Bibliographic record
Abstract
Polymer foams are utilized in a range of applications such as absorption, mechanical cushioning, thermal and sound insulation, and medical devices. In comparison with amorphous polymer foams, foams of semicrystalline polymers are desirable in applications that require higher service temperatures and chemical resistance, but preparing foams of semicrystalline polymers is challenging due to their weak melt strength near processing temperatures. Blending of immiscible polymers has emerged as a convenient route to tune the viscoelastic and crystallization properties of polymers. Here we show that the in-situ generation of a fibrillar morphology during physical blending of immiscible polymers can broaden the foam processing window of semicrystalline polymers. These results formed the basis for a W.O. Patent. We anticipate that these results will help in the development of alternative polymer formulations and optimization of existing ones for the large-scale manufacture of semicrystalline polymer foams with unique properties for advanced applications. For example, novel superhydrophobic and oleophilic open-cell foams were obtained from polypropylene (PP) containing polytetrafluoroethylene (PTFE) fibrillar dispersed phases in a fully scalable extrusion process. These open-cell foams are technologically promising for applications such as oil-spill cleanup, organic pollutant removal, and field water remediation.
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 | 0.000 |
| 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) | 0.002 | 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".