生物分子
检出限
介孔材料
小分子
分析物
纳米颗粒
质谱法
纳米技术
化学
基质辅助激光解吸/电离
基质(化学分析)
纳米复合材料
材料科学
解吸
色谱法
吸附
有机化学
生物化学
催化作用
作者
Dantong Zhao,Chunxia Ma,Meng Gao,Yong Li,Bo Yang,Hui Li,Runhao Zhang,Minglu Hao,Jing Huang,Kang Liang,Pu Chen,Lei Xie,Rong Rong,Biao Kong
出处
期刊:Nano Research
[Springer Science+Business Media]
日期:2021-08-12
卷期号:15 (3): 2722-2733
被引量:21
标识
DOI:10.1007/s12274-021-3741-0
摘要
Small biomolecules (m/z < 500) are the material basis of organisms and participate in life activities, but their comprehensive and accurate detection in complex samples remains a challenge. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a powerful detection tool for molecular analysis with high throughput. The development of a new matrix is essential to improve the efficiency of the MALDI-MS for molecular compound detection. In this work, the sandwich-like gold nanoparticles@mesoporous silica nanocomposite@silver nanoparticles (Au@MSN@Ag) nanospheres were prepared by layer-by-layer super-assembly strategy, and can be used as a novel matrix for the quantitative detection and enrichment of small biomolecules by LDI-MS. The sandwich-like nanospheres form a unique plasma resonant cavity that effectively absorbs the laser energy, while the homogeneous mesoporous structure of MSN can lock the analyte, which is essential for efficient LDI of small molecules. Compared to traditional matrices, Au@MSN@Ag shows the advantages of low background, wide application range, high sensitivity, super high salt and protein tolerance, and good stability. For example, the detection limit of glucose was as low as 5 fmol, and showed a good linear relationship in the range of 1–750 µg/mL. Au@MSN@Ag assisted LDI-MS allows the enrichment and detection of small molecules in traditional Chinese medicine (TCM) without derivatization and purification, classification of herbs using the accurate quantitative results oligosaccharides, and identification of gelatin by amino acid content. This research could help in designing more efficient nanostructure matrices and further explored the application of LDI-MS.
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