Search Results (7,445)

Precise regulation of the postharvest storage environment is critical for reducing losses and maintaining potato quality. Semi-underground storage facilities are widely used in major potato-producing regions of northern China; however, pronounced spatiotemporal heterogeneity in the internal temperature field often leads to localized quality deterioration. To enable accurate sensing and proactive prediction of temperature dynamics in such facilities, this study investigated a typical semi-underground potato storage cellar in Wuchuan County, Inner Mongolia. A high-density sensor network was deployed to collect temperature data, and the spatiotemporal variation patterns of the internal temperature field were systematically analyzed. The results indicate that, at the same vertical height, spatial temperature gradually increases from the entrance toward the interior of the cellar. Both the maximum and minimum temperatures in the entrance zone are lower than those in other regions, while the highest temperatures are observed near the rear wall. Based on the collected data, hierarchical clustering was employed to partition the internal temperature field into three spatiotemporal pattern clusters with significant differences. Key representative monitoring locations were then identified using the Spearman correlation coefficient. An AdaBoost-based prediction model was subsequently developed to estimate the temperatures at other test locations within each cluster using measurements from the representative points. The results demonstrate that the proposed model maintains high prediction accuracy while substantially reducing dependence on a dense sensor network. The overall MAE ranges from 0.075 to 0.373 °C, and the sensor reduction ratio reaches 87%. This approach provides a paradigm for low-cost intelligent monitoring and offers theoretical support and decision-making guidance for the smart regulation of potato storage environments. By optimizing the monitoring of potato storage environments, this study can reduce monitoring system costs and resource consumption, providing technical support for building a sustainable potato supply chain and delivering significant economic benefits in promoting the development of a resource-conserving potato industry. Full article

This study presents the development of a low-cost H₂ gas sensor made from a titanium dioxide–zinc oxide composite by means of a simple, cost-effective screen-printing method. The sensing material was created by mixing titanium dioxide and zinc oxide nanoparticles with an organic binder, which was screen-printed onto a glass substrate containing silver electrodes. These samples were then characterized using X-ray diffraction (XRD) and field-emission scanning electron microscopy (FESEM). The XRD results confirmed that the films boasted well-defined crystallinity, with predominant anatase and hexagonal ZnO phases, as well as uniformity of grains. Sensor performance was evaluated in a custom-built chamber at hydrogen concentrations of 100 to 1000 ppm and at operating temperatures of 100 °C, 200 °C, and 300 °C. The results indicate improved sensor performance as the operating temperature increased to 300 °C, with the best sensitivity values of 0.99, 1.17, and 1.31 at hydrogen concentrations of 100, 500, and 1000 ppm, respectively. The sensor showed stable and reproducible response characteristics, and its responses were retimed after a few hundred seconds. Low-cost fabrication, ease of processing, and reliable sensor performance make titanium oxide–zinc oxide composites promising candidates for hydrogen detection. Full article

Textile materials represent a versatile class of engineering substrates widely used in apparel, domestic products, and medical protective systems. Despite their extensive application, large-scale textile production has seen limited integration of fundamentally new functionalization strategies. In recent years, however, advances in materials science have enabled the development of textiles with tailored electrical, adaptive, and biological functionalities. This review summarizes recent progress in the functionalization of textile materials with a focus on approaches relevant to engineering and industrial implementation. Particular attention is given to conductive textiles designed for operation under extreme environmental conditions, including low-temperature climates. Methods for integrating electrically conductive elements into fibrous structures are discussed, highlighting their potential for sensing, thermal regulation, and energy-related applications such as powering portable electronic devices. Inkjet printing is presented as a scalable technique for high-resolution deposition of conductive patterns while preserving the mechanical integrity and aesthetic properties of textile substrates. In addition, adaptive and stimuli-responsive textile systems are reviewed, including materials capable of responding to thermal, optical, or chemical stimuli, with applications in camouflage, wearable systems, and multifunctional surfaces. The review further addresses the development of bioactive textiles, emphasizing antibacterial functionalization using organic and inorganic agents to mitigate the spread of pathogenic microorganisms. The relevance of such materials has been underscored by recent global viral outbreaks. Overall, this work aims to provide a materials science perspective on emerging textile functionalization strategies and to facilitate the transition of these technologies from laboratory-scale research to practical engineering applications. Full article

High-resolution long-wave infrared imaging is critical for lunar mineralogy. However, it must balance a large FOV, a small F-number, chromatic aberration correction, optical efficiency, and system compactness. We introduce a push-broom multispectral imager employing a collaborative integrated filter array and an off-axis two-mirror Gregorian telescope. The system, utilizing an uncooled Vanadium Oxide detector, has an F-number of 1.0, an IFOV of 0.04943 mrad, and a 2.90° × 2.83° FOV that covers eight bands ranging between 7.38 and 14.3 μm. Optical simulation confirms that the modulation transfer function exceeds 0.25 at the Nyquist frequency of 42 lp/mm, with a maximum RMS spot radius of less than 12 μm. The system has remarkable versatility within an operating temperature range of 0 °C to 40 °C. Thermal background radiation analysis, stray light analysis, and detection sensitivity were conducted, which indicated that the system has good compliance with indicators and engineering feasibility. This high-throughput optical design meets the rigorous criteria for lunar remote sensing and provides a reliable device for site evaluation in future manned lunar missions. Full article

Under the global trend of population aging, providing a safe and reliable living environment for the elderly who live at home has become a major social issue. This study reports a monitoring technology for elderly-friendly residential buildings based on distributed acoustic sensing (DAS) and distributed temperature sensing (DTS), which is used to monitor and identify the physical behaviors of residents and temperature changes at different locations in the space. The results show that the distributed acoustic sensing (DAS) system can initially identify typical behavioral states such as walking, squatting, and falling. The fiber DTS technology can not only monitor the temperature distribution at different locations indoors, but also be used for the monitoring and early warning of local fires in different areas of the room. The sensing probes of the monitoring system proposed in this paper are linear optical cables, which have the advantages of easy installation, strong anti-interference ability, intrinsic explosion-proof, less likely to leak residents’ privacy, all-weather operation, precise event location, and low cost for large-scale distributed measurement systems. By integrating the sensing optical cables, fiber signal processing systems, and application software introduced in this paper, an intelligent management and early warning platform for elderly-friendly residential buildings can be established, providing a new solution for remote supervision of the living safety of the elderly. Full article

Compaction quality governs asphalt pavement durability, but conventional density checks are intermittent. Reliable compaction control of asphalt mixtures requires real-time information on internal responses rather than relying solely on endpoint density measurements. In this study, an embedded smart-particle framework is developed for in situ monitoring and index-based evaluation of vibratory compaction quality, integrating multi-source sensing, feature extraction, and compaction degree mapping. The smart particle integrates inertial/orientation sensing together with thermal–mechanical measurements, and its high-temperature survivability and calibratability are verified through thermal exposure and calibration tests. During laboratory vibratory compaction of representative asphalt mixtures, raw signals are converted into stable attitude responses via attitude estimation and filtering; posture-dominant descriptors are then extracted and used to establish a data-driven mapping from internal responses to compaction degree using regression models. Results show that the device remains stable under typical hot-mix asphalt conditions, with calibration exhibiting high linearity (temperature channel R² > 0.990; force channel R² > 0.980 in the relevant range). Filtering markedly enhances inertial-signal usability under strong vibration and improves the interpretability of attitude-response evolution during compaction. The evolution of attitude features is consistent with the “rapid-to-slow densification” process, yielding correlations of |r| ≈ 0.35–0.47 with compaction degree evolution. Nonlinear regressors outperform linear baselines, and the better-performing nonlinear models achieve strong predictive performance across all six specimens, with R² values reaching 0.740–0.960 and RMSE reaching 0.016–0.043. Moreover, machine-learning-based feature-importance analysis reveals distinct mixture-type-dependent characteristics, indicating that AC and SMA transmit compaction-state information through partly different dominant response features. These findings demonstrate the feasibility of embedded smart particles for online compaction-quality evaluation and provide a basis for real-time feedback in intelligent compaction. Full article

Full-penetration laser welding (FPLW) is increasingly adopted in manufacturing pipelines, yet its industrial scalability is constrained by in-process defect formation, particularly incomplete penetration. To address this, we propose a sensor-driven framework for non-destructive monitoring and automated defect detection that uses infrared (IR) thermography as the primary in situ sensing modality and applies deep learning to the acquired thermal signals. High-speed IR camera recordings were processed to track spatter and the weld zone, yielding a time series of physically interpretable spatiotemporal features (mean spatter area, mean spatter temperature, number of spatters, and mean welding zone temperature). Defect recognition is formulated as a multi-label classification problem targeting incomplete penetration, sagging, shrinkage groove, and linear misalignment, and multiple temporal models were evaluated on the same sensor-derived feature sequences. Experimental validation on 09G2S pipeline steel demonstrates that the proposed time series pipeline based on a hybrid CNN–transformer achieves a mean Average Precision (mAP) of 0.85 while preserving near-real-time inference on a CPU. The results indicate that IR thermography-based spatter dynamics provide actionable sensing signatures for automated defect prediction and can serve as a foundation for closed-loop quality control in industrial laser pipeline welding. Full article

The low-frequency distributed acoustic sensing (LF-DAS) data acquired through fiber-optic cables cemented behind the fracturing well casing can dynamically capture the hydraulic fracturing process. After removing the thermal effect, the LF-DAS data can reveal the strain evolution induced by the initiation of hydraulic fractures. This paper presented an improved strain–temperature decoupling method for LF-DAS measurements based on joint LF-DAS/distributed temperature sensing (DTS) monitoring. The decoupling method was based on strain change and temperature change pre-processed from the raw DAS and DTS data to avoid the enhancement of DTS data noise. The moving window function method and the image processing parameter cosine similarity was introduced to cope with the differences in temporal and spatial resolution between LF-DAS and DTS data. The region significantly affected by temperature change could be identified automatically and the mechanical strain change could be extracted. The tensile strain response generally reached a local peak at perforation clusters and increased significantly at those with dominant fracture fluid inflow. By analyzing the evolution of strain profile during fracturing, the effectiveness of multi-cluster fracture initiation and fracture temporary plugging could be evaluated. Full article

The rapid chilling and aging stages following pork slaughter represent a critical window for determining final physicochemical quality and flavor development. To address the destructive nature of conventional meat quality assessment methods and the limitations of rigid spectral probes when applied to irregular biological surfaces, this study developed and validated a wireless monitoring system integrating a flexible visible/near-infrared (VIS/NIR) sensing array with ensemble learning algorithms. The proposed system enables non-destructive, continuous monitoring of pork quality during cold-chain storage. A DeepEnsemble regression model based on a stacking framework was constructed by integrating Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) to predict pH, moisture content, and total amino acid concentration. During a 26 h dynamic aging experiment, the proposed model achieved coefficients of determination (R²) of 0.9019, 0.9687, and 0.9600 for pH, moisture content, and total amino acids, respectively, with prediction performance exceeding that of individual regression models. The wireless transmission module maintained stable data communication under low-temperature and high-humidity conditions (−20 °C and 0–4 °C), with packet loss rates below 0.1%. These results indicate that the proposed system can effectively capture the dynamic evolution of pork quality during aging and provides a practical non-destructive approach for intelligent pork quality evaluation, cold-chain monitoring, and digital management of meat supply chains. Full article

Hydrogels are endowed with exceptional hydrophilicity and biocompatibility by their network structure, while also exhibiting soft physical properties similar to living tissues, which renders them ideal biomaterials. Responsive hydrogels—particularly those constructed from multicomponent systems including proteins, polysaccharides, peptides, and polyphenols—have emerged as a frontier research focus owing to their tunable responsiveness and controllable functional properties. In this review, hydrogel response mechanisms were categorized according to pH, ionic strength, temperature, light, enzymes, and multi-stimuli interactions. Key preparation strategies, encompassing chemical, physical, and enzymatic crosslinking, were systematically introduced. The preparation of hydrogels from various food-grade matrices, such as polysaccharide-based, protein-based, peptide-based, and polyphenol-based systems, was also summarized, with emphasis placed on how their tailored structures govern functional performance. Furthermore, innovative applications of responsive hydrogels were highlighted, including targeted delivery of nutrients and bioactive substances (e.g., probiotics, anthocyanins, vitamins) in functional foods, smart packaging and sensing for real-time freshness monitoring of meat and fruits, food quality detection through colorimetric and photothermal sensors, and 4D food printing for personalized nutrition and dysphagia-friendly foods. Full article

To address the technical bottlenecks of strong background radiation interference and weak target signals in mid-wave infrared (MWIR) hyperspectral mineral detection over complex terrain, this paper proposes a “full-link background radiation suppression” methodological framework. A coupled illumination-terrain-atmosphere-sensor radiative transfer model is constructed to systematically quantify how multidimensional parameters—such as observation geometry, surface temperature, elevation, aerosol optical depth, and water vapor content—influence the target background radiation contrast. The findings reveal that daytime observation, lower surface temperature, higher altitude, dry atmosphere, and moderate solar and observation zenith angles are key factors for maximizing the signal-to-noise ratio. Comprehensive optimization analysis demonstrates that observations during midday in autumn and winter achieve optimal performance, with the target background relative contrast potentially enhanced by up to 6.29 times compared to unfavorable conditions such as summer nights. This work elucidates the physical mechanisms governing MWIR hyperspectral detection efficacy in complex scenarios, provides direct parameter-optimization strategies for intelligent mission planning of spaceborne imaging systems, and holds significant value for advancing mineral remote sensing from “passive acquisition” to “cognitive detection”. Full article

In this study, a highly crystalline 2H (hexagonal)-phase MoS₂ sensing layer with a precisely controlled crystal structure was realized through a combination of DC sputtering and sulfurization annealing processes, and subsequently integrated with a D-shaped optical fiber to develop a highly sensitive carbon dioxide (CO₂) sensor. Conventionally sputtered MoS₂ thin films often suffer from the presence of unstable metallic 1T (tetragonal) phases and a high density of sulfur vacancies, which significantly degrade sensor reversibility and long-term stability. Here, high-temperature annealing under a sulfur-rich atmosphere was employed to induce a complete phase transition from the metastable 1T phase to the stable semiconducting 2H phase, while simultaneously healing sulfur vacancies. Enhanced crystallinity was confirmed by Raman spectroscopy. The fabricated sensor exhibited excellent linearity (R² > 0.99) and markedly improved repeatability over a CO₂ concentration range of 1000–10,000 ppm. This significant performance enhancement is attributed to reversible charge transfer induced by sulfur vacancy passivation, which modulates the complex refractive index of the MoS₂ layer and optimizes optical interaction with the evanescent field of the D-shaped fiber. The phase engineering and defect-healing strategy presented in this work effectively addresses the drift issues commonly observed in conventional electrical gas sensors and provides a crucial pathway toward the realization of high-performance optical gas sensors. Full article

Microclimatic stress strongly influences the ecological resilience of Vasconcellea stipulata (Toronche), yet current monitoring approaches rely on sparse measurements and lack real-time predictive capability. This work introduces an AI-enhanced virtual sensing framework based on laser-induced graphene (LIG) designed to emulate the thermoresistive response of an LIG transducer and generate high-resolution environmental indicators for microclimatic analysis. Unlike conventional LIG sensors or standalone IoT systems, the proposed framework integrates experimental calibration, data-driven modeling, and embedded inference into a unified architecture suitable for lightweight deployment on edge devices. A multilayer perceptron (MLP) model trained on laboratory data reproduced the temperature- and humidity-dependent electrical behavior of the transducer with high fidelity, achieving an RMSE of 0.016 k$\Omega$ in the calibrated range (10–60 °C) and remaining below 0.09 k$\Omega$ under noisy and extrapolated conditions. Sensitivity analysis identified temperature as the dominant driver (71%), followed by solar irradiance (19%) and relative humidity (10%), consistent with the microstructural mechanisms governing LIG’s response. The virtual sensor enables continuous, low-cost environmental monitoring and provides quantitative variables that can support downstream ecological interpretation. Overall, the results highlight the potential of AI-enhanced LIG–IoT architectures for advancing real-time microclimatic assessment in resource-limited Andean ecosystems. Full article

Deep eutectic solvent (DES), when used as the continuous phase of eutectogels, can significantly improve their electrical and mechanical properties due to its excellent conductivity, freeze resistance and chemical stability. The development of eutectogels effectively solves the key limitations of traditional hydrogels and organogels, such as low-temperature freezing, high-temperature volatilization, and organic solvent leakage. It also realizes the collaborative optimization of environmental friendliness and comprehensive performance, which makes it show broad application prospects in the field of flexible sensing. This review summarizes the design principles, material selection, sensing mechanisms, and flexible sensing applications of eutectogels. By examining the design of eutectogels, the selection of DES, and the synthesis of the gel network, it provides a theoretical basis for the development of eutectogel-based sensor devices. A detailed description of the sensing mechanism is provided to elucidate the signal generation and transition in eutectogels toward the purpose of the practical applications. Finally, the application prospects of eutectogels for high-performance sensors and detection devices are discussed. Additionally, we provide a theoretical support for their structural design, performance optimization, and practical application. Full article

Metal–organic frameworks (MOFs) have received significant attention as multifunctional platforms for chemical sensing due to their adjustable porosity, high specific surface area, and modular chemical architecture, which allow for customized host-guest interactions and signal transduction. This work presents a critical overview of recent advances in electrochemical and optical sensors based on MOFs for the detection of emerging contaminants, including toxic metal ions, pharmaceutical residues, and industrial pollutants in environmental and biological matrices. Special emphasis is placed on the underlying sensing mechanisms, such as redox activity, charge transfer, and luminescence modulation, as well as the main challenges related to structural stability under realistic operating conditions, including variations in pH, humidity, and temperature. Furthermore, the development of hybrid and hierarchical architecture based on MOFs is discussed as an effective strategy to improve sensitivity, selectivity, and long-term robustness. Finally, the perspective highlights how to optimize sensor performance and enable more reliable and scalable applications in monitoring emerging contaminants. Full article