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

Mechanical properties and fracture patterns of graphene (graphitic) nanowiggles

publisher: Elsevier articletitle: Mechanical properties and fracture patterns of graphene (graphitic) nanowiggles journaltitle: Carbon articlelink: http://dx.doi.org/10.1016/j.carbon.2017.04.018 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved.publisher: Elsevier articletitle: Mechanical properties and fracture patterns of graphene (graphitic) nanowiggles journaltitle: Carbon articlelink: http://dx.doi.org/10.1016/j.carbon.2017.04.018 content_type: article copyright: © 2017 Elsevier Ltd. All rights reserved.This work was supported in part by the Brazilian Agencies CNPq, CAPES and FAPESP. The authors would like to thank the Center for Computational Engineering and Sciences at Unicamp for financial support through the FAPESP/CEPID Grant 2013/08293-7. N.M.P. is supported by the European Research Council PoC 2015 “Silkene” No. 693670, by the European Commission H2020 under the Graphene Flagship Core 1 No. 696656 (WP14 “Polymer Nanocomposites”) and under the Fet Proactive “Neurofibres” No. 732344

Mechanical properties of graphene nanowiggles

CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPIn this work we have investigated the mechanical properties and fracture patterns of some graphene nanowiggles (GNWs). Graphene nanoribbons are finite graphene segments with a large aspect ratio, while GNWs are nonaligned periodic repetitions of graphene nanoribbons. We have carried out fully atomistic molecular dynamics simulations using a reactive force field (ReaxFF), as implemented in the LAMPPS (Large-scale Atomic/Molecular Massively Parallel Simulator) code. Our results showed that the GNW fracture patterns are strongly dependent on the nanoribbon topology and present an interesting behavior, since some narrow sheets have larger ultimate failure strain values. This can be explained by the fact that narrow nanoribbons have more angular freedom when compared to wider ones, which can create a more efficient way to accumulate and to dissipate strain/stress. We have also observed the formation of linear atomic chains (LACs) and some structural defect reconstructions during the material rupture. The reported graphene failure patterns, where zigzag/armchair edge terminated graphene structures are fractured along armchair/zigzag lines, were not observed in the GNW analyzed cases.16581418CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPSem informaçãoSem informação2013/08293-7Symposium RR - Large-Area Graphene and Other 2D-Layered Materials - Synthesis, Properties and Applications1 a 6 de Dezembro de 2013Boston, MA, Estados UnidosThis work was supported in part by the Brazilian Agencies CNPq, CAPES and FAPESP. The authors would like to thank the Center for Computational Engineering and Sciences at Unicamp for financial support through the FAPESP/CEPID Grant 2013/08293-7

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Mechanical Properties And Fracture Dynamics Of Silicene Membranes

The advent of graphene created a new era in materials science. Graphene is a two-dimensional planar honeycomb array of carbon atoms in sp2-hybridized states. A natural question is whether other elements of the IV-group of the periodic table (such as silicon and germanium), could also form graphene-like structures. Structurally, the silicon equivalent to graphene is called silicene. Silicene was theoretically predicted in 1994 and recently experimentally realized by different groups. Similarly to graphene, silicene exhibits electronic and mechanical properties that can be exploited to nanoelectronics applications. In this work we have investigated, through fully atomistic molecular dynamics (MD) simulations, the mechanical properties of single-layer silicene under mechanical strain. These simulations were carried out using a reactive force field (ReaxFF), as implemented in the LAMMPS code. We have calculated the elastic properties and the fracture patterns. Our results show that the dynamics of the whole fracturing processes of silicene present some similarities with that of graphene as well as some unique features.1549299107Harris, P.J.F., (2009) Carbon Nanotube Science, , Cambridge University Press, CambridgeBaughman, R., Eckhardt, H., Kertesz, M., (1987) J. Chem. Phys., 87, p. 6687Coluci, V.R., Braga, S.F., Legoas, S.B., Galvao, D.S., Baughman, R.H., (2003) Phys. Rev. B, 68, p. 035430Coluci, V.R., Braga, S.F., Legoas, S.B., Galvao, D.S., Baughman, R.H., (2004) Nanotechnology, 15, p. S142Novoselov, K.S., (2004) Science, 306, p. 666Cheng, S.H., (2010) Phys. Rev. B, 81, p. 205435Withers, F., Duboist, M., Savchenko, A.K., (2010) Phys. Rev. B, 82, p. 073403Takeda, K., Shiraishi, K., (1994) Phys. Rev. B, 50, p. 14916Cahangirov, S., Topsakal, M., Akturk, E., Sahin, H., Ciraci, S., (2009) Phys. Rev. Lett., 102, p. 236804Nakano, H., (2006) Angew. Chem., 118, p. 6451Lalmi, B., (2010) Appl. Phys. Lett., 97, p. 223109Psofogiannakis, G.M., Froudakis, G.E., (2012) J. Phys. Chem. C, 116, p. 19211Aufray, B., (2010) Appl. Phys. Lett., 96, p. 183101De Padova, P., (2010) Appl. Phys. Lett., 96, p. 261905Vogt, P., (2012) Phys. Rev. Lett., 108, p. 155201Bianco, E., (2013) Nano Lett., 7, p. 4414Friedlein, R., Fleurence, A., Ozaki, T., Yamada-Takamura, Y., SPIE Newsroom, , in pressVan Duin, A.C.T., Dasgupta, S., Lorant, F., Goddard, W.A., III, (2001) J. Phys. Chem. A, 105, p. 9396Plimpton, S., (1995) J. Comp. Phys., 117, p. 1. , http://lammps.sandia.govPaupitz, R., (2013) Nanotechnology, 24, p. 035706Yang, Y., Xu, X., (2012) Comp. Mater. Sci., 61, p. 83Pei, Q.X., Zhang, Y.W., Shenoy, V.B., (2010) Carbon, 48, p. 898Kim, K., (2011) Nano Lett., 12, p. 293Koskinen, (2008) Phys. Rev. Lett., 101, p. 115502Koskinen, (2009) Phys. Rev. B, 80, p. 07340

Semi-supervised machine-learning classification of materials synthesis procedures

Digitizing large collections of scientific literature can enable new informatics approaches for scientific analysis and meta-analysis. However, most content in the scientific literature is locked-up in written natural language, which is difficult to parse into databases using explicitly hard-coded classification rules. In this work, we demonstrate a semi-supervised machine-learning method to classify inorganic materials synthesis procedures from written natural language. Without any human input, latent Dirichlet allocation can cluster keywords into topics corresponding to specific experimental materials synthesis steps, such as “grinding” and “heating”, “dissolving” and “centrifuging”, etc. Guided by a modest amount of annotation, a random forest classifier can then associate these steps with different categories of materials synthesis, such as solid-state or hydrothermal synthesis. Finally, we show that a Markov chain representation of the order of experimental steps accurately reconstructs a flowchart of possible synthesis procedures. Our machine-learning approach enables a scalable approach to unlock the large amount of inorganic materials synthesis information from the literature and to process it into a standardized, machine-readable database

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 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 the "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