Search Results (4,995)

Cerium oxide (CeO₂) nanozymes, also known as nanoceria have emerged as a versatile class of catalytic nanomaterials capable of mimicking key redox enzymes, including oxidases and peroxidases. Their tunable Ce³⁺/Ce⁴⁺ redox cycling, high density of oxygen vacancies, and exceptional resistance to thermal, pH, and storage stress distinguish CeO₂ from conventional enzyme labels, such as horseradish peroxidase (HRP). In immunoassays, CeO₂ enables H₂O₂-free TMB (3,3’,5,5’-tetramethylbenzidine) oxidation, generating strong chromogenic signals with minimal background. Although CeO₂ nanozymes have been explored in colorimetric, chemiluminescent, and photoactive immunoassays, their integration into lateral flow immunoassays (LFIAs) remains limited, with only a few hybrid CeO₂-containing systems reported to date. This mini-review highlights the limitations of conventional peroxidase-based formats and explains how CeO₂’s redox cycling (Ce³⁺/Ce⁴⁺) and oxygen-vacancy-driven catalysis deliver stable, reagent-free signal amplification. Emphasis is placed on the synthetic control of CeO₂, conjugation chemistry with antibodies, and integration into LFIA architectures. CeO₂ enables hydrogen-peroxide-free colorimetric detection with improved robustness and sensitivity, positioning it as a promising catalytic label for point-of-care testing. However, it may aggregate in high-ionic-strength buffers, and its synthesis cost increases for highly uniform, vacancy-engineered materials. Surface functionalization with polymers or dopants and optimized dispersion strategies can mitigate these issues, guiding future practical implementations. Full article

Semiconductor manufacturing relies heavily on automatic test equipment (ATE), and yet thermal aging poses a critical risk to equipment reliability. This study proposes a novel anomaly detection framework for ATE driver boards by integrating cumulative degradation time (CDT)—derived from accelerated life testing (ALT)—with artificial intelligence models. Specifically, the approach quantifies the cumulative effects of thermal stress as CDT and utilizes it as a key input feature to enable the early detection of degradation under prolonged high-temperature conditions. The proposed framework successfully demonstrates the capability to diagnose real-time anomalies before critical CDT thresholds are reached. Consequently, this approach allows for efficient management, significantly contributing to reduced maintenance costs, minimized downtime, and enhanced equipment reliability, serving as a foundational strategy for condition-based maintenance (CBM) strategies in semiconductor manufacturing. Full article

This review deals with the issue of operational coal quality control using instrumental nuclear–physical methods. The existing traditional method of coal testing, characterized by high labor intensity and low representativeness, cannot serve as a basis for operational management of mining and processing processes. Instrumental nuclear–physical methods are free from these drawbacks; they are based on various processes of interaction of gamma and neutron radiation with substances. The main modifications of instrumental methods using gamma radiation are discussed: backscattering, forward gamma scattering, gamma absorption, gamma annihilation, and natural gamma activity. Various modifications of gamma methods are related to the energy of the primary and recorded radiation, the prevalence of a particular interaction process, the depth of the method, characteristics of the test object, the measurement geometry, and the other factors. The features of gamma methods are described in the context of the tasks being solved, interfering factors (variations in the bulk density, the moisture content, and the elemental composition), and methodological approaches for increasing the sensitivity and accuracy of the coal quality assessment. The variety of modifications of neutron methods is associated with irradiation of the analyzed coal with neutrons of different energies and detection of secondary gamma radiation arising from neutron activation of elements, inelastic scattering of fast neutrons, and radiative capture of thermal neutrons by the elements composing the coal. The methodological features of neutron activation, the neutron–gamma method of inelastic scattering and radiative capture are considered in the context of elemental analysis for Al, Si, S, Ca, Fe, H, C, and O and determining the ash content of coal in general. The main trends of the instrumental quality control are highlighted and recommendations are given for their use depending on the metrological characteristics and physical and chemical properties of the control object. The gamma-albedo method with registration of X-ray fluorescence of heavy gold-forming elements is the most promising for express analysis of powder samples. To test coarse coal in large amounts, multiparameter neutron methods are needed that comprehensively utilize high-precision equipment and instrumental signals from carbon, oxygen, and major ash-forming elements. Full article

To address the clinical urgency of simultaneously monitoring multiple biomarkers in chronic wound infections, this study presents the innovative development of an electrochemical sensor based on a MWCNTs/MXene/PVA composite hydrogel. A dual-channel conductive network is constructed via the electrostatic self-assembly of the two-dimensional material MXene and multi-walled carbon nanotubes (MWCNTs). This strategy not only enhances the charge transfer efficiency but also effectively suppresses the aggregation of MWCNTs and exposes the electrocatalytic active sites. Additionally, the thermal annealing process is incorporated to facilitate the ordered arrangement of polyvinyl alcohol (PVA) nanocrystalline domains, strengthening the hydrogen bond-mediated interfacial adhesion and resolving the issues of hydrogel swelling and delamination. The detection limit (LOD) of the optimized sensor (MWCNTs_0.6/MXene_0.4/PVA) for pyocyanin (PCN) within complex biological matrices, including phosphate-buffered saline (PBS), Luria–Bertani (LB) broth, and saliva, was decreased to a range of 0.84~0.98 μM. Leveraging the disparities in the characteristic oxidation potentials (ΔE > 0.3 V) of PCN, uric acid (UA), and histamine (HA) in simulated wound exudate (SWE), the multi-component synchronous detection functionality of the non-specific sensor was expanded for the first time. This study offers a high-precision and multi-parameter integrated approach for point-of-care testing (POCT) of wound infections. Full article

The field of developing effective catalysts for heterogeneous catalysis has recently focused on controlling the structures of catalysts themselves to optimise the density and energy of crystal lattice defects. This can significantly influence catalytic activity in terms of both reaction rates and reaction mechanisms, and thus the selective production of desired substances as well. In many cases, these crystal lattice defects manifest themselves as so-called electron traps (ETs) and thus significantly influence charge transfer between the catalyst and reactants. ETs provide the missing electronic link between atomic-scale defects and macroscopic performance in heterogeneous catalysis. Therefore, the importance of ETs for catalysis is particularly evident in areas where charge transfer plays a fundamental role in the reaction mechanism, such as photocatalysis and electrocatalysis. In the field of thermally initiated reactions, the importance of ETs in heterogeneous catalysis has not yet been fully appreciated. However, several studies have already addressed the importance of ETs for this type of reaction. This review consolidates and extends the concept of ETs to purely thermal-initiated reactions, with a focus on CO₂ hydrogenation using typical transition metal catalysts. Firstly, in this review, ETs are defined as band gap states associated with internal and external defects, and their depth, density, spatial location, and dynamics are then coupled with key steps in thermocatalytic cycles, including charge storage/release, reactant activation, intermediate stabilisation, and redox turnover. Secondly, electron trap detection is reviewed based on advanced spectroscopic techniques, including reversed double-beam photoacoustic spectroscopy (RDB-PAS), thermally stimulated current (TSC), deep-level transient spectroscopy (DLTS), thermoluminescence (TL), electron paramagnetic resonance (EPR), and photoluminescence (PL), highlighting how each method describes trap energetics and populations under realistic operating conditions. Finally, case studies on the application of metal oxides and supported metals are discussed, as these are typical catalysts for the reaction mentioned above. This review highlights how oxygen vacancies (OVs), polarons, and metal–support interfacial sites act as robust electron reservoirs, lowering the barriers for CO₂ activation and hydrogenation. By reframing thermocatalysts through the lens of ET chemistry, this review identifies ETs as actionable targets for the rational design of next-generation materials for CO₂ hydrogenation and related high-temperature transformations. Full article

Hot-spots represent a significant failure mechanism in photovoltaic (PV) modules, typically attributable to electrical mismatching. However, thermo-optical degradation of the encapsulant, including discoloration and delamination, can both trigger and amplify mismatch by inducing localized optical losses and temperature rise. The present paper proposes a compact circuit-level electro-thermal–optical model that explicitly captures the short-term closed-loop interaction between mismatching, cell temperature, and temperature-dependent optical properties. The photogenerated current is formulated as a function of irradiance, cell temperature, and encapsulant degradation, enabling dynamic feedback between heating and optical losses. Numerical simulations are carried out on a commercial 40-cell PV module under four representative operating static scenarios. The results demonstrate that, even in the absence of shading, optical degradation can generate multimodal P–V characteristics, drive cells into reverse bias, and produce hot-spots. When optical degradation coexists with irradiance mismatch, the feedback loop significantly amplifies mismatching and shifts the maximum power point toward thermally unsafe operating conditions. These findings demonstrate that maximizing instantaneous power does not necessarily maximize lifetime energy yield, underscoring the need for thermal-aware MPPT strategies and providing a practical framework for early detection of thermo-optical faults in PV modules. Full article

Humidity sensing is essential in medical fields such as respiratory support, neonatal care, sterilization, and pharmaceutical storage. However, current sensors face limitations, including slow response/recovery, low sensitivity, and poor long-term stability. To address these challenges, we developed borophene-multiwalled carbon nanotube (MWCNT) heterostructures using a stepwise in situ thermal decomposition method. The resulting humidity sensor exhibits an ultrabroad detection range (11–97% RH), ultra-high sensitivity (55,000% at 97% RH), and fast response/recovery times (10.04 s/4.8 s). Through interfacial charge-transfer engineering, the system facilitates rapid electron migration, enhances Schottky barrier modulation, and provides abundant active adsorption sites for water molecules, thereby achieving comprehensive improvement in sensing performance. It also demonstrates excellent selectivity, mechanical flexibility, and operational stability. Notably, the sensor’s sensitivity at 97% RH surpasses that of sensors based on pure borophene or MWCNT by 37–462 times, highlighting the advantages of heterostructure engineering. The multifunctionality of the device suggests its potential in areas beyond conventional sensing, including non-contact voice recognition, skin humidity mapping, and real-time breath monitoring. These results lay a solid foundation for developing borophene-MWCNT heterostructures into a high-performance platform for next-generation medical diagnostics and intelligent health monitoring. Full article

Topography regulates vegetation functioning by controlling water redistribution, microclimate, and solar exposure. In Mediterranean ecosystems, where water availability constitutes a fundamental limiting factor, vegetation functioning is also influenced by environmental drivers such as temperature, climatic seasonality, drought recurrence, and soil properties that interact with terrain heterogeneity. Understanding how these elements operate at the micro-scale is essential for interpreting the spatial variability of photosynthetic vigor and canopy water condition. This study evaluates the relationships between the topographic metrics Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), and Diurnal Anisotropic Heat Index (DAH) and two spectral proxies of vegetation condition, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), in Los Nogales Nature Sanctuary (central Chile). Multitemporal Sentinel-2 time series (2017–2025) were analyzed using Generalized Additive Models (GAMs) with Gaussian distribution and cubic splines to detect non-linear topographic responses. All topographic predictors were statistically significant (p < 0.001). NDVI and NDMI values were higher in concave and less rugged areas, decreasing toward convex and thermally exposed slopes. NDMI exhibited greater sensitivity to topographic position and thermal anisotropy, indicating the strong dependence of vegetation water condition on topographically driven water redistribution. These results highlight the role of terrain in modulating vegetation vigor and moisture in Mediterranean ecosystems. Full article

This paper proposes a novel motion-aware framework to address key challenges in 4D millimeter-wave radar detection for autonomous driving. While existing methods struggle with sparse point clouds and dynamic object characterization, our approach introduces three key innovations: (1) A Bird’s Eye View (BEV) fusion network incorporating velocity vector decomposition and dynamic gating mechanisms, effectively encoding motion patterns through independent XY-component convolutions; (2) a gradient-aware multi-task balancing scheme using learnable uncertainty parameters and dynamic weight normalization, resolving optimization conflicts between classification and regression tasks; and (3) a two-phase progressive training strategy combining multi-frame pre-training with sparse single-frame refinement. Evaluated on the TJ4D benchmark, our method achieves 33.25% mean Average Precision (mAP)_3D with minimal parameter overhead (1.73 M), showing particular superiority in pedestrian detection (+4.16% AP) while maintaining real-time performance (24.4 FPS on embedded platforms). Comprehensive ablation studies validate each component’s contribution, with thermal map visualization demonstrating effective motion pattern learning. This work advances robust perception under challenging conditions through principled motion modeling and efficient architecture design. Full article

This study presents a detailed evaluation of how inlet air filter differential pressure (Filter DP) affects the operational performance of a gas turbine, focusing on its influence on power generation and thermal efficiency. Real operating data combined with machine learning (ML) techniques were used. Following the installation of new filters, the turbine operated for 10,000 h, and 4438 h under base-load conditions were selected for modeling. The impact of Filter DP was examined using Multiple Linear Regression (MLR), Quadratic Support Vector Regression (SVR), Regression Tree, and Artificial Neural Network (ANN) models, allowing both linear and nonlinear behavior to be captured. Results show that each 1 mbar increase in Filter DP leads to roughly a 1.67 MW drop in power output and a 0.094% reduction in thermal efficiency. Additionally, higher Filter DP raises fuel consumption and causes an extra 0.45 kgCO₂e of emissions per 1 MWh of electricity produced. These findings underline that even small increases in inlet pressure loss significantly affect economic and environmental performance. Filter fouling increases natural gas demand, CO₂e emissions, and overall carbon footprint. The ML-based approach enhances predictive maintenance by enabling early detection of filter degradation and supporting more efficient and sustainable turbine operation. Full article

This paper presents Sem4EDA, an ontology-driven and rule-based framework for automated fault diagnosis and energy-aware optimization in Electronic Design Automation (EDA) and Internet of Things (IoT) environments. The escalating complexity of modern hardware systems, particularly within IoT and embedded domains, presents formidable challenges for traditional EDA methodologies. While EDA tools excel at design and simulation, they often operate as siloed applications, lacking the semantic context necessary for intelligent fault diagnosis and system-level optimization. Sem4EDA addresses this gap by providing a comprehensive ontological framework developed in OWL 2, creating a unified, machine-interpretable model of hardware components, EDA design processes, fault modalities, and IoT operational contexts. We present a rule-based reasoning system implemented through SPARQL queries, which operates atop this knowledge base to automate the detection of complex faults such as timing violations, power inefficiencies, and thermal issues. A detailed case study, conducted via a large-scale trace-driven co-simulation of a smart city environment, demonstrates the framework’s practical efficacy: by analyzing simulated temperature sensor telemetry and Field-Programmable Gate Array (FPGA) configurations, Sem4EDA identified specific energy inefficiencies and overheating risks, leading to actionable optimization strategies that resulted in a 23.7% reduction in power consumption and 15.6% decrease in operating temperature for the modeled sensor cluster. This work establishes a foundational step towards more autonomous, resilient, and semantically-aware hardware design and management systems. Full article

Quartz Crystal Microbalance with Dissipation monitoring (QCM-D) systems are widely used for the real-time analysis of mass changes and viscoelastic properties in biological samples, enabling applications such as biomolecular interaction studies, biosensing, and fluid characterization. However, their accessibility has been limited by high acquisition costs. To address this limitation, a low-cost, open-source QCM-D system was developed. Unlike other affordable, open-hardware alternatives, this system is specifically optimized for potential biomedical applications by integrating active thermal control to preserve the physical properties of the samples and dissipation monitoring to characterize their viscoelastic behavior. A 10 MHz quartz crystal with a sensor module and a control and acquisition unit were integrated. The full system was built at a total cost below USD 500. Performance validation showed a temperature stability of ±0.13 °C, a frequency stability of ±2 Hz in air, and a limit of detection (LOD) of 0.46% polyethylene glycol (PEG), thereby enabling stable, reproducible measurements and the sensitive detection of small mass and interfacial changes in low-concentration samples. These results demonstrate that key QCM-D sensing capabilities can be achieved at a fraction of the cost, providing an accessible and reliable platform for potential biomedical research. Full article

AI-driven imaging is becoming central to crop monitoring, with proximal and unmanned aerial vehicle (UAV) platforms now routinely used for disease and stress detection, yield estimation, canopy structure, and fruit counting. Yet, as these models move from plots to farms, the main bottleneck is no longer raw accuracy but robustness under distribution shift. Systems trained in one field, season, cultivar, or sensor often fail when the scene, sensor, protocol, or timing changes in realistic ways. This review synthesizes recent advances on robustness and transferability in proximal and UAV imaging, drawing on a corpus of 42 core studies across field crops, orchards, greenhouse environments, and multi-platform phenotyping. Shift types are organized into four axes, namely scene, sensor, protocol, and time. The article also maps the empirical evidence on when RGB imaging alone is sufficient and when multispectral, hyperspectral, or thermal modalities can potentially improve robustness. This serves as a basis to synthesize acquisition and evaluation practices that often matter more than architectural tweaks, which include phenology-aware flight planning, radiometric standardization, metadata logging, and leave-one-field/season-out splits. Adaptation options are consolidated into a practical symptom/remedy roadmap, ranging from lightweight normalization and small target-set fine-tuning to feature alignment, unsupervised domain adaptation, style translation, and test-time updates. Finally, a benchmark and dataset agenda are outlined with emphasis on object-oriented splits, cross-sensor and cross-scale collections, and longitudinal datasets where the same fields are followed across seasons under different management regimes. The goal is to outline practices and evaluation protocols that support progress toward deployable and auditable systems, noting that such claims require standardized out-of-distribution testing and transparent reporting as emphasized in the benchmark specification and experiment suite proposed here. Full article

Winter road safety is critically influenced by microclimatic factors that determine where frost and ice persist on pavement surfaces. Among these, shadow duration plays a decisive yet often under quantified role in mountainous regions, where complex topography and variable solar exposure create localized cold zones. This study presents a GIS-based methodology for detecting and characterizing shadow-prone areas along high-altitude roads, extending previous national-scale models of winter risk toward local, geometry-driven analysis. Using high-resolution Digital Terrain Models (DTM02) and solar radiation simulations, four representative mountain roads (CL-505, AV-501, and CA-820) were analyzed to evaluate how orientation, slope, and surrounding relief control solar incidence. The resulting shadow maps were validated through UAV-derived thermal orthophotos and ground-based temperature measurements, confirming strong correspondence between simulated low-irradiance areas and observed cold surfaces. The integration of geometric and radiometric data demonstrates that topographic shading is a reliable predictor of frost persistence and can be incorporated into winter maintenance planning. By combining high-resolution terrain analysis with empirical thermal validation, this approach not only enhances predictive accuracy but also provides actionable insights for prioritizing road sections at greatest risk. Ultimately, it offers a scalable, data-driven framework for improving infrastructure resilience, optimizing maintenance operations, and mitigating winter hazards in cold-climate mountainous environments, supporting both safety and cost-effectiveness in road management strategies. Full article

Viticulture is facing growing economic and environmental pressures that demand a transition toward intelligent and autonomous crop management systems. Phytopathologies remain one of the most critical threats, causing substantial yield losses and reducing grape quality, while regulatory restrictions on agrochemicals and sustainability goals are driving the development of precision agriculture solutions. In this context, early disease detection is crucial; however, current visual inspection methods are hindered by subjectivity, cost, and delayed symptom recognition. This study presents a fully autonomous robotic platform developed within the Agrimet project, enabling continuous, high-frequency monitoring in vineyard environments. The system integrates a tracked mobility base, multimodal sensing using RGB-D and thermal cameras, an AI-based perception framework for leaf localisation, and a compliant six-axis manipulator for biological sampling. A custom control architecture bridges standard autopilot PWM signals with industrial CANopen motor drivers, achieving seamless coordination among all subsystems. Field validation in a Sicilian vineyard demonstrated the platform’s capability to navigate autonomously, acquire multimodal data, and perform precise georeferenced sampling under unstructured conditions. The results confirm the feasibility of holistic robotic systems as a key enabler for sustainable, data-driven viticulture and early disease management. The YOLOv10s detection model achieved good precision and F1-score for leaf detection, while the integrated Kalman filtering visual servoing system demonstrated low spatial tolerance under field conditions despite foliage sway and vibrations. Full article