The tip-enhanced Raman spectroscopy (TERS) provides a non-destructive and label-free molecular detection approach with high sensitivity due to the hotspot excited at tip apex, which depends crucially on the tip parameters. Here, we employed an Au cladded atomic force microscopy probe to investigate enhancement effect of the Au claddings, both in theories and experiments. The simulation optimized a highest enhancement with Au cladding thickness of 50 nm under given experimental condition, being consistent with the experimental results. It provides a practical guide to optimize the probes to improve the TERS sensitivity and exert potentials in further TERS applications.

Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)₂(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives-high conversion and high diastereomeric excess-the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories.