Methanol synthesis reaction over copper-group IV metal amorphous alloys as catalyst precursor

Shibata, Masatoshi; Ohbayashi, Yasuo; Kawata, Noboru; Masumoto, T.; Aoki, Kiyoshi

doi:10.1016/0021-9517(85)90384-7

Cited by 55 publications

(2 citation statements)

References 2 publications

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“…According to Figure 2, the BO made a significant change by suggesting strictly copper-based catalysts supported on zirconia, and completely ignoring the other supports (silica, titania, and alumina). These results show that the first set of experiments already managed to identify the well-known strong support effect on the catalyst performance for this reaction 44 , and the better performing ZrO2 support combined to Cu and Zn active phase, from an extremely large chemical space [45][46][47][48][49][50][51][52] . Noteworthily, it took historically about 60 years for scientists to move from the initial ZnO-Cr2O3 catalyst, introduced by BASF in 1920s, to ZrO2-based catalysts (Re/Cu), passing through the development of Cu-Al2O3 53 in the 1940's and ZnO-Cu-Al2O3 catalysts by Imperial Chemical Industries in the 1960's.…”

Section: Resultsmentioning

confidence: 99%

Accelerated Exploration of Heterogeneous CO2 Hydrogenation Catalysts by Bayesian Optimized High-throughput and Automated Experimentation.

Ramirez,

Lam,

Pacheco

et al. 2023

Preprint

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Automated high-throughput platforms and Artificial Intelligence (AI) are already accelerating discovery and optimization in various fields of chemistry and chemical engineering. However, despite some promising solutions, little to no attempts have targeted the full heterogeneous catalyst discovery workflow, with most chemistry laboratories continuing to perform research with a traditional one-at-a-time experiment approach and limited digitization. In this work, we present a closed-loop data-driven approach targeting the optimization of catalysts’ composition for the direct transformation of carbon dioxide (CO2) into methanol, by combining Bayesian Optimization (BO) algorithm, automated synthesis by incipient wetness impregnation and high-throughput catalytic performance evaluation in fixed bed mode. The BO algorithm optimized a four-objective function simultaneously (high CO2 conversion, high methanol selectivity, low methane selectivity, and low metal cost) with a total of 11 parameters (4 supports, 6 metals salts, and one promoter). In 6 weeks, 144 catalysts were synthesized and tested, with limited manual laboratory activity. The results show a significant improvement in the objectives at the end of each iteration. Between the first and fifth catalyst generation, the average CO2 conversion and methanol formation rates have been multiplied by 5.7 and 12.6 respectively, while simultaneously reducing the methane production rate by 3.2 and dividing the metal cost by 6.3 times. Notably, through the exploration process, the BO algorithm rapidly focuses on copper-based catalysts supported on zirconia doped with Zinc and/or Cerium, with the best catalysts, according to the model, showing an optimized composition of 1.85wt% Cu, 0.69wt% Zn, and 0.05wt% Ce supported on ZrO2. When changing the objective, i.e. removing the metal cost as a constrain, the BO algorithm suggests compositions centered on Indium-based catalysts, highlighting an alternative family of catalysts, testifying of the algorithm adaptability and the reusability of the data when targeting different objectives. In only 30 days, the BO, coupled with automated synthesis and high-throughput testing, has been able to replicate the major development stages in the field of heterogeneous catalysts research for CO2 conversion to methanol, made over of the last 100 years with a conventional experimental approach. This data-driven approach proves to be very efficient in exploring and optimizing catalyst composition from the vast multi-parameter space towards multiple performance objectives simultaneously and could be easily extrapolated to different parameter spaces, objectives, and be transposed to other applications.

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Section: Resultsmentioning

confidence: 99%

Accelerated Exploration of Heterogeneous CO2 Hydrogenation Catalysts by Bayesian Optimized High-throughput and Automated Experimentation.

Ramirez,

Lam,

Pacheco

et al. 2023

Preprint

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show abstract

“…Ozin found that CO 2 was formed when oxygen/carbon monoxide and gold steam were mixed at 30-40 K. In 1985, G.J. Hutchings proved that gold can catalyze the hydrochlorination of acetylene to vinyl chloride [5,8]. In 1980s, M. Haruta et al determined that gold dispersed on the surface of metal oxide by co-precipitation and deposition-precipitation can catalyze CO oxidation at room temperature [9].…”

Section: Literature Reviewmentioning

confidence: 97%

Catalysis of oxidation of carbon monoxide on supported gold nanoparticle

Tseng

Yang

et al. 2009

Journal of Hazardous Materials

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Metallic Glasses

Baiker

Molnár²

2008

Handbook of Heterogeneous Catalysis

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The sections in this article are Introduction Preparation Chemical and Structural Properties Metallic Glasses in Catalysis Research Research into Metallic Glasses in As‐Quenched State Metallic Glasses as Precursors to Catalytically Active Materials Factors Influencing Chemical and Structural Properties of Catalytic Materials Derived from Metallic Glasses Chemical Composition Chemical and Structural Homogeneity Thermal Stability and Crystallization Behavior Oxidation Behavior Dissolution of Gases Segregation Phenomena Case Studies CO Oxidation Catalysts Prepared from Metallic Glasses Pd / ZrO 2 Catalysts from Amorphous Pd  Zr Alloys Promoted Au  Zr Catalysts from Ternary Au ‐Containing Glassy Alloys Methanation over Ni Alloys Dehydrogenation over Cu  M ( Ti , Zr or Hf ) Catalysts Conclusions and Outlook

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Methanol synthesis reaction over copper-group IV metal amorphous alloys as catalyst precursor

Cited by 55 publications

References 2 publications

Accelerated Exploration of Heterogeneous CO2 Hydrogenation Catalysts by Bayesian Optimized High-throughput and Automated Experimentation.

Accelerated Exploration of Heterogeneous CO2 Hydrogenation Catalysts by Bayesian Optimized High-throughput and Automated Experimentation.

Catalysis of oxidation of carbon monoxide on supported gold nanoparticle

Metallic Glasses

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