journal article·2021-09

Porous silicon from industrial waste engineered for superior stability lithium-ion battery anodes

Tongyu Yang, Yang Gao, Yakun Tang, Yang Zhang, Xiaohui Li, Lang Liu

Journal of Nanoparticle Research· Volume 23· Issue 9
Publisher page

Abstract

Silicon waste from industrial cutting silicon rod process is assessed as an anode for lithium-ion batteries (LIBs) to expand utilization of silicon waste and effectively reduce the cost. However, it is still a big challenge to achieve a large-scale and green-effective manufacture. Hence, it is important to propose a facile, low-cost, and scalable method which prepares porous silicon as an anode by chemical etching of silicon waste using acid and alkaline solution. The silicon etched by HF solution possesses porous structure, which exhibits higher capacity than unetched samples. Comparatively, the silicon etched by NaOH solution has the flaky shape with about 110 nm and shows superior cycling performance and stability (600.7 mAh g−1 after 200 cycles at 420 mA g−1), which can be ascribed to its abundant porous structure, as well as the amorphous SiOx layer accommodating the volume expansion of embedded Si. The silicon waste modified by scalable etching reveals greatly enhanced electrochemical properties for LIB anodes. It is a promising method of improving the electrochemical properties of porous Si anode materials for commercial LIBs. The two kinds of porous silicon were cleverly fabricated using KL-Si through facile ball-milling and chemical etching. H-Si owns the high initial coulombic efficiency and reversible capacity at lower current density. However, N-Si exhibits the superior cycling stability, which can be attributed to larger specific area and the SiOx layer to buffer enormous volume expansion of Si during discharge/charge process.
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Research context

OpenAlex topics: Advancements in Battery Materials · Supercapacitor Materials and Fabrication · Semiconductor materials and devices

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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