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  • KENTECH
    Distinguished Prof.
    Chinho Park’s Group

Welcome to Prof. Chinho Park’s Group

We innovatively design new materials and devices for clean energy generation and storage applications with strong prominence on green hydrogen production from photoelectrochemical water splitting and green electricity generation from earth-abundant thin film solar cells.

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Prof. Chinho Park’s Group, Hydrogen Energy And Photovoltaics Research & Technology Development Laboratory (HEART) Researches

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  • Green hydrogen
    production
  • Thin film
    photovoltaics
    devices
  • Next generation
    battery materials
    and devices

Prof. Chinho Park’s Group, Hydrogen Energy And Photovoltaics Research & Technology Development Laboratory (HEART) Professor

Prof. Chinho Park

  • Acting President and Distinguished Professor,
  • Korean Institute of Energy Technology (KENTECH), 21 KENTECH-gil, Naju-si, Jeollanam-do, Republic of Korea
  • Tel : +82-61-330-9602
  • Email : chpark@kentech.ac.kr

Prof. Chinho Park’s Group, Hydrogen Energy And Photovoltaics Research & Technology Development Laboratory (HEART) Members

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Prof. Chinho Park’s Group, Hydrogen Energy And Photovoltaics Research & Technology Development Laboratory (HEART) Publications

  • Mohammad R. Thalji, Abdullah Al Mahmud, Farzaneh Mahmoudi, Amr Hussein Mady, Deivasigamani Ranjith Kumar, Kwok Feng Chong, Chinho Park*, Jae-Jin Shim*, "Ethyl xanthate-driven in situ synthesis of Ni-Fe sulfide@ Ti3C2Tx MXene hybrid electrodes for ultra-high-performance supercapacitors,"Chemical Engineering Journal, 522, 167789 (2025).

  • Mohammad R. Thalji, Farzaneh Mahmoudi, Leonidas G Bachas, and Chinho Park*, "MXene-Based Electrocatalysts for Water Splitting: Material Design, Surface Modulation, and Catalytic Performance", International journal of Molecular Sciences, 26, 8019 (2025).

  • Nagesh D. Kolhe, Mohaseen S. Tamboli, Hassan Zuhaib, Chinho Park*, Mukund G Mali* et.al., MIL-Fe-88 derived α-Fe2O3/C@ g-C3N4 ternary composite with boosted electrocatalytic activity: OER stability prediction and forecasting via machine learning, "Journal of Industrial and Engineering Chemistry, Accepted (2025)