Dr. Fan Zhang | Energy Technologies | Best Researcher Award

Dr. Fan Zhang | Energy Technologies | Best Researcher Award

Research Associate | Queensland University of Technology | Australia

Dr. Fan Zhang is a distinguished researcher at the Queensland University of Technology whose work focuses on the advancement of next-generation aqueous zinc-ion batteries and sustainable energy storage technologies. Their research integrates bioinspired materials design, electrolyte optimization, and interfacial engineering to address key challenges such as dendrite formation, hydrogen evolution, and low reversibility in Zn-based systems. With significant contributions to materials science and electrochemistry, Dr. Zhang has established a strong reputation for innovative approaches that enhance the safety, energy density, and long-term stability of aqueous batteries. Their studies combine experimental synthesis with advanced characterization techniques, leading to impactful findings published in high-impact journals such as Advanced Materials, Journal of the American Chemical Society, National Science Review, and Nano Energy. Dr. Zhang’s scholarly influence is evidenced by a Scopus citation count of 370 (h-index: 12, 17 documents) and a Google Scholar citation count of 352 (h-index: 11, i10-index: 11). Their research continues to drive progress in electrochemical energy storage, contributing to the global shift toward sustainable and environmentally friendly power solutions.

Profile

Scopus | Google Scholar

Featured Publications

Zhang, F., Liao, T., Liu, C., Peng, H., Luo, W., Yang, H., Yan, C., & Sun, Z. (2022). Biomineralization-inspired dendrite-free Zn-electrode for long-term stable aqueous Zn-ion battery. Nano Energy, 103, 107830.

Zhang, F., Liao, T., Peng, H., Xi, S., Qi, D. C., Micallef, A., Yan, C., Jiang, L., & Sun, Z. (2024). Outer sphere electron transfer enabling high-voltage aqueous electrolytes. Journal of the American Chemical Society, 146(15), 10812–10821.

Zhang, F., Liao, T., Qi, D. C., Wang, T., Xu, Y., Luo, W., Yan, C., Jiang, L., & Sun, Z. (2024). Zn-ion ultrafluidity via bioinspired ion channel for ultralong lifespan Zn-ion battery. National Science Review, 11(8), nwae199.

Zhang, F., Liao, T., Yan, C., & Sun, Z. (2024). Bioinspired designs in active metal-based batteries. Nano Research, 17(2), 587–601.

Zhang, F., Liao, T., Zhou, Q., Bai, J., Li, X., & Sun, Z. (2025). Advancements in ion regulation strategies for enhancing the performance of aqueous Zn-ion batteries. Materials Science and Engineering: R: Reports, 165, 101012.

Dr. Dawei Qiu | Smart Grid | Best Researcher Award

Dr. Dawei Qiu | Smart Grid | Best Researcher Award

Lecturer, University of Exeter, United Kingdom

Dr. Dawei Qiu is a distinguished scholar in smart energy systems, currently serving as a Lecturer at the University of Exeter, UK 🏫. With a strong background in electrical engineering and power systems, he specializes in AI-driven reinforcement learning, market design for low-carbon energy transition, and resilience enhancement of energy systems ⚡. His extensive research contributions in smart grids and power systems have earned him recognition in academia, with a Google Scholar citation count of 2,109, an h-index of 24, and an h10-index of 35 📊.

Publication Profile

Google Scholar

🎓 Education

Dr. Qiu holds a Ph.D. in Electrical Engineering from Imperial College London (2016–2020) 🎓, where he conducted pioneering research on local flexibility’s impact on electricity retailers under the supervision of Prof. Goran Strbac. Prior to this, he completed his M.Sc. in Power System Engineering from University College London (2014–2015) and obtained his B.Eng. in Electrical and Electronic Engineering from Northumbria University at Newcastle (2010–2014) ⚙️. His academic journey has been shaped by esteemed mentors, including Dr. Ben Hanson and Dr. Zhiwei (David) Gao, IEEE Fellow.

💼 Experience

Dr. Qiu’s professional career spans academia and research institutions, where he has contributed significantly to energy systems innovation 🌍. Before joining the University of Exeter in 2024, he was a Research Fellow at Imperial College London (2023–2024), specializing in market design for low-carbon energy systems. He also served as a Research Associate at the same institution from 2020 to 2023 🔬. His work in smart grids and energy resilience has been instrumental in shaping sustainable and intelligent power infrastructure.

🏆 Awards and Honors

Dr. Qiu’s research excellence has been acknowledged through various accolades 🏅. His contributions to smart energy systems, AI-driven reinforcement learning, and low-carbon market design have positioned him as a leading researcher in the field. His studies have been published in top-tier journals, and his work has received high citations, demonstrating its impact on the global research community 🌟.

🔬 Research Focus

Dr. Qiu’s research is centered on leveraging artificial intelligence and reinforcement learning for power and energy applications 🤖. His work explores market mechanisms for cost-effective and sustainable energy transitions, as well as the resilience enhancement of energy systems in response to climate change 🌍. His expertise in AI-driven optimization and machine learning applications in energy systems makes him a key contributor to the advancement of smart grid technologies.

🔚 Conclusion

Dr. Dawei Qiu is a leading researcher in smart energy systems, with a strong academic background and impactful contributions to power systems engineering 🔬. His expertise in AI-driven market optimization, reinforcement learning, and resilient energy systems has made him a valuable asset to the research community 🌍. With his ongoing work at the University of Exeter, he continues to drive innovation in low-carbon and intelligent energy solutions ⚡.

🔗 Publications

A knowledge-based safe reinforcement learning approach for real-time automatic control in a smart energy hub – Applied Energy (Under review, 2025) 🔗 Link

Enhanced Meta Reinforcement Learning for Resilient Transient Stabilization – IEEE Transactions on Power Systems (Under review, 2025) 🔗 Link

Machine learning-based economic model predictive control for energy hubs with variable energy efficiencies – Energy (First round revision, 2024) 🔗 Link

A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems – Proceedings of the IEEE (Under review, 2025) 🔗 Link

Coordinated Optimal Dispatch Based on Dynamic Feasible Operation Region Aggregation – IEEE Transactions on Smart Grid (First round revision, 2024) 🔗 Link

A Sequential Multi-Agent Reinforcement Learning Method for Coordinated Reconfiguration of Substation and MV Distribution Networks – IEEE Transactions on Power Systems (Under review, 2024) 🔗 Link

Enhancing Microgrid Resilience through a Two-Layer Control Framework for Electric Vehicle Integration and Communication Load Management – IEEE Internet of Things Journal (Under review, 2024) 🔗 Link

Coordinated Electric Vehicle Control in Microgrids Towards Multi-Service Provisions: A Transformer Learning-based Risk Management Strategy – Energy (Under review, 2024) 🔗 Link

Adaptive Resilient Control Against False Data Injection Attacks for a Multi-Energy Microgrid Using Deep Reinforcement Learning – IEEE Transactions on Network Science and Engineering (Under review, 2024) 🔗 Link