High-throughput experimentation for photocatalytic water purification in practical environments

Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering

Solid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights across different catalyst families. To overcome this fragmentation, we introduce a framework that integrates high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework is demonstrated for oxidative coupling of methane (OCM), where active learning is continued until the machine learning model achieves robustness for each of the BaO-, CaO-, La2O3-, TiO2-, and ZrO2-supported catalysts, with 333 catalysts newly tested. The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. Moreover, we propose a method for transferring knowledge between supports, showing that features refined on one support can improve predictions on others. This framework advances the understanding of catalyst design and promotes reliable machine learning.

Design of novel solar-light-induced KBi6O9I/Ag-AgVO3 nanophotocatalyst with Ag-bridged Z-scheme charge carriers separation and boosted photo-elimination of hospital effluents

In this research, a novel solar-light-induced KBi6O9I/Ag-AgVO3 nanophotocatalyst with an Ag-bridged Z-scheme structure has been designed and synthesized through a sonochemical method to photo-degrade antibiotic hospital contaminants under simulated solar-light irradiation. Synthesized nanophotocatalysts with varying KBi6O9I to Ag-AgVO3 weight ratios underwent N2 Adsorption-Desorption, XRD, TEM, UV-Vis DRS, FESEM and PL analyses. The Ag-bridged Z-scheme-structured KBi6O9I/Ag-AgVO3 (1:1) nanophotocatalyst, demonstrated broad light absorption within the solar-light spectrum and showcased effective photocatalytic efficacy in degrading tetracycline antibiotic (88.3% and 83.5% removal for 25 and 50 mg/L, respectively, after 120 min). This performance outperformed other composited photocatalysts, as well as pure Ag-AgVO3 and KBi6O9I photocatalysts. The enhanced degradation efficiency of the KBi6O9I/Ag-AgVO3 (1:1) composite can be ascribed to the synergistic interaction of various elements. These include the surface plasmon resonance impact of silver nanoparticles, their pronounced sensitivity to solar irradiation, and the Z-scheme heterojunction configuration. Collectively, these factors work together to minimize the recombination rate of photoinduced electron-hole pairs, thereby amplifying the efficacy of photodegradation. Furthermore, the KBi6O9I/Ag-AgVO3 (1:1) composite photocatalyst displayed sustained pollutants elimination performance even after undergoing four consecutive cycles.