Crystalline silver thin layers were obtained using high-energy pulsed laser ablation without the heating of the deposition substrate. The fluid Plateau–Rayleigh (PRI), Rayleigh–Taylor (RTI), and Richtmyer–Meshkov (RMI) instabilities, as well as the crown splash induced during the pulsed laser deposition (PLD) in the high energy regime, resulting in ring and pearl-shaped structures, offer the benefit of an increased sorption surface. These morphological structures obtained for the silver thin layers make them of interest for catalytic applications. This study addresses both fundamental and applied issues on the morphological structures obtained for the silver thin layers and their catalytic function in organic processes. In this sense, the catalytic action of the thin silver layer was highlighted by modifications of the Reactive Blue 21 dye (C.I.) in an aqueous solution with sodium bicarbonate. Specific investigations and analyses were carried out using electron microscopy and elemental analysis (SEM-EDX), atomic force microscopy (AFM) and profilometry, mass spectrometry, ablation plasma diagnosis, diffractograms (XRD), as well as IR spectroscopy (FTIR). In addition to the experimental investigation and analyses, the simulation of the ionization energy threshold was conducted in COMSOL for complementary evaluation on the involved processes and phenomena.

This study aimed to investigate the behavior of chitosan/quaternized chitosan fibers in media mimicking wound exudates to understand their capacities as wound dressing. Fiber analysis of the fibers using dynamic vapor sorption proved their ability to adsorb moisture up to 60% and then to desorb it as a function of humidity, indicating their outstanding breathability. Dissolution analyses showed that quaternized chitosan leached from the fibers in water and PBS, whereas only small portions of chitosan were solubilized in water. In media containing lysozyme, the fibers degraded with a rate determined by their composition and pH, reaching a mass loss of up to 47% in media of physiologic pH. Notably, in media mimicking the wound exudate during healing, they adsorbed moisture even when their mass loss due to biodegradation was high, whereas they were completely degraded in the media of normal tissues, indicating bioabsorbable dressing capacities. A mathematical model was constructed, which characterized the degradation rate and morphology changes of chitosan/quaternized chitosan fibers through analyses of dynamics in scale space, using the Theory of Scale Relativity. The model was validated using experimental data, making it possible to generalize it to the degradation of other biopolymeric systems that address wound healing.

Crystalline silver thin layers were obtained by high-energy pulsed laser ablation without heating of the deposition substrate. The fluid Plateau-Rayleigh (PRI), Rayleigh – Taylor (RTI) and Richtmyer – Meshkov (RMI) instabilities, as well as crown splash induced during pulsed laser deposition (PLD) in high energy regime, resulting in ring and pearl shaped structures, offer the benefit of increased sorption surface. These morphological structures obtained for the silver thin layers make them of interest for catalytic applications. The study presented with this work addresses both the fundamental and applied issues on the morphological structures obtained for the silver thin layer and their catalytic function in organic processes. In this sense, the catalytic action of the thin silver layer was highlighted by modifications of the Reactive Blue 21 dye (C.I.) in aqueous solution with sodium bicarbonate. Specific investigations and analyzes were carried out by electron microscopy and elemental analysis (SEM-EDX), atomic force microscopy (AFM) and profilometry, mass spectrometry, ablation plasma diagnosis, diffractograms (XRD), as well as IR spectroscopy (FTIR). In addition to the experimental investigation and analyzes, simulation of the ionization energy threshold was conducted in COMSOL for complementary evaluation on the involved processes and phenomena.

This paper investigates the nonlinear dynamics of atmospheric aerosols during the transition from laminar to turbulent flows using the framework of Scale Relativity Theory. It is proposed that the transition from multifractal to non-multifractal scales (in the dynamics of the atmospheric aerosols) can be assimilated to the transition between laminar and turbulent states. These transitions are determined by the multifractal diffusion and deposition processes. The methodology used involves the application of the principle of scale covariance, which states that the laws of atmospheric physics remain invariant with respect to spatial and temporal transformations as well as scale transformations. Based on this principle, several conservation laws are constructed. In such context, the conservation law of the density of states associated with the multifractal-non-multifractal scale transition in a one-dimensional case is then considered. The model describes the non-linear behaviour of atmospheric aerosols undergoing diffusion and deposition processes. The theoretical approach was correlated using experimental data from a ceilometer and radar reflectivity factor data.

The Planetary Boundary Layer (PBL) is a crucial parameter to investigate for characterizing the atmosphere, particularly concerning aerosol concentrations. Understanding the PBL allows us to estimate air quality, provide weather forecasts, and establish correlations with astronomical seeing conditions and atmospheric turbulence intensity. The planetary boundary layer can be defined in many ways, but its importance remains constant as it is the atmospheric layer where most socio-economic activities occur. In this letter, we present a method to predict the stochastic PBL height (SPBLH) using ceilometer data and a deep learning approach based on a fully connected neural network. We found a correlation between the Lyapunov coefficient calculated for each SPBLH time series and the loss function, which is influenced by various factors such as atmospheric parameters, pollution, aerosols, and more. The performance of a typical neural network used to predict a time series is significantly affected by the degree of chaos, quantified by the largest Lyapunov exponents (LLE). Our results show a decrease in accuracy as a function of increasing LLE. Moreover, an increased number of virtual neurons in the neural network can be detrimental to SPBLH prediction for the complex dynamics of the PBL due to atmospheric conditions and unforeseen events.