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To address the growing demand for temperature control precision and uniformity in wafer processing, a specialized electrostatic chuck temperature control system based on thermal control coatings is proposed, aiming to enhance thermal management robustness and homogeneity. This study employs a zoned control methodology using metal-oxide conductive coatings on silicon carbide wafer heating plates. A quadrant-based thermal control coating model was established, and finite element analysis was conducted to compare temperature distribution characteristics across three geometric configurations: sectorial, spiral, and zoned designs. The zoned structure was identified as the optimal configuration. The heating mechanism and heat transfer principles of the specialized chuck were analyzed, encompassing thermal conduction, convection, and radiation, with key factors influencing temperature distribution elucidated. Finite element simulation was utilized to optimize the thermal control system design, incorporating structured meshing to ensure computational accuracy. Experimental results demonstrate that precise regulation of coating current variations achieves maximum temperature difference control below 0.2°C and surface temperature uniformity stabilized at approximately 0.05°C, validating the efficacy of the methodology. These findings establish a robust theoretical foundation for further optimization of temperature control systems in semiconductor thermal management applications.
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http://dx.doi.org/10.1177/00368504251377216 | DOI Listing |
Light Sci Appl
September 2025
Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 230031, Hefei, China.
Marine vessels play a vital role in the global economy; however, their negative impact on the marine atmospheric environment is a growing concern. Quantifying marine vessel emissions is an essential prerequisite for controlling these emissions and improving the marine atmospheric environment. Optical imaging remote sensing is a vital technique for quantifying marine vessel emissions.
View Article and Find Full Text PDFJ R Soc Interface
September 2025
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
In temperate regions, respiratory viruses such as SARS-CoV-2 are better transmitted in winter than in summer. Understanding how the weather is associated with SARS-CoV-2 transmissibility can enhance projections of COVID-19 incidence and improve estimation of the effectiveness of control measures. During the pandemic, transmissibility was tracked by the reproduction number .
View Article and Find Full Text PDFBioresour Technol
September 2025
College of Forestry, Beijing Forestry University, Beijing 100083, PR China. Electronic address:
The timing of microbial inoculation is a decisive factor influencing both the efficiency and quality of green waste (GW) composting. This study evaluated the effects of applying a self-developed lignocellulose-degrading compound microbial inoculum at different composting phases (mesophilic, thermophilic, and cooling) compared to a commercial Effective Microorganisms agent. Thermophilic-phase inoculation (T2) was most effective by enhancing the complementary metabolic functions between strains, thus establishing an efficient lignocellulose degradation system.
View Article and Find Full Text PDFJ Environ Manage
September 2025
University of Maryland Center for Environmental Science, Annapolis, MD, USA.
River water quality degradation is a prevailing problem in coastal China with intensifying human-nature interaction. However, the spatial and temporal dynamics of water quality and their drivers remain poorly understood. In this study, we developed an analytical framework integrating self-organizing mapping (SOM) with partial least squares structural equation models (PLS-SEMs) to analyze the patterns and drivers of river water quality at 49 stations from 2021 to 2023 in Fujian Province, a coastal region in southeastern China.
View Article and Find Full Text PDFDriven by eutrophication and global warming, the occurrence and frequency of harmful cyanobacteria blooms (CyanoHABs) are increasing worldwide, posing a serious threat to human health and biodiversity. Early warning enables precautional control measures of CyanoHABs within water bodies and in water works, and it becomes operational with high frequency in situ data (HFISD) of water quality and forecasting models by machine learning (ML). However, the acceptance of early warning systems by end-users relies significantly on the interpretability and generalizability of underlying models, and their operability.
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