Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation
Process optimisation for NASICON-type solid electrolyte synthesis using a combination of experiments and bayesian optimisation
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DOI：
10.1039/d2ma00731b
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发表时间：
2022-10-11
影响因子：
5
通讯作者：
Natori, Takaaki
中科院分区：
文献类型：
--
作者：
Takeda, Hayami;Fukuda, Hiroko;Natori, Takaaki
Na超离子导体(NASICON)型LiZr2(PO4)(3) (LZP)是一种用于全固态锂离子电池的氧化物基固体电解质候选材料。然而，由于离子电导率不足，人们通过控制组成和晶体结构进行异价阳离子掺杂以提高锂离子电导率。锂离子电导率还受到烧结体的微观结构特性（例如密度、形态和元素分布）的影响，因此，控制工艺参数（例如固态反应期间的加热条件）可以提高电导率。采用详尽的实验方法，在各种两步加热条件下通过固态反应合成了Ca和Si共掺杂的富锂NASICON型LZP，通过优化条件获得了最高的锂离子电导率。当样品首先在1050℃加热，然后在1250℃加热时，获得了最高的总锂离子电导率3.3 x 10(-5)Scm(-1)。对材料的晶体结构、相对密度、微观形貌和锂离子电导率进行了表征，并研究了它们之间的关系。这些关系很复杂，仅通过少量实验就可以直观地确定最佳条件。相反，作为概念验证研究，收集的数据用于证明贝叶斯优化 (BO) 有效地改进了最佳加热条件的实验确定。与材料行业采用的传统试错方法相比，BO 引导的实验研究可以更快地确定最佳条件。效率系数大约是穷举搜索的两倍。
Na superionic conductor (NASICON)-type LiZr2(PO4)(3) (LZP) is an oxide-based solid electrolyte candidate for use in all-solid-state Li-ion batteries. However, as the ionic conductivity is insufficient, doping with aliovalent cations has been carried out to improve the Li-ion conductivity by controlling the composition and crystal structure. Li-ion conductivity is also affected by the microstructural properties of a sintered body, such as density, morphology, and elemental distribution, and thus, controlling process parameters, such as heating conditions during the solid-state reaction, improves conductivity. Using an exhaustive experimental approach, Ca and Si co-doped Li-rich NASICON-type LZP was synthesised via solid-state reactions under various two-step heating conditions to yield the highest Li-ion conductivity by optimising the conditions. The highest total Li-ion conductivity of 3.3 x 10(-5) S cm(-1) was obtained when the sample was first heated at 1050 degrees C and then heated at 1250 degrees C. The crystal structures, relative densities, micromorphologies, and Li-ion conductivities of the materials were characterised, and their relationships were investigated. These relationships were complex, and intuitively determining the optimal conditions was challenging with only a few experiments. Instead, as a proof-of-concept study, the collected data were used to demonstrate that Bayesian optimisation (BO) efficiently improved the experimental determination of the optimal heating conditions. The BO-guided experimental investigation determined the optimal conditions more rapidly compared to conventional trial-and-error approaches employed in the materials industry. The efficiency factor was approximately double that of the exhaustive search.