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

Langmuir, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

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

Language: Английский

Electrochemical performance of polyoxometalate-based composite materials fabricated by a localization-coating strategy

Colloids and Surfaces A Physicochemical and Engineering Aspects, Journal Year: 2025, Volume and Issue: 720, P. 137071 - 137071

Published: April 28, 2025

Language: Английский