TY - JOUR
T1 - Synergistic ternary deep eutectic solvents
T2 - An archetype for sustainable and eco-conscious Li and Co recovery from spent batteries
AU - Amusa, Hussein K.
AU - Lemaoui, Tarek
AU - Almustafa, Ghaiath
AU - Darwish, Ahmad
AU - Banat, Fawzi
AU - Arafat, Hassan A.
AU - AlNashef, Inas M.
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/11/1
Y1 - 2024/11/1
N2 - The development of sustainable materials for energy is a critical challenge in the pursuit of a circular economy. This study presents an innovative approach for recycling lithium-ion batteries (LIBs) using a novel, sustainable deep eutectic solvent (DES) composed of glycine, ascorbic acid, and water. This natural, chloride-free ternary DES leverages the biocompatible and eco-friendly properties of its constituents, achieving exceptional leaching efficiency of 99.1 % for lithium and 97.9 % for cobalt under optimized conditions. A multifaceted methodology integrating advanced factorial design, kinetic modeling, and machine learning was employed to refine the process parameters and enhance leaching efficiency. The extraction kinetics were predominantly governed by endothermic surface reactions, characterized by activation energies of 81.5 kJ/mol for Co and 110.9 kJ/mol for Li. An ensemble neural network model accurately predicted the extraction with an R2 > 0.95, demonstrating the robustness of the computational approach. The DES exhibited excellent recyclability over five cycles, thereby emphasizing its potential for practical industrial applications in the field of sustainable materials management. This approach facilitated the selective recovery of high-purity lithium oxalate and cobalt oxalate, underscoring the efficiency and environmental benefits of the process. DFT calculations provided insights into the extraction mechanism, revealing the crucial role of hydrogen bonding and synergistic effects of the DES components. This pioneering study not only advances LIB recycling but also establishes a comprehensive, quantitatively validated framework, paving the way for future innovations in sustainable materials management and supporting global efforts towards a circular economy in energy storage.
AB - The development of sustainable materials for energy is a critical challenge in the pursuit of a circular economy. This study presents an innovative approach for recycling lithium-ion batteries (LIBs) using a novel, sustainable deep eutectic solvent (DES) composed of glycine, ascorbic acid, and water. This natural, chloride-free ternary DES leverages the biocompatible and eco-friendly properties of its constituents, achieving exceptional leaching efficiency of 99.1 % for lithium and 97.9 % for cobalt under optimized conditions. A multifaceted methodology integrating advanced factorial design, kinetic modeling, and machine learning was employed to refine the process parameters and enhance leaching efficiency. The extraction kinetics were predominantly governed by endothermic surface reactions, characterized by activation energies of 81.5 kJ/mol for Co and 110.9 kJ/mol for Li. An ensemble neural network model accurately predicted the extraction with an R2 > 0.95, demonstrating the robustness of the computational approach. The DES exhibited excellent recyclability over five cycles, thereby emphasizing its potential for practical industrial applications in the field of sustainable materials management. This approach facilitated the selective recovery of high-purity lithium oxalate and cobalt oxalate, underscoring the efficiency and environmental benefits of the process. DFT calculations provided insights into the extraction mechanism, revealing the crucial role of hydrogen bonding and synergistic effects of the DES components. This pioneering study not only advances LIB recycling but also establishes a comprehensive, quantitatively validated framework, paving the way for future innovations in sustainable materials management and supporting global efforts towards a circular economy in energy storage.
KW - Circular economy
KW - Deep eutectic solvent
KW - Density functional theory
KW - Kinetic modeling
KW - Lithium-ion battery recycling
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85204806057&partnerID=8YFLogxK
U2 - 10.1016/j.cej.2024.156114
DO - 10.1016/j.cej.2024.156114
M3 - Article
AN - SCOPUS:85204806057
SN - 1385-8947
VL - 499
JO - Chemical Engineering Journal
JF - Chemical Engineering Journal
M1 - 156114
ER -