Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles

While gold nanoparticles (Au NPs) are widely employed in modern technology, their large-scale synthesis still faces challenges related to cost and sustainability. In addition, chemical contaminants are a problem when the highest purity is demanded, such as for biomedicine, catalysis, and several processes mediated by the NP surface. Laser ablation in liquid (LAL) is a promising technique for producing surface-clean Au NPs, although its scalability has not yet matched that of conventional chemical methods. In this work, the LAL synthesis of 5 nm Au NPs in a batch configuration was optimized using machine learning. A 3.4-fold increase in investment-specific productivity was achieved compared to the previous LAL record, and at 1/18 of the initial investment. This makes the laser synthesis of Au NPs "greener" and four times cheaper than gram-scale chemical synthesis via the classical Turkevich-Frens method. Besides, the chemical-free and surface-clean Au NPs showed better cytocompatibility, superior performance as MALDI substrates, higher catalytic activity in the reduction of nitrothiophenol, higher surface thiol coverage, and a more intense plasmon absorption compared to that of the commercial counterpart. This study highlights the positive prospects of machine learning-optimized LAL for the low-cost and environmentally sustainable production of metal NPs possessing convenient properties not achievable through wet-chemistry routes.