Application and biomechanical analysis of bio inspired strategies in the recycling of lithium-ion cathode materials
Abstract
This study explores bio-inspired strategies for recycling cathode materials in lithium batteries by integrating biomechanical models with optimization algorithms to enhance recycling efficiency. We developed a biomechanical model to examine the recovery process of metal ions, analyzing their dynamic behavior and reaction rates to assess the potential of bio-inspired algorithms for model optimization. Based on this model, we designed an optimization algorithm to boost metal ion recovery by varying experimental conditions such as reaction temperature, solvent concentration, pH, and reaction time. Experimental results indicate that reaction temperature, solvent concentration, adsorption and desorption rates, and pH significantly influence recovery efficiency. The optimal conditions identified were 55 ℃, a solvent concentration of 0.7 mol/L, and a pH of 5.5, yielding a recovery efficiency of 80.3%. Additionally, extending the reaction time positively correlated with recovery rates, achieving a maximum of 86.4% at 50 min. By combining biomechanical analysis with algorithm optimization, this research enhances our understanding of material recycling mechanisms and provides a theoretical foundation and technical support for future industrial recycling processes. These findings offer valuable insights for optimizing lithium battery recycling technologies and improving resource utilization efficiency.
References
1. Maoucha A, Berghout T, Djeffal F, et al. Machine learning-assisted investigation of CIGS thin-film solar cell degradation using deep learning analysis. Journal of Physics and Chemistry of Solids. 2025; 199: 112526.
2. Zhao H, Meng J, Peng Q. Early perception of Lithium-ion battery degradation trajectory with graphical features and deep learning. Applied Energy. 2025; 381: 125214.
3. Karthikeyan M, Manimegalai D, Rajagopal K. Enhancing voltage control and regulation in smart micro-grids through deep learning–optimized EV reactive power management. Energy Reports. 2025; 13: 1095–1107.
4. Mahek MK, Ramadan M, Ghazal M, et al. Advanced thermal management with heat pipes in lithium-ion battery systems: Innovations and AI-driven optimization. Next Energy. 2025; 7: 100223.
5. Tian Y, Lin C, Meng X, et al. Accelerated commercial battery electrode-level degradation diagnosis via only 11-point charging segments. eScience. 2025; 5(1): 100325.
6. Elachhab A, Laadissi EM, Tabine A, et al. Deep learning and data augmentation for robust battery state of charge estimation in electric vehicles. Electrical Engineering. 2024; 1–15.
7. Hosseini M, Bagheri H. Improving the resolution of solar energy potential maps derived from global DSMs for rooftop solar panel placement using deep learning. Heliyon. 2025; 11(1), e41193.
8. Salehi Z, Tofigh M, Kharazmi A, et al. Transfer learning-based deep neural network model for performance prediction of hydrogen-fueled solid oxide fuel cells. International Journal of Hydrogen Energy. 2025; 99: 102–111.
9. Liang J, Yang Q, Zhang C, et al. Triboelectric materials with UV protection, anti-bacterial activity, and green closed-loop recycling for medical monitoring. Chemical Engineering Journal. 2025; 503: 158407.
10. Khosravi N, Oubelaid A, Belkhier Y. Energy management in networked microgrids: A comparative study of hierarchical deep learning and predictive analytics techniques. Energy Conversion and Management: X. 2025; 25: 100828.
11. Qiu Y, Wen S, Zhao Q, et al. Multi-model deep learning-based state of charge estimation for shipboard lithium batteries with feature extraction and Spatio-temporal dependency. Journal of Power Sources. 2025; 629: 235983.
12. Chen ZL, Zhang BX, Zhang CL, et al. Deep learning-based fault diagnosis of high-power PEMFCs with ammonia-based hydrogen sources. Journal of Power Sources. 2025; 629: 236018.
13. Wang S, Wang P, Wang L, et al. An enhanced deep learning framework for state of health and remaining useful life prediction of lithium-ion battery based on discharge fragments. Journal of Energy Storage. 2025; 107: 114952.
14. Chen B, Wang K, Xu D, et al. Global–local attention network and value-informed federated strategy for predicting power battery state of health. Energy. 2024; 313: 134088.
15. Palaniandy SS, The PL, Che WM, et al. The Effect of Particle Size of Recovered Carbon Black (rCB) on the Properties of Epoxy Conductive Material. Key Engineering Materials. 2024; 997: 45–52.
16. Wang J, Zhu Q, E. Ragab A. Application of multi-directional FG material to improve natural frequencies of perovskite solar cells under mechanical shock validated by deep-learning approach. Mechanics of Advanced Materials and Structures. 2024; 31(28): 10179–10200.
17. Flores-Martin D, Laso S, Herrera JL. Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behavior. Electronics. 2024; 13(24): 4897.
18. Chae SG, Bae SJ, Oh KY. State-of-health estimation and remaining useful life prediction of lithium-ion batteries using DnCNN-CNN. Journal of Energy Storage. 2025; 106: 114826.
19. Zhao J, Han X, Wu Y, et al. Opportunities and challenges in transformer neural networks for battery state estimation: Charge, health, lifetime, and safety. Journal of Energy Chemistry. 2025; 102: 463–496.
20. Tang Z, Zhang Z, Shen X, et al. Evolutionary hybrid deep learning based on feature engineering and deep projection encoded echo-state network for lithium batteries state of health estimation. Energy. 2024; 313: 133978.
21. Li X, Zhao X, Zhong S, et al. BMSFormer: An efficient deep learning model for online state-of-health estimation of lithium-ion batteries under high-frequency early SOC data with strong correlated single health indicator. Energy. 2024; 313: 134030–134030.
22. Cheng D, Sha W, Guo Y, et al. A real-time deep learning model to narrow the gap between atomic scanning transmission electron microscopy and theory calculations: Recognition, reconstruction, and simulation. MRS Bulletin. 2024; 50: 1–14.
23. Meng K, Bai K, Sun S. Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method. Journal of Energy Storage. 2024; 101: 113809.
24. Jiang Y, Sun Y, Zhou P, et al. Recycled lithium battery nanomaterials as a sustainable nanofertilizer: Reduced peanut allergenicity and improved seed quality. The Science of the total environment. 2024; 955: 176900.
25. Wen G, Yuan S, Dong Z, et al. Recycling of spent lithium iron phosphate battery cathode materials: A review. Journal of Cleaner Production. 2024; 474: 143625–143625.
26. Qi X, Xiong R, Sa B, et al. Efficient rhombohedral GeTe thermoelectrics for low-grade heat recovery. Materials Today Physics. 2024; 45: 101466.
27. Liu Y, Zhang X, Ma W, et al. Research on the recycling of waste lithium battery electrode materials using ammonium sulfate roasting. Materials Chemistry and Physics. 2024; 318: 129221.
28. Petrus R, Kowaliński A, Lis T. Recycling primary lithium batteries using a coordination chemistry approach: recovery of lithium and manganese residues in the form of industrially important materials. Dalton transactions. 2024.
29. Singh R, Dogra S, Dixit S, et al. Advancements in thermoelectric materials for efficient waste heat recovery and renewable energy generation. Hybrid Advances. 2024; 5: 100176.
30. Wang L, Zhu H, Bi H, et al. Efficient recovery of electrode materials from lithium iron phosphate batteries through heat treatment, ball milling, and foam flotation. Journal of Material Cycles and Waste Management. 2024; 26(3): 1622–1632.
31. Yue S, Shao S, He W, et al. Pioneer exploration on the energy recovery technology for waste heat in solid rocket motors by utilizing thermoelectric materials. Energy Conversion and Management. 2024; 302: 118151.
32. Lei S, Sun W, Yang Y. Comprehensive Technology for Recycling and Regenerating Materials from Spent Lithium Iron Phosphate Battery. Environmental science & technology. 2024; 58(8).
33. Wang S, Ji Y, Liu J, et al. Integrating crystal structure and numerical data for predictive models of lithium-ion battery materials: A modified crystal graph convolutional neural networks approach. Journal of Energy Storage. 2024; 80: 110220.
34. Wang Q, Luo H, Xu Z, et al. Porous pyroelectric material for waste heat harvesting. Functional Materials Letters. 2023; 16(7).
35. Milkin P, Zhanbassynova A, Ionov L. Superelastic, soft, stress-healable, recyclable conductive materials. Composite Structures. 2024; 327.
36. Zhou Z, Liu Y, Tang Z, et al. Facile and efficient recycling of cathode materials of spent lithium manganate batteries. Chemical communications. 2023; 59(26).
37. Sun D, Saw BLH, Onyianta AJ, et al. Preparation of elastomeric nanocomposites using nanocellulose and recycled alum sludge for flexible dielectric materials. Journal of Advanced Dielectrics. 2023; 13(1).
38. Ma J, Shi T, Li Y, et al. Selective sulfidation-vacuum volatilization processes for tellurium and bismuth recovery from bismuth telluride waste thermoelectric material. Journal of Environmental Management. 2023; 327: 116845.
39. Sgura I, Mainetti L, Negro F, et al. Deep-learning based parameter identification enables rationalization of battery material evolution in complex electrochemical systems. Journal of Computational Science. 2023; 66.
40. Yu J, Ma B, Wang C, et al. Recovery of cobalt as mesoporous Co3O4 from ammonia leaching solution of spent lithium battery cathode material via reductive ammonia distillation and thermal decomposition. Separation and Purification Technology. 2023; 306.
41. Michaud Paradis MC, Doucet FR, Rousselot S, et al. Deep Learning Classification of Li-Ion Battery Materials Targeting Accurate Composition Classification from Laser-Induced Breakdown Spectroscopy High-Speed Analyses. Batteries. 2022; 8(11): 231.
42. Cheng K, Bu Z, Tang J, et al. Efficient Mg2Si0.3Sn0.7 thermoelectrics demonstrated for recovering heat of about 600 K. Materials Today Physics. 2022; 28.
43. Singh G, Mittal N, Chouhan SS. A Systematic Review of Deep Learning Approaches for Natural Language Processing in Battery Materials Domain. IETE Technical Review. 2022; 39(5): 1046–1057.
44. Zhou C, Wang R, Gao L, et al. Unveiling the Synthetic Potential of 1,3,5-Tri(10H-phenothiazin-10-yl)benzene-Based Optoelectronic Material: A Metal-Free and Recyclable Photocatalyst for Sequential Functionalization of C(sp2)-H Bonds. ACS applied materials & interfaces. 2022; 14(27).
45. Cao J, Sim Y, Tan XY, et al. Upcycling Silicon Photovoltaic Waste into Thermoelectrics. Advanced Materials. 2022; 34(19).
46. Morina R, Callegari D, Merli D, et al. Cathode active material recycling from spent lithium batteries: A green (circular) approach based on deep eutectic solvents. ChemSusChem. 2022; 15(2).
47. Yang H. Research progress on recycling technology of waste lithium battery anode materials. In: Proceedings of the 3rd International Conference on Green Energy and Sustainable Development; 14–15 November 2020; Shenyang City, China.
48. Bi H, Zhu H, Zu L, et al. Environment-friendly Technology for Recovering Cathode Materials from Spent Lithium Iron Phosphate Batteries. Waste management & research. 2020; 38(8): 911–920.
49. Amouzegar K, Bouchard P, Turcotte N, et al. Process for The Recycling of Lithium Battery Electrode Materials Patent US11050098B2. 2019.
Copyright (c) 2025 Author(s)

This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.