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A semiclassical model describing the charge transfer collisions of C 60 fullerene with different slow ions has been developed to analyze available observations. These data reveal multiple Breit–Wigner-like peaks in the cross sections, with subsequent peaks of reactive cross sections decreasing in magnitude. Calculations of charge transfer probabilities, quasi-resonant cross sections, and cross sections for reactive collisions have been performed using semiempirical interaction potentials between fullerenes and ion projectiles. All computations have been carried out with realistic wave functions for C 60 ’s valence electrons derived from the simplified jellium model. The quality of these electron wave functions has been successfully verified by comparing theoretical calculations and experimental data on the small angle cross sections of resonant [Formula: see text] collisions. Using the semiempirical potentials to describe resonant scattering phenomena in C 60 collisions with ions and Landau–Zener charge transfer theory, we calculated theoretical cross sections for various C 60 charge transfer and fragmentation reactions which agree with experiments. 

Indoor dust can be a major source of heavy metals, nutrients, and bacterial contamination in residential environments and may cause serious health problems. The goal of this research is to characterize chemical and bacterial contaminants of indoor, settled house dust in the Houston Metropolitan region. To achieve this, a total of 31 indoor dust samples were collected, along with household survey data, which were subsequently analyzed for elemental and bacterial concentrations. Microscopic and geospatial analysis was conducted to characterize and map potential hotspots of contamination. Interestingly Cd, Cr, Cu, Pb, and Zn concentrations of all 31 indoor dust samples were significantly enriched and exceeded soil background concentrations. Furthermore, As, Cd, Pb, and Zn concentrations in the dust samples were significantly correlated to the enteric bacterial load concentrations. Human health assessment revealed that cancer risk values via ingestion for Cd, Cr, and Ni were greater than the acceptable range. Of our 31 dust sample isolates, three Gram-negative and 16 Gram-positive pathogenic bacteria were identified, capable of causing a wide range of diseases. Our results demonstrate that both chemical and bacterial characterization of indoor dust coupled with spatial mapping is essential to assess and monitor human and ecological health risks. 

Scientific Machine Learning (SciML) is a new multidisciplinary methodology that combines the data-driven machine learning models and the principle-based computational models to improve the simulations of scientific phenomenon and uncover new scientific rules from existing measurements. This article reveals the experience of using the SciML method to discover the nonlinear dynamics that may be hard to model or be unknown in the real-world scenario. The SciML method solves the traditional principle-based differential equations by integrating a neural network to accurately model the nonlinear dynamics while respecting the scientific constraints and principles. The paper discusses the latest SciML models and apply them to the oscillator simulations and experiment. Besides better capacity to simulate, and match with the observation, the results also demonstrate a successful discovery of the hidden physics in the pendulum dynamics using SciML. 

The nonlinearity of activation functions used in deep learning models is crucial for the success of predictive models. Several simple nonlinear functions, including Rectified Linear Unit (ReLU) and Leaky-ReLU (L-ReLU) are commonly used in neural networks to impose the nonlinearity. In practice, these functions remarkably enhance the model accuracy. However, there is limited insight into the effects of nonlinearity in neural networks on their performance. Here, we investigate the performance of neural network models as a function of nonlinearity using ReLU and L-ReLU activation functions in the context of different model architectures and data domains. We use entropy as a measurement of the randomness, to quantify the effects of nonlinearity in different architecture shapes on the performance of neural networks. We show that the ReLU nonliearity is a better choice for activation function mostly when the network has sufficient number of parameters. However, we found that the image classification models with transfer learning seem to perform well with L-ReLU in fully connected layers. We show that the entropy of hidden layer outputs in neural networks can fairly represent the fluctuations in information loss as a function of nonlinearity. Furthermore, we investigate the entropy profile of shallow neural networks as a way of representing their hidden layer dynamics. 

Scattering phenomena between charged particles and highly excited Rydberg atoms are of critical importance in many processes in plasma physics and astrophysics. While a Maxwell–Boltzmann (MB) energy distribution for the charged particles is often assumed for calculations of collisional rate coefficients, in this contribution we relax this assumption and use two different energy distributions, a bimodal MB distribution and a $\unicode[STIX]{x1D705}$ -distribution. Both variants share a high-energy tails occurring with higher probability than the corresponding MB distribution. The high-energy tail may significantly affect rate coefficients for various processes. We focus the analysis to specific situations by showing the dependence of the rate coefficients on the principal quantum number of hydrogen atoms in $n$ -changing collisions with electrons in the excitation and ionization channels and in a temperature range relevant to the divertor region of a tokamak device. We finally discuss the implications for diagnostics of laboratory plasmas. 

We employ force-field molecular dynamics simulations to investigate the kinetics of nucleation to new liquid or solid phases in a dense gas of particles, seeded with ions. We use precise atomic pair interactions, with physically correct long-range behaviour, between argon atoms and protons. Time dependence of molecular cluster formation is analysed at different proton concentration, temperature and argon gas density. The modified phase transitions with proton seeding of the argon gas are identified and analysed. The seeding of the gas enhances the formation of nano-size atomic clusters and their aggregation. The strong attraction between protons and bath gas atoms stabilises large nano-clusters and the critical temperature for evaporation. An analytical model is proposed to describe the stability of argon-proton droplets and is compared with the molecular dynamics simulations.