Electron mobility and diffusion coefficients in monolayer silicene are calculated by Monte Carlo simulations using simplified band structure with linear energy bands. Results demonstrate reasonable agreement with the full-band Monte Carlo method in low applied electric field conditions. Negative differential resistivity is observed and an explanation of the origin of this effect is proposed. Electron mobility and diffusion coefficients are studied in low applied electric field conditions. We demonstrate that a comparison of these parameter values can provide a good check that the calculation is correct. Low-field mobility in silicene exhibits -T 3 temperature dependence for nondegenerate electron gas conditions and -T 1 for higher electron concentrations, when degenerate conditions are imposed. It is demonstrated that to explain the relation between mobility and temperature in nondegenerate electron gas the linearity of the band structure has to be taken into account. It is also found that electron-electron scattering only slightly modifies low-field electron mobility in degenerate electron gas conditions.

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Compared with traditional gas chromatography–mass spectrometry techniques, electronic noses are non-invasive and can be a rapid, cost-effective option for several applications. This paper presents comparative studies of differentiation between odors emitted by two forest pathogens: Pythium and Phytophthora, measured by a low-cost electronic nose. The electronic nose applies six non-specific Figaro Inc. metal oxide sensors. Various features describing shapes of the measurement curves of sensors’ response to the odors’ exposure were extracted and used for building the classification models. As a machine learning algorithm for classification, we use the Support Vector Machine (SVM) method and various measures to assess classification models’ performance. Differentiation between Phytophthora and Pythium species has an important practical aspect allowing forest practitioners to take appropriate plant protection. We demonstrate the possibility to recognize and differentiate between the two mentioned species with acceptable accuracy by our low-cost electronic nose.

Standard Lugli-Ferry method for including Pauli principle in Monte Carlo simulations yields unphysical results when the system is strongly degenerate. For example, one finds that distribution function is higher than unity in low energy region. In this article we explain the origin of these errors. We propose simple correction in order to overcome these problems without significant increase of required computational resources. We applied our method to the study of electron transport in degenerate In 0.53 Ga 0.47 As at 77 K. We found good agreement between obtained results and theoretical Fermi-Dirac distribution in zero field limit.

Recent advances in the field of electronic noses (e-noses) have led to new developments in both sensors and feature extraction as well as data processing techniques, providing an increased amount of information. Therefore, feature selection has become essential in the development of e-nose applications. Sophisticated computation techniques can be applied for solving the old problem of sensor number optimization and feature selections. In this way, one can find an optimal application-specific sensor array and reduce the potential cost associated with designing new e-nose devices. In this paper, we examine a procedure to extract and select modeling features for optimal e-nose performance. The usefulness of this approach is demonstrated in detail. We calculated the model’s performance using cross-validation with the standard leave-one-group-out and group shuffle validation methods. Our analysis of wine spoilage data from the sensor array shows when a transient sensor response is considered, both from gas adsorption and desorption phases, it is possible to obtain a reasonable level of odor detection even with data coming from a single sensor. This requires adequate extraction of modeling features and then selection of features used in the final model.

Articles you may be interested inMonte Carlo simulation of terahertz quantum cascade laser structures based on wide-bandgap semiconductors Dependence of saturation effects on electron confinement and injector doping in Ga As ∕ Al 0.45 Ga 0.55 As quantum-cascade lasers Electronic spatial distribution of In 0.53 Ga 0.47 As ∕ Al As 0.56 Sb 0.44 quantum-cascade lasers J. Appl. Phys. 98, 086106 (2005); 10.1063/1.2113416 Simultaneous measurement of the electronic and lattice temperatures in GaAs/Al 0.45 Ga 0.55 As quantumcascade lasers: Influence on the optical performance Results of multiparticle Monte Carlo simulations of midinfrared quantum cascade lasers structure initially fabricated by Page et al. are presented.The main aim of this paper is to discuss in details how electric current flows through the structure and which subbands are involved in this process. Monte Carlo method allows to predict the electron population inversion between the lasing levels and gives microscopic insight into processes leading to such behavior. Importance of a subband belonging to the laser injector region, with energy slightly below the upper lasing level, is demonstrated. The electron-electron Coulomb interactions influence the shapes of electron distribution functions; the values of average electron energies and effective subbands' temperatures are calculated.

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Electronic noses can be applied as a rapid, cost-effective option for several applications. This paper presents the results of measurements of samples of two pathogenic fungi, Fusarium oxysporum and Rhizoctonia solani, performed using two constructions of a low-cost electronic nose. The first electronic nose used six non-specific Figaro Inc. metal oxide gas sensors. The second one used ten sensors from only two models (TGS 2602 and TGS 2603) operating at different heater voltages. Sets of features describing the shapes of the measurement curves of the sensors’ responses when exposed to the odours were extracted. Machine learning classification models using the logistic regression method were created. We demonstrated the possibility of applying the low-cost electronic nose data to differentiate between the two studied species of fungi with acceptable accuracy. Improved classification performance could be obtained, mainly for measurements using TGS 2603 sensors operating at different voltage conditions.