Proceedings of MATSUS Fall 2023 Conference (MATSUSFall23)
DOI: https://doi.org/10.29363/nanoge.matsus.2023.099
Publication date: 18th July 2023
Colloidal nanoparticles are of growing importance in a broad range of practical applications, from medicine and biodiagnostics through to energy and environmental science. However, the fact that they are non-discreet reaction products, and their properties vary extremely sensitively as a function of size, shape and composition, makes materials discovery, characterization and optimization a daunting task in the face of an extensive reaction parameter space.
Here we present our experience in the development and application of segmented-flow microfluidic systems for the advanced synthesis, analysis and optimization of colloidal nanoparticles [1]. These systems allow rapid and efficient exploration of the mentioned extensive reaction parameter space. Intrinsic advantages include rapid heat and mass transfer in micro-isolated reaction volumes, leading to highly uniform and reproducible reaction conditions. Incorporation of in-line analytics (e.g. photoluminescence and absorption spectroscopy) allows real-time characterization and data feedback for powerful real-time reaction optimization. We discuss how this approach interfaces with new opportunities in data science, including machine learning, and how we are pushing to move materials chemistry into a new ‘big data’ regime. We present examples from our lab of the application of such systems in nanomaterials investigations, with a particular focus on the synthesis and characterization of cesium lead bromide perovskite nanocrystals. Here, we have used multiparametric reaction mapping to elucidate how nanocrystal optical and morphological properties vary with ligand type, concentrations, and blend ratios.
There are many opportunities and challenges in the use of automated microfluidic systems in the future of colloidal nanocrystal synthesis, and there is vast potential for such systems to be developed and applied by a variety of research groups for wide impact.
We acknowledge London South Bank Univeristy (LSBU) School of Engineering for supporting the work. Neal Munyebvu acknowledges LSBU School of Egnineering for a doctoral studentship. Philip Howes acknowledges support from the Royal Society of Chemistry for a Research Enablement Grant (E21-2246074803).