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遗传算法指导的催化剂在基于插件的微流控装置中的演化,该装置用于测试甲烷与氧气的氧化反应。

Evolution of catalysts directed by genetic algorithms in a plug-based microfluidic device tested with oxidation of methane by oxygen.

机构信息

Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, 929 East 57th Street, Chicago, Illinois 60637, USA.

出版信息

J Am Chem Soc. 2010 Mar 10;132(9):3128-32. doi: 10.1021/ja909853x.

DOI:10.1021/ja909853x
PMID:20148558
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2861856/
Abstract

This paper uses microfluidics to implement genetic algorithms (GA) to discover new homogeneous catalysts using the oxidation of methane by molecular oxygen as a model system. The parameters of the GA were the catalyst, a cocatalyst capable of using molecular oxygen as the terminal oxidant, and ligands that could tune the catalytic system. The GA required running hundreds of reactions to discover and optimize catalyst systems of high fitness, and microfluidics enabled these numerous reactions to be run in parallel. The small scale and volumes of microfluidics offer significant safety benefits. The microfluidic system included methods to form diverse arrays of plugs containing catalysts, introduce gaseous reagents at high pressure, run reactions in parallel, and detect catalyst activity using an in situ indicator system. Platinum(II) was identified as an active catalyst, and iron(II) and the polyoxometalate H(5)PMo(10)V(2)O(40) (POM-V2) were identified as active cocatalysts. The Pt/Fe system was further optimized and characterized using NMR experiments. After optimization, turnover numbers of approximately 50 were achieved with approximately equal production of methanol and formic acid. The Pt/Fe system demonstrated the compatibility of iron with the entire catalytic cycle. This approach of GA-guided evolution has the potential to accelerate discovery in catalysis and other areas where exploration of chemical space is essential, including optimization of materials for hydrogen storage and CO(2) capture and modifications.

摘要

本文使用微流控技术实现了遗传算法 (GA),以甲烷氧化为模型体系,寻找新的均相催化剂。GA 的参数包括催化剂、能够将分子氧用作末端氧化剂的共催化剂和可以调节催化体系的配体。GA 需要运行数百次反应来发现和优化高适应性的催化剂体系,而微流控技术使得这些大量的反应能够并行运行。微流控的小尺寸和小体积提供了显著的安全优势。微流控系统包括形成含有催化剂的多样化塞子阵列、在高压下引入气态试剂、并行运行反应以及使用原位指示剂系统检测催化剂活性的方法。铂 (II) 被鉴定为活性催化剂,铁 (II) 和多金属氧酸盐 H(5)PMo(10)V(2)O(40) (POM-V2) 被鉴定为活性共催化剂。Pt/Fe 体系进一步通过 NMR 实验进行了优化和表征。经过优化,Pt/Fe 体系的转化率约为 50,甲醇和甲酸的产量大致相等。Pt/Fe 体系证明了铁在整个催化循环中的兼容性。这种遗传算法 (GA) 引导进化的方法有可能加速催化和其他需要探索化学空间的领域的发现,包括对用于储氢和二氧化碳捕集的材料的优化和改性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/e45ba0a8d416/nihms178778f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/d8bf4a8d0bff/nihms178778f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/1fb2ab023a07/nihms178778f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/84f908e20b4d/nihms178778f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/e45ba0a8d416/nihms178778f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/d8bf4a8d0bff/nihms178778f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/1fb2ab023a07/nihms178778f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/84f908e20b4d/nihms178778f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2e9/2861856/e45ba0a8d416/nihms178778f4.jpg

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