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.
Nanoparticles and biological molecules high throughput robust separation is of significant interest in many healthcare and nanoscience industrial applications. In this work, we report an on-chip automatic efficient separation and preconcentration method of dissimilar sized particles within a microfluidic platform using integrated membrane valves controlled microfiltration. Micro-sized E. coli bacteria are sorted from nanoparticles and preconcentrated on a microfluidic chip with six integrated pneumatic valves (sub-100 nL dead volume) using hydrophilic PVDF filter with 0.45 μm pore diameter. The proposed on-chip automatic sorting sequence includes a sample filtration, dead volume washout and retentate backflush in reverse flow. We showed that pulse backflush mode and volume control can dramatically increase microparticles sorting and preconcentration efficiency. We demonstrate that at the optimal pulse backflush regime a separation efficiency of E. coli cells up to 81.33% at a separation throughput of 120.45 μL/min can be achieved. A trimmed mode when the backflush volume is twice smaller than the initial sample results in a preconcentration efficiency of E. coli cells up to 121.96% at a throughput of 80.93 μL/min. Finally, we propose a cyclic on-chip preconcentration method which demonstrates E. coli cells preconcentration efficiency of 536% at a throughput of 1.98 μL/min and 294% preconcentration efficiency at a 10.9 μL/min throughput.
The work considers the stages of design and operation of the information expert system of predictive maintenance analytical support of industrial equipment exemplifi ed by vacuum devices. Special attention was paid to the study of the methods of maintenance of the equipment and also to the development of a concept of a modern system of predictive maintenance. The formalization of the test system of predictive maintenance was performed in the package MS Excel using the programming language Visual Basic for Applications. The result of work is the development of an automated expert system of analysis of the methods and means of predictive maintenance of vacuum devices. The particular results were obtained with the support of the Ministry of Education and Science of the Russian Federation within the project of the Agreement No. 14.579.21.0142 UID RFMEFI57917X0142.
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