Biological decontamination using a nonthermal gas discharge at atmospheric pressure in air is the subject of significant research effort at this time. The mechanism for bacterial deactivation undergoes a lot of speculation, particularly with regard to the role of ions and reactive gas species. Two mechanisms have been proposed: electrostatic disruption of cell membranes and lethal oxidation of membrane or cytoplasmic components. Results show that death is accompanied by cell lysis and fragmentation in Gram-negative bacteria but not Gram-positive species, although cytoplasmic leakage is generally observed. Gas discharges can be a source of charged particles, ions, reactive gas species, radicals, and radiation (ultraviolet, infrared, and visible), many of which have documented biocidal properties. The individual roles played by these in decontamination are not well understood or quantified. However, the reactions of some species with biomolecules are documented otherwise in the literature. Oxidative stress is relatively well studied, and it is likely that exposure to gas discharges in air causes extreme oxidative challenge. In this paper, a review is presented of the major reactive species generated by nonthermal plasma at atmospheric pressure and the known reactions of these with biological molecules. Understanding these mechanisms becomes increasingly important as plasma-based decontamination and sterilization devices come closer to a wide-scale application in medical, healthcare, food processing, and air purification applications. Approaches are proposed to elucidate the relative importance of reactive species

Axisymmetric finite element models have been developed for the simulation of negative discharges in air without and with the presence of dielectrics. The models are based on the hydrodynamic drift-diffusion approximation. A set of continuity equations accounting for the movement, generation and loss of charge carriers (electrons, positive and negative ions) is coupled with Poisson's equation to take into account the effect of space and surface charges on the electric field. The model of a negative corona discharge (without dielectric barriers) in a needle-plane geometry is analysed first. The results obtained show good agreement with experimental observations for various Trichel pulse characteristics. With dielectric barriers introduced into the discharge system, the surface discharge exhibits some similarities and differences to the corona case. The model studies the dynamics of volume charge generation, electric field variations and charge accumulation over the dielectric surface. The predicted surface charge density is consistent with experimental results obtained from the Pockels experiment in terms of distribution form and magnitude.

Partial discharge (PD) classification in power cable accessories and high voltage equipment in general is essential in evaluating the severity of the damage in the insulation. In this article, the PD classification was realised as a two-fold process. Firstly, measurements taken from a high-frequency current transformer (HFCT) sensor were represented as features by means of a transformation to the classifier and secondly, the probabilistic neural network (PNN) classifier itself was capable of effectively recognising features coming from different types of discharges. The feature that was used as a fingerprint for PD characterisation was extracted from the moments of the probability density function (PDF) of the wavelet coefficients at various scales, obtained through the wavelet packets transformation. The PNN classifier was used to classify the PDs and assess the suitability of this feature vector in PD classification. Four types of artificial PDs were created in a high voltage laboratory, namely corona discharge in air, floating discharge in oil, internal discharge in oil and surface discharge in air, at different applied voltages, and were used to train the PNN algorithm. The results obtained here (97.49, 91.9, 100 and 99.8% for the corona, the floating, the internal and the surface discharges, respectively) are very encouraging for the use of PNN in PD classification with this particular feature vector. This article suggests a feature extraction and classification algorithm for PD classification, which when combined together reduced the dimensionality of the feature space to a manageable dimension, and achieved very high levels of classification.