办公地址：中山大学深圳校区工学园1栋339室

工作邮箱：liujp59@mail.sysu.edu.cn

中山大学“百人计划”助理教授，硕士生导师。

主要致力于新能源材料的开发与研究，研究领域包括：固态氧化物燃料电池，锂离子电池，机器学习。
目前共发表论文（Nature Catalysis, Nature Communications, Energy & Environmental Science, Advanced Energy Materials等）共44篇，其中第一作者（含共一）文章20篇。
曾任Joule, Journal of Materials Chemstry A等期刊审稿人。

教育与工作经历

2022-至今  中山大学先进能源学院，助理教授
2021-2022 香港生产力局，博士后
2020-2021 香港科技大学，机械与航空系，博士后
2016-2020 香港科技大学，机械与航空系，博士，专业：机械工程
2015-2016 香港城市大学，能源及环境学院，科研助理
2013-2014 西安交通大学，科研助理
2009-2013 西北工业大学，航海学院，本科，专业：热能与动力工程

代表性论文

1. Liu, J., Kim, J. K., Wang, Y., Kim, H., Belotti, A., Koo, B., Wang, Z., Jung, W.⋆, and Ciucci, F.⋆, 2021. Understanding and mitigating A-site surface enrichment in Ba-containing perovskites: A combined computational and experimental study of BaFeO3. Energy & Environmental Science, 15, 4069-4082. (Front Cover)
2. Wang, J.⋆, Kim S. J., Liu, J., Gao, Y., Choi, S., Han, J., Shin, H., Jo, S., Kim, J., Ciucci, F., Kim, H., Li, Q., Yang, W., Long, X., Yang, S.⋆, Cho, S. P., Chae, K. H., Kim, M. G., Kim, H.⋆, and Lim, J.⋆, 2021. Redirecting dynamic surface restructuring of a layered transition metal oxide catalyst for superior water oxidation. Nature Catalysis, 4, 212–222.
3. Wang, Z., Wang, Y., Wang, J., Song, Y., Robson, M. J., Seong, A., Yang, M., Zhang, Z., Belotti, A., Liu, J., Kim, G., Lim, J., Shao, Z., and Ciucci, F.⋆, 2022. Rational design of perovskite ferrites as high-performance proton-conducting fuel cell cathodes. Nature Catalysis, 5, 777–787.
4. Zhang, Z.†, Liu, J.†, Wang, J.†, Wang, Q., Wang, Y., Wang, K., Wang, Z., Gu, M., Tang, Z., Lim, J., Zhao, T., and Ciucci, F.⋆. Single-atom catalyst for high-performance methanol oxidation. Nature Communications, 12(1), 5235.
5. Liu, J., Lu, Z., Effat, M. B., and Ciucci, F.⋆, 2019. A theoretical study on the stability and ionic conductivity of the Na11M2PS12 (M= Sn, Ge) superionic conductors. Journal of Power Sources, 409, 94-101.
6. Lin, X.†, Zhou, G.†, Liu, J.†, Yu, J., Effat, M. B., Wu, J., and Ciucci, F.⋆, 2020. Rechargeable battery electrolytes capable of operating over wide temperature windows and delivering high safety. Advanced Energy Materials, 10(43), 2001235.
7. Song, Y.†, Liu, J.†, Wang, Y.⋆, Guan, D., Zhang, Z., Shao, Z.⋆, Ciucci, F.⋆. Nanocomposites: A new opportunity for developing highly active and durable bifunctional air electrodes for reversible pro- tonic ceramic cells. Advanced Energy Materials, 11(36), 2101899.
8. Yu, J.†, Lin, X.†, Liu, J.† Yu, J. T., Zhou, G., Robson, M. J., Law, H. M., Kwok, S. C., Ciucci, F.⋆, 2021. In situ fabricated quasi-solid polymer electrolyte for high-Energy-density lithium metal battery capable of subzero operation. Advanced Energy Materials, 12(2), 202102932.
9. Lin, X.†, Zhou, G.†, Liu, J.†, Robson, M. J., Yu, J., Wang, Y., Zhang, Z., Kwok, S. C., and Ciucci F.⋆, 2021. Bifunctional hydrated gel electrolyte for long-cycling Zn-ion battery with NASICON-type cathode. Advanced Functional Materials, 31(42), 2105717.
10. Liu, J., and Ciucci, F.⋆, 2017. Modeling the impedance spectra of mixed conducting thin films with exposed and embedded current collectors. Physical Chemistry Chemical Physics, 19(38), 26310-26321.
11. Wang, Y.†, Song, Y.†, Liu, J.†, Yang, K., Lin, X., Yang, Z., and Ciucci, F.⋆, 2021. Functionalized metal-supported reversible protonic ceramic cells with exceptional performance and durability. Advanced Energy and Sustainability Research, 3(2), 202100171.
12. Liu, J., Wang, J., Belotti, A. and Ciucci, F.⋆ , 2019. P-substituted Ba0.95La0.05FeO3-d as a cath- ode material for SOFCs. ACS Applied Energy Materials, 2(8), 5472-5480.
13. Liu, J., and Ciucci, F.⋆, 2019. The Gaussian process distribution of relaxation times: A machine learning tool for the analysis and prediction of electrochemical impedance spectroscopy data. Electrochimica Acta, 331, 135316.
14. Liu, J., and Ciucci, F.⋆, 2020. The deep-prior distribution of relaxation times. Journal of The Electrochemical Society, 167, 026506.
15. Liu, J.†, Wan, T. H.†, and Ciucci, F.⋆, 2020. A Bayesian view on the Hilbert transform and the Kramers-Kronig transform of electrochemical impedance data: probabilistic estimates and quality scores. Electrochimica Acta, 357, 136864.
16. Zhou, M.†, Liu, J.†, Ye, Y., Sun, X., Chen, H., Yin, Y., Ling, Y., Ciucci F.⋆ and Chen, Y.⋆, 2021. Enhancing the intrinsic activity and stability of perovskite cobaltite at elevated temperature through surface stress. Small, 17(45), 2104144.
17. Sun, H.†, Liu, J.†, Chen, G., Kim, H., Kim, S., Hu, Z., Lin, H. J., Chen, C. T., Ciucci, F.⋆, and Jung, W.⋆, 2021. Hierarchical structure of CuO nanowires decorated with Ni LDH supported on Cu foam for hydrogen production via urea electrocatalysis. Small Methods, 6(1), 202101017.
18. Wang, K.†, Liu, J.†, Tang, Z.⋆, Li, L., Wang, Z., Ciucci, F.⋆, Thomsen, L. Wright, J., Chen, S., and Bedford, N.⋆, 2021. Establishing structure/property relationships in atomically dispersed Co-Fe dual site M-Nx catalysts on mesoporous carbon for oxygen reduction reaction. Journal of Materials Chemistry A, 9(22), 13044-13055.
19. Yang, K.†, Liu, J.†, Wang, Y.†, Shi, X., Lu, Q.⋆, Ciucci, F.⋆, and Yang, Z.⋆, 2022. Machine-learning- assisted prediction of long-term performance degradation on solid oxide fuel cell cathodes induced by chromium poisoning. Journal of Materials Chemistry A, 10, 23683-23690.
20. Sun, H.†, Liu, J.†, Kim, H., Song, S., Fei, L., Hu, Z., Lin, H.-J., Chen, C.-T., Ciucci, F.⋆, and Jung, W.⋆, 2022. Ni-doped CuO Nanoarrays Activate Urea Adsorption and Stabilizes Reaction Intermediates to Achieve High-performance Urea Oxidation Catalysts. Advanced Science, 9(34), 2204800.
21. Zhang, Z.†, Liu, J.†, Curcio, A., Wang, Y., Wu, J., Zhou, G., Tang, Z., and Ciucci, F.⋆, 2020. Atomically dispersed materials for rechargeable batteries. Nano Energy, 76, 105085.
22. Kim, J. H.†, Kim, J. K.†, Liu, J.†, Curcio, A., Jang, J. S., Kim, I. D., Ciucci, F.⋆, and Jung, W.⋆, 2020. Nanoparticle ex-solution for supported catalysts: Materials design, mechanism and future perspectives. ACS Nano, 15(1), 81-110.
23. Simona, P., Liu, J., Quattrocchi, E., and Ciucci, F.⋆, 2021. Neural ordinary differential equations and recurrent neural networks for predicting the state of health of batteries. Journal of Energy Storage, 50, 104209.