We report the development and applications of a computer vision based reaction monitoring method for parallel and high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics of one reaction from one video, this new approach enables one video to capture bulk kinetics of multiple reactions running in parallel. Case studies, in and beyond well‐plate high throughput settings, are described. Analysis of parallel dye‐quenching hydroxylations, DMAP‐catalysed esterification, solid‐liquid sedimentation dynamics, metal catalyst degradation, and biologically‐relevant sugar‐mediated nitro reduction reactions have each provided insight into the scope and limitations of camera‐enabled high throughput kinetics as a means of widening known analytical bottlenecks in HTE for reaction discovery, mechanistic understanding, and optimisation. It is envisaged that the nature of the multi‐reaction time‐resolved datasets made available by this analytical approach will later serve a broad range of downstream efforts in machine learning approaches to exploring chemical space.

中文翻译：

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使用计算机视觉进行并行和高通量反应监控


我们报告了基于计算机视觉的并行和高通量实验（HTE）反应监测方法的开发和应用。之前的工作报道了从一个视频中提取一个反应的整体动力学的方法，而这种新方法使一个视频能够捕获并行运行的多个反应的整体动力学。描述了孔板高通量设置内外的案例研究。对平行染料淬灭羟基化、DMAP 催化酯化、固液沉降动力学、金属催化剂降解和生物相关糖介导的硝基还原反应的分析都提供了对相机支持的高通量动力学的范围和局限性的深入了解，例如一种拓宽 HTE 已知分析瓶颈的方法，以实现反应发现、机理理解和优化。预计通过这种分析方法提供的多反应时间解析数据集的性质稍后将服务于探索化学空间的机器学习方法的广泛下游工作。