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.

Electrostatic properties of individually separated single-walled carbon nanotubes (SWCNTs), double-walled carbon nanotubes (DWCNTs), and multiwalled carbon nanotubes (MWCNTs) deposited on insulating layers have been investigated by charge injection and electric force microscopy (EFM) experiments. Delocalized charge patterns are observed along the CNTs upon local injection from the EFM tip, corresponding to (i) charge storage in the nanotubes and to (ii) charge trapping in the oxide layer along the nanotubes. The two effects are dissociated easily for CNTs showing abrupt discharge processes in which the charge stored in the CNT are field emitted back to the EFM tip, while trapped oxide charge can subsequently be imaged by EFM, clearly revealing field-enhancement patterns at the CNT caps. The case of continuous discharge processes of SWCNTs, DWCNTs, and MWCNTs is discussed, as well as the evolution of the discharge time constants with respect to the nanotube diameter.

It is shown that a $4d$ metal (palladium) orders ferromagnetically in GaN, forming a dilute magnetic semiconductor. The ferromagnetic ordering occurs despite Pd being nonmagnetic in its natural phase. ${\mathrm{Pd}}_{0.0625}{\mathrm{Ga}}_{0.9375}\mathrm{N}$ is shown to possess a magnetic moment of $1.3{\mu }_B$ per supercell, with a spin-polarized impurity band in the GaN band gap and the Fermi level lying within this band. The impurity band is shown to arise from the hybridization of the Pd $4d$ level with N $2p$ states. Though the largest experimental and theoretical focus up to now has been on $3d$ metals such as Mn in III–V and II–VI semiconductors, the results presented here show that $4d$ metals such as palladium, may also be considered as candidates for ferromagnetic dopants in semiconductors. This broadens the range of dopants which may be taken into account in attempts to overcome technological and other barriers related to the dopants used presently to obtain magnetically ordered semiconductors for potential spintronic applications.

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.