Following morphology detection, seed-mediated recipes were screened by matches with keywords including "seed-mediated" and "growth solution". Publications that had two or more methods including seed-mediated growth and other non-seed-mediated methods (with keywords 'one-pot' or 'seedless') are regarded as having multiple recipes and therefore were filtered out. The next step in the workflow is recipe paragraph classification. The goal of this step is to identify which of the paragraphs of the full text contain information on AuNP synthesis recipes (as opposed to introduction, conclusion, etc.). Candidate recipe paragraphs were identified using regex to screen for paragraphs containing AuNP or with keywords like "synthesize" and "prepared". After this filter, the recipe paragraphs were identified using a trained classifier for enhanced precision of the recipe filter. Briefly, we trained a Support Vector Classifier (SVC), using MatBERT embeddings 28 of each paragraph. Recipe paragraphs and non-recipe paragraphs were each sampled and annotated to construct a dataset of 920 paragraphs, which was split 9:1 into training and test sets. Test set accuracy was 0.89 for this SVC classifier. Using MatBERT embeddings (784 dimension) as features showed classification performance comparable to trainings using GPT-3 embeddings (1536 dimension, test set accuracy 0.90).