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.