Role of Morphology on Zinc Oxide Nanostructures for Efficient Photoelectrochemical Activity and Hydrogen Production.

Energy generation today heavily relies on the field of photocatalysis, with many conventional energy generation strategies now superseded by the conversion of solar energy into chemical or thermal energy for a variety of energy-related applications. Global warming has pointed to the urgent necessity of moving away from non-renewable energy sources, with a resulting emphasis on creating the best photocatalysts for effective solar conversion by investigating a variety of material systems and material combinations. The present study explores the influence of morphological changes on the photoelectrochemical activity of zinc oxide nanostructures by exploiting electrodeposition and capping agents to control the growth rates of different ZnO facets and obtain well-defined nanostructures and orientations.

Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry with GPT-4-Turbo.

The rapid advancement in artificial intelligence and natural language processing has led to the development of large-scale datasets aimed at benchmarking the performance of machine learning models. Herein, we introduce "RetChemQA", a comprehensive benchmark dataset designed to evaluate the capabilities of such models in the domain of reticular chemistry. This dataset includes both single-hop and multi-hop question-answer pairs, encompassing approximately 45,000 question and answers (Q&As) for each type.