Professor Jin Xuan

Associate Dean (Research and Innovation)


University roles and responsibilities

  • Associate Dean (Research and Innovation) for the Faculty of Engineering and Physical Sciences
  • Professor of Sustainable Processes
  • Member of Council
  • Member of Senate


    Research interests



    Recent representative publications

    1. Jiao K, Xuan J, Du Q, Bao Z, Xie B, Wang B, Zhao Y, Fan L, Wang H, Hou Z, Huo S, Brandon NP, Yin Y, Guiver MD, Designing the next-generation of proton exchange membrane fuel cells, Nature, 2021, 595, 361-369
    2. Niu Z, Pinfield VJ, Wu B, Wang H, Jiao K, Leung DYC, Xuan J*, Towards the digitalisation of porous energy materials: Evolution of digital approaches for microstructural design, Energy & Environmental Science, 2021, 14, 2549-2576. (Highlighted as journal front cover)
    3. Niu Z, Zhao W, Wu B, Wang H, Lin W, Pinfield VJ, Xuan J*, π Learning: A Performance-informed framework for microstructural electrode design, Advanced Energy Materials, 2023, 13, 2300244. (Highlighted as journal front cover)
    4. Leong KW, Pan W, Yi X, Luo S, Zhao X, Zhang Y, Wang Y, Mao J, Chen Y, Xuan J, Wang H, Leung DYC, Next-generation magnesium-ion batteries: The quasi-solid-state approach to multivalent metal ion storage, Science Advances, 2023, 9, eadh1181.
    5. Pan W, Zhao Y, Mao J, Wang Y, Zhao X, Leung KW, Luo S, Liu X, Wang H, Xuan J, Yang S, Chen Y, Leung DYC, High-energy single-walled carbon nanotube cathode for aqueous Al-ion battery boosted by multi-ion intercalation chemistry, Advanced Energy Materials, 2021, 11, 2101514. (Highlighted as journal back cover)
    6. Wang B, Zhang G, Wang H, Xuan J*, Jiao K, Multi-physics-resolved digital twining of proton exchange membrane fuel cells with a data-driven surrogate model, Energy and AI, 2020, 1, 100004.
    7. Lu X, Zhu C, Wu Z, Xuan J, Francisco JS, Wang H, In-situ observation of the pH gradient near the gas diffusion electrode of CO2 reduction in alkaline electrolyte, Journal of the American Chemical Society, 2020, 142, 15438–15444. (Highlighted as journal front cover)
    8. Xu H, Ma J, Tan P, Chen B, Wu Z, Zhang Y, Xuan J*, Ni M, Towards online optimisation of solid oxide fuel cell performance: combining deep learning with multi-physics simulation, Energy and AI, 2020, 1, 100003.
    9. Zhakeyev A, Wang P, Zhang L, Shu W, Wang H, Xuan J*, Additive manufacturing: Unlocking the evolution of energy materials, Advanced Science, 2017, 4, 1700187. (Most Accessed Article in November 2017)
    10. Wang B, Prinsen P, Wang H, Bai Z, Wang H, Luque R, Xuan J*, Macroporous materials: microfluidic production, functionalization and application, Chemical Society Reviews, 2017, 46, 855-914.
    H Jiang, Shuo Wang, Lei Xing, Valerie J. Pinfield, Jin Xuan (2023)Machine learning based techno-economic process optimisation for CO2 capture via enhanced weathering, In: Energy and AI Elsevier

    This work evaluated the practicability and economy of the enhanced weathering (EW)-based CO2 capture in series packed bubble column (S-PBC) contactors operated with different process configurations and conditions. The S-PBC contactors are designed to fully use the advantages of abundant seawater and highly efficient freshwater through a holistic M4 model, including multi-physics, machine learning, multi-variable and multi-objective optimisation. An economic analysis is then performed to investigate the cost of different S-PBC configurations. A data-driven surrogate model based on a novel machine learning algorithm, extended adaptive hybrid functions (E-AHF), is implemented and trained by the data generated by the physics-based models. GA and NSGA-II are applied to perform single- and multi-objective optimisation to achieve maximum CO2 capture rate (CR) and minimum energy consumption (EC) with the optimal values of eight design variables. The R2 for the prediction of CR and EC is higher than 0.96 and the relative errors are lower than 5%. The M4 model has proven to be an efficient way to perform multi-variable and multi-objective optimisation, that significantly reduces computational time and resources while maintaining high prediction accuracy. The trade-off of the maximum CR and minimum EC is presented by the Pareto front, with the optimal values of 0.1014 kg h−1 for CR and 6.1855 MJ kg−1CO2 for EC. The calculated net cost of the most promising S-PBC configuration is around 400 $ t−1CO2, which is about 100 $ t−1CO2 lower than the net cost of current direct air capture (DAC), but compromised by slower CO2 capture rate.