Version 1
: Received: 3 October 2024 / Approved: 7 October 2024 / Online: 8 October 2024 (11:22:13 CEST)
How to cite:
Taylor, B.; Kumar, N.; Mishra, D. K.; Simmons, B. A.; Choudhary, H.; Sale, K. L. Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches. Preprints2024, 2024100430. https://doi.org/10.20944/preprints202410.0430.v1
Taylor, B.; Kumar, N.; Mishra, D. K.; Simmons, B. A.; Choudhary, H.; Sale, K. L. Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches. Preprints 2024, 2024100430. https://doi.org/10.20944/preprints202410.0430.v1
Taylor, B.; Kumar, N.; Mishra, D. K.; Simmons, B. A.; Choudhary, H.; Sale, K. L. Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches. Preprints2024, 2024100430. https://doi.org/10.20944/preprints202410.0430.v1
APA Style
Taylor, B., Kumar, N., Mishra, D. K., Simmons, B. A., Choudhary, H., & Sale, K. L. (2024). Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches. Preprints. https://doi.org/10.20944/preprints202410.0430.v1
Chicago/Turabian Style
Taylor, B., Hemant Choudhary and Kenneth L Sale. 2024 "Computational Advances in Ionic Liquid Applications for Green Chemistry: A Critical Review of Lignin Processing and Machine Learning Approaches" Preprints. https://doi.org/10.20944/preprints202410.0430.v1
Abstract
The valorization and dissolution of lignin using ionic liquids (ILs) is critical for developing sustainable biorefineries and a circular bioeconomy. This review aims to critically assess the current state of computational and machine learning methods for understanding and optimizing IL-based lignin dissolution and valorization processes reported since 2022. The paper examines various computational approaches, from quantum chemistry to machine learning, highlighting their strengths, limitations, and recent advances in predicting and optimizing lignin-IL interactions. Key themes include the challenges in accurately modeling lignin’s complex structure, the development of efficient screening methodologies for ionic liquids to enhance lignin dissolution and valorization processes, and the integration of machine learning with quantum calculations. These computational advances will drive progress in IL-based lignin valorization by providing deeper molecular-level insights and facilitating the rapid screening of novel IL-lignin systems.
Chemistry and Materials Science, Chemical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
