Search Results (483)

Nanocellulose materials (CNMs), encompassing cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC), have emerged as a versatile and sustainable class of bio-based nanomaterials with significant promise for applications in mechanical engineering. This review systematically examines the processing of nanocellulose via mechanical, chemical, and enzymatic routes, alongside surface modification strategies that enhance performance and address scalability challenges. A principal advantage of CNMs lies in their exceptional mechanical properties, including superior strength, stiffness, and toughness, which position them as high-performance, sustainable reinforcement agents for advanced composites. Beyond mechanical reinforcement, CNMs exhibit a suite of functional properties critical for engineering design, such as thermal stability, tunable conductivity, effective gas/moisture barrier performance, and improved tribological behavior. These characteristics enable their use in diverse high-value applications, including lightweight composites, protective coatings, energy storage devices, sensors, actuators, and intelligent material systems. Furthermore, the inherent renewability, biodegradability, and recyclability of nanocellulose align closely with the principles of a circular economy and green engineering. However, the successful integration of CNMs into mainstream manufacturing requires overcoming key challenges. These include the energy intensity of certain production processes, inherent moisture sensitivity, long-term stability under operational conditions, and compatibility with established industrial techniques. Life-cycle analyses reveal important environmental trade-offs that must be navigated. Overall, nanocellulose represents a renewable, multi-functional material platform whose unique combination of mechanical performance, functional versatility, and environmental benefits is poised to drive innovation in next-generation engineering materials. Full article

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha⁻¹) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO₂ assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds. Full article

High-precision detection of hazardous gases with low refractive indices ranging from 1.000 to 1.100, specifically including methane, carbon monoxide, and sulfur dioxide, is critical for industrial safety, yet conventional sensors often suffer from limited sensitivity and severe thermal cross-sensitivity. This work presents a Magneto-Optical Differential Photonic Crystals Sensor (MO-DPCS) utilizing indium antimonide (InSb) to address these constraints. Employing the Multi-Objective Dragonfly Algorithm (MODA), the system was inversely optimized to maximize magneto-optical polarization splitting while rigorously maintaining an ultra-high transmission efficiency. Crucially, an angular interrogation architecture operating under oblique incidence was established to maximize the magneto-optical non-reciprocity, where the detection was realized by fixing the terahertz source frequency and monitoring the precise angular displacements of the steep spectral edges. A differential detection technique was employed to utilize the non-reciprocal phase changes wherein Transverse Electric (TE) and Transverse Magnetic (TM) modes display contrasting kinematic characteristics in the presence of an external magnetic field. The findings indicate that with an adjusted magnetic field of 0.033 T, the MO-DPCS attains an exceptional differential sensitivity of 30.8°/RIU, much above the 0.8°/RIU seen in the unmagnetized condition. The differential approach efficiently eliminates common-mode thermal noise, minimizing temperature-induced drift to below 0.35° across a 1 K range. The suggested MO-DPCS offers a robust, self-referencing solution for stable and high-sensitivity gas sensing applications with a detection limit of 4.18 × 10⁻⁴ RIU. Full article

Early detection of fire-related risks in lithium-ion batteries (LIBs) remains a critical challenge, as conventional protection mechanisms typically activate only after irreversible degradation or macroscopic failure occurs. In this study, an innovative sensor-based diagnostic framework is proposed for proactive fire prediction in LIBs by simultaneously monitoring low-frequency and high-frequency electrical signatures generated during battery charge–discharge processes. An electromagnetic (EM) antenna sensor and a high-frequency current transformer (HFCT) sensor were employed to capture complementary voltage- and current-based transient signals associated with internal degradation phenomena. Cell-level experiments were conducted under various C-rates and temperature conditions, including high-stress environments, while module-level validation was performed on a 4-series, 1-parallel (4S1P) configuration at a 2C-rate under ambient temperature. Time–frequency characteristics of the measured signals were systematically evaluated using MATLAB-based continuous wavelet transform (CWT) and discrete wavelet transform (DWT) techniques. The results reveal that degradation-induced transient events exhibit non-stationary, impulsive voltage and current signatures with distinct frequency-band localization, which intensify with increasing C-rate, elevated temperature, and aging progression. At the module level, although signal amplitudes were partially attenuated due to current redistribution, characteristic wavelet energy patterns and time–frequency concentrations remained clearly distinguishable, demonstrating the scalability of the proposed approach. The combined EM antenna–HFCT sensing strategy, together with multi-resolution wavelet analysis, enables effective phenomenological differentiation between normal operational noise and incipient internal fault signatures well before conventional thermal or capacity-based indicators become evident. These findings demonstrate feasibility of the proposed method for early-stage fault diagnosis and highlight its potential applicability to advanced battery management systems for proactive fire prevention in large-scale energy storage and electric vehicle applications. Unlike conventional voltage-, temperature-, or gas-based diagnostics, the proposed approach enables the detection of incipient degradation phenomena at the microsecond scale by exploiting complementary low- and high-frequency electrical signatures. This study provides experimental evidence that wavelet-based EM and HFCT sensing can identify MISC-related precursors significantly earlier than conventional battery management indicators. Full article

Hydrogen is a key energy carrier for fuel cell vehicles and hydrogen energy systems. However, its colorless and odorless nature, combined with a wide flammability range, poses significant safety risks in the event of leakage. Accordingly, compact and reliable hydrogen sensors capable of low-ppm detection at moderate operating temperatures are essential for early-stage safety monitoring. In this study, a bias-optimized hydrogen gas sensor based on a Pd-functionalized SnO₂ thin film with Mo electrodes and an integrated microheater is designed, fabricated, and systematically characterized. The sensor employs a Mo-based vertical microheater and a multilayer thermal insulation stack, enabling thermally efficient and stable operation at 250–280 °C with low power consumption. The electrical and sensing properties of the SnO₂ layer are optimized by controlling the oxygen partial pressure during reactive sputtering and post-deposition annealing. The Pd catalytic layer promotes hydrogen dissociation and spillover, resulting in pronounced resistance modulation through surface redox reactions and interfacial charge transport effects. By systematically optimizing the sensing bias voltage, a clear trade-off between sensitivity enhancement and electrical noise is identified, which allows stable and repeatable operation in the low-ppm regime. The sensor response follows a power-law dependence on hydrogen concentration, and an automated measurement platform is employed to evaluate repeatability and statistical performance. Based on baseline noise analysis and concentration-dependent resistance variation, a limit of detection of approximately 6.4 ppm is achieved. Furthermore, a concentration-normalized figure of merit that combines response magnitude and concentration dependence is introduced to quantitatively assess low-concentration hydrogen sensing performance. These results demonstrate that the proposed Mo-electrode Pd/SnO₂ thin-film sensor, enabled by bias-optimized operation and integrated thermal control, provides a robust and scalable platform for safety-critical hydrogen leak detection. Full article

Objective neural network-based two-phase flow regime classifiers are developed for vertical, narrow, rectangular channels and a 1 inch round pipe using Kohonen Self-Organizing Maps. In the rectangular channel, the classifier uses five geometric inputs obtained from a two-sensor droplet-capable conductivity probe (DCCP-2): the bulk gas void fraction ${\alpha }_g$, ligament void fraction ${\alpha }_{\mathrm{lig}}$, normalized ligament chord length $y_{\mathrm{lig}}$, normalized large bubble chord length $y_{\ell ,\mathrm{bb}}$, and a droplet indicator. These parameters allow for the objective identification of bubbly/distorted bubbly, cap-turbulent, churn-turbulent, annular, rolling wispy, and wispy flow regimes, and yield quantitative transition boundaries in the $(j_f,j_g)$ plane for a densely populated test matrix. In the round pipe, a four-sensor droplet-capable conductivity probe (DCCP-4) provides the mean and standard deviation of droplet, bubble, and ligament chord length distributions, which are used as inputs to a Self-Organizing Map (SOM) classifier that separates rolling annular and wispy annular regimes at high void fractions. The resulting regime maps are discussed in terms of the associated phase geometries and their impact on interfacial area, drag, and entrainment, providing regime-dependent geometric inputs that can be used to improve Two-Fluid Model closures for reactor downcomers, core channels, and other nuclear thermal–hydraulic applications. Full article

Effective thermal runaway (TR) detection is critical for the safety of lithium-ion battery packs, particularly in electric vehicles. However, deploying laboratory-validated methods into resource-constrained battery management systems (BMS) presents significant engineering challenges. This review surveys the state of the art in on-board TR monitoring, with an emphasis on the practical constraints of automotive applications. We first examine available precursor signals, including thermal, electrical, gas, and acoustic emissions, and evaluate their trade-offs regarding response speed and integration complexity. Second, diagnostic algorithms, from threshold-based logic to deep learning, are assessed against key performance metrics such as computational latency, false alarm rates, and lead time. Furthermore, the review discusses essential deployment considerations, including model compression techniques, inference hardware architectures, and compliance with functional safety standards. Specifically, the review discusses the implementation challenges of multi-modal data fusion, with a particular focus on the constraints imposed by limited hardware resources and long-term sensor reliability. Future directions regarding data standardization and cloud-edge collaboration are also discussed. Full article

Benzo[a]pyrene (BaP), a potent carcinogenic polycyclic aromatic hydrocarbon, is a critical food contaminant originating from environmental deposition and thermal processing, posing a significant threat to public health and driving stringent global regulations. This review critically examines recent advancements in analytical methodologies for BaP determination, giving particular emphasis to sample preparation and detection techniques. The discussion covers the evolution from conventional methods, such as solid-phase extraction, towards more efficient and sustainable approaches, including magnetic, dispersive, and molecularly imprinted solid-phase extraction, as well as microextraction techniques and gel permeation chromatography. For detection, the performance of established chromatographic methods, such as gas chromatography–mass spectrometry (GC-MS) and high-performance liquid chromatography with fluorescence detection (HPLC-FLD), is evaluated against emerging rapid techniques such as sensors, immunoassays, and spectroscopic methods. The analysis reveals that while significant progress has been made in improving sensitivity, selectivity, and throughput, challenges remain in balancing speed with accuracy, managing matrix effects, and translating novel materials from research to routine application. The review concludes by underscoring the necessity for future development to focus on the integration of smart materials, automation, and advanced data science to achieve robust, on-site, and holistic monitoring solutions for ensuring food safety against BaP contamination. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

The detection of trace nitrogen dioxide (NO₂) is critical for environmental monitoring and industrial safety. Among various sensing technologies, chemiresistive sensors based on semiconducting metal oxides are prominent due to their high sensitivity and fast response. However, their application is hindered by inherent limitations, including low selectivity and elevated operating temperatures, which increase power consumption. Two-dimensional metal oxysulfides have recently attracted attention as room-temperature sensing materials due to their unique electronic properties and fully reversible sensing performance. Meanwhile, their combination with optoelectronic gas sensing has emerged as a promising solution, combining higher efficiency with minimal energy requirements. In this work, we introduce non-layered 2D indium oxysulfide (In₂S_xO_3−x) synthesized via a two-step process: liquid metal printing of indium followed by thermal annealing of the resulting In₂O₃ in a H₂S atmosphere at 300 °C. The synthesized material is characterized by a micrometer-scale lateral dimension with 6.3 nm thickness and remaining n-type semiconducting behavior with a bandgap of 2.53 eV. It demonstrates a significant response factor of 1.2 toward 10 ppm NO₂ under blue light illumination at room temperature. The sensor exhibits a linear response across a low concentration range of 0.1 to 10 ppm, alongside greatly improved reversibility, selectivity, and sensitivity. This study successfully optimizes the application of 2D metal oxysulfide and presents its potential for the development of energy-efficient NO₂ sensing systems. Full article

Lithium-ion batteries (LIBs) face the safety hazard of thermal runaway (TR). Gas-sensing-based monitoring is one of the viable warning approaches for batteries during operation, and TR warning using semiconductor gas sensors has garnered widespread attention. This review presents a comprehensive analysis of the latest advances in this field. It details the gas release characteristics during the TR failure process and identifies H₂, electrolyte vapor, CO, CO₂, and CH₄ as effective TR warning markers. The core of this review lies in an in-depth critical analysis of gas-sensing materials designed for these target gases, systematically summarizing the design, performance, and application research of semiconductor gas-sensing materials for each aforementioned gas in battery monitoring. We further summarize the current challenges of this technology and provide an outlook on future development directions of gas-sensing materials, including improved selectivity, integration, and intelligent advancement. This review aims to provide a roadmap that directs the rational design of next-generation sensing materials and fast-tracks the implementation of gas-sensing technology for enhanced battery safety. Full article

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains. Full article

Peatlands play a critical role in global and regional climate regulation by functioning as long-term carbon sinks, regulating hydrology, and modulating land–atmosphere energy exchange. Intact peat ecosystems store large amounts of organic carbon and stabilize local climate through high water retention and evapotranspiration, whereas peatland degradation disrupts these functions and can transform peatlands into significant sources of greenhouse gas emissions and climate extremes such as drought and fire. Indonesia contains approximately 13.6–40.5 Gt of carbon, around 40% of which is stored on the island of Sumatra. However, tropical peatlands in this region are highly vulnerable to climate anomalies and land-use change. This study investigates the impacts of major climate anomalies—specifically El Niño and positive Indian Ocean Dipole (pIOD) events in 1997/1998, 2015/2016, and 2019—on peatland cover change across South Sumatra, Jambi, Riau, and the Riau Islands. Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery were analyzed using a Random Forest machine learning classification approach. Climate anomaly periods were identified using El Niño-Southern Oscillation (ENSO) and IOD indices from the National Oceanic and Atmospheric Administration. To enhance classification accuracy and detect vegetation and hydrological stress, spectral indices including the Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI) were integrated. The results show classification accuracies of 89–92%, with kappa values of 0.85–0.90. The 2015/2016 El Niño caused the most severe peatland degradation (>51%), followed by the 1997/1998 El Niño (23–38%), while impacts from the 2019 pIOD were comparatively limited. These findings emphasize the importance of peatlands in climate regulation and highlight the need for climate-informed monitoring and management strategies to mitigate peatland degradation and associated climate risks. Full article

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

Two-dimensional carbide crystals (MXenes) are emerging as a promising platform for the development of novel gas sensors, offering advantages in energy efficiency and tunable analyte selectivity. One of the most effective strategies to enhance and tailor their functional performance involves forming hetero-structured composites with metal oxides. In this work, we explore a chemiresistive effect in double-metal MXene of Ti_0.2V_1.8C and its composites with 2 mol. % SnO₂ and Co₃O₄ nanocrystalline oxides toward feasibility tests with alcohol and ammonia vapor probes. The materials were characterized by simultaneous thermal analysis, X-ray diffraction analysis, Raman spectroscopy, and scanning/transmission electron microscopy. Gas-sensing experiments were carried out on composite layers deposited on multi-electrode substrates to be exposed to the test gases, 200–2000 ppm concentrations, at an operating temperature of 370 °C. The developed sensor array demonstrated clear analyte discrimination. The distinct sensor responses enabled a selective identification of vapors through linear discriminant analysis, demonstrating the further potential of MXene-based materials for integrated electronic nose applications. Full article