Large Language Modeling to Assist Natural Polyphenols as Green Precipitants for Recycling Spent Batteries

Abstract

The growing demand for energy storage batteries, driven by the need to alleviate global warming and reduce fossil fuel dependency, has led to environmental concerns surrounding spent batteries. Efficient recycling of these batteries is essential to prevent pollution and recover valuable metal ions such as nickel (Ni²⁺), cobalt (Co²⁺), and manganese (Mn²⁺). Conventional hydrometallurgical methods for battery recycling, while effective, often involve harmful chemicals and processes. Natural polyphenols offer a greener alternative due to their ability to coordinate with metal ions. However, optimizing polyphenol selection for efficient recovery remains a labor-intensive challenge. This study presents a strategy combining natural polyphenols as green precipitants with the power of GPT-4, a large language model (LLM), to enhance the precipitation and recovery of metal ions from spent batteries. By leveraging the capabilities of GPT-4 in natural language processing, we enable a dynamic, iterative collaboration between human researchers and the LLM, optimizing polyphenol selection for different experimental conditions. The results show that tannic acid achieved precipitation rates of 94.8, 96.7, and 96.7% for Ni²⁺, Co²⁺, and Mn²⁺, respectively, outperforming conventional methods. The integration of GPT-4 enhances both the efficiency and accuracy of the process, ensuring environmental sustainability by minimizing secondary pollution and utilizing biodegradable materials. This innovative strategy demonstrates the potential of combining artificial intelligence-driven analysis with green chemistry to address battery recycling challenges, paving the way for more sustainable and efficient methods.