Computational bioinspired structural design for sustainable and secure high-performance energy storage materials
As energy storage technologies advance rapidly, high-performance materials with higher electrochemical efficiency, mechanical robustness, and long-term durability are needed. Energy storage materials may benefit from bioinspired structural designs. These designs mimic natural hierarchies and biologi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025016251 |
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| Summary: | As energy storage technologies advance rapidly, high-performance materials with higher electrochemical efficiency, mechanical robustness, and long-term durability are needed. Energy storage materials may benefit from bioinspired structural designs. These designs mimic natural hierarchies and biological processes. However, current research does not give a full foundation for autonomously modifying these structures to improve performance. Biomechanics and computational optimization are used to build and evaluate bioinspired energy storage materials. Examples include Monte Carlo Simulations, Multi-scale Statistical Modeling, Bayesian Optimization, and Frame Analysis. According to multi-scale statistical modeling, hierarchical structures have 25 % more energy storage capacity, 30 % more ion diffusion, and 20 % more mechanical toughness than standard materials. Bayesian optimization cut experimental trials by 35 %, making the design more efficient and cost-effective. Additionally, charge retention increased by 15 %, and efficiency increased. Energy storage capacity increased by 20 % in materials with fractal dimensions of 1.5 to 2.5.Monte Carlo simulations validated the probabilistic durability of the optimized materials, demonstrating a 98 % confidence level in their mechanical stability over 10,000 charge cycles. |
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| ISSN: | 2590-1230 |