微尺度三维微通道中的磁性微流体:释放锂离子微型电池的纳米多孔电极潜力

Energy Storage Pub Date : 2024-06-13 DOI:10.1002/est2.662
Adeel Ashraf, Tareq Manzoor, Shaukat Iqbal, Tauseef Anwar, Muhammad Farooq-i-Azam, Zeashan Khan, Habib Ullah Manzoor
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引用次数: 0

摘要

要提高锂驱动微型电池(LIMB)的整体效率、安全性和寿命,就必须增强这些能源设备中纳米电解质的特性,而设计具有宽电化学稳定性窗口的纳米电解质,同时保持其与电极材料的兼容性,正是改进的目标之一。电池技术必须经过这一优化过程才能得到实际应用。通过磁化监测锂离子能源设备健康状况的传感机制。磁性微流体图案的变化可能是电池性能恶化或出现其他问题的信号。锂离子电池的健康状况是磁感应的一种应用。电池健康状况的变化和其他性能问题可以通过磁性质量传输模式来发现。本研究探讨了磁场对拉伸片上纳米多孔通道中艾林-鲍尔质量传输的影响。利用相似性变换,通过二阶近似将表现该现象的主方程转化为非线性微分方程。此外,还使用了一种名为最优同调渐近法(OHAM)的半解析技术来求解转换后的艾林-鲍威尔模型。数值结果表明了速度、表皮摩擦系数和舍伍德数的变化对拟议方案的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Magnetic micro-fluidics in 3D microchannel at the micro-scale: Unlocking nano-porous electrode potential for lithium-ion micro-batteries

Magnetic micro-fluidics in 3D microchannel at the micro-scale: Unlocking nano-porous electrode potential for lithium-ion micro-batteries

Enhancing the nanosized-electrolyte's characteristics in Lithium-driven micro-batteries (LIMBs) is indispensable to improve the overall efficiency, security, and lifespan of these energy devices, designing nano-sized electrolyte with a wide electrochemical stability window while keeping them compatible with electrode materials is one of the improvement goals. Battery technologies must go through this optimization process in order to be used practically. A sensing mechanism to keep an eye on the health of Li-ion energy devices through the magnetization. Magnetic micro-fluidic patterns that change could be a sign of battery deterioration or other problems with performance. Li-ion battery health is one application of magnetic sensing that you can do with magnetic sensing. Battery health variations and other performance problems can be found using magnetic mass transport patterns. Present study examines the effects of magnetic field on Eyring–Powel mass transport in nano-porous channels over a stretching sheet. The principal equations exhibiting the phenomenon are transformed into non-linear differential equation by second-order approximation by using a similarity transformation. Furthermore, a semi-analytic technique named optimal homotopy asymptotic method (OHAM) is used to solve the transformed Eyring–Powell model. The numerical results demonstrated the impact of variations in velocity, skin-friction coefficient and Sherwood number for the proposed scheme.

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