Calibration-free quantification and automated data analysis for high-throughput reaction screening†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY
Felix Katzenburg, Florian Boser, Felix R. Schäfer, Philipp M. Pflüger and Frank Glorius
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引用次数: 0

Abstract

The accelerated generation of reaction data through high-throughput experimentation and automation has the potential to boost organic synthesis. However, efforts to generate diverse reaction datasets or identify generally applicable reaction conditions are still hampered by limitations in reaction yield quantification. In this work, we present an automatable screening workflow that facilitates the analysis of reaction arrays with distinct products without relying on the isolation of product references for external calibrations. The workflow is enabled by a flexible liquid handler and parallel GC-MS and GC-Polyarc-FID analysis while we introduce pyGecko, an open-source Python library for processing GC raw data. pyGecko offers comprehensive analysis tools allowing for the determination of reaction outcomes of a 96-reaction array in under a minute. Our workflow's utility is showcased for the scope evaluation of a site-selective thiolation of halogenated heteroarenes and the comparison of four cross-coupling protocols for challenging C–N bond formations.

Abstract Image

免校准定量和自动化数据分析高通量反应筛选†
通过高通量实验和自动化加速生成反应数据具有促进有机合成的潜力。然而,生成不同的反应数据集或确定普遍适用的反应条件的努力仍然受到反应产率量化的限制。在这项工作中,我们提出了一种自动化的筛选工作流程,可以方便地分析具有不同产品的反应阵列,而无需依赖于外部校准的产品参考的隔离。该工作流由灵活的液体处理程序和并行的GC- ms和GC- polyarc - fid分析实现,同时我们引入了pyGecko,一个用于处理GC原始数据的开源Python库。pyGecko提供全面的分析工具,允许在一分钟内确定96个反应阵列的反应结果。我们的工作流程的实用程序展示了对卤化杂芳烃的选择性巯基化的范围评估,以及对具有挑战性的C-N键形成的四种交叉偶联方案的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
2.80
自引率
0.00%
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