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    • Data and code for "Predicting the toughness of compatibilized polymer blends"

    Metadata Updated: September 30, 2023

    This code and example files were used to run the self-consistent field theory parameterization in the publication "Predicting the toughness of compatibilized polymer blends" by Robert J. S. Ivancic (OrcID: https://orcid.org/0000-0001-9969-2534, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1) and Debra J. Audus (OrcID: https://orcid.org/0000-0002-5937-7721, National Institute of Standards and Technology, Material Measurement Laboratory, Division 642, Group 1). The data/README.md and code/README.md files describe the code and dataset.Abstract from the publication : Polymer blends can yield superior materials by merging the unique properties of their components. However, these mixtures often phase separate, leading to brittleness. While compatibilizers can toughen these blends, their vast design space makes optimization difficult. Here, we design a model to predict the toughness of compatibilized glassy polymer mixtures. This theory reveals thatcompatibilizers increase blend toughness by creating molecular bridges that stitch the interface together. We validate this theory by directly comparing its predictions to extensive molecular dynamics simulations in which we vary polymer incompatibility, chain stiffness, compatibilizer areal density, and blockiness of copolymer compatibilizers. We then parameterize the model using self-consistent field theory and confirm its ability to make predictions for practical applications through comparison with simulations and experiments. These results suggest the theory can optimize compatibilizer design for industrial glassy polymer blends extit{in silico} while providing microscopic insight, allowing for the development of next-generation mixtures.

    Access & Use Information

    Public: This dataset is intended for public access and use. License: See this page for license information.

    Downloads & Resources

    Dates

    Metadata Created Date September 30, 2023
    Metadata Updated Date September 30, 2023
    Data Update Frequency irregular

    Metadata Source

    Harvested from NIST

    Other Data Resources

    Additional Metadata

    Resource Type Dataset
    Metadata Created Date September 30, 2023
    Metadata Updated Date September 30, 2023
    Publisher National Institute of Standards and Technology
    Maintainer
    Identifier ark:/88434/mds2-3057
    Language en
    Data Last Modified 2023-08-01 00:00:00
    Category Mathematics and Statistics:Modeling and simulation research:Chemistry:Theoretical chemistry and modeling
    Public Access Level public
    Data Update Frequency irregular
    Bureau Code 006:55
    Metadata Context https://project-open-data.cio.gov/v1.1/schema/data.json
    Schema Version https://project-open-data.cio.gov/v1.1/schema
    Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
    Harvest Object Id d4aeba44-c296-42ec-9bb8-a86b1e441724
    Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
    Harvest Source Title NIST
    Homepage URL https://data.nist.gov/od/id/mds2-3057
    License https://www.nist.gov/open/license
    Program Code 006:045
    Source Datajson Identifier True
    Source Hash ea7ee5a0e577edcc5b2ae77a287755e08bd2fda673d9b207328ba00bd1bbc83b
    Source Schema Version 1.1

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