Using waste to treat waste: facile synthesis of hollow carbon nanospheres from lignin for water decontamination

Lignin, the most abundant natural material, is considered as a low-value commercial biomass waste from paper mills and wineries. In an effort to turn biomass waste into a highly valuable material, herein, a new-type of hollow carbon nanospheres (HCNs) is designed and synthesized by pyrolysis of biom...

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Veröffentlicht in:Chemical science (Cambridge) 2023-12, Vol.15 (1), p.24-212
Hauptverfasser: Liu, Xiang, Hao, Zixuan, Fang, Chen, Pang, Kun, Yan, Jiaying, Huang, Yingping, Huang, Di, Astruc, Didier
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Sprache:eng
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Zusammenfassung:Lignin, the most abundant natural material, is considered as a low-value commercial biomass waste from paper mills and wineries. In an effort to turn biomass waste into a highly valuable material, herein, a new-type of hollow carbon nanospheres (HCNs) is designed and synthesized by pyrolysis of biomass dealkali lignin, as an efficient nanocatalyst for the elimination of antibiotics in complex water matrices. Detailed characterization shows that HCNs possess a hollow nanosphere structure, with abundant graphitic C/N and surface N and O-containing functional groups favorable for peroxydisulfate (PDS) activation. Among them, HCN-500 provides the maximum degradation rate (95.0%) and mineralization efficiency (74.4%) surpassing those of most metal-based advanced oxidation processes (AOPs) in the elimination of oxytetracycline (OTC). Density functional theory (DFT) calculations and high-resolution mass spectroscopy (HR-MS) were employed to reveal the possible degradation pathway of OTC elimination. In addition, the HCN-500/PDS system is also successfully applied to real antibiotics removal in complex water matrices ( e.g. river water and tap water), with excellent catalytic performances. Herein, a new type of hollow carbon nanospheres (HCNs) is designed and synthesized by pyrolysis of biomass dealkali lignin, as an efficient nanocatalyst for the elimination of antibiotics in complex water matrices.
ISSN:2041-6520
2041-6539
DOI:10.1039/d3sc05275c