Surface-enhanced Raman spectroscopy (SERS) has been intensely studied as a possible solution in the fields of analytical chemistry and biosensorics for decades. Substantial research has been devoted to engineering signal enhanced SERS-active substrates based on semi-continuous nanostructured silver and gold films, or agglomerates of micro- and nanoparticles in solution. Herein, we demonstrate the high-amplitude spectra of myoglobin precipitated out of ultra-low concentration solutions (below 10 μg/mL) using e-beam evaporated continuous self-assembled silver films. We observe up to 10⁵ times Raman signal amplification with purposefully designed SERS-active substrates in comparison with the control samples. SERS-active substrates are obtained by electron beam evaporation of silver thin films with well controlled nanostructured surface morphology. The characteristic dimensions of the morphology elements vary in the range from several to tens of nanometers. Using optical confocal microscopy we demonstrate that proteins form a conformation on the surface of the self-assembled silver film, which results in an effective enhancement of giant Raman scattering signal. We investigate the various SERS substrates surface morphologies by means of atomic force microscopy (AFM) in combination with deep data analysis with Gwyddion software and a number of machine learning techniques. Based on these results, we identify the most significant film surface morphology patterns and evaporation recipe parameters to obtain the highest amplitude SERS spectra. Moreover, we demonstrate the possibility of automated selection of suitable morphological parameters to obtain the high-amplitude spectra. The developed AFM data auto-analysis procedures are used for smart optimization of SERS-active substrates nanoengineering processes.

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.

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.

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.

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.

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.