We started from an initial database of nearly 5 million articles. Details for the initial database acquisition and filtration are described by Kononova et al.. Briefly, publications are sourced from materials science-related journals from Springer, Wiley, Elsevier, the Royal Society of Chemistry, the Electrochemical Society, and the American Chemical Society. Then, we collected 1,108,803 nanomaterial papers by performing a keyword search for papers containing "nano*", where "*" could be any contiguous set of characters. Figure depicts the schematics of the synthesis extraction process. Before describing this process in detail, we provide a brief overview of the procedure. First, a "clean" dataset 6 of 328 recipes was obtained via mainly search-based parser components. Then, a subset of this dataset was used to fine-tune Llama-2, which was then deployed to process "hard" and "partial" cases to obtain 91 and 73 validated recipes, respectively. "Hard" cases were where the search-based parser failed to parse a recipe, and "partial" cases were where it extracted a partially complete recipe. Additionally, we extracted precursor-only recipes, disregarding the amounts, which we referred to as the 'categorical' dataset, in contrast to the dataset containing complete amounts. The categorical dataset comprised 139 and 124 recipes from hard and partial cases, respectively. Overall, we obtained a dataset consisting of 492 recipes with complete amounts and 591 in the categorical format.