Comparative study of the artificial and natural, indoor ageing of crosslinked, polyurethane ester closed-cell foams
Bibliographic record
Abstract
This study compares naturally aged (NA) closed-cell polyester urethane (PU-ES) foams with an artificially aged (AA) chemically crosslinked, comparable PU-ES. The foams are shoe soles part of a museum collection from a well-defined time, place, and use, and were thoroughly characterised visually, chemically, and, where possible, mechanically. Under museum conditions, naturally aged materials displayed hydrolysis related phenomena namely blooming, pox, and dried fluids, consisting either of adipic acid deposits, or resulting from reactions between polyester polyol oligomers and shoe metal components. Shore hardness, only method applicable to both sets of samples, showed that 56 days at 70°C and 98 %RH produced a loss of elasticity in AA material comparable to ∼25 years of natural ageing. Yet, artificial ageing was not able to replicate NA surface phenomena, signalling a fundamental difference between material undergoing both kinds of ageing. Particular effort was devoted to the mechanical and chemical characterization, and to evaluating pyrolysis-GCMS for monitoring ageing in PU-ES. While detailed MS interpretation enabled proposal of several structures of pyrolysis products, certain key issues intrinsically affected the repeatability of py-GCMS analysis, making MDA, a proposed marker for PU-ES degradation, unsuitable for following ageing. Alternative methods for this purpose included the infrared band ratio 1727 cm⁻¹ / 1706 cm⁻¹, which increased consistently with accelerated ageing but was reliable only when adipic acid did not obscure the region. In contrast, GCMS of material extracts showed higher repeatability and promise for identifying molecular ageing markers. Overall, the results indicate that while artificial ageing can reproduce certain mechanical changes, it cannot fully replicate the chemical and morphological complexity of long-term natural ageing, highlighting the importance of complementary analytical strategies and the potential of the study of naturally aged materials from industrial heritage collections. By leveraging their repetitive nature and well-established, industrially manufactured formulations from collections, this study demonstrates how such materials can uniquely bridge natural and artificial ageing research— not only refining conservation strategies to ensure their safeguarding for future generations, but also allowing the study of long-term polymer degradation processes otherwise difficult to access within current material science strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".