Dr Zhiqiang Niu

PhD, FHEA

  • Lecturer in Sustainable Transport Technologies
Publications
Google Scholar
Zhiqiang-Niu

Background:

I’m an interdisciplinary researcher at the intersection of advanced energy materials, multiscale modelling, and data-driven design. My research is motivated by the urgent need to develop next-generation clean energy technologies, including hydrogen fuel cells, batteries, and water electrolysers to decarbonise those hard-to-debate sectors such as heavy-duty transportation, chemical, and aviation. My research brings together microstructural characterisation, computational physics, and machine-learning methods to explore how the architecture of porous electrodes and catalyst layers influences performance, longevity, and materials sustainability. My current research group focuses on micro-/nano-structure evolution in energy materials, bridging experimental imaging (e.g., X-ray CT, FIB-SEM), physics-based simulation, and generative AI. Our goals are to 1) understand how electrode and catalyst-layer morphology affects cell performance and degradation; 2) Develop multiscale simulation tools that integrate pore-scale physics with device-scale operation; and 3) Apply generative AI and digital twins to accelerate the design of next-generation electrode architectures.

I welcome strong and motivated students, postdoctoral researchers, and collaborators who are interested in joining my group. If you share an interest in microstructure-informed modelling, clean-energy materials, or generative-AI for materials design, please feel free to contact me. I am open for PhD applications and also welcome visiting scholars.

Key awards:

  • Newton International Fellow
  • Royal Society Kan Tong Po International Fellow
  • Special Award of BaoSteel Excellent Student, Baosteel Education Foundation, China
  • Undergraduate/Postgraduate National Scholarship, the Ministry of Education of China.

Outline of main research interests:

  • Hydrogen Fuel Cell and Battery Technologies
  • AI-driven multi-physics, multi-scale computing technologies
  • Hydrogen Production via Water Electrolysis (SOECs, PEMWEs)
  • Sustainable fuel production
  • Waste Heat Recovery via Thermoelectric Generators

Grants and contracts:

  • Royal Society Research Grant, H2Truck: Innovating Cost-effective Hydrogen Fuel Cells for Net-zero Heavy-duty Trucks (RG\R1\251493)
  • Royal Society Kan Tong Po International Fellowship, 3D Multi-scale Modelling of Na-ion Battery Electrodes via the Fusion of Statistical Generation and 2D Imaging (KTP\R1\241088)
  • Newton International Fellowship, Deep Optimisation of Fuel Cell Electrodes via Reinforcement Learning Integrated with Physically-Meaningful Multiphase Electrochemical Modelling (NIF\R1\191864)

Current teaching responsibilities:

  • TTA003 Thermofluids
  • TTD020 Advanced CFD
  • TTC201 Machine Intelligence

Current administrative responsibilities:

  • Coordinator of Departmental Seminar Series
  • Member of ERA Research Committee

Recent publications:

  1. Niu, Z., Pinfield, V. J., Wu, B., Wang, H., Jiao, K., Leung, D. Y. C., & Xuan, J. (2021). Towards the Digitalisation of Porous Energy Materials: Evolution of Digital Approaches for Microstructural Design. Energy & Environmental Science, 14(5), 2549-2576 (Featured as journal cover). https://doi.org/10.1039/D1EE00398D
  2. Niu, Z.*, Zhao, W., Wu, B., Wang, H., Lin, W.–F., Pinfield, V. J., & Xuan, J. (2023). π Learning: A Performance-Informed Framework for Microstructural Electrode Design. Advanced Energy Materials, 13(17), 2300244(Featured as journal cover). https://doi.org/10.1002/aenm.202300244
  3. Niu, Z.*, Zhao, W., Deng, H., Tian, L., Pinfield, V. J., Ming, P., & Wang, Y. (2024). Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures. ACS Nano, 18 (31), 2024 (Featured as journal cover). https://doi.org/10.1021/acsnano.4c04943
  4. Zhao, W., Pinfield, V. J., Wang, H., Xuan, J., & Niu, Z*. (2023). An Open Source Framework for Advanced Multi-physics and Multiscale Modelling of Solid Oxide Fuel Cells. Energy Conversion and Management, 280, 116791. https://doi.org/10.1016/j.enconman.2023.116791
  5. Niu, Z.*, Zhou, Z., Perrenot, P., Villevieille, C., Zhao, W., Cai, Q., Pinfield, V. J., & Wang, Y. (2025). Seeing the Middle: Reconstructing 3D Internal Electrode Microstructures from Low-Resolution Surfaces with Generative Diffusion Artificial Intelligence. Small Science, 2025, Article 2500414. https://doi.org/10.1002/smsc.202500414

External collaborators:

  • The University of California, Irvine, USA
  • The University of Hong Kong, China
  • Fraunhofer-Gesellschaft, Germany
  • Zhejiang University, China

External roles and appointments:

  • Member of EPSRC, UKRI Peer Review College
  • International Grant Reviewer for the Research Grants Council (RGC) of Hong Kong
  • Guest Editor for the special issue “AI for Predictive Analysis” at the Elsevier Journal Energy and AI (IF: 9.7)