Impact ionization fronts in Si diodes: Numerical evidence of superfast propagation due to nonlocalized preionization

We present numerical evidence of a novel propagation mode for superfast impact ionization fronts in high-voltage Si $p^+$-$n$-$n^+$ structures. In nonlinear dynamics terms, this mode corresponds to a pulled front propagating into an unstable state in the regime of nonlocalized initial conditions. Before the front starts to travel, field-ehanced emission of electrons from deep-level impurities preionizes initially depleted $n$ base creating spatially nonuniform free carriers profile. Impact ionization takes place in the whole high-field region. We find two ionizing fronts that propagate in opposite directions with velocities up to 10 times higher than the saturated drift velocity.Comment: 3 pages, 4 figure

Deposition of Thick SiO2 Coatings to Carbonyl Iron Microparticles for Thermal Stability and Microwave Performance

Thick dielectric SiO2 shells on the surface of iron particles enhance the thermal and electrodynamic parameters of the iron. A technique to deposit thick, 500-nm, SiO2 shell to the surface of carbonyl iron (CI) particles was developed. The method consists of repeated deposition of SiO2 particles with air drying between iterations. This method allows to obtain thick dielectric shells up to 475 nm on individual CI particles. The paper shows that a thick SiO2 protective layer reduces the permittivity of the ‘Fe-SiO2—paraffin’ composite in accordance with the Maxwell Garnett medium theory. The protective shell increases the thermal stability of iron, when heated in air, by shifting the transition temperature to the higher oxide. The particle size, the thickness of the SiO2 shells, and the elemental analysis of the samples were studied using a scanning electron microscope. A coaxial waveguide and the Nicholson–Ross technique were used to measure microwave permeability and permittivity of the samples. A vibrating-sample magnetometer (VSM) was used to measure the magnetostatic data. A synchronous thermal analysis was applied to measure the thermal stability of the coated iron particles. The developed samples can be applied for electromagnetic compatibility problems, as well as the active material for various types of sensors

Deposition of Thick SiO₂ Coatings to Carbonyl Iron Microparticles for Thermal Stability and Microwave Performance

Thick dielectric SiO2 shells on the surface of iron particles enhance the thermal and electrodynamic parameters of the iron. A technique to deposit thick, 500-nm, SiO2 shell to the surface of carbonyl iron (CI) particles was developed. The method consists of repeated deposition of SiO2 particles with air drying between iterations. This method allows to obtain thick dielectric shells up to 475 nm on individual CI particles. The paper shows that a thick SiO2 protective layer reduces the permittivity of the ‘Fe-SiO2—paraffin’ composite in accordance with the Maxwell Garnett medium theory. The protective shell increases the thermal stability of iron, when heated in air, by shifting the transition temperature to the higher oxide. The particle size, the thickness of the SiO2 shells, and the elemental analysis of the samples were studied using a scanning electron microscope. A coaxial waveguide and the Nicholson–Ross technique were used to measure microwave permeability and permittivity of the samples. A vibrating-sample magnetometer (VSM) was used to measure the magnetostatic data. A synchronous thermal analysis was applied to measure the thermal stability of the coated iron particles. The developed samples can be applied for electromagnetic compatibility problems, as well as the active material for various types of sensors

Influence of Hydrogen Reduction Stage Conditions on the Microwave Properties of Fine Iron Powders Obtained via a Spray-Pyrolysis Technique

The relationship between the chemical purity of one-size particles and microwave properties in ferromagnetic materials is not clearly studied. Ferromagnetic nanostructured iron powders were synthesized from iron nitrate solution using ultrasonic spray-pyrolysis and then reduced in H2 flow at 350, 400, 450, and 500 °C. A rise in the concentration of solutions of a precursor from 10 to 20 wt. % led to an increase in mean particle size. The interrelationship was studied between chemical composition and the microwave dispersion of the powders obtained. An increase in the temperature of reduction changes the chemical composition and increases the amplitude of complex microwave permeability, which was studied using solid-state physics methods (XRD, STA, SEM, and VNA). It was found that annealing at 400 °C is the optimal treatment that allows the production of iron powders, consisting of about 90% of α-Fe phase, possessing a particle surface with low roughness and porosity, and demonstrating intense microwave absorption. Annealing at a higher temperature (500 °C) causes an even higher increase in permeability but leads to the destruction of nanostructured spheres into smaller particles due to grain growth. This destruction causes an abrupt increase in permittivity and therefore significantly reduces potential applications of the product. The insight into chemical–magnetic relationships of these materials enhances the data for design applications in magnetic field sensing

Deposition of a SiO2 Shell of Variable Thickness and Chemical Composition to Carbonyl Iron: Synthesis and Microwave Measurements

Protective SiO2 coating deposited to iron microparticles is highly demanded both for the chemical and magnetic performance of the latter. Hydrolysis of tetraethoxysilane is the crucial method for SiO2 deposition from a solution. The capabilities of this technique have not been thoroughly studied yet. Here, two factors were tested to affect the chemical composition and the thickness of the SiO2 shell. It was found that an increase in the hydrolysis reaction time thickened the SiO2 shell from 100 to 200 nm. Moreover, a decrease in the acidity of the reaction mixture not only thickened the shell but also varied the chemical composition from SiO3.0 to SiO8.6. The thickness and composition of the dielectric layer were studied by scanning electron microscopy and energy-dispersive X-ray analysis. Microwave permeability and permittivity of the SiO2-coated iron particles mixed with a paraffin wax matrix were measured by the coaxial line technique. An increase in thickness of the silica layer decreased the real quasi-static permittivity. The changes observed were shown to agree with the Maxwell Garnett effective medium theory. The new method developed to fine-tune the chemical properties of the protective SiO2 shell may be helpful for new magnetic biosensor designs as it allows for biocompatibility adjustment

SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods

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

SERS Sensor for Human Glycated Albumin Direct Assay Based on Machine Learning Methods

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