen bonding, π–π stacking, and framework confinement in amplifying enantioselectivity. Finally, we discuss current challenges and future perspectives, including the rational design of ratiometric sensors, integration into practical devices, and the convergence with machine learning to advance the field of smart chiral sensing. Full article

Polyvinyl alcohol (PVA)-based hydrogels suffer from insufficient mechanical strength, while aramid nanofibers (ANF) have intrinsic insulation that limits their sensing applications, and the synergistic effect of composite fillers remains underexplored. This study aims to develop a multifunctional PVA/ANF/NaCl composite organohydrogel for high-performance flexible sensors. The gel was fabricated via freeze–thaw crosslinking, solvent exchange and NaCl impregnation, with systematic investigations of its microstructure, mechanical, electrical and multifunctional sensing properties, and a corresponding triboelectric nanogenerator (TENG) and self-powered handwriting recognition system were constructed. Results show that 2% ANF significantly enhances the gel’s mechanical performance, 0.5 M NaCl achieves optimal mechanical-electrical balance, the gel-based sensor exhibits excellent distance, pressure and strain sensing with high cyclic stability, the TENG delivers stable electrical output, and the recognition system achieves 95% accuracy on the test set. This work provides a new material and design strategy for advanced flexible electronic devices. Full article

Conductive hydrogels are functional materials that combine soft, highly hydrated properties with electrical signal transmission capabilities. Their conductivity arises from ionic or electronic pathways, and the key design challenge is achieving good conductivity and long-term stability without compromising mechanical performance and biocompatibility. Among various conductive components, conductive polymers have attracted considerable attention due to their tunable mechanical properties, high electrical conductivity, good biocompatibility, and facile synthesis routes. In this study, a series of conductive hydrogels were rationally designed and fabricated by copolymerizing acrylamide and N,N′-methylenebisacrylamide with functionalized poly(vinyl alcohol) (PVA) and poly(3,4-ethylenedioxythiophene-co-thiophene) [P(EDOT-co-Th)]. The functionalized PVA provided multiple dynamic hydrogen-bonding sites, significantly enhancing the toughness of the hydrogel and its adhesion to various substrates, while the P(EDOT-co-Th) copolymer imparted good and stable electrical conductivity. By systematically adjusting the amount of functionalized PVA, the mechanical strength, adhesiveness, and durability of the conductive hydrogels were effectively optimized. The optimized hydrogel exhibited robust adhesion to a wide range of surfaces, excellent fatigue resistance, and long-term stability under repeated mechanical deformation. Moreover, the combination of mechanical resilience and good conductivity enabled precise and reliable signal transduction, highlighting its strong potential as a next-generation material for wearable strain and pressure sensors. Full article

The reliable identification of impairment relevant to safety-critical activities remains a significant challenge for public safety, motivating the exploration of unobtrusive and widely accessible sensing technologies. This study examines the viability of utilising inertial data acquired from consumer-grade smartphones to characterise gait disturbances associated with simulated visual impairment. The study simulates intoxication-related effects using alcohol-impairment goggles and does not involve the measurement of real alcohol intoxication. Two supervised experimental protocols were conducted in which participants traversed predefined walking routes under normal conditions and while wearing alcohol-impairment simulation goggles representing five manufacturer-declared blood alcohol concentration (BAC)-related goggle conditions plus a no-goggles control condition. An initial indoor trial, conducted in a structured corridor environment, yielded limited discrimination of gait dynamics due to strong spatial and visual stabilisation cues. To address this limitation, a subsequent outdoor experiment was conducted along a 100 m path lacking prominent visual reference points, resulting in motion patterns that more closely reflect unconstrained, real-world locomotion. Tri-axial accelerometer and gyroscope signals were recorded using smartphones, followed by artefact removal, segmentation, and standardisation to ensure inter-trial comparability. The resulting curated dataset comprises 290,919 multi-channel samples derived from 96 walking trials involving 16 participants and is released as an openly accessible resource to support further research in gait analysis and classification of gait alterations associated with simulated impairment. Model evaluation was performed using an 80/20 train–test split conducted within each traversal, with training and test windows originating from the same participant and walking session. Consequently, the reported results reflect within-subject performance instead of subject-independent generalisation. Multiple deep learning architectures combining convolutional feature extraction, bidirectional long short-term memory layers, and self-attention mechanisms were systematically evaluated. Using a subject-dependent evaluation protocol, the best-performing architecture achieved an accuracy of 71.4% and a weighted F1-score of 71.5% in distinguishing gait patterns associated with different levels of simulated visual impairment. The best-performing architectures yielded classification performance consistent with exploratory, low-stakes assessment of gait alterations associated with simulated visual impairment, using accelerometer data alone. These findings illustrate the feasibility of using smartphones as auxiliary tools for exploratory, low-stakes screening or educational applications and contribute a publicly released dataset and benchmark results to facilitate methodological advancement in inertial sensor-based gait impairment analysis. Full article

A reflective plasmonic humidity sensor based on an Au–PVA–Au nanohole sandwich structure is investigated. The device consists of a periodic gold nanohole array, a poly(vinyl alcohol) (PVA) spacer, and a continuous gold film. A humidity-dependent model considering both the refractive-index decrease and thickness swelling of PVA is established to analyze the optical response and resonance-modulation mechanism. Within the relative humidity range of 20–98%RH, the reflection resonance dip exhibits a continuous blueshift with a total wavelength shift of approximately 135 nm. Piecewise linear fitting shows sensitivities of 1.3857 nm/%RH in the 20–74%RH range and 2.5000 nm/%RH in the 74–98%RH range. At approximately 74%RH, the resonance wavelength, full width at half maximum, and quality factor are about 830 nm, 19 nm, and 43.7, respectively. Decoupling analysis confirms that both PVA refractive-index reduction and thickness swelling contribute to the blueshift, while their combined effect produces the largest response. These results demonstrate that the proposed structure converts humidity-induced optical and geometric variations in PVA into a pronounced wavelength response, providing a mechanism-guided design route for reflective nanoplasmonic humidity sensors based on polymer-assisted cavity modulation. Full article

Exhaled breath analysis offers a non-invasive route for metabolic monitoring and disease screening, but its practical implementation requires sensing platforms that combine high sensitivity, robustness, and simplicity. Here, we report an evanescent wave-excited fiber-optic surface-enhanced Raman scattering (SERS) sensor based on silver nanocubes (Ag NCs) assembled onto a fiber taper waist (FTW), and the design is further extended to an Ag/graphene oxide (GO) hybrid interface for enhanced gas detection. Finite element and finite-difference time-domain simulations were employed to optimize the FTW geometry and Ag NC dimensions for efficient evanescent-field excitation and plasmonic enhancement. The fabricated FTW-SERS probe achieved a minimum detectable concentration of 10⁻⁹ M for crystal violet, together with good linearity and a relative standard deviation below 5%. For gas sensing, ethanol and acetone vapors were detected down to 50 ppm using the Ag NC-based FTW-SERS probe. After introducing a 0.3 mg/mL GO functional layer, the minimum detectable concentrations of both analytes were further reduced to 25 ppm. In addition, proof-of-concept monitoring of exhaled ethanol after alcohol consumption revealed dynamic spectral changes consistent with ethanol metabolism. These results demonstrate the potential of evanescent wave-excited FTW-SERS probes for compact and sensitive breath-analysis applications. Full article

This study presents a headset-type biofluorometric gas sensor incorporating a CMOS camera for continuous, non-invasive monitoring of transcutaneous ethanol from the ear canal. The sensor employs alcohol dehydrogenase (ADH) to catalyze the NAD⁺-to-NADH conversion during ethanol oxidation, enabling quantitative measurement through NADH fluorescence detection (λ_ex = 340 nm, λ_em = 490 nm). The integrated system comprises a wireless CMOS camera, an ADH-immobilized cotton mesh enzyme membrane, UV-LED excitation source, optical bandpass filters, and a dual convex lens assembly housed in a 3D-printed headset powered by a lithium battery. Key improvements include a 3.5-fold enhancement in fluorescence collection efficiency achieved through optimized dual convex lens configuration. Systematic screening of seven cotton mesh materials identified Iwatsuki cotton mesh as the optimal enzyme immobilization substrate, exhibiting minimal autofluorescence and 14.2-fold higher water retention capacity compared to H-PTFE membranes. The glutaraldehyde-crosslinked ADH-immobilized cotton mesh maintained enzymatic activity for over 45 min with a 10-fold improvement in signal-to-noise ratio. The system demonstrated a dynamic detection range spanning 10 ppb to 10 ppm for gaseous ethanol and exhibited high selectivity against interfering volatile organic compounds in skin gas, including methanol, acetaldehyde, formaldehyde, and acetone. Human experiments validated the system’s practical performance. Following alcohol consumption, subjects wore the device for 50 min while real-time fluorescence monitoring captured dynamic ethanol concentration changes in the ear canal. The dose-dependent fluorescence response—approximately 2-fold higher at 0.4 g/kg versus 0.04 g/kg alcohol intake—correlated well with calibration data. This headset-type biofluorometric sensor enables unrestrained continuous monitoring of ear canal ethanol, providing a novel wearable platform for alcohol metabolism assessment with potential applications in health monitoring and clinical research. Full article

This study demonstrates a molecular size-dependent strategy to regulate the network structure of poly(vinyl alcohol) (PVA) hydrogels using a series of saccharides with increasing molecular size—glucose, maltose, raffinose, soluble starch, and amylose. FTIR, XPS, XRD, and TG analyses reveal that increasing saccharide size shifts the network from plasticization to reinforcement, which is further confirmed by mechanical testing and rheological analysis. Small-molecule saccharides disrupt hydrogen bonds and enhance chain mobility, while macromolecular starches promote network regularity through strong hydrogen bonding and crystallization induction. This structural tunability ndows the resulting hydrogels with integrated functionalities: tensile strain increases from 640% to 1500%, self-healing efficiency reaches up to 90.6%, and high-fidelity electrocardiogram (ECG) signal acquisition is achieved with a signal-to-noise ratio of 39.84 dB, comparing favorably with commercial electrodes. This work establishes a structure–property relationship linking saccharide molecular size to network architecture and provides a versatile material platform for next-generation flexible wearable sensors and bioelectrodes. Full article

Despite many efforts to curtail drunk driving, alcohol-related traffic fatalities and injuries continue to be a major public health problem in the United States (U.S.) and most of the world. Technologies exist that prevent an automobile from starting if the driver’s breath alcohol exceeds 20 milligrams per deciliter (mg/dL), but these devices are only fitted to vehicles of individuals who have been convicted of Driving Under the Influence (DUI). A new approach must be taken to reduce the incidence of drunk driving by integrating an alcohol sensor system in vehicles as part of the delivered hardware. The system must be fast, accurate, and contactless—meaning that a forced exhalation is not required to measure the concentration of alcohol on the breath. We report on a novel device, the Driver Alcohol Detection System for Safety (DADSS) Breath Alcohol Sensor System, which uses the mid-infrared region of the electromagnetic spectrum to concurrently monitor alcohol and expired carbon dioxide (CO₂) to accurately quantify the breath alcohol concentration in samples that have been diluted in the atmosphere before being measured. The system was validated in a research laboratory with 70 male and female volunteers in 187 individual study days. Participants were given various doses of alcohol to consume and then breath and blood samples were collected simultaneously. Pearson correlation coefficients between the DADSS Breath Alcohol Sensor system and blood samples indicate a strong correlation between the measures, with an overall Pearson correlation of 0.8875 over an alcohol concentration range of 0–220 mg/dL. These results indicate that incorporating the DADSS system into motor vehicles has the potential to reduce the incidence of drunk driving. Full article

This study presents an E-nose system for the identification and classification of volatile compounds in traditional Filipino alcoholic beverages, Basi, Bignay, Lambanog, and Tapuy. The system utilizes a gas sensor array composed of MQ3, MQ6, MQ8, MQ135, and MQ136 sensors, and implements a Support Vector Machine (SVM) algorithm with principal component analysis for classification and dimensionality reduction. The experimental process involves three main phases: absorption, data acquisition, and desorption. A total of 225 training samples per class and a total of 20 testing samples were used, evenly distributed among all classes. The SVM model achieved an accuracy of 85%, highlighting its effectiveness in distinguishing between the beverages. This work contributes to the advancement of low-cost, sensor-based solutions for quality control, standardization, and the cultural preservation of traditional Filipino wines. Full article

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article