Nanomaterials as a Service (NaaS) concept: on-demand protocols for volume synthesis of nanomaterials

Abstract

Establishing scalable nanomaterials synthesis protocols remains a bottleneck towards their commercialisation and, thus, a topic of intense research and development. Herein, we present an automated machine-learning microfluidic platform capable of synthesising optically active nanomaterials from target spectra originating from prior experience, theorised or published. Implementing unsupervised Bayesian optimisation with Gaussian processes reduces the optimisation time and the need for prior knowledge to initiate the process. Using PTFE tubing and connectors enables facile change in reactor design. Ultimately, the platform substitutes the labour-intensive trial-and-error synthesis and provides a pathway to standardisation and volume synthesis, slowing down the translation and commercialisation of high-quality nanomaterials. As a proof-of-concept, Ag nanoplates and Prussian-blue nanoparticle protocols were optimised and validated for volume production.