Assessment of fine-tuned large language models for real-world chemistry and material science applications.

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens doors to a wider chemical audience, as field-specific featurization techniques can be omitted.

Acid selenites as new selenium precursor for CdSe quantum dot synthesis.

Chemical precursors for nanomaterials synthesis have become essential to tune particle size, composition, morphology, and unique properties. New inexpensive precursors investigation that precisely controls these characteristics is highly relevant. We studied new Se precursors, the acid selenites (R-O-SeOOH), to synthesize CdSe quantum dots (QDs).

Advances in Engineered Metal Oxide Thin Films by Low-Cost, Solution-Based Techniques for Green Hydrogen Production.

Functional oxide materials have become crucial in the continuous development of various fields, including those for energy applications. In this aspect, the synthesis of nanomaterials for low-cost green hydrogen production represents a huge challenge that needs to be overcome to move toward the next generation of efficient systems and devices. This perspective presents a critical assessment of hydrothermal and polymeric precursor methods as potential approaches to designing photoelectrodes for future industrial implementation.