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Neural network and experimental thermodynamics study of YCrO3-δ for efficient solar thermochemical hydrogen production

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Listed:
  • Cong, Jian
  • Ma, Tianzeng
  • Chang, Zheshao
  • Zhang, Qiangqiang
  • Akhatov, Jasurjon S.
  • Fu, Mingkai
  • Li, Xin

Abstract

ABO3-type perovskites have been demonstrated as promising redox materials for solar thermochemical H2 production. In this work, a new energy material screening method based on the neural network system is designed as a feasible way to search for promising H2 production materials. The predicted oxygen vacancy formation energy of the selected YCrO3-δ is 4.199 eV, hinting excellent H2 production potential. Thermogravimetric analysis shows that the doping of Zr into YCrO3-δ improves oxygen formation capacity, leading to the maximum δ of 0.106. The molar enthalpy and entropy of YCr0.75Zr0.25O3-δ have the positive relationship with δ, and the maximum values of which are 273.7 kJ mol−1 and 164.9 J mol−1 K−1 respectively. Based on the equilibrium thermodynamic principle, the peak H2 yield is predicted to be 444.6 μmol g−1. Considering material kinetic limitation, gas-solid heat recovery and parameter sensitivity, the maximum H2 production efficiency of YCr0.75Zr0.25O3-δ is 17.3%. The combination of neural network and material thermodynamics provides a new pathway to design promising H2 production materials, and the screened YCrO3-δ presents excellent solar thermochemical H2 production capacity.

Suggested Citation

  • Cong, Jian & Ma, Tianzeng & Chang, Zheshao & Zhang, Qiangqiang & Akhatov, Jasurjon S. & Fu, Mingkai & Li, Xin, 2023. "Neural network and experimental thermodynamics study of YCrO3-δ for efficient solar thermochemical hydrogen production," Renewable Energy, Elsevier, vol. 213(C), pages 1-10.
    • Handle: RePEc:eee:renene:v:213:y:2023:i:c:p:1-10
    DOI: 10.1016/j.renene.2023.05.085
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