Automation and Autonomous Experimentation for Sol-Gel Nanomaterial Synthesis

Abstract

The nexus of laboratory automation and machine learning enables a new paradigm of autonomousexperimental materials research. Autonomous experimentation integrates highly automated materials synthesis and characterization experiments with machine-learning guided experimental design strategies to adaptively execute experiments that advance specific research goals. These systems have the potential to accelerate materials development timelines compared to traditional manual processes by efficiently targeting experimental efforts. Sol-gel processes are used to synthesize a diverse array of metal oxide nanomaterials for many applications. In particular, mesoporous colloidal silicas are promising materials for use as catalysis support matrices, 5 chromatographic separation media, and drug delivery systems. Achieving retrosynthetic control over particle morphologies is critical for advancing use in these applications. In this work, automated and autonomous systems for the synthesis and optimization of colloidal silicas are developed. An open-source platform for flexible laboratory automation is developed to enable democratized access to autonomous experimentation. A system for the fully automated synthesis and characterization of colloidal mesoporous silicas is developed which integrates the open- hardware automation platform to perform automated sol-gel synthesis with synchrotron and laboratory X-ray scattering instruments for characterization. Synthesis campaigns executed with this system have produced colloidal silicas with a range of particle morphologies and mesopore phase structures. Finally, progress towards integrating a Bayesian optimization based experimental design strategy to optimize the morphology of silica nanoparticles is discussed. This work represents an important step towards accelerating the development of sol-gel materials with autonomous experimentation.