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The thermal process parameters are crucial in metal-sulfides ceramics as they affect significantly the resulting physico-chemical properties. In the present work, we investigated the sintering effect in the kesterite Cu2.125Zn0.875SnS4 on its structural, microstructural, and thermoelectric (TE) properties to highlight the non-negligible contribution of the thermal process often ignored in metal-sulfide ceramics. For this purpose, we developed an approach combining data science with the conventional material experiment/theory approach which can be used as a tool to shortcut the time-consuming steps of TE material optimization. We confirmed that the optimization and control of the densification process is critical in unravelling the highest potential on metal sulfide TE ceramics with a non-negligible increase of its zT up to 60%. We propose a scientific tool, the synergic combination of active machine learning with conventional chemistry/theory approaches, to either identify the most proficient sintering process as well as the process to avoid the degradation of the metal-sulfide ceramic properties and thus in a shorten number of experiments. This approach can be extended not only to other metal-sulfide ceramics for thermoelectricity but also to other research fields.

Rights:

• Creative Commons BY-NC-ND Attribution-NonCommercial-NoDerivs 4.0 International
  

Keyword: Kesterite, Machine learning, Process, Thermoelectric, Ceramic

Date published: 2024-08-29

Publisher: Elsevier BV

Journal:

• Acta Materialia (ISSN: 13596454) vol. 281 120342

Funding:

• JST-Mirai Program
• Japan Society for the Promotion of Science

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1016/j.actamat.2024.120342

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Updated at: 2024-09-20 16:30:30 +0900

Published on MDR: 2024-09-20 16:30:30 +0900