Bibliographic Details
| Title: |
Kinetic modeling of the synthesis of styrene–DVB resins for estimating chain density distribution. |
| Authors: |
Aguiar, Leandro G.1 (AUTHOR) leandroaguiar@usp.br, Reis, Maria L. T.1 (AUTHOR), Godoy, William M.1 (AUTHOR) |
| Source: |
Polymer Bulletin. Dec2025, Vol. 82 Issue 18, p12731-12750. 20p. |
| Subjects: |
Crosslinking (Polymerization), Copolymerization, Swelling of materials, Dynamic models, Crosslinked polymers, Model validation |
| Abstract: |
A cross-linking copolymerization model based on species and sequences balance was developed and applied to styrene-based resins with 1–25% divinylbenzene (DVB). The swelling indices of these resins were estimated using a modified Flory–Rehner equation. The chain density was calculated as a function of the radius of gyration of sequences between cross-links, employing the Flory exponent as an adjustable parameter. The mathematical model was successfully validated against morphology data, yielding an average R2 of 0.936. The model provided the fraction of linear chains and the distribution of sequences between cross-links. This distribution facilitated the calculation of swelling index distribution along the resin, following a logarithmic function of M C with an R2 of approximately 0.920. The results suggest that the radius of gyration equation R g = 0.03 M c r α is applicable for chain segments between cross-links ( L E r ) in sulfonated styrene–DVB resins. For lightly cross-linked sulfonated resins (1.5–2.5% DVB), water could be considered a theta solvent based on the coiling factors identified ( α ≈ 0.5 ). At higher DVB percentages, the coiling behavior resembled that of polystyrene in apolar media ( α ≈ 0.6 ), aligning with literature reports. [ABSTRACT FROM AUTHOR] |
|
Copyright of Polymer Bulletin is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |