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

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-M...

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Bibliographic Details
Published in:Nanomaterials (Basel, Switzerland) Switzerland), 2024-01, Vol.14 (1), p.75
Main Authors: Shin, Sangsoo, Song, Hyeongyun, Shin, Yeon Su, Lee, Jaegeun, Seo, Tae Hoon
Format: Article
Language:English
Subjects:
Aluminum oxide
Bayesian analysis
Bayesian optimization
Carbon
carbon nanotube
Carbon nanotubes
catalyst
Catalysts
Chemical synthesis
Chemical vapor deposition
Design of experiments
Maximization
Mechanical properties
Nanoparticles
Nanotechnology
Nanotubes
Optimization
Robustness (mathematics)
Scanning electron microscopy
wet-impregnation
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Summary: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 consider noise, and one-shot knowledge gradient (OKG), which takes noise into account. As a result, both acquisition functions successfully optimized the carbon yield with similar performance. The result suggests that the use of EI, which has a lower computational load, is acceptable if the system has sufficient robustness. The investigation of the contour plots showed that the addition of Mo has a negative effect on carbon yield.
ISSN:2079-4991
2079-4991
DOI:10.3390/nano14010075