Literature DB >> 27524957

Stalking the Materials Genome: A Data-Driven Approach to the Virtual Design of Nanostructured Polymers.

Curt M Breneman1, L Catherine Brinson2, Linda S Schadler3, Bharath Natarajan3, Michael Krein1, Ke Wu1, Lisa Morkowchuk1, Yang Li2, Hua Deng2, Hongyi Xu2.   

Abstract

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this "Materials Genomics" approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2-vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso-scale morphologies and macro-scale properties under quasi-equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.

Entities:  

Year:  2013        PMID: 27524957      PMCID: PMC4981086          DOI: 10.1002/adfm.201301744

Source DB:  PubMed          Journal:  Adv Funct Mater        ISSN: 1616-301X            Impact factor:   18.808


  7 in total

1.  The effect of hydrophilicity-hydrophobicity and solubility on the immunogenicity of some natural and synthetic polymers.

Authors:  J O Naim; C J van Oss
Journal:  Immunol Invest       Date:  1992-12       Impact factor: 3.657

Review 2.  Quantitative structure-property relationship modeling of diverse materials properties.

Authors:  Tu Le; V Chandana Epa; Frank R Burden; David A Winkler
Journal:  Chem Rev       Date:  2012-01-17       Impact factor: 60.622

3.  Model polymer nanocomposites provide an understanding of confinement effects in real nanocomposites.

Authors:  Perla Rittigstein; Rodney D Priestley; Linda J Broadbelt; John M Torkelson
Journal:  Nat Mater       Date:  2007-03-18       Impact factor: 43.841

Review 4.  Monopolar surfaces.

Authors:  C J van Oss; M K Chaudhury; R J Good
Journal:  Adv Colloid Interface Sci       Date:  1987-11       Impact factor: 12.984

5.  Quantitative equivalence between polymer nanocomposites and thin polymer films.

Authors:  Amitabh Bansal; Hoichang Yang; Chunzhao Li; Kilwon Cho; Brian C Benicewicz; Sanat K Kumar; Linda S Schadler
Journal:  Nat Mater       Date:  2005-08-07       Impact factor: 43.841

Review 6.  Nanocomposites: structure, phase behavior, and properties.

Authors:  Sanat K Kumar; Ramanan Krishnamoorti
Journal:  Annu Rev Chem Biomol Eng       Date:  2010       Impact factor: 11.059

7.  Effects of dispersion and interfacial modification on the macroscale properties of TiO(2) polymer matrix nanocomposites.

Authors:  Lesley M Hamming; Rui Qiao; Phillip B Messersmith; L Catherine Brinson
Journal:  Compos Sci Technol       Date:  2009-09-01       Impact factor: 8.528

  7 in total
  5 in total

1.  Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns.

Authors:  Iman Hassaninia; Ramin Bostanabad; Wei Chen; Hooman Mohseni
Journal:  Sci Rep       Date:  2017-11-10       Impact factor: 4.379

2.  Attribution-Driven Explanation of the Deep Neural Network Model via Conditional Microstructure Image Synthesis.

Authors:  Shusen Liu; Bhavya Kailkhura; Jize Zhang; Anna M Hiszpanski; Emily Robertson; Donald Loveland; Xiaoting Zhong; T Yong-Jin Han
Journal:  ACS Omega       Date:  2022-01-07

3.  Epoxy resin composites with commercially available graphene: toward high toughness and rigidity.

Authors:  Jianxiang Sun; Jingqi Ji; Zhigeng Chen; Shumei Liu; Jianqing Zhao
Journal:  RSC Adv       Date:  2019-10-16       Impact factor: 4.036

4.  Prediction of Hardenability Curves for Non-Boron Steels via a Combined Machine Learning Model.

Authors:  Xiaoxiao Geng; Shuize Wang; Asad Ullah; Guilin Wu; Hao Wang
Journal:  Materials (Basel)       Date:  2022-04-26       Impact factor: 3.623

5.  Materials Discovery With Machine Learning and Knowledge Discovery.

Authors:  Osvaldo N Oliveira; Maria Cristina F Oliveira
Journal:  Front Chem       Date:  2022-07-07       Impact factor: 5.545

  5 in total

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