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A 3D MXene–CNT–Ag SERS platform with high enhancement and uniformity for quantitative analysis of complex botanical extracts

Journal 2026 · IF 6 · 发表于 2026 年 8 月 10 日
作者: Xueying Zhang,Wei Lai,Mengdi Guo
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摘要

Quality assessment of herbal medicines is complicated by their complex matrices and variability in chemical composition. In this study, a three-dimensional surface-enhanced Raman scattering (SERS) substrate was constructed for the analysis of Astragalus membranaceus extracts. The substrate consists of carboxylated carbon nanotubes (HOOC CNTs) integrated with Ti 3 C 2 T x MXene and decorated with in situ-grown silver nanoparticles (AgNPs), forming an interconnected porous network. This hierarchical architecture provides accessible plasmonic sites, while the CNT–MXene framework improves material dispersion and limits MXene restacking in aqueous media. The AgNP/HOOC CNT/Ti 3 C 2 T x substrate exhibited a measured SERS enhancement factor of 1.82 × 10 6 , together with improved signal uniformity and stability under the tested conditions. Quantitative calibration models were established for astragalosides, polysaccharides, and calycosin in A. membranaceus extracts with limited sample pretreatment, and the analytical results were compared with those obtained using reference methods. The measured chemical profiles also enabled preliminary discrimination among the investigated samples from different geographical origins and assessment of batch-to-batch variation. Among the samples included in this study, those collected from Jilin showed relatively high levels of the target constituents. These results indicate that the proposed SERS substrate has potential as a rapid analytical platform for compositional assessment and comparative quality evaluation of A. membranaceus . Further validation using larger and more geographically representative sample sets is required before the method can be applied to routine origin authentication and quality grading. Download: Download high-res image (370KB) Download: Download full-size image

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