Xiaodong Zhou | High energy physics | Research Excellence Award

Prof. Xiaodong Zhou | High energy physics | Research Excellence Award 

East China University of Science and Technology | China 

Prof. Xiaodong Zhou is a Professor at the Chemical Engineering School, East China University of Science and Technology (ECUST), Shanghai, where he has been a core faculty member for more than two decades. He received his doctoral degree from Nanjing University of Science and Technology and has built a distinguished academic career focused on advanced composite materials, functional interfaces, and high-performance materials for extreme environments. His research integrates fundamental colloid and interface chemistry with applied materials engineering, addressing both scientific challenges and industrial needs. Prof. Zhou’s primary research interests encompass composite materials, biodegradable and bio-based materials, graphene and related nanomaterials, antistatic materials, and composite material interface engineering. A significant part of his work is dedicated to high-energy laser protection materials, including fibrous felt-reinforced aerogels, ceramic-based composites, and polymer matrix composites designed to withstand ultra-high laser power densities. Through innovative structural design and interfacial regulation, his group has achieved materials exhibiting high reflectivity, low absorptivity, and excellent ablation resistance under continuous-wave laser irradiation. In parallel, Prof. Zhou has made notable contributions to sustainable materials and biodegradable composites. His research on starch-, cellulose-, and lignin-based composites, as well as polylactic acid and poly(vinyl alcohol) systems, has advanced the understanding of interfacial modification, processing–structure–property relationships, and mechanical and thermal performance optimization. These studies provide valuable pathways for developing environmentally friendly materials with enhanced functionality. Prof. Zhou is also actively engaged in graphene and nanostructured material research, including high-yield liquid-phase production of high-quality graphene and the design of graphene-based aerogels and composites for energy, environmental, and protection applications. His work emphasizes scalable processing methods and the translation of nanomaterial advantages into macroscopic performance.

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Jian Du | Machine Learning in Physics | Best Scholar Award

Dr. Jian Du | Machine Learning in Physics | Best Scholar Award 

Politecnico di Milano | Italy

Mr. Jian Du is a fourth-year Ph.D. candidate in Petroleum and Natural Gas Engineering at China University of Petroleum–Beijing, and a visiting Ph.D. researcher at the Department of Energy, Politecnico di Milano, Italy. His research focuses on the integration of physics-based knowledge and advanced machine learning techniques to address complex industrial challenges in liquid and multi-product pipeline systems. His core interests include explainable machine learning for pipeline process monitoring, physics-informed neural networks (PINNs) for efficient simulation of complex fluid dynamics, and knowledge-embedded data science frameworks for intelligent pipeline management. Through these efforts, he aims to bridge the gap between traditional physical modeling and data-driven approaches, improving reliability, interpretability, and real-time applicability in energy transportation systems. Jian Du has made significant research contributions in the areas of contamination tracking, hydraulic transient simulation, batch tracking, corrosion prediction, and energy system forecasting. He has authored or co-authored more than 30 peer-reviewed publications, with over 17 papers as first or second author, published in leading journals such as Energy, Engineering Applications of Artificial Intelligence, Journal of Industrial Information Integration, Renewable and Sustainable Energy Reviews, and Chemical Engineering Research and Design. His cumulative journal impact factor exceeds 95, and his work includes an ESI Hot Paper and Highly Cited Paper ranked in the top 1% of the engineering field. A recurring theme in his research is the development of the “DeepPipe” framework—a series of theory-guided, physics-enhanced, and multi-modal neural networks tailored for real-time pipeline monitoring and decision support.

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