Investigation of the Hydrolytic Stability of Polyurethane Applied to Vehicle Suspension Components
Bibliographic record
Abstract
Process parameters such as temperature and humidity, as well as formulation are the key factors in the manufacture of a polymeric component through a polyurethane prepolymer. These define the reaction kinetics, bonding and the resulting chemical interactions which determine the final characteristics of the material. One of the expected skills of polyurethane, when applied to components used in contact with water, is hydrolysis resistance. Consequently this research focused on exposure of a polymerized TDI (toluene diisocyanate) polyether polyurethane to different proportions of the curing agent, MOCA (4,4'-methylene-bis), in an environment susceptible to reactions with water at 70 °C. In this case, this material is applied in the manufacture of coil spring solid axle with trailing arms and Panhard rod suspension bushings. Mechanical tests and DSC (differential scanning calorimetry), TGA (thermogravimetry) and FT-IR (Fourier transform infrared) evaluations of the samples and prepolymer are conducted for the characterization of the different formulations, showing the negative relationship of the curing agent proportion parameters with the hydrolysis resistance. Here depolymerization of urethane and ether groups, as well as lower retention of yield stress are verified. These findings can subsidize developing predictive models for performance and lifetime of polyurethanes.
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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 | 0.000 |
| 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) | 0.001 | 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".