Assistant Researcher | University of Oxford | United Kingdom
Dr. Zhexu Xi is an Assistant Researcher at the Inorganic Chemistry Laboratory of the University of Oxford, an invited Visiting Professor at the Hong Kong Institute of Technology, and a Guest Professor with the North American Artificial Intelligence Agency, specializing in inorganic nanoscience, interfacial functional nanomaterials, and AI-assisted materials design. His professional experience spans leading projects on two-dimensional transition-metal clusters for electrocatalysis, magnetic nanoparticle platforms for microfluidic enrichment, and ultrafast carrier dynamics in quantum-dot heterostructures, along with advancing AI-driven prediction frameworks for nanomaterials and contributing to climate-adaptive permeable pavement research. He has published more than thirty peer-reviewed papers across SCI journals and major WoS-indexed conferences, authored patents and book chapters, and delivered interdisciplinary contributions integrating nanoscience, materials chemistry, machine learning, and environmental engineering. Dr. Xi has received distinctions including the Emerging Scientist Award and a Best Paper Award nomination, and he serves as Youth Editorial Board Member of J. Mater. Sci., invited editor for MC Pharm. Sci., annual fellow of J. Water Res., peer reviewer for leading journals such as Nat. Commun. and ACS Appl. Mater. Interfaces, and guest editor for multiple special issues across SCI journals and international conferences, while also contributing to academic leadership through conference chair roles and professional memberships supporting innovation in materials chemistry and AI-driven science. His research impact includes 106 citations, 11 publications, and an h-index of 3.
Profiles: Scopus | ORCID | Google Scholar
Featured Publications
1. Z. Xi, Revisiting the Marcus Inverted Regime: Modulation Strategies for Photogenerated Ultrafast Carrier Transfer from Semiconducting Quantum Dots to Metal Oxides. RSC Adv., 2025, 15, 26897–26918.
2. G. Jin, C. Liu, Z. Xi, H. Sha, Y. Liu, J. Huang, Adaptive dual-view wavenet for urban spatial–temporal event prediction. Inf. Sci., 2022, 588, 315–330.
3. G. Jin, Z. Xi, H. Sha, Y. Feng, J. Huang, Deep multi-view graph-based network for citywide ride-hailing demand prediction. Neurocomputing, 2022, 510, 79–94.
4. R. Kang, H. Li, Z. Xi, S. Ringgard, A. Baatrup, K. Rickers, M. Sun, D.Q.S. Le, et al., Surgical repair of annulus defect with biomimetic multilamellar nano/microfibrous scaffold in a porcine model. J. Tissue Eng. Regen. Med., 2018, 12(1), 164–174.
5. G. Jin, Z. Xi, H. Sha, Y. Feng, J. Huang, Deep multi-view spatiotemporal virtual graph neural network for significant citywide ride-hailing demand prediction. arXiv preprint, 2020, arXiv:2007.15189.
Dr. Xi’s work advances the scientific understanding of nanomaterial interfaces and ultrafast charge dynamics while integrating AI-driven modelling to accelerate material discovery, supporting innovations that strengthen clean energy technologies and sustainable urban systems.