Effect of Thermal Aging on Polyurethane Degradation and the Influence of Unsaturations in the Hard Segment
Bibliographic record
Abstract
ABSTRACT This research focuses on synthesizing two types of polyurethanes (PUs): one with saturated C–C bonds (SPU) and another with unsaturated CC bonds (UPU), primarily in the hard segment. Both types of PUs underwent thermal aging for 30 days at 150°C in an air atmosphere to investigate the influence of unsaturation on their thermal degradation. The samples, both before and after aging, were characterized using FT‐IR, XRD, TGA, DSC, mechanical testing, and SEM. Additionally, the percentage of weight loss was measured. Similar changes were observed in both SPU and UPU after thermal aging. Thermal degradation resulted in mass losses of approximately 5% for SPU and 10% for UPU, accompanied by chemical alterations in the PUs, as evidenced by FT‐IR. Mechanical testing revealed a decline in performance for both materials, with notable reductions in yield strength and elongation at break. UPU exhibited a more pronounced degradation in mechanical properties compared to SPU. SEM analysis further confirmed surface degradation in both materials. Among the two PUs, UPU demonstrated higher susceptibility to thermal degradation. This study provides valuable insights into how the unsaturation in diols or polyols used in PU synthesis influences thermal degradation and the resulting changes in material properties. The findings also highlight the potential performance implications for SPU and UPU when exposed to temperatures exceeding their processing or operational limits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".