Glycated albumin (GA) is rapidly emerging as a robust biomarker forscreening and monitoring of diabetes. To facilitate its rapid, point-of-caremeasurements, a label-free surface-enhanced Raman spectroscopy (SERS)sensing platform is reported that leverages the specificity of molecularvibrations and signal amplification on silver-coated silicon nanowires(Ag/SiNWs) for highly sensitive and reproducible quantification of GA. Thesimulations and experimental measurements demonstrate that thedisordered orientation of the nanowires coupled with the wicking of theanalyte molecules during the process of solvent evaporation facilitatesmolecular trapping at the generated plasmonic hotspots. Highly sensitivedetection of glycated albumin is shown with the ability to visually detectspectral features at as low as 500 × 10-9 m, significantly below thephysiological range of GA in body fluids. Combined with chemometricregression models, the spectral data recorded on the Ag/SiNWs also allowaccurate prediction of glycated concentration in mixtures of glycated andnon-glycated albumin in proportions that reflect those in the bloodstream.

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

Annalisa Convertino
;
Valentina Mussi;Luca Maiolo;
2020

Abstract

Glycated albumin (GA) is rapidly emerging as a robust biomarker forscreening and monitoring of diabetes. To facilitate its rapid, point-of-caremeasurements, a label-free surface-enhanced Raman spectroscopy (SERS)sensing platform is reported that leverages the specificity of molecularvibrations and signal amplification on silver-coated silicon nanowires(Ag/SiNWs) for highly sensitive and reproducible quantification of GA. Thesimulations and experimental measurements demonstrate that thedisordered orientation of the nanowires coupled with the wicking of theanalyte molecules during the process of solvent evaporation facilitatesmolecular trapping at the generated plasmonic hotspots. Highly sensitivedetection of glycated albumin is shown with the ability to visually detectspectral features at as low as 500 × 10-9 m, significantly below thephysiological range of GA in body fluids. Combined with chemometricregression models, the spectral data recorded on the Ag/SiNWs also allowaccurate prediction of glycated concentration in mixtures of glycated andnon-glycated albumin in proportions that reflect those in the bloodstream.
2020
Istituto per la Microelettronica e Microsistemi - IMM
biosensing
diabetes screening
glycated albumin
machine learning
nanowires
plasmonics
surface enhanced Raman spectroscopy (SERS)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/383978
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