| 期刊介绍 | ACS Engineering Au是一本开放获取期刊，报道化学工程、应用化学和能源方面的重大进展，涵盖了基础知识、过程和产品。该期刊的收稿范围包括来自学术和工业环境的实验、理论、数学、计算、化学和物理研究。
期刊收录研究方向：
热力学、传递现象（流动、混合、质量和传热）、化学反应动力学和工程、催化、分离、界面现象和材料等领域的基础研究
工艺设计、开发和强化（例如，化学品和材料的工艺技术、合成和设计方法、工艺强化、多相反应器、放大生产、系统分析、工艺控制、数据关联方案、建模、机器学习、人工智能）
涉及化学和工程方面的产品研发（例如，催化剂、塑料、弹性体、纤维、粘合剂、涂料、纸张、膜、润滑剂、陶瓷、气溶胶、流体装置、强化工艺设备）
能源和燃料（例如，可再生能源的预处理、加工和利用，燃料的加工和利用，原料燃料和精炼产品的性质和结构或分子组成，燃料电池，氢气，电池，光化学燃料和能源生产，脱碳，电气化，微波;空化）
测量技术、计算模型和材料热物理、热力学和传输特性以及相平衡行为的数据
新方法、模型和工具（例如，实时数据分析、多尺度模型、物理知情机器学习模型、机器学习增强型基于物理的模型、软传感器、高性能计算）
ACS Engineering Au is an open access journal that reports significant advances in chemical engineering, applied chemistry, and energy covering fundamentals, processes, and products. The journal's broad scope includes experimental, theoretical, mathematical, computational, chemical, and physical research from academic and industrial settings. Short letters, comprehensive articles, reviews, and perspectives are welcome on topics that include:
Fundamental research in such areas as thermodynamics, transport phenomena (flow, mixing, mass & heat transfer), chemical reaction kinetics and engineering, catalysis, separations, interfacial phenomena, and materials
Process design, development, and intensification (e.g., process technologies for chemicals and materials, synthesis and design methods, process intensification, multiphase reactors, scale-up, systems analysis, process control, data correlation schemes, modeling, machine learning, Artificial Intelligence)
Product research and development involving chemical and engineering aspects (e.g., catalysts, plastics, elastomers, fibers, adhesives, coatings, paper, membranes, lubricants, ceramics, aerosols, fluidic devices, intensified process equipment)
Energy and fuels (e.g., pre-treatment, processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells, hydrogen, batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)
Measurement techniques, computational models and data on thermo-physical, thermodynamic, and transport properties of materials and phase equilibrium behavior
New methods, models and tools (e.g., real-time data analytics, multi-scale models, physics informed machine learning models, machine learning enhanced physics-based models, soft sensors, high-performance computing) |
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