journal article·2026-07Open access

Tailored graphene quantum dots for heavy metal stabilization in MSW incineration fly ash: A potential scalable engineering pathway

Yufei Gao, Zhao Gao, Jiaji Hu, Xingqiang Li, Qiqi Zheng, Chuang He, Wenjie Zhang, Lei Xu, Haijie He

Materials & Design· Volume 267

Abstract

Carbon dots (CDs) have attracted increasing attention as emerging carbon-based nanomaterials due to their excellent optical properties, low toxicity, and abundant surface functional groups. However, their practical use in large-scale engineering systems remains limited. Meanwhile, municipal solid waste incineration fly ash (IFA), a hazardous byproduct containing leachable heavy metals, requires effective treatment to reduce environmental risks. In this study, amino-functionalized graphene quantum dots (A-GQDs) were synthesized and incorporated into a cement-based solidification/stabilization (S/S) system for IFA treatment, and their effects on heavy metal immobilization and the underlying mechanism were systematically investigated. The results show that the addition of 0.2 wt% A-GQDs significantly reduced the leaching concentrations of Pb, Cd, and Zn by 50–90%. Sequential extraction further confirmed that A-GQDs promoted the transformation of heavy metals from unstable fractions to more stable forms. Microstructural characterization suggests that A-GQDs enhanced the formation of dense hydration products and refined the cement matrix, while their surface functional groups may also contribute to heavy metal immobilization through chemical interactions. Overall, A-GQDs show promise as multifunctional nano-additives for improving the cement-based treatment of IFA and provide a potential application pathway for carbon dots in hazardous waste management.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Coal and Its By-products · Recycling and utilization of industrial and municipal waste in materials production · Advanced Photocatalysis Techniques

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

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