法拉第效率
杂原子
阳极
锂(药物)
纳米技术
电池(电)
电解质
石墨
碳纤维
炭黑
插层(化学)
材料科学
生物量(生态学)
锂离子电池
废物管理
化学工程
电化学
比表面积
城市固体废物
吸附
原材料
作者
Abdulafeez O. Orilonise,Kingsley O. Iwuozor,Ebuka Chizitere Emenike,Joshua Emeghai,Adewale George Adeniyi
标识
DOI:10.1016/j.esi.2026.01.001
摘要
Waste derived carbon materials have advanced as sustainable alternatives to graphite for lithium ion battery anodes, yet existing studies remain fragmented because biomass and plastic wastes are often examined separately. This review integrates these research streams and establishes a unified framework linking feedstock composition, co-carbonization behaviour, activation pathways, heteroatom doping, and microstructural evolution to electrochemical performance. The analysis demonstrates that blended biomass-plastic feedstocks generate synergistic effects that shape yield, porosity, interlayer spacing, defect density, and surface chemistry. These structural features govern dual lithium storage mechanisms involving pseudocapacitive adsorption at defect sites and intercalation within turbostratic microdomains. Reported capacities frequently exceed 500 mAh g -1 with superior rate performance compared to graphite. The review shows that excessive surface area and uncontrolled activation reduce initial coulombic efficiency through extensive solid electrolyte interphase formation, whereas moderated activation and controlled defect engineering improve cyclability. The study also shows the performance gains achieved by forming hybrids with metal oxides, silicon, and MXenes, which enhance conductivity, buffer volume change, and accelerate ion transport, delivering capacities between 700 and 1200 mAh g -1 . Key barriers include low initial coulombic efficiency, variable feedstock quality, and the limited scalability of chemical activation. The review identifies targeted pre-lithiation, multi heteroatom co doping, and data driven synthesis optimisation as essential strategies for advancing waste derived carbons toward commercial anode applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI