Prof. Runwei Mo | Energy Storage Systems (ESS) and Batteries | Editorial Board Member

Professor | East China University of Science and Technology | China

Professor Runwei Mo, a distinguished faculty member at East China University of Science and Technology, is an expert in advanced energy materials and intelligent manufacturing whose work spans innovative energy-storage systems, carbon-based material design, and next-generation battery technologies. He holds a PhD with specialization in energy materials engineering and has built a dynamic professional trajectory through academic, research, and collaborative roles across leading global institutions, contributing to major interdisciplinary projects in materials innovation, nanotechnology, and high-performance electrochemical systems. His research contributions encompass the development of novel electrode architectures, advanced graphene-based materials, CO₂ conversion strategies, flexible and high-energy battery systems, and breakthrough approaches to lithium-ion and sodium-ion storage, supported by an extensive publication record that includes high-impact journal papers, book chapters, and multiple authorized patents. He has led and contributed to significant national and international research initiatives, advancing scientific outcomes that have achieved practical application and received broad academic recognition. His professional standing is further reflected in his service as guest editor and editorial board member for several reputable journals, as well as active participation in scholarly communities focused on materials science and energy technologies. His achievements have earned him numerous honors, including multiple fellowships from respected scientific and professional societies, prestigious innovation and scientific contribution awards, and recognition for leadership in energy and environmental research. Through sustained excellence in research, mentorship, and scientific service, Professor Mo continues to advance the global progress of energy materials and sustainable technological development.

Profile: Scopus

Featured Publications

Runwei Mo*, A microstructure-enhanced dual-mode LC sensor with a PSO-BP algorithm for precise detection of temperature and pressure. Adv. Funct. Mater., Accepted.

Runwei Mo*, Autonomous self-healing strategy for flexible fiber lithium-ion batteries with ultra-high mechanical properties and volumetric energy densities. Chem. Eng. J., 154153.

Runwei Mo*, Structure engineering and heteroatom doping-enabled high-energy and fast-charging dual-ion batteries. Chem. Eng. J., 490, 151537.

Runwei Mo*, Covalently bonded MXene@Antimonene heterostructure anode for fast lithium-ion storage. Chem. Eng. J., 485, 149837.

Mr. Jufeng Han | Materials Informatics | Best Researcher Award

Master | Institute of Semiconductors | China

Dr. Jufeng Han, currently pursuing a Master’s degree in Artificial Intelligence for Science at the Institute of Semiconductors, Chinese Academy of Sciences, is an emerging researcher specializing in materials informatics and semiconductor and optoelectronic materials. His academic foundation combines advanced studies in artificial intelligence with applications in materials science, focusing on the integration of data-driven modeling with physical principles. Professionally, he has contributed to innovative research projects, most notably the development of a symbolic–neural hybrid modeling framework for perovskite bandgap prediction—an approach that enhances accuracy and interpretability in photovoltaic material screening. His work has been recognized with a Best Paper Candidate nomination and publication acceptance in Materials Today Energy. Beyond research, he collaborates within interdisciplinary teams at the Institute of Semiconductors, demonstrating leadership in bridging AI methodologies with materials discovery. His research contributions have strengthened the role of AI in accelerating semiconductor innovation, particularly in energy-efficient and sustainable technologies. He is a member of the Association for the Advancement of Artificial Intelligence (AAAI) and maintains a strong academic presence through his Google Scholar profile. Jufeng Han’s combination of technical expertise, academic excellence, and forward-looking research vision positions him as a promising scholar in AI-driven materials science and a deserving nominee for the Best Researcher Award.

Profile: Scopus

Featured Publications

Han, Jufeng*, Bandgap prediction for perovskite materials based on symbolic–neural hybrid modeling. Materials Today Energy, Accepted.

Han, Jufeng*, Symbolic–neural hybrid framework for enhanced interpretability and accuracy in perovskite bandgap prediction. Institute of Semiconductors, Chinese Academy of Sciences, In production.

Han, Jufeng, AI-driven modeling approaches for semiconductor and optoelectronic material discovery. AI for Science Research Series, Under review.