Incipience of Plastic Flow in Aluminum with Nanopores: Molecular Dynamics and Machine-Learning-Based Description

Incipience of plastic flow in nanoporous metals under tension is an important point for the development of mechanical models of dynamic (spall) fracture. Here we study axisymmetric deformation with tension of nanoporous aluminum with different shapes and sizes of nanopores by means of molecular dynamics (MD) simulations. Random deformation paths explore a sector of tensile loading in the deformation space. The obtained MD data are used to train an artificial neural network (ANN), which approximates both an elastic stress–strain relationship in the form of tensor equation of state and a nucleation strain distance function. This ANN allows us to describe the elastic stage of deformation and the transition to the plastic flow, while the following plastic deformation and growth of pores are described by means of a kinetic model of plasticity and fracture. The parameters of this plasticity and fracture model are identified by the statistical Bayesian approach, using MD curves as the training data set. The present research uses a machine-learning-based approximation of MD data to propose a possible framework for construction of mechanical models of spall fracture in metals

3D in vitro platform produced by two-photon polymerization for the analysis of neural network formation and function

Zr-Si organic-inorganic scaffolds fabricated by a two-photon polymerization technique were used for the primary culture of mouse embryonic neural cells. We observed that dissociated hippocampal cells adhere to the scaffolds, produce neurites, elongate and differentiate into adult neurons. Neuronal outgrowth and synaptogenesis were confirmed by immunohistochemical staining with antibodies against βIII-Tubulin and synaptophysin. The formation of a functional neural network was assessed by the measurement of spontaneous activity using Ca2+ imaging of dissociated hippocampal cultures grown on Zr-Si scaffolds. The results of this study suggest that two-photon-induced polymerization of organic-inorganic hybrid biomaterials provides a robust model for 3D neuronal tissue engineering studies

Structure and Spectral Properties of Thianthrene and Its Benzoyl-Containing Derivatives

The IR absorption spectra of the recently synthesized series of benzoyl-containing thianthrene derivatives were studied in the context of their structural identification. Geometry optimizitation of the ground singlet state by density functional theory (DFT) calculations with the gradient and Hessian search were performed for thianthrene molecule in the framework of the C2v symmetry restriction. The excited singlet and triplet states of thianthrene were found to be distorted along the b3u vibrational mode of the D2h point group, as well as the ground state, which leads to the non-planar batterfly-like structure (C2v). But the excited states require additional symmetry reduction; they are closer to planarity but have no symmetry elements. Optimized ground states structure for the thianthrene-benzoyle molecule and its four derivatives with fluoro-substituents and different substitution positions were analysed through complete assignment of all their vibrational modes and comparison with experimental infrared absorption spectra. A good agreement between experimental data and DFT calculated IR spectra provides additional structural support to results of the X-ray diffraction analysis of all synthesized compounds. The Hirshfeld surfaces analysis of the crystalline 3-fluorobenzoylthianthrene (T3F) was performed in order to analyze intermolecular interactions in T3F crystal. It indicates the presence of weak CH...F, CH...S and CH...O intermolecular contacts, stabilizing the crystal structure of T3F. The CH...O interactions appear in the IR spectrum of T3F crystal as two vibrational modes with frequencies 3084 and 3078 cm-1. The intermolecular interactions CH...F and CH...S do not affect the IR spectrum of T3F