UC Berkeley

Gold nanoparticles (AuNPs) are widely used functional nanomaterials that exhibit adjustable properties depending on their shapes and sizes. Creating a comprehensive dataset of AuNP syntheses is useful for understanding how to control their morphology and size. Here, we employed search-based algorithms and fine-tuned the Llama-2 large language model to extract 492 multi-sourced seed-mediated AuNP synthesis recipes from the literature. With this dataset which we share online, we verified that the type of seed capping agent such as CTAB or citrate plays a crucial role in determining the morphology of the AuNPs, aligning with established findings in the field. We also observe a weak correlation between the final AuNR aspect ratio and silver concentration, although a large variance reduces the significance of this relationship. Overall, our work demonstrates the value of literature-based datasets in advancing knowledge in the field of nanomaterial synthesis for further exploration and better reproducibility.