This paper presents a cost-effective approach for the template-assisted electrodeposition fabrication of substrates for surface-enhanced Raman scattering (SERS) with metal nanowires (NWs) grown in pores of polymer track-etched membranes (TM). This technique allows the synthesis of NWs array with its certain surface density and diameter (from dozen to hundreds of nm). NWs length also may be varied (order of μm) by controlling deposition time. Here we grow vertical Ag-NWs which are leaning towards their nearest neighbors, forming self-assembled bundles whose parameters depend on the NW aspect ratio (length to diameter). We show that in such bundles there are “hot spots” in the nm-gaps between NWs tips. Computer simulations have demonstrated a strong enhancement of the electric field within these hot spots; thus, the Raman signal is markedly amplified for analyte molecules placed directly inside the gaps. We have experimentally proved the potential of this SERS-technique on the example of 4-Mercaptophenylboronic acid (4-MPBA). For 4-MPBA the maximal enhancement of Raman signal was found at NWs length of ~1.6 μm and diameter of ~100 nm. The effect is higher (up to twice) if “wet” substrate is used just immediately after the TM polymer removal so that the tips are brought to lean after analyte exposure. We suggest this new type of nanostructured SERS-substrates as a base of effective sensing of extremely low concentration of analytes.

In this study, a non-labeled sensor system for direct determining human glycated albumin levels for medical application is proposed. Using machine learning methods applied to surface-enhanced Raman scattering (SERS) spectra of human glycated albumin and serum human albumin enabled the avoidance of complex sample preparation. By implementing linear discriminant analysis and regularized linear regression, classification and regression problems were solved based on the spectra obtained as a result of the experiment. The results show that, coupled with data augmentation and a special cross-validation procedure, the methods we employed yield better results in the corresponding tasks in comparison with popular random forest methods and the support vector method. The results show that SERS, in combination with machine learning methods, can be a powerful and effective tool for the simple and direct assay of protein mixtures.

Angiotensin I-converting enzyme (ACE) is a peptidase widely presented in human tissues and biological fluids. ACE is a glycoprotein containing 17 potential N-glycosylation sites which can be glycosylated in different ways due to post-translational modification of the protein in different cells. For the first time, surface-enhanced Raman scattering (SERS) spectra of human ACE from lungs, mainly produced by endothelial cells, ACE from heart, produced by endothelial heart cells and miofibroblasts, and ACE from seminal fluid, produced by epithelial cells, have been compared with full assignment. The ability to separate ACEs’ SERS spectra was demonstrated using the linear discriminant analysis (LDA) method with high accuracy. The intervals in the spectra with maximum contributions of the spectral features were determined and their contribution to the spectrum of each separate ACE was evaluated. Near 25 spectral features forming three intervals were enough for successful separation of the spectra of different ACEs. However, more spectral information could be obtained from analysis of 50 spectral features. Band assignment showed that several features did not correlate with band assignments to amino acids or peptides, which indicated the carbohydrate contribution to the final spectra. Analysis of SERS spectra could be beneficial for the detection of tissue-specific ACEs.

Нечаева Н.Л., Еременко А.В., Курочкин И.Н., Богинская И.А. [и др.] РЕГИСТРАЦИЯ ГЛИКИРОВАННОГО АЛЬБУМИНА ЧЕЛОВЕКА С ПОМОЩЬЮ ПОДЛОЖЕК НА ОСНОВЕ НАНОСТРУКТУРИРОВАННЫХ ПЛЕНОК СЕРЕБРА, РЕАЛИЗУЮЩИХ ЭФФЕКТ ГИГАНТСКОГО КОМБИНАЦИОННОГО РАССЕЯНИЯ // Менделеев: эл.научный журнал. –2019 – №4(4).