**Abstract:** This research proposes a novel methodology for drastically improving the efficiency of photoelectrochemical (PEC) water splitting using TiO₂ photocatalysts through adaptive gradient optimization of their nanostructure. By integrating reinforcement learning (RL) with real-time electrochemical impedance spectroscopy (EIS) data, we develop a closed-loop system that dynamically adjusts fabrication parameters to iteratively refine the catalyst morphology, maximizing hydrogen evolution efficiency (HEE). This approach enables the creation of highly-optimized TiO₂ nanostructures exhibiting significantly improved charge carrier separation and transport compared to conventionally fabricated catalysts, ultimately yielding a projected 35% increase in HEE within a 5-year commercialization timeframe.

Shaw, M. H., Twilton, J. & MacMillan, D. W. C. Photoredox catalysis in organic chemistry. J. Org. Chem. 81, 6898–6926 (2016). Hopkinson, M. N., Tlahuext-Aca, A. & Glorius, F. Merging visible light photoredox and gold catalysis. Acc. Chem. Res. 49,...

Nature Synthesis - Heck-type reactions with bismuth photocatalysts

Environmental pollution from organic contaminants poses a significant threat to ecosystems and human health, which require innovative and efficient remediation strategies. Porphyrin-based materials, r...

**Abstract:** This research investigates a novel approach to boosting solar energy conversion efficiency in Z-scheme heterojunction photocatalysts exceeding 20% by leveraging machine learning algorithms for compositional optimization of constituent materials. Current limitations in Z-scheme photocatalysis arise from interfacial charge recombination and suboptimal band alignment. Our method employs a hybrid machine learning model combining Bayesian Optimization and Graph Neural Networks (GNNs) to predict and optimize the composition of TiO₂, WO₃, and Cu₂O, minimizing recombination loss and maximizing light harvesting.

Environmental pollution from organic contaminants poses a significant threat to ecosystems and human health, which require innovative and efficient remediation strategies. Porphyrin-based materials, r...

**Abstract:** The deactivation of TiO₂ photocatalysts, primarily due to surface trapping of photogenerated electrons and subsequent recombination, severely limits their efficiency. This research proposes a novel approach to enhance stability through dynamic surface functionalization with self-assembled monolayers (SAMs) of thiol-terminated phosphonic acids (TPA-SH), whose inhibits surface electron trapping. A multi-fidelity assessment pipeline, incorporating high-throughput computational screening, experimental validation, and real-time activity monitoring, is developed to optimize the SAM design and deposition process.

**Abstract:** This paper introduces a novel computational framework employing Hyperdimensional Graph Neural Networks (HGNNs) to model and interpret the intricate passivation dynamics on TiO₂ photocatalyst surfaces under UV-Vis illumination. Unlike traditional computational approaches relying on simplistic kinetic models and limited feature representation, our HGNN framework captures the complex interplay of surface defects, adsorbates, and illumination-induced reactions within a higher-dimensional feature space.

**Abstract:** This paper presents a novel approach to optimizing the performance of Z-scheme heterojunction photocatalysts for solar-to-hydrogen conversion. We investigate LaFeO₃/TiO₂ heterojunctions, focusing on the critical role of doping gradient control within the LaFeO₃ component. Employing a machine learning (ML) framework coupled with Density Functional Theory (DFT) simulations and experimental validation, we demonstrate that a precisely engineered doping gradient of Fe²⁺ within LaFeO₃ significantly enhances charge separation and reduces electron-hole recombination, leading to a 18.3% improvement in hydrogen evolution efficiency compared to undoped controls.

**Abstract:** This research proposes a novel method for enhancing hydrogen production efficiency in TiO₂ photocatalysts through precise defect engineering, guided by a machine learning (ML) framework. Current TiO₂ photocatalysts suffer from limitations due to rapid electron-hole recombination. Introducing controlled defects – oxygen vacancies and nitrogen doping – is known to improve performance, but the optimal combination and concentration remain experimentally challenging to determine.