Search Results (10,086)

By integrating cathodic arc evaporation (CAE) with magnetron sputtering (MS) or high-power impulse magnetron sputtering (HiPIMS), hard coatings with diverse multicomponent compositions can be fabricated. Depending on the deposition conditions, the coatings with nano-composite or nano-multilayered microstructures are produced. During the mixing deposition conditions, nano-composite coatings are fabricated, which can be tailored to possess combining properties of super hardness, low friction coefficient, and excellent thermal/chemical stability. For the deposition with larger rotating periods, layer-by-layer deposition was observed. By the nano-multilayered coating design, superior mechanical properties (hardness ≥ 35 GPa), modulated residual stresses, and enhanced high-temperature properties can be obtained. In addition, lubricious elements, low friction (friction coefficient < 0.4), and low wear (<10⁻⁵ mm³/N∙m) both at ambient temperature and high temperature can be realized. Among these coatings, some have been specifically designed to achieve outstanding cutting performance in high-speed cutting applications. Several nitride and oxide hard coatings, such as AlTiN, TiAlN/TiSiN, AlCrN/Cu, and AlCrO, were deposited using a hybrid industrial physical vapor deposition (PVD) coating system. The microstructure, mechanical properties, and cutting performance of these coatings will be discussed. Full article

The Haiyang-2B (HY-2B) satellite, launched on 25 October 2018, carries both active and passive microwave sensors, including a scanning microwave Radiometer (SMR), to deliver high-precision, all-weather global observations. Sea surface temperature (SST) is among its key products. We evaluated the HY-2B SMR Level-4A (L4A) SST (25 km resolution) over the North Pacific (0–60°N, 120°E–100°W) for the period 1 October 2023 to 31 March 2025 using the extended triple collocation (ETC) and dual-pairing methods. These comparisons were made against the Remote Sensing System (RSS) microwave and infrared (MWIR) fused SST product and the National Oceanic and Atmospheric Administration (NOAA) in situ SST Quality Monitor (iQuam) observations. Relative to iQuam, HY-2B SST has a mean bias of –0.002 °C and a root mean square error (RMSE) of 0.279 °C. Compared to the MWIR product, the mean bias is 0.009 °C with an RMSE of 0.270 °C, indicating high accuracy. ETC yields an equivalent standard deviation (ESD) of 0.163 °C for HY-2B, compared to 0.157 °C for iQuam and 0.196 °C for MWIR. Platform-specific ESDs are lowest for drifters (0.124 °C) and tropical moored buoys (0.088 °C) and highest for ship and coastal moored buoys (both 0.238 °C). Both the HY-2B and MWIR products exhibit increasing ESD and RMSE toward higher latitudes, primarily driven by stronger winds, higher columnar water vapor, and elevated cloud liquid water. Overall, HY-2B SST performs reliably under most conditions, but incurs larger errors under extreme environments. This analysis provides a robust basis for its application and future refinement. Full article

Chemical vapor deposition (CVD) is a crucial technique for fabricating high-performance amorphous silicon coatings, leveraging its process flexibility and microstructural controllability. Optimizing processes like hot-wire chemical vapor deposition, plasma-enhanced chemical vapor deposition, and catalytic chemical vapor deposition enable precise regulation of coating density, surface roughness, and chemical bonding. These amorphous silicon coatings exhibit outstanding tribological properties and exceptional corrosion resistance, primarily attributed to their unique amorphous structure eliminating grain boundary defects and forming dense passivation films. Future research should focus on intelligent process development, multi-field coupling failure analysis, environmental friendliness enhancement, and lifespan prediction models to advance this technology. Full article

Chinese forest ecosystems are key carbon sinks that significantly contribute to lowering carbon emissions. Accurate Net Ecosystem Productivity (NEP) estimations are essential for evaluating their carbon sequestration capabilities and overall health. This study employed the Physiological Principles Predicting Growth-Satellites (3-PGS) and soil heterotrophic respiration models to simulate China’s forest carbon sinks and sources distribution from 2013 to 2023. Then, climatic factors influencing NEP changes were examined through the application of a geographical detector model. The net carbon sequestered was 1.71 ± 0.09 PgC with an annual average of 0.156 ± 0.0071 PgC, signifying a substantial carbon sink in China’s forest. The annual NEP was highest in evergreen broadleaf forests (352.12 gC m⁻²) and lowest in deciduous needleleaf forests (148.31 gC m⁻²). NEP in China’s forests increased by a rate of 1.67 gC m⁻² annually, with most regions exhibiting a 275.32 gC m⁻² annual carbon sink. The geographical detector model analysis showed that solar radiation, precipitation, and vapor pressure deficit were the main drivers of NEP change, while temperature and frost days had a secondary influence. Furthermore, the interaction between solar radiation and temperature variables showed the greatest impact. This study can enhance the understanding of carbon sink and source distribution in China, serve as a reference for regional carbon cycle research, and provide key insights for policymakers in developing effective climate strategies. Full article

This review summarizes the recent advances in the application of nanomaterial coatings in optical fiber sensors, with a particular focus on deposition techniques and the research progress over the past five years in humidity sensing, gas detection, and biosensing. Benefiting from the high specific surface area, abundant surface active sites, and quantum confinement effects of nanomaterials, advanced thin-film fabrication techniques—including spin coating, dip coating, self-assembly, physical/chemical vapor deposition, atomic layer deposition (ALD), electrochemical deposition (ECD), electron beam evaporation (E-beam evaporation), pulsed laser deposition (PLD) and electrospinning, and other techniques—have been widely employed in the construction of functional layers for optical fiber sensors, significantly enhancing their sensitivity, response speed, and environmental stability. Studies have demonstrated that nanocoatings can achieve high-sensitivity detection of targets such as humidity, volatile organic compounds (VOCs), and biomarkers by enhancing evanescent field coupling and enabling optical effects such as surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), and lossy mode resonance (LMR). This paper first analyzes the principles and optimization strategies of nanocoating fabrication techniques, then explores the mechanisms by which nanomaterials enhance sensor performance across various application domains, and finally presents future research directions in material performance optimization, cost control, and the development of novel nanocomposites. These insights provide a theoretical foundation for the functional design and practical implementation of nanomaterial-based optical fiber sensors. Full article

In tropical regions like sub-Saharan Africa, conventional vapor compression HVAC systems contribute disproportionately to energy use, operating costs, and carbon emissions—particularly in coastal urban areas where humidity-driven cooling demand is extreme. Despite these challenges, viable low-energy alternatives remain largely underexplored for this region. This study presents the first simulation-based assessment of a desiccant indirect evaporative cooling (DIEC) system optimized for the hot–humid coastal climate of Dar es Salaam, Tanzania, addressing a critical gap in sustainable cooling research for coastal Africa. Using OpenStudio (version 3.9.0) and a custom EnergyPlus(version 9.3.0) latent heat removal algorithm, this study models a DIEC-equipped medium office building with 100% outdoor air ventilation and exhaust-air-based desiccant regeneration. The model reflects local construction practices, occupancy profiles, and weather data and is validated with >90% accuracy against experimental benchmarks. Results demonstrate that the DIEC system (1) maintains indoor thermal comfort (23.8–24.0 °C) during peak humidity periods, and (2) reduces annual cooling energy consumption by 10.2% relative to single-speed DX systems. These savings are particularly impactful in a context where electricity prices are rising, and HVAC loads consume 25–40% of building operational budgets. Furthermore, the system’s superior humidity control and stable power demand make it well-suited for integration with decentralized renewable energy sources. By establishing a context-specific benchmark for DIEC performance, this study delivers a novel, regionally tailored strategy for decarbonizing urban cooling in coastal tropical Africa. Full article

With the rapid advancement of electronic devices, Internet of Things (IoT) technology has become increasingly integrated into everyday life. However, its broader development has been restricted by challenges related to long-term maintenance and the frequent need for power source replacements. Among the available power supply solutions, thermoelectric power generation has garnered significant interest due to its high reliability. Nevertheless, the widespread application of thermoelectric generators (TEGs) in IoT remains limited due to their relatively low conversion efficiency and structural fragility. This review systematically summarizes recent strategies aimed at enhancing the output performance and durability of TEGs through improvements in manufacturing processes and performance optimization techniques. It highlights several fabrication methods capable of endowing devices with superior flexibility and reliability, including screen printing, chemical vapor deposition (CVD), and electrospray deposition. Additionally, we discuss two key approaches for improving power generation performance: advanced material selection and multi-mechanism hybridization. Finally, the article explores the applications of TEGs in thermal energy harvesting from wearable devices, ambient environments, and aerospace fields, demonstrating their substantial potential to provide sustainable energy for IoT devices. Full article

Rapid on-site detection of chemical warfare agents (CWAs) is vital for security and environmental monitoring. In this work, copper(I) oxide (Cu₂O) nanowire (NW) chemiresistors were investigated as gas sensors for low-concentration organophosphorus chemical warfare agent (CWA) simulants. The NWs were hydrothermally synthesized and deposited onto microheater platforms, enabling them to operate at elevated working temperatures. Their sensing performance was tested against a range of vapor-phase simulants, including dimethyl methylphosphonate (DMMP), triethyl phosphate (TEP), diethyl ethylphosphonate (DEEP), diphenyl phosphoryl chloride (DPPCl), parathion, diethyl phosphite (DEP), diethyl adipate (DEA), and cyanogen chloride (ClCN). Fully oxidized P(V) simulants (DMMP, DEEP, TEP) produced modest, predominantly reversible responses (~3–6% RR). On the contrary, DPPCl and DEP induced the strongest relative responses (RR −94.67% and >200%, respectively), accompanied by irreversible surface modification as revealed by SEM and EDS. ClCN produced a substantial but reversible negative response (RR −9.5%), consistent with transient oxidative interactions. Surface poisoning was confirmed after exposure to DEP and DPPCl, which left phosphorus or chlorine residues on the Cu₂O surface. These results highlight both the promise and limitations of Cu₂O NW chemiresistors for selective CWA detection. Full article

Active packaging supports sustainable development by extending food shelf life and reducing spoilage, contributing to global food security. In this study, cellulose dialdehyde was synthesized and blended with polyvinyl alcohol in varying ratios to produce composite films. The incorporation of dialdehyde cellulose into films tended to increase puncture strength and Young’s modulus, decrease elongation, reduce water solubility, and enhance resistance to water vapor transmission because of crosslinking. Carbon quantum dots were subsequently incorporated into composite films to enhance their antibacterial property. This represents a novel combination of a natural bio-based crosslinker and fluorescent nanomaterials in a single packaging system. Carbon quantum dots were synthesized by an electrochemical method and incorporated as functional agents. The addition of carbon quantum dots influenced the mechanical properties of the films due to interactions between polymers and carbon quantum dots. This interaction also slightly reduced the antibacterial effectiveness of the films, consisting of dialdehyde cellulose and PVA in ratios of 3:1 and 4:0. Nevertheless, the composite films maintained sufficient antimicrobial activity against common foodborne bacteria, including Staphylococcus aureus, Escherichia coli, and Salmonella Typhimurium. Overall, the findings demonstrate that multifunctional material made from dialdehyde cellulose, polyvinyl alcohol, and carbon quantum dots are a promising alternative to conventional plastic packaging. Full article

Chemical vapor deposition (CVD) is a high-precision thin-film fabrication technique that is widely applied in semiconductor manufacturing, optical component manufacturing, and materials science. The performance of the deposition process plays a critical role in determining the quality of the final product. However, multiple variables in CVD processes have a highly nonlinear nature that involves complex interactions. Therefore, conventional experimental methods exhibit limitations in quality control and process optimization for CVD. In this study, we developed a predictive model based on process parameters and quality indicators using machine learning techniques to analyze and optimize the CVD processes. Through data collection, feature selection, model training, and model validation, the developed machine-learning algorithms were tested and evaluated. The adopted machine learning algorithms effectively captured the nonlinear relationships between multiple variables in CVD processes, accurately predicted thin-film quality indicators, and provided data for optimizing process parameters. In addition, the analysis results of feature importance revealed the effect of each key parameter on product quality, offering a basis for process improvement. Overall, the results of this study highlight the capability of machine learning algorithms for quality control and optimization in CVD processes for future advancements in smart manufacturing. Full article

The significant energy consumption and contribution to greenhouse gas emissions by the construction sector need careful attention to explore innovative sustainable solutions for improving the energy efficiency and thermal comfort of building envelopes. The integration of phase-change materials (PCMs) into building commodities is a favorable technology for minimizing energy consumption and enhancing thermal performance. This review paper covers the impact of PCM incorporation into construction materials, such as walls, roofs, and glazing units. Additionally, it examines different embedding techniques like direct incorporation, immersion, macro and micro-encapsulation, and form and shape-stable PCM. Factors affecting the thermal performance of PCM-integrated buildings, including melting temperature, thickness, position, volumetric change, vapor pressure, density, optical properties, latent heat, thermal conductivity, chemical stability, and climate conditions, are elaborated. Furthermore, the latest experimental and numerical simulations, as well as modeling techniques, evident from case studies, are investigated. Ultimately, the advantages of PCM integration, including energy savings, peak load reduction, improvement in interior comfort, and reduced heating, ventilation, and air-conditioning dependence, are explained alongside the limitations. Finally, the recent progress and future potential of PCM-integrated construction materials are discussed, focusing on innovations in this field, addressing the status of policies in line with the United Nations Sustainable Development Goals, and outlining research potential for the future. Full article

Solar greenhouses are an important component of modern facility agriculture, and the dynamic changes in their internal environment directly affect crop growth and yield. Among these factors, crop transpiration releases water vapor through transpiration, directly altering the indoor humidity balance and forming a dynamic coupling with factors such as temperature and light. The environment of solar greenhouses exhibits highly nonlinear and multivariate coupling characteristics, leading to insufficient prediction accuracy in existing models. However, accurate predictions are crucial for regulating crop growth and yield. However, current mainstream greenhouse environmental prediction models still have obvious limitations when dealing with such complexity: traditional machine learning models and single-variable-driven models have issues such as insufficient accuracy (average MAE is 15–20% higher than in this study) and weak adaptability to nonlinear environmental changes in multi-environmental factor coupling predictions, making it difficult to meet the needs of precision farming. A review of relevant research over the past five years shows that while LSTM-based models perform well in time series prediction, they ignore the spatial correlations between environmental factors. Models incorporating attention mechanisms can capture key variables but suffer from high computational costs. To address these issues, this study proposes a prediction model based on multi-strategy optimization and gradient-boosting (GBDT) algorithms. By introducing a multi-scale feature fusion module, it addresses the accuracy issues in multi-factor coupling prediction. Additionally, it employs a lightweight network design to balance prediction performance and computational efficiency, filling the gap in existing research applications under complex greenhouse environments. The model optimizes data preprocessing and model parameters through Sobol sequence initialization, adaptive t-distribution perturbation strategies, and Gaussian–Cauchy mixture mutation strategies and combines CatBoost for modeling to enhance prediction accuracy. Experimental results show that the MSCSO–CatBoost model performs excellently in temperature prediction, with the mean absolute error (MAE) and root mean square error (RMSE) reduced by 22.5% (2.34 °C) and 24.4% (3.12 °C), respectively, and the coefficient of determination (R²) improved to 0.91, significantly outperforming traditional regression methods and combinations of other optimization algorithms. Additionally, the model demonstrates good generalization capability in predicting multiple environmental variables such as temperature, humidity, and light intensity, adapting to environmental fluctuations under different climatic conditions. This study confirms that combining multi-strategy optimization with gradient-boosting algorithms can significantly improve the prediction accuracy of solar greenhouse environments, providing reliable support for precision agricultural management. Future research could further explore the model’s adaptive optimization in complex climatic regions. Full article

The increasing demand for renewable energy has intensified research on lignocellulosic biomass pyrolysis as a versatile route for sustainable energy and resource recovery. This study provides a comparative overview of main pyrolysis regimes (slow, intermediate, fast, and flash), emphasizing operational parameters, typical product yields, and technological readiness levels (TRLs). Reactor configurations, including fixed-bed, fluidized-bed, rotary kiln, auger, and microwave-assisted systems, are analyzed in terms of design, advantages, limitations, and TRL status. Key process parameters, such as temperature, heating rate, vapor residence time, reaction atmosphere, and catalyst type, critically influence the yields and properties of biochar, bio-oil, and syngas. Increased temperatures and fast heating rates favor liquid and gas production, whereas lower temperatures and longer residence times enhance biochar yield and carbon content. CO₂ and H₂O atmospheres modify product distribution, with CO₂ increasing gas formation and biochar surface area and steam enhancing bio-oil yield at the expense of solid carbon. Catalytic pyrolysis improves selectivity toward target products, though trade-offs exist between char and oil yields depending on feedstock and catalyst choice. These insights underscore the interdependent effects of process parameters and reactor design, highlighting opportunities for optimizing pyrolysis pathways for energy recovery, material valorization, and sustainable bioeconomy applications. Full article

The monitoring of gases and vapors using portable instruments is critical in a variety of fields, such as industrial and household safety, environmental monitoring, process control, and national security, owing to gas pollution. In this study, we design a portable and simple two-dimensional photonic crystal microcavity sensor for detecting gases such as ammonia, methane, carbon monoxide, acetylene, ethylene, and ethane. The basic structure of the sensor consists of silicon rods arranged in a square lattice pattern in air. Waveguide input and output channels are realized by engineering line defects within the lattice structure. Moreover, the sensor’s performance is continuously optimized by adding point defects, introducing a ring cavity, and varying the radius of the dielectric rods in the microcavity. Using the transmission spectrum obtained from the output waveguide, the performance parameters of the gas sensor are calculated. Based on the simulation analysis, the optimized gas sensor exhibits excellent performance, achieving a sensitivity S of 932.43 nm/RIU and a quality factor Q of 2421.719. Full article

The vaporization of urea–water solution (AdBlue) plays a critical role in the performance of selective catalytic reduction (SCR) systems for modern diesel engines. This study presents mathematical models describing the vaporization of AdBlue droplets under ultrasonic excitation generated by a magnetostrictive effect, focusing on both film and volumetric regimes. The models rigorously incorporate heat and mass transfer equations, including acoustic cavitation effects induced by ultrasound. The influence of magnetostrictive-induced atomization and combined inductive preheating on droplet detachment and SCR catalyst efficiency was analyzed. Additionally, the impact of ultrasound frequency and amplitude on thermal vaporization efficiency and reactive mixture formation was investigated with the aim of enhancing NOx emission reduction. Model validation against literature data confirmed the practical applicability of the proposed approach, offering valuable insights for optimizing ultrasound-assisted AdBlue injection systems. Full article