Effect of Moisture on Shape Memory Polyurethane Polymers for Extrusion-Based Additive Manufacturing
Bibliographic record
Abstract
Extrusion-based additive manufacturing (EBAM) or 3D printing is used to produce customized prototyped parts. The majority of the polymers used with EBAM show moisture sensitivity. However, moisture effects become more pronounced in polymers used for critical applications, such as biomedical stents, sensors, and actuators. The effects of moisture on the manufacturing process and the long-term performance of Shape Memory Polyurethane (SMPU) have not been fully investigated in the literature. This study focuses primarily on block-copolymer SMPUs that have two different hard/soft (h/s) segment ratios. It investigates the effect of moisture on the various properties via studying: (i) the effect of moisture trapping within these polymers and the consequences when manufacturing; (ii) and the effect on end product performance of plasticization by moisture. Results indicate that higher h/s SMPU shows higher microphase separation, which leads to an increase of moisture trapping within the polymer. Understanding moisture trapping is critical for EBAM parts due to an increase in void content and a decrease in printing quality. The results also indicate a stronger plasticizing effect on polymers with lower h/s ratio but with a more forgiving printing behavior compared to the higher h/s ratio.
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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 | 0.001 |
| 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) | 0.002 | 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".