Deep Learning for Determination of Properties of Semidilute Polymer Solutions
The ability to predict polymer solution viscosity is essential for polymer characterization and processing. Here, we use a synergistic approach combining the scaling theory of polymer solutions and convolutional neural network (CNN) models to obtain system-specific parameters and describe semidilute solution viscosity in the unentangled and entangled solution regimes. The scaling approach relies on the existence of a characteristic microscopic length scalethe solution correlation length (correlation blob size) ξwhich uniquely defines macroscopic solution properties. It is based on a relationship between the solution correlation length ξ = lg ν/B and the number of monomers g per correlation volume for polymers with the monomer projection length l. The system-specific set of parameters B g, B th, and 1 in the corresponding solution regime with the scaling exponent ν = 0.588, 0.5, and 1, respectively. Applying two CNN models, we obtained the sets B g and B th from the solution specific viscosity, ηsp, as a function of concentration, c, and weight-average degree of polymerization, N w. The CNN was trained on theoretically generated datasets converted to sparse images representing the normalized specific viscosity ηsp/N w (cl 3)1/(3ν–1) in the unentangled Rouse regime. The trained CNN was utilized in automated data analysis of the solution viscosity of polystyrene, poly­(ethylene oxide), poly­(methyl methacrylate), poly­(acrylonitrile-co-itaconic acid), cellulose, sodium hyaluronate, hydroxypropyl methyl cellulose, methyl cellulose, hydroxypropyl cellulose, cellulose tris­(phenyl carbamate), xanthan gum, galactomannan, and sodium κ-carrageenan in water, organic solvents, and ionic liquids. This approach produced values of the B-parameters with mean absolute percentage differences of less than 6% from the corresponding values determined by the manual data analysis. The B-parameters are then used to obtain the packing number P e defining the onset of entanglements in polymer solutions and to describe semidilute solution viscosity as a function of concentration and N w.
Authors
- Michael Jacobs (ORCID: https://orcid.org/0000-0002-7255-3451)
- Andrey V. Dobrynin (ORCID: https://orcid.org/0000-0002-6484-7409)
- Ryan Sayko (ORCID: https://orcid.org/0000-0002-5986-4829)
- Marissa Dominijanni
Institutions
- University of North Carolina at Chapel Hill (US)
Publication Details
- Journal
- UNC Libraries
- Published
- 2025-02-04
- DOI
- https://doi.org/10.17615/0e6h-d630
- Primary Topic
- Analytical Chemistry and Chromatography
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- Division of Materials Research