4.7 Article

Silver-Coated Disordered Silicon Nanowires Provide Highly Sensitive Label-Free Glycated Albumin Detection through Molecular Trapping and Plasmonic Hotspot Formation

期刊

ADVANCED HEALTHCARE MATERIALS
卷 10, 期 3, 页码 -

出版社

WILEY
DOI: 10.1002/adhm.202001110

关键词

biosensing; diabetes screening; glycated albumin; machine learning; nanowires; plasmonics; surface enhanced Raman spectroscopy (SERS)

资金

  1. Joint research project Scalable nanoplasmonic platform for differentiation and drug response monitoring of organ-tropic metastatic cancer cells of the Italian Minister of Foreign Affairs and International Collaboration (MAECI) within the Scientific and C
  2. National Institute of Biomedical Imaging and Bioengineering [2-P41-EB015871-31]
  3. National Institute of General Medical Sciences [DP2GM128198]

向作者/读者索取更多资源

A label-free surface-enhanced Raman spectroscopy sensing platform utilizing silver-coated silicon nanowires has been developed to achieve highly sensitive and reproducible quantification of glycated albumin. This technology allows for visual detection of glycated albumin at levels as low as 500 x 10(-9) m, significantly below the physiological range.
Glycated albumin (GA) is rapidly emerging as a robust biomarker for screening and monitoring of diabetes. To facilitate its rapid, point-of-care measurements, a label-free surface-enhanced Raman spectroscopy (SERS) sensing platform is reported that leverages the specificity of molecular vibrations and signal amplification on silver-coated silicon nanowires (Ag/SiNWs) for highly sensitive and reproducible quantification of GA. The simulations and experimental measurements demonstrate that the disordered orientation of the nanowires coupled with the wicking of the analyte molecules during the process of solvent evaporation facilitates molecular trapping at the generated plasmonic hotspots. Highly sensitive detection of glycated albumin is shown with the ability to visually detect spectral features at as low as 500 x 10(-9) m, significantly below the physiological range of GA in body fluids. Combined with chemometric regression models, the spectral data recorded on the Ag/SiNWs also allow accurate prediction of glycated concentration in mixtures of glycated and non-glycated albumin in proportions that reflect those in the bloodstream.

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