Bayesian Optimization of Wet-Impregnated Co-Mo/Al₂O₃ Catalyst for Maximizing the Yield of Carbon Nanotube Synthesis.

ABSTRACT: Multimetallic catalysts have demonstrated their high potential for the controlled synthesis of carbon nanotubes (CNTs), but their development requires a more complicated optimization than that of monometallic catalysts. Here, we employed Bayesian optimization (BO) to optimize the preparation of Co-Mo/Al₂O₃ catalyst using wet impregnation, with the goal of maximizing carbon yield in the chemical vapor deposition (CVD) synthesis of CNTs. In the catalyst preparation process, we selected four parameters to optimize: the weight percentage of metal, the ratio of Co to Mo in the catalyst, the drying temperature, and the calcination temperature. We ran two parallel BO processes to compare the performance of two types of acquisitions: expected improvement (EI), which does not

SUBMITTER: Shin S 

PROVIDER: S-EPMC10780783 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature