PBPU Elastomer Network Architecture Determination via Corresponding States Analysis of Mechanical Behavior
Sushanta Das (Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India Defence Research, Development Organization, New Delhi, India), Hari Ramakrishna Sudhakar (Department of Chemical Engineering, Indian Institute of Technology Bombay

TL;DR
This study investigates how the R=[NCO]/[OH] ratio influences the structural topology of PBPU elastomer networks, using stress-elongation behavior modeling and analysis of network defects, revealing non-correlation between chain size and crosslink density.
Contribution
It introduces a combined modeling approach with a normalized Mooney-Rivlin representation to analyze network topology and defect effects in PBPU elastomers based on R ratio variations.
Findings
Network defects increase with R in the <1 regime.
Non-correlation between chain size and crosslink density.
Stress-elongation behavior reveals structural topology insights.
Abstract
In this work we examine the effect of R=[NCO]/[OH] in the R=<1 regime, on the resultant structural topology of polybutadiene polyurethane (PBPU) elastomer networks based on hydroxy-terminated polybutadiene (HTPB). We employ stress-elongation behavior and its modeling, as a tool. We examine this property via a combination of our model for the finite chain phantom networks incorporating the HTPB structural information, with the slip-tube model from the literature, suitably modified phenomenologically. We implement a further normalized Mooney-Rivlin (MR) representation (corresponding deformation states plots), to remove any magnitude bias on the model parameters. The now revealed curvatures of all the MR plots, in turn, reveals the non-correlation between the chain size and crosslink density. This discrepancy occurs due to the R-dependent majority presence of network defects due to sol…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
