Automation isn't automatic.

Automation has become an increasingly popular tool for synthetic chemists over the past decade. Recent advances in robotics and computer science have led to the emergence of automated systems that execute common laboratory procedures including parallel synthesis, reaction discovery, reaction optimization, time course studies, and crystallization development. While such systems offer many potential benefits, their implementation is rarely automatic due to the highly specialized nature of synthetic procedures.

Automated solubility screening platform using computer vision.

Solubility screening is an essential, routine process that is often labor intensive. Robotic platforms have been developed to automate some aspects of the manual labor involved. However, many of the existing systems rely on traditional analytic techniques such as high-performance liquid chromatography, which require pre-calibration for each compound and can be resource consuming.

Automated Experimentation Powers Data Science in Chemistry.

Data science has revolutionized chemical research and continues to break down barriers with new interdisciplinary studies. The introduction of computational models and machine learning (ML) algorithms in combination with automation and traditional experimental techniques has enabled scientific advancement across nearly every discipline of chemistry, from materials discovery, to process optimization, to synthesis planning. However, predictive tools powered by data science are only as good as their data sets and, currently, many of the data sets used to train models suffer from several limitations, including being sparse, limited in scope and requiring human curation.

Exploration of continuous-flow benchtop NMR acquisition parameters and considerations for reaction monitoring.

This study focused on fundamental data acquisition parameter selection for a benchtop nuclear magnetic resonance (NMR) system with continuous flow, applicable for reaction monitoring. The effect of flow rate on the mixing behaviors within a flow cell was observed, along with an exponential decay relationship between flow rate and the apparent spin-lattice relaxation time (T1*) of benzaldehyde. We also monitored sensitivity (as determined by signal-to-noise ratios; SNRs) under various flow rates, analyte concentrations, and temperatures of the analyte flask.