Ilya A. Ryzhikov

In this study, we used the SERS method for the first time to measure the spectra of four polysaccharide markers of fungal infections: linear β-(1→3)- and β-(1→6)-linked D-glucans, branched mannan of Candida albicans and galactomannan of Aspergillus fumigatus. Aqueous solutions of the polysaccharides were studied in concentrations from 10 pg/mL to 100 μg/mL. The spectra were analyzed using machine learning methods: principal component analysis for data visualization and partial least squares with a ridge regularizer, which were used to construct metrics reflecting the accuracy of substance recognition relative to each other. The spectral changes with varying analyte concentration were observed and stable calibration has been achieved. Subsequent measurements of fungal polysaccharides in the presence of a physiological concentration of human serum albumin (45 mg/mL), used to model blood serum, enabled accurate analyte detection in a clinically relevant concentration range of 10 pg/mL to 100 ng/mL. In this case, the calibration dependence was calculated using the partial least squares method with the L1-regularizer. Blind testing was evaluated using a train-derived applicability-domain criterion based on the disagreement between the model prediction and an independent concentration estimate.

Specific monosaccharide residue, β-D-galactofuranose (Galf) featuring a five-membered ring structure, is found in the glycans of fungi and bacteria, but is normally absent in healthy mammals and humans. In this study, synthetic oligosaccharides mimicking bacterial and fungal glycans were investigated by SERS (Surface-Enhanced Raman Scattering) techniques for the first time to distinguish between different types of glycan chains. SERS spectra of oligosaccharides related to fungal α-(1→2)-mannan, β-(1→3)-glucan, β-(1→6)-glucan, galactomannan of Aspergillus, galactan I of Klebsiella pneumoniae, and diheteroglycan of Enterococcus faecalis were measured. To analyze the spectra, a number of machine learning methods were used that complemented each other: principal component analysis (PCA), confidence interval estimation (CIE), and logistic regression with L1 regularization. Each of the methods has shown own effectiveness in analyzing spectra. Namely, PCA allows the visualization of the divergence of spectra in the principal component space, CIE visualizes the degree of overlap of spectra through confidence interval analysis, and logistic regression allows researchers to build a model for determining the belonging of the analyte to a given class of carbohydrate structures. Additionally, the methods complement each other, allowing the determination of important features representing the main differences in the spectra containing and not containing Galf residue. The developed mathematical models enabled the reliable identification of Galf residues within glycan compositions. Given the high sensitivity of SERS, this spectroscopic technique serves as a promising basis for developing diagnostic test systems aimed at detecting biomarkers of fungal and bacterial infections.

The modifications of the microstructure of myoglobin deposited onto SERS-active Ag-based substrates by drying a drop of aqueous solution with and without laser irradiation and the corresponding surface-enhanced Raman scattering (SERS) spectra are studied. It is shown that drying with laser irradiation leads to the formation of protein aggregates of various types, including crystal-like aggregates. It is also shown that after such drying, the aggregates generally have SERS spectra characterized by a change in the position of the vibration bands and the ratios of their amplitudes compared to the spectra of proteins dried without additional treatment. In particular, parts of the SERS spectra of aggregates formed under laser irradiation are characterized by an additional enhancement (up to 100×) compared to the SERS spectra of myoglobin dried in air at room temperature. The crystallization processes were modeled using the results of atomic force microscopy morphology studies of dried myoglobin on the SERS-active substrates to determine the conditions under which crystal-like aggregates start to grow at surface irregularities, specifically those with a volume close to that of the critical-size nucleus, and where the lowest energy of formation occurs. A correlation is established between surface irregularities, the amplitude, and the change in the SERS spectra during the drying of a myoglobin solution sample on a nanostructured Ag-based surface.

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.

Substrates based on the metal-polymer nanocomposites 2,3-dichloro-p-xylylene-silver (PCPX-Ag) that realize the effect of surface-enhanced Raman scattering (SERS) were developed. To obtain nanocomposites, the vapor-phase polymerization method was used (VDP), which makes it possible to control the nanocomposite microstructure. In the process of self-assembly during VDP, nanocomposite films with inclusions of silver particles were formed on the polycore substrates. Silver content varied from 2.5 to 16% vol. The possibility of using such substrates for the detection of low-molecular substances, for example 5,5′-dithiobis- (2-nitrobenzoic acid) (DTNB) analyte, by the SERS method with an enhancement factor of up to 104, was demonstrated. The dependence of the SERS spectra enhancement on the microstructure of the nanocomposite and the silver content was determined. The optical and morphological properties of nanocomposites were also investigated and their dependence on the silver content was shown. It has been demonstrated that the nanocomposite is SERS selective since when working with complex solutions in the presence of high molecular weight substances, signal enhancement was only observed for low molecular weight substances.

Advanced microsystems widely used in integrated optoelectronic devices, energy harvesting components, and microfluidic lab-on-chips require high-aspect silicon microstructures with a precisely controlled profile. Such microstructures can be fabricated using the Bosch process, which is a key process for the mass production of micro-electro-mechanical systems (MEMS) devices. One can measure the etching profile at a cross-section to characterize the Bosch process quality by cleaving the substrate into two pieces. However, the cleaving process of several neighboring deeply etched microstructures is a very challenging and uncontrollable task. The cleaving method affects both the cleaving efficiency and the metrology quality of the resulting etched microstructures. The standard cleaving technique using a diamond scriber does not solve this issue. Herein, we suggest a highly controllable cross-section cleaving method, which minimizes the effect on the resulting deep etching profile. We experimentally compare two cleaving methods based on various auxiliary microstructures: (1) etched transverse auxiliary lines of various widths (from 5 to 100 μm) and positions; and (2) etched dashed auxiliary lines. The interplay between the auxiliary lines and the etching process is analyzed for dense periodic and isolated trenches sized from 2 to 50 μm with an aspect ratio of more than 10. We experimentally showed that an incorrect choice of auxiliary line parameters leads to silicon “build-up” defects at target microstructures intersections, which significantly affects the cross-section profile metrology. Finally, we suggest a highly controllable defect-free cross-section cleaving method utilizing dashed auxiliary lines with the stress concentrators.

The creation of composite coatings with assigned optical properties is an important problem of modern science and technology. We have considered the metal-polymer nanocomposites based on poly(p-xylylene), with various concentrations of plasmon components—silver nanoparticles. We have carried out the optical measurements of the reflection, transmission, scattering and absorption coefficients for the manufactured films. We have retrieved the effective optical parameters of the composite coatings. The theoretical estimations based on the scattering and absorption data show that the scattering effectively occurs on the clusters of metal nanoparticles. This significantly influences the optical properties of the composite coating.

Spectroscopy based on surface enhanced Raman scattering (SERS) is widely used as a method with extremely high sensitivity for molecular and chemical analysis. We have developed thin-film sandwich structures, in which, when used as sensitive elements for detecting organic compounds at low concentrations, high-amplitude spectra of surface enhanced Raman scattering are observed. Using gas-phase cryochemical synthesis and thermal sputtering in vacuum, SERS active sandwich structures Ag–poly(chloro-p-xylylene)–Ag (Ag–PCPX–Ag) were obtained. In the process of creating sandwich structures, the upper silver film takes the form of a complex island topology with submicron sizes. A series of samples were made with different thicknesses of the polymer and upper silver layers. SERS spectra of the analyte chemically adsorbed on the film surface were obtained, demonstrating a significant amplification (up to 10⁴) compared with the control sample. The dependence of the gain on the silver concentration is characterized by a maximum polymer layer thickness of 600 nm and a 30 nm thick upper silver layer. A selective amplification of the low molecular weight compound spectra with respect to proteins was observed. A semi-empirical model is proposed that is in good agreement with the experimental results.

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).