By selecting specific sentences, we assessed the model's precision, recall, and F1-score before proceeding with further analyses. For some cases where we observed chatGPT was not accurate, we resorted to a manual verification to remove erroneous relations. This rigorous evaluation helps to ensure the accuracy and effectiveness of our strategy. It is worth noting that similar approaches have been attempted recently in the field of CO 2 RR, but with a narrower focus on Cubased catalysts for CO 2 ER and relying on only a few articles , or using these NLP tools in a general fashion . Unlike these previous analyses, our approach considers a larger corpus and has a broader scope: CO 2 catalysts beyond Cubased materials. By expanding the range of materials under investigation, we were able to uncover novel materials that have gained attention in the field of CO 2 RR and CORR. Additionally, we employed a different strategy to leverage large language models (LLMs). While these tools offer powerful language processing capabilities, there is a concern regarding the generation of inaccurate or false information, commonly referred to as "hallucinations". However, by limiting their responses and providing appropriate context, we could efficiently mitigate these risks. Figure charts the pipeline adopted in our work.