Enhanced Photoelectrochemical Water Splitting Efficiency via Defect-Engineered Fluorine-Doped Tin Oxide Nanoparticles and Machine Learning-Driven Electrolyte Optimization

**Abstract:** This paper presents a novel approach to significantly enhance the photoelectrochemical (PEC) water splitting efficiency of fluorine-doped tin oxide (FTO) nanoparticles by combining precise defect engineering techniques with machine learning (ML) driven electrolyte optimization. We demonstrate that controlled introduction of oxygen vacancies and fluorine incorporation within the FTO lattice creates synergistic effects, leading to improved charge separation and transport. Further optimization of the electrolyte composition, guided by a Reinforcement Learning (RL) algorithm, achieves a 25% increase in photocurrent density compared to conventional FTO electrodes. This study offers a commercially viable pathway towards scalable and cost-effective hydrogen production via PEC water splitting.