Automated Digital Discovery and Synthesis of CuO-Based Nanoparticle Heterostructures for Catalysis

The discovery and
synthesis of composite nanomaterials often rely
on molecular self-assembly and crystallization, posing significant
challenges due to the vast chemical space and the irreproducibility
of experimental methods. We present a programmable robotic platform,
controlled by the universal Chemical Description Language (χDL),
that enables the solid-phase synthesis of composite nanomaterials.
In addition to synthesis, the platform validates its catalytic performance
through an automated workflow. This platform enables open-ended exploration
of composition-morphology-activity relationships, with high accuracy
and reproducibility, while also reducing synthesis time and cost.
In this study, we are moving beyond the colloidal, plasmonic-focused
systems previously explored in robotic platforms to the discovery,
synthesis, and catalytic properties of CuO-based nanomaterials, such
as CuO-Au and CuO-Ag2O NP heterostructures that show good
reproducibility across repeated syntheses. Remarkably, even at very
low metal loadings, as confirmed by ICP (Au wt % = 0.06%, Ag wt %
= 0.03%), the heterostructures exhibited enhanced photodegradation
efficiency of the dye Methyl Green (MG) compared with pristine CuO.
The degradation yield increased from 45 ± 2% for pristine CuO
to 57 ± 3% for CuO-Au and 65 ± 2% for CuO-Ag2O, as observed through real-time UV–vis spectroscopy. Additionally,
a kinetic assay of the synthesis process provided insights into the
self-assembly mechanism, highlighting the interactions between the
core material (CuO NPs) and the surface coatings (Au or Ag2O). This work demonstrates a shift from traditional manual experimentation
to programmable, data-driven workflows, highlighting both the progress
and the remaining challenges in the automation of solid-phase nanomaterial
synthesis in the field of materials science.