Solvent Free Generation of Open and Skinless Foam in Poly(<scp>l</scp>-lactic acid)/Poly(<scp>d</scp>,<scp>l</scp>-lactic acid) Blends Using Carbon Dioxide
Bibliographic record
Abstract
Foams generated via carbon dioxide (CO 2 ) processing typically exhibit a solid skin layer on the exterior surface and a closed-pore structure with limited interconnectivity in the core section thus limiting its application for biomedical intent. By controlling the properties of poly( l -lactic acid)/poly( d, l -lactic acid) (PLLA/PDLLA) blends and using CO 2 with specific processing parameters, skinless foams with interconnected porous structure were prepared in this work using only CO 2 as a physical foaming agent, which overcome the necessity to use organic solvents and solid porogens. The crystallization behaviors and sorption kinetics of PLLA and its blends were studied. Addition of PDLLA reduces the crystallinity of PLLA/PDLLA blends while treated with CO 2 as compared to neat PLLA. The solubility and diffusion coefficients of CO 2 in PLLA and its blends were found to be similar. Furthermore, the effect of PLLA/PDLLA blend ratio and CO 2 treatment conditions on the foam morphologies was investigated. Through fine parameter control, well interconnected pore structures with a porous surface were generated. Results indicated that by controlling the physical properties of samples combined with optimizing CO 2 foaming process, it is indeed possible to create biodegradable interconnected porous structures for potential biomedical applications.
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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) | 0.000 | 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 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".