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

Abstract

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.