Numerical and Analytical Methods for Laser-Plasma Acceleration Physics

Theories and numerical modeling are fundamental tools for understanding, optimizing and designing present and future laser-plasma accelerators (LPAs). Laser evolution and plasma wave excitation in a LPA driven by a weakly relativistically intense, short-pulse laser propagating in a preformed parabolic plasma channel, is studied analytically in 3D including the effects of pulse steepening and energy depletion. At higher laser intensities, the process of electron self-injection in the nonlinear bubble wake regime is studied by means of fully self-consistent Particle-in-Cell simulations. Considering a non-evolving laser driver propagating with a prescribed velocity, the geometrical properties of the non-evolving bubble wake are studied. For a range of parameters of interest for laser plasma acceleration, The dependence of the threshold for self-injection in the non-evolving wake on laser intensity and wake velocity is characterized. Due to the nonlinear and complex nature of the Physics involved, computationally challenging numerical simulations are required to model laser-plasma accelerators operating at relativistic laser intensities. The numerical and computational optimizations, that combined in the codes INF&RNO and INF&RNO/quasi-static give the possibility to accurately model multi-GeV laser wakefield acceleration stages with present supercomputing architectures, are discussed. The PIC code jasmine, capable of efficiently running laser-plasma simulations on Graphics Processing Units (GPUs) clusters, is presented. GPUs deliver exceptional performance to PIC codes, but the core algorithms had to be redesigned for satisfying the constraints imposed by the intrinsic parallelism of the architecture. The simulation campaigns, run with the code jasmine for modeling the recent LPA experiments with the INFN-FLAME and CNR-ILIL laser systems, are also presented.

Atmospheric plasma processes for microbial inactivation: food applications and stress response in Listeria monocytogenes

This PhD thesis is focused on cold atmospheric plasma treatments (GP) for microbial inactivation in food applications. In fact GP represents a promising emerging technology alternative to the traditional methods for the decontamination of foods. The objectives of this work were to evaluate: - the effects of GP treatments on microbial inactivation in model systems and in real foods; - the stress response in L. monocytogenes following exposure to different GP treatments. As far as the first aspect, inactivation curves were obtained for some target pathogens, i.e. Listeria monocytogenes and Escherichia coli, by exposing microbial cells to GP generated with two different DBD equipments and processing conditions (exposure time, material of the electrodes). Concerning food applications, the effects of different GP treatments on the inactivation of natural microflora and Listeria monocytogenes, Salmonella Enteritidis and Escherichia coli on the surface of Fuji apples, soya sprouts and black pepper were evaluated. In particular the efficacy of the exposure to gas plasma was assessed immediately after treatments and during storage. Moreover, also possible changes in quality parameters such as colour, pH, Aw, moisture content, oxidation, polyphenol-oxidase activity, antioxidant activity were investigated. Since the lack of knowledge of cell targets of GP may limit its application, the possible mechanism of action of GP was studied against 2 strains of Listeria monocytogenes by evaluating modifications in the fatty acids of the cytoplasmic membrane (through GC/MS analysis) and metabolites detected by SPME-GC/MS and 1H-NMR analyses. Moreover, changes induced by different treatments on the expression of selected genes related to general stress response, virulence or to the metabolism were detected with Reverse Transcription-qPCR. In collaboration with the Scripps Research Institute (La Jolla, CA, USA) also proteomic profiles following gas plasma exposure were analysed through Multidimensional Protein Identification Technology (MudPIT) to evaluate possible changes in metabolic processes.