Mechanical Properties and Fracture Dynamics of Silicene Membranes

As graphene became one of the most important materials today, there is a renewed interest on others similar structures. One example is silicene, the silicon analogue of graphene. It share some the remarkable graphene properties, such as the Dirac cone, but presents some distinct ones, such as a pronounced structural buckling. We have investigated, through density functional based tight-binding (DFTB), as well as reactive molecular dynamics (using ReaxFF), the mechanical properties of suspended single-layer silicene. We calculated the elastic constants, analyzed the fracture patterns and edge reconstructions. We also addressed the stress distributions, unbuckling mechanisms and the fracture dependence on the temperature. We analysed the differences due to distinct edge morphologies, namely zigzag and armchair

A model of community-centred education: the evolution of post-secondary education programming on the Stoney Indian Reserve

Bibliography: p. 155-164

Fast simulation of railway pneumatic brake systems

The pneumatic brake system is the most important safety component in freight trains. Understanding its behavior during operation is crucial to identify the causes of technical problems and to improve driving techniques. Commercial codes can be used to analyze the system, aiming to develop better braking procedures and to predict eventual problems. However, these codes are not aimed to improve driver's skills or to predict the effects of unexpected driver's actions on the system, which can be done using train simulators and requires real-time simulations. This work focuses on modeling the pneumatic brake system to estimate the pressure along the brake line in real time by employing two different methods: the lumped characteristics approach and an isothermal approach using the Navier–Stokes equation. The results from both models match with the ones from a commercial software, which is known to represent adequately the system's behavior on the field. Using processing time as the selection criteria, this work shows that the lumped characteristics method is the best choice to be employed in real-time simulations2334420430The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge VALE S.A for sponsoring the projec

Mechanical and structural properties of graphene-like carbon nitride sheets

CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCarbon nitride-based nanostructures have attracted special attention (from theory and experiments) due to their remarkable electromechanical properties. In this work we have investigated the mechanical properties of some graphene-like carbon nitride membranes through fully atomistic reactive molecular dynamics simulations. We have analyzed three different structures of these CN families, the so-called graphene-based g-CN, triazine-based g-C3N4 and heptazine-based g-C3N4. The stretching dynamics of these membranes was studied for deformations along their two main axes and at three different temperatures: 10 K, 300 K and 600 K. We show that g-CN membranes have the lowest ultimate fracture strain value, followed by heptazine-based and triazine-based ones, respectively. This behavior can be explained in terms of their differences in density values, topologies and types of chemical bonds. The dependency of the fracture patterns on the stretching directions is also discussed.Carbon nitride-based nanostructures have attracted special attention (from theory and experiments) due to their remarkable electromechanical properties. In this work we have investigated the mechanical properties of some graphene-like carbon nitride membranes through fully atomistic reactive molecular dynamics simulations. We have analyzed three different structures of these CN families, the so-called graphene-based g-CN, triazine-based g-C3N4 and heptazine-based g-C3N4. The stretching dynamics of these membranes was studied for deformations along their two main axes and at three different temperatures: 10 K, 300 K and 600 K. We show that g-CN membranes have the lowest ultimate fracture strain value, followed by heptazine-based and triazine-based ones, respectively. This behavior can be explained in terms of their differences in density values, topologies and types of chemical bonds. The dependency of the fracture patterns on the stretching directions is also discussed.6807691576921CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOA085/2013Sem informação2013/08293-7This work was supported in part by the Brazilian Agencies CAPES, CNPq and FAPESP. The authors thank the Center for Computational Engineering and Sciences at Unicamp for financial support through the FAPESP/CEPID Grant # 2013/08293-7. J. M. S. acknowledges the support from CAPES through the Science Without Borders program (project number A085/2013)

Similarity of Precursors in Solid-State Synthesis as Text-Mined from Scientific Literature

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that "chemical similarity" of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.Comment: Chemistry of Materials (2020