Self-assembly of an amino acid derivative as an anode interface layer for advanced alkaline Al–air batteries

Guo, Lei; Zhu, Lei; Huang, Yue; Tan, Yan; Ritacca, Alessandra Gilda; Zheng, Xingwen; Leng, Senlin; Wang, Baoguo

doi:10.1039/d3cp05767d

Phys. Chem. Chem. Phys.

2024

DOI: 10.1039/d3cp05767d

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Self-assembly of an amino acid derivative as an anode interface layer for advanced alkaline Al–air batteries

Lei Guo,

Lei Zhu,

Yue Huang

et al.

Abstract: Alkaline Al-air batteries (AABs) are gaining increasing attention for large-scale energy storage systems due to their attractive intrinsic safety and cost-effectiveness. Nonetheless, future development of AABs is substantially hampered by...

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A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes

Xia,

Li,

Sun

et al. 2024

Crystals

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Aluminum–air batteries are characterized as “green energy for the 21st century” due to their clear advantages in terms of high current discharge, high specific energy, low cost, and easy-to-obtain electrode materials. This study develops the SLM aluminum anode quality prediction model and evaluates its learning and training results using the BP neural network architecture. By altering the network topology of the SLM aluminum anode quality prediction model, we create a process parameter backpropagation model that takes advantage of the extremely adaptable capabilities of artificial neural networks. The quick and exact selection of process parameters meets the goals of density, self-corrosion current, and anode usage, hence improving the forming quality and processing efficiency of SLM aluminum anodes. The experimental results show that the process parameter backpropagation model’s parameter configurations match to the real densities and self-corrosion currents, which are somewhat higher than the specified target values. The maximum error rate for the aluminum anode forming quality prediction model is 8.23%. Furthermore, the actual anode utilization rate is somewhat lower than the projected target value, indicating that the backpropagation model can satisfy actual production needs.

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A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes

Xia,

Li,

Sun

et al. 2024

Crystals

View full text Add to dashboard Cite

show abstract

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Self-assembly of an amino acid derivative as an anode interface layer for advanced alkaline Al–air batteries

Abstract: Alkaline Al-air batteries (AABs) are gaining increasing attention for large-scale energy storage systems due to their attractive intrinsic safety and cost-effectiveness. Nonetheless, future development of AABs is substantially hampered by...

Cited by 1 publication

References 54 publications

A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes

A Multi-Objective Genetic Algorithm-Based Predictive Model and Parameter Optimization for Forming Quality of SLM Aluminum Anodes

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