Pub Date : 2024-09-04DOI: 10.1149/1945-7111/ad7297
Sheng S. Zhang
Reducing particle size has been widely adopted to mitigate the cracking and pulverization of silicon particles and to enhance electrode reaction kinetics for silicon electrodes in cycling. However, the increased surface area promotes parasitic reactions with electrolyte solvents. This work comparatively studies nano-sized silicon (Si-NP) and micro-sized silicon (Si-MP) as anodes in Li-ion cells using nickel-rich LiNi0.80Co0.1Mn0.1O2 (NCM811) as the cathode. The focus is on capacity, capacity retention, Coulombic efficiency (CE), and rate capability by changing the negative-to-positive capacity (N/P) ratio and charging cutoff voltage. It is found that Si-NP initially exhibits a CE above 90%, however, it rarely exceeds 98% in subsequent cycles, leading to rapid capacity fade. Additionally, increasing the N/P ratio and lowering the charging cutoff voltage does not obviously improve the cycling stability of Si-NP cells. Compared with Si-NP, Si-MP experiences lower capacity and lower CE in the initial several cycles. However, with continued cycling, both the capacity and CE gradually increase to a maximum and stably remain at ∼99.9%. The findings of this work suggest that, with its excellent rate capability, Si-MP may be more advantageous than Si-NP in developing practical Li-ion batteries, provided its low CE during initial cycles can be successfully addressed.
减小硅颗粒的尺寸已被广泛采用,以减轻硅颗粒的开裂和粉碎,并增强硅电极在循环过程中的电极反应动力学。然而,表面积的增加会促进与电解质溶剂的寄生反应。本研究比较性地研究了纳米级硅(Si-NP)和微米级硅(Si-MP)在锂离子电池中用作阳极的情况,阴极使用富镍 LiNi0.80Co0.1Mn0.1O2 (NCM811)。研究重点是通过改变负负容量比(N/P)和充电截止电压来研究容量、容量保持率、库仑效率(CE)和速率能力。研究发现,Si-NP 最初的 CE 超过 90%,但在随后的循环中很少超过 98%,从而导致容量快速衰减。此外,提高 N/P 比和降低充电截止电压并不能明显改善 Si-NP 电池的循环稳定性。与 Si-NP 相比,Si-MP 在最初的几个循环中容量较低,CE 也较低。然而,随着循环的持续进行,容量和 CE 都会逐渐增加到最大值,并稳定地保持在 ∼ 99.9%。这项工作的研究结果表明,Si-MP 具有优异的速率能力,在开发实用锂离子电池方面可能比 Si-NP 更具优势,但前提是能成功解决其在初始循环期间的低 CE 问题。
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The burgeoning intersection of machine learning (ML) with electrochemical sensing heralds a transformative era in analytical science, pushing the boundaries of what’s possible in detecting and quantifying chemical substances with unprecedented precision and efficiency. This convergence has accelerated a number of discoveries, improving electrochemical sensors’ sensitivity, selectivity, and ability to comprehend complicated data streams in real-time. Such advancements are crucial across various applications, from monitoring health biomarkers to detecting environmental pollutants and ensuring industrial safety. Yet, this integration is not without its challenges; it necessitates navigating intricate ethical considerations around data use, ensuring robust data privacy measures, and developing specialized software tools that balance accessibility and security. As the field progresses, addressing these challenges head-on is essential for harnessing the full potential of ML-enhanced electrochemical sensing. This review briefly explores these dimensions, spotlighting the significant technological strides, the ethical landscape, and the dynamic interplay between open-source and proprietary software solutions while also casting a forward gaze at the promising future directions of this interdisciplinary venture.