In present work we compare kinetics and efficiency of hydrogen release from stain etched porous silicon (average size of 3-100 nm), silicon powder obtained from waste of the metallurgical silicon with different chemical content. The influence of powder fraction by particles size and mode of treatment on the amount of bonded hydrogen have been studied. Size of silicon nanocrystallites was controlled by DLS method, the chemical composition of metallurgical silicon was analysed by Energy-dispersive X-ray spectroscopy. Metallurgical silicon was processed in a blender, milled, filtered and centrifuged. This allowed us to obtain crystallites measuring 100-1000 nm. The H2 release intensity in the reaction of PS with water and a catalyst can be controlled by changing the catalyst concentration and temperature. The presence of impurities (iron, carbon, aluminium, calcium, cerium and others) in metallurgical silicon influences the yield of hydrogen generation. Physicochemical processes associated with the catalytic properties of additives in metallurgical silicon to increase the yield of hydrogen are discussed.

A software-algorithmic method for processing temperature characteristics of a catalytic sensor to recognize the type and determine the concentration of combustible gases in multicomponent mixtures has been developed and its feasibility has been evaluated. This method integrates principal component analysis (PCA) for processing Scharacteristics of catalytic sensors with machine learning classification algorithms to enhance selectivity. A flowchart of this software-algorithmic method is presented, and experimental studies of one-, two- and multicomponent mixtures consisting of hydrocarbons and hydrogen are carried out. In this work, a sample of six flammable gases and vapors of VOCs (methane, propane, butane, ethylene, hexane, and hydrogen) was examined. The original gases were calibration gas mixtures in the range of pre-explosive concentration. The results proved that the software-algorithmic method could recognize the type of combustible gas and determine the concentration with an accuracy of up to 90 %.

This work investigates an interferometric chemical sensor based on modulating optical bandgap parameters in a porous silicon photonic crystal. The proposed system features a fast, inexpensive setup using a single, narrow spectral response photodetector, which is advantageous for studying analyte adsorption/desorption kinetics compared to bulkier dispersive systems. Reflectance spectra were computed using the transfer matrix method, considering variations in refractive indices and bilayer numbers (Nbi). Modeling and experiments confirmed that reflectance oscillation frequency is proportional to analyte parameters like refractive index and evaporation rate. Short fast Fourier transformation (FFT) yielded analyte-specific spectrograms for ethanol and isopropanol. Sensitivity is enhanced for photonic structures designed at shorter wavelengths and remains invariant to Nbi.

Fluorinated carbon dots (FCDs) represent a promising class of nanomaterials for biomedical applications owing to their biocompatibility, high fluorescence, and chemical stability. In this work, we report for the first time the synthesis and systematic investigation of Gd3+-doped FCDs (Gd-FCDs) obtained via a solvothermal route using urea, anhydrous citric acid, 3-(trifluoromethyl)aniline, and gadolinium(III) chloride hexahydrate. By incorporating paramagnetic Gd3+ ions into fluorinated fluorescent carbon dots, we aimed to create multifunctional nanoprobes capable of simultaneous fluorescence and magnetic resonance imaging (MRI). Comprehensive characterization demonstrated that Gd3+ doping significantly altered the structural and optical properties of the FCDs. While pristine FCDs were ultrasmall (2-8 nm), Gd-FCDs exhibited larger sizes (40-80 nm) due to ion-induced aggregation. During the synthesis process, Gd3+ ions are efficient positively charged centers stimulating formation of FCDs around them, resulting in bigger final complexes. Zeta potentials of FCDs and Gd-FCDs are -27.8 mV and -1.5 mV, respectively. UV-vis and fluorescence analyses revealed changes in electronic transitions and reduced fluorescence intensity, consistent with the introduction of nonradiative pathways by Gd3+ ions. Time-resolved fluorescence studies further confirmed the modified exciton dynamics. Importantly, proton relaxation measurements showed drastic reductions in both T-1 and T-2 relaxation times for Gd-FCDs in the concentration range 0.015-4 g/L, highlighting their strong MRI contrast capability across different magnetic field strengths. Cell toxicity measurements on 3T3-L1 biological cells show that all the FCDs revealed no toxicity against cells, indicating their complete biological compatibility at the concentration levels between 0.175 and 0.334 mg/mL. Its efficient penetration into cell nuclei enables robust fluorescent cell labeling across the visible spectrum.

A novel and sensitive approach has been investigated for discerning similar alcoholic beverages, utilizing 3D analyte “taste” patterns derived from analyte photoluminescent responses in the presence of synthesized fluorinated carbon nanoparticles (FCNP). Fabrication of fluorescent FCNP was carried out by implementing a modified solvothermal synthesis followed by nanoparticle parameters monitoring using atomic force microscopy (AFM) imaging and dynamic light scattering spectroscopy (DLS). The method of concentration-dependent photoluminescence was used to determine the optimal parameters of the colloidal solution of the FCNP-analyte sensor system to maximize the selectivity of the 3D patterns sensor response on analytes presence. The photoluminescence patterns of the colloidal solution of FCNP were obtained for similar production technology alcoholic beverages such as armagnac, cognac, and whiskey. The photoluminescence response pattern exhibited by the FCNP-based sensor system is distinctive for the analyzed analytes and the reference mixture of ethanol and water. The action of the aromatic molecules of a specific analyte forms a unique photoluminescent response pattern of FCNP that can be used for systems like “artificial tongue”. Principal component analysis (PCA) depicts clear clusters for beverage and references. The clusters corresponding to cognac and armagnac closely align with each other, illustrating the inherent affinity between these alcoholic beverages.

In this article the possibility of determining the hydrogen concentration in a multicomponent gas mixture using the principal component analysis is investigated. Source data were obtained by a system, consisting of 8 sensors, each of which measured its own response values. It was found that, the values of the principal components form linear dependences of concentration, which are proportional to each other. At the same time, a different hydrogen concentration, pure or in a multicomponent mixture, is uniquely determined. The results showed that the principal component analysis allows both visually distinguishing sensor responses at different concentrations, and using additional mathematical operations to obtain the concentration value.

The purpose of this letter is to prove that the reaction of hydrogen combustion in a catalytic sensor can occur at room temperature. This letter was designed to examine the response of catalytic hydrogen sensors to heating voltage (S-characteristics) as the ambient temperature is changed from 17 °С to −48 °С. A platinum group catalyst based on Pt and Pd composite with molar ratio Pt:Pd = 1:3 was used as a catalyst. It was shown that the sensor starts responding to hydrogen upon zero heating voltage at 17 °С. When the ambient temperature decreases, an initial section appears on the dependences of the sensor response to hydrogen. This section contains no response and expands rightwards to the area of higher heating voltages from 0 to 442 mV as the ambient temperature decreases from 17 °С to −48 °С. This research identified the threshold temperature at which catalytic hydrogen combustion starts in a sensor with a Pt+3Pd composite catalyst. This temperature was found to fall in the range of 17–22 °С.

On March 3, 2024, sad news spread among Russian plant physiologists: an outstanding scientist and wonderful person, Farida Minnikhanovna Shakirova, died. Friends and colleagues exchanged letters in an attempt to honor her memory. Members of Editorial board of the journal “Ecobiotech”, created by colleagues F.M. Shakirova, decided to write about her scientific activities and her amazing qualities: kindness, dedication to science, efficiency, talent as a researcher, willingness to share her experience with colleagues. We would like young colleagues who did not have the opportunity to know her personally to learn from her example.

Prevention of emergencies associated with flammable gases explosions remains an urgent task. A ranking place in solving of this task plays environment monitoring for combustible gases presence. The environmental explosiveness level can also be assessed overall, without measuring separate gases concentrations. The assessment of environmental explosiveness level can be performed using catalytic gas sensors based on combustion heat measurements of flammable gases combustion in the sensors. Modern data processing methods application, such as machine learning, allows to increase the measurements accuracy. However, machine learning needs a Iot of data. To collect that data enough measurements for different gases should be performed. At the same time, the question remains of whether it is possible to apply the machine learning models to gases, which have not been used while training. In this work, the results of the research are presented, where the possibility of successful models training using limited gases set and the ability of such models to assess the explosiveness level of other gases and mixtures were examined.

Well-known that porous silicon is currently under consideration as new promising material for solid state hydrogen reservoirs. In present work a hydrogen adsorption processes in porous silicon are simulated. A model of hydrogen passivation of the surface of silicon pores are developed using the methods of molecular dynamics. Hydrogen-like spherical Lennard-Jones particles are used as the adsorbate. Such choice is explained by the physical nature of the adsorption processes of hydrogen on initial chemically adsorbed H-monolayer, covered the pore walls. A distribution of potential energy for interaction of the drifting species with the pore walls is calculated. Current-voltage and impedance characteristics of free standing porous silicon layers were selected as control method to study hydrogen accumulation process. The charge transfer processes were analyzed based on dependence of the impedance of porous silicon samples on voltage in the atmosphere with H2 and during relaxation in air.

The response of catalytic hydrogen sensors with platinum-group catalysts (Pt + 3Pd, Pt, Pd, Ir, and Rh) in the room temperature range has been studied. It was demonstrated that a flameless catalytic hydrogen combustion reaction on the Pt and Pt + 3Pd catalysts occurs at 20 degrees C, which results in the heating of the sensing element of a catalytic sensor, and the microheater resistance increases. The microheater temperature was first measured, and it was shown that the temperature increases by 99 and 83 degrees C in the calibration gas mixture containing 0.96% vol. hydrogen for the Pt and Pt + 3Pd catalysts, respectively. A method has been provided for measuring hydrogen levels using a catalytic sensor without heating voltage input.

In this article, the authors theoretically and experimentally investigated ways to improve the efficiency of porous silicon (PS)-based optical microcavity sensors as a 1D/2D host matrix for electronic tongue/nose systems. The transfer matrix method was used to compute reflectance spectra of structures with different [nLnH] sets of low nL and high nH bilayer refractive indexes, the cavity position λc, and the number of bilayers Nbi. Sensor structures were prepared by electrochemically etching a silicon wafer. The kinetics of adsorption/desorption processes of ethanol-water-based solution was monitored in real time with a reflectivity probe-based setup. It was theoretically and experimentally demonstrated that the sensitivity of the microcavity sensor is higher for structures with refractive indexes in the lower range (and the corresponding porosity values in the upper range). The sensitivity is also improved for structures with the optical cavity mode (λc) adjusted toward longer wavelengths. The sensitivity of a distributed Bragg reflector (DBR) with cavity increases for a structure with cavity position λc in the long wavelength region. The full width at half maximum (fwhmc) of the microcavity is smaller and the quality factor of microcavity (Qc) is higher for the DBR with a larger number of structure layers Nbi. The experimental results are in good agreement with the simulated data. We believe that our results can help in developing rapid, sensitive, and reversible electronic tongue/nose sensing devices based on a PS host matrix.

Hydrogen monitoring in industrial premises, when other gases are also present in air, is an urgent task. For safety reasons, it is necessary that a hydrogen sensor provides measurements at the lowest possible temperature. In this paper, we propose an approach to the selective measurement of the concentration of hydrogen, which is part of multicomponent hydrocarbon mixtures. Binary and ternary mixtures of hydrogen with methane, propane, and butane were used in this study. The traditional method is based on measuring the catalytic sensor response, and a new method that is based on measuring the amount of heat released during hydrogen combustion was used to solve the problem of selectivity. An industrial catalytic sensor was used for the measurements. It was shown that for the selective measurement of hydrogen in hydrocarbon mixtures, it is necessary to reduce the sensor temperature below 200 degrees C. Measurements of hydrogen concentration and a comparison of results were carried out at 105 degrees C. Such a low operating temperature is an excellent result for a catalytic sensor. It is shown that the method based on measuring the amount of heat released during hydrogen combustion is more accurate than the traditional method, and the average error was 7.7%.

In order to carry out efficient flammable gas leaks and emissions monitoring of pipelines, wireless sensor nodes have to be deployed at different locations and heights. Therefore, the power grid could be unavailable and the power supply of gas sensors should be based on battery-renewable energy which needs an optimal low-power switching algorithm considering low cost. In addition, it is important to save battery life by protecting the battery from full discharge and overcharging. This paper introduces a DC/DC single-inductor, multi-input, multi-output (SI-MIMO) converter that can operate in three modes: buck, buck-boost, and boost. This converter is going to be designed in the discrete form. The basis is that the CPU of the microcontroller be turned on as little as possible and to be used only when it is necessary. Digital algorithms are considered in such a way that the system can intelligently and, depending on the energy level of the inputs, can use them to charge the outputs and determine the optimal switching frequency for each output. The system can work with variable input. Each output can be charged at different frequencies and according to their load, and the maximum switching frequency is equal to 10 kHz, which is a relatively low frequency.

Fast Fourier transform (FFT) and principal component analysis (PCA) were successfully applied to realize a selective technique of adsorbate discrimination by spectral analysis of the waveforms of current through a gas-sensitive ITO/nanostructured TiO2 heterojunction when a sinusoidal alternating voltage is applied to it. The novel technique shows good differentiation of sensor responses for adsorption of water, ammonia, ethanol and isopropanol molecules with sequential injection of analytes and fair differentiation of methanol, ethanol and isopropanol adsorption even for random injection of analytes. Comparison of the score plots and current epures obtained at various frequencies of the probing signal and various sets of harmonics used as source data for PCA allowed to find the optimal measurement conditions in terms of selectivity and reveal the basic factor, which predetermines substance-dependent variations in the harmonic spectrum of current waveforms. It is considered that substance-dependent changes in the shape of current waveforms are due to the processes of recharging of electron and/or hole traps caused by the interaction of adsorbed molecules with the surface of the structure and their diffusion deep into the porous TiO2 layer.

Environment explosiveness level monitoring is the crucial method to prevent emergencies related to combustible gas leakages. In this paper, the results of advanced signal processing for environment integral explosiveness evaluation are presented. The signal processing is based on machine learning techniques application to take into account the information pre-sented in the multidimensional signal retrieved by the temper-ature modulated measurements. The signal measurements were performed for clean air, hydrogen (1 %vol.), methane (1 %vol.), ethylene (1 %vol.), propane (1 %vol.), butane (0.7 %vol.) and n-hexane (0.5 %vol.). To measure the signal, the node prototype with the catalytic gas sensor was used. The data from the prototype was transmitted by wireless channel to a personal computer to be stored and processed. Two models were trained: a linear regression and a neural network. The models were trained using stochastic gradient descent algorithm. The training was performed while the validation error value was decreased. The results for the models were compared with the known method of environment explosiveness level estimation. It was shown that the available method has a low performance for specific gases. The presented models are able to decrease the average error of explosiveness level estimation for the gases in research from 7.8 %LEL down to 0.12 %LEL. Thereat the error level stays almost the same for all gases.

The research demonstrates for the first time the high selectivity of catalytic hydrogen sensors to other hydrocarbons (methane, propane, hexane, butane, ethane, and ethylene) at a temperature less than 70 °С. Two circuits were used to measure the response of the sensors: a Wheatstone bridge circuit and a divider circuit. Hydrogen measurement was conducted within a temperature range of 66–130 °С. The sensors exhibited high sensitivity (25–35 mV/%) and low power consumption (approximately 8.6 mW). The Wheatstone bridge circuit was observed to have the maximum value of selectivity and sensitivity.

The results of research to improve hydrogen measurement selectivity of catalytic gas sensors are presented. The method uses temperature modulation to obtain the multidimensional signal, which includes a temperature-dependent sensor response to the presence of different gases in the environment: hydrogen, methane, ethylene, propane, butane, and hexane. The signal processing was performed using machine learning methods. Two models were trained: the linear regression and the neural network. The neural network model showed substantially better accuracy in comparison to the linear one. The absolute mean error for this model is 0.0095 %vol. The level of error is consistent for all gases. The results show that the presented method allows the selective measurements of target gases, hydrogen in particular, and minimize the interference of other gases. In comparison to other methods, the method does not need any changes in the sensor construction and allows selective results even when gases have a strong response to similar temperatures.

In this article, the authors performed computer simulations and experimentally investigated the application of the colorimetric approach based on CIE1931 color space for characterization of the optical response of porous silicon (PS) based photonic crystals (PC). The colorimetric parameters as dominant wavelength (lambda(dom)) and excitation purity (EP) were determined and compared with conventional PC parameters as Bragg peak position lambda(0) and FWHM. The colorimetric parameters were obtained for PC with different sets of porosity values of bi-layers and the position of Bragg peak lambda(0)(air). The PS sensor structures were fabricated by the method of electrochemical anodization of Si wafer. The colorimetric sensitivity of the PC sensors is higher for structure with smaller refractive indexes. Colorimetric parameters characterize the modification of the entire complex spectrum of the optical sensor as opposed to the conventional parameter as the Bragg peak position. The time dependences of colorimetric parameters on PC parameters during adsorption/ desorption of ethanol are determined. The colorimetric approach for sensor characterization is preferable to develop the rapid not dispersive sensor systems as artificial tongue/nose with coordinate-dependent parameters of the spectrum. (c) 2021 Elsevier B.V. All rights reserved.

The colorimetric approaches based on CIE1931 and HSV [Hue Saturation Value] color space were considered in the article as a prospective method of characterization of the reflectance response of the optical sensor. The transfer matrix method was used for the determination of reflectivity spectra of porous silicon based distributed Bragg reflectors with pores filled with analyte with refractive index in the range 1.3...1.4. Traditional parameters of the sensor as the position of peak maxima and FWHM, CIE1931 colorimetric parameters as dominant wavelength and excitation purity, [H S V] components were determined for different low and high values of refractive indexes of layers of the optical sensor.