Large Language\nModeling to Assist Natural Polyphenols\nas Green Precipitants for Recycling Spent Batteries

The growing demand for energy storage batteries, driven\nby the\nneed to alleviate global warming and reduce fossil fuel dependency,\nhas led to environmental concerns surrounding spent batteries. Efficient\nrecycling of these batteries is essential to prevent pollution and\nrecover valuable metal ions such as nickel (Ni<sup>2+</sup>), cobalt\n(Co<sup>2+</sup>), and manganese (Mn<sup>2+</sup>). Conventional hydrometallurgical\nmethods for battery recycling, while effective, often involve harmful\nchemicals and processes. Natural polyphenols offer a greener alternative\ndue to their ability to coordinate with metal ions. However, optimizing\npolyphenol selection for efficient recovery remains a labor-intensive\nchallenge. This study presents a strategy combining natural polyphenols\nas green precipitants with the power of GPT-4, a large language model\n(LLM), to enhance the precipitation and recovery of metal ions from\nspent batteries. By leveraging the capabilities of GPT-4 in natural\nlanguage processing, we enable a dynamic, iterative collaboration\nbetween human researchers and the LLM, optimizing polyphenol selection\nfor different experimental conditions. The results show that tannic\nacid achieved precipitation rates of 94.8, 96.7, and 96.7% for Ni<sup>2+</sup>, Co<sup>2+</sup>, and Mn<sup>2+</sup>, respectively, outperforming\nconventional methods. The integration of GPT-4 enhances both the efficiency\nand accuracy of the process, ensuring environmental sustainability\nby minimizing secondary pollution and utilizing biodegradable materials.\nThis innovative strategy demonstrates the potential of combining artificial\nintelligence-driven analysis with green chemistry to address battery\nrecycling challenges, paving the way for more sustainable and efficient\nmethods.