Thermal stress wave and spallation induced by an electron beam

Thermal stress wave and spallation in aluminium alloy exposed to a high fluency and low energy electron beams are studied theoretically. A simple model for the study of energy deposition of electrons in materials is presented on the basis of some empirical formulae. Under the stress wave induced by energy deposition, microcracks and/or microvoids may appear in target materials, and in this case, the inelastic volume deformation should not vanish. The viscoplastic model proposed by Bodner and Partom with corresponding Gurson's yield function requires modification for this situation. The new constitutive model contains a scalar field variable description of the material damage which is taken as the void volume fraction of the polycrystalline material. Incorporation of the damage parameter permits description of rate-dependent, compressible, inelastic deformation and ductile fracture. The melting phenomenon has been observed in the experiment, therefore one needs to take into account the melting process in the intermediate energy deposition range. A three-phase equation of state used in the paper provides a more detailed and thermodynamical description of metals, particularly, in the melting region. The computational results based on the suggested model are compared with the experimental test for aluminium alloy, which is subjected to a pulsed electron beam with high fluency and low energy. (C) 1997 Elsevier Science Ltd.

Study on the ions' behavior in an electron cyclotron resonance plasma

The energy, velocity, angle distribution of ions in magnetoactive electron cyclotron resonance plasma have been studied with a two-dimension hybrid mode. The dependence of these distribution functions versus position and pressure are discussed. Our simulation results are in good agreement with many experimental measurements. (C) 1997 American Institute of Physics.

Self-consistent kinetic modeling of electron distribution function in a low-pressure inductively coupled plasma

Based upon the spatially inhomogeneous Boltzmann equation in two-term approximation coupled with electromagnetic and fluid model analysis for the recently developed inductively coupled plasma sources, a self-consistent electron kinetic model is developed. The electron distribution function, spatial distributions of the electron density and ionization rate are calculated and discussed.

The effects of coil current distribution in a cylindrical electron-cyclotron-resonance reactor

Using a 2-D hybrid model, the authors have found that external currents play an important role in the plasma parameters in the reactor. The plasma density, temperature and electrostatic potential would be significantly influenced by the applied external currents.

Measurement of Electron Density Profile Behind Strong Shock Waves with a Langmuir Probe

At the shock velocity range of 7~9km/s, the variations of electron density behind strong normal shock waves are measured in a low-density shock tube by using the Langmuir electrostatic probe technique. The electron temperature, calculated based on Park’s three-temperature model, is used in interpreting the probe current data. The peak electron densities determined in the present experiment are shown to be in a good agreement with those predicted by Lin’s calculation. The experimentally obtained ratios of the characteristic ionization distance to the mean free path of freestream ahead of the shock wave are found to be in a good agreement with the existing experiments and Park’s calculation.

Long-lived oscillatory incoherent electron dynamics in molecules: trans-polyacetylene oligomers

We identify an intriguing feature of the electron-vibrational dynamics of molecular systems via a computational examination of trans-polyacetylene oligomers. Here, via the vibronic interactions, the decay of an electron in the conduction band resonantly excites an electron in the valence band, and vice versa, leading to oscillatory exchange of electronic population between two distinct electronic states that lives for up to tens of picoseconds. The oscillatory structure is reminiscent of beating patterns between quantum states and is strongly suggestive of the presence of long-lived molecular electronic coherence. Significantly, however, a detailed analysis of the electronic coherence properties shows that the oscillatory structure arises from a purely incoherent process. These results were obtained by propagating the coupled dynamics of electronic and vibrational degrees of freedom in a mixed quantum-classical study of the Su-Schrieffer-Heeger Hamiltonian for polyacetylene. The incoherent process is shown to occur between degenerate electronic states with distinct electronic configurations that are indirectly coupled via a third auxiliary state by vibronic interactions. A discussion of how to construct electronic superposition states in molecules that are truly robust to decoherence is also presented

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.