High Throughput Parallel Reaction Monitoring with Computer Vision

We report the development and applications of a computer vision based reaction monitoring method for high throughput experimentation (HTE). Whereas previous efforts reported methods to extract bulk kinetics from a single 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 towards exploring chemical space.