A scalable post-treatment method for modifying polylactic acid to create nanoporous polymers
Bibliographic record
Abstract
Dip-dip-dry (DDD) is a solvent-swelling based process developed to take solid poly-lactic acid (PLA) components and modify them post-treatment to render the material porous. DDD is notably designed for easy scaleup and high throughput with the whole process taking at most 25 s in the current batch setup. Porous polymers have applications in numerous fields including insulation, packaging, biomedicine, and agriculture, and DDD is one of the only ways to produce porous PLA that does not require starting from a melt or a solution of dissolved polymer. This work investigates the characteristics of the porous structures formed by DDD and seeks to tune the process, so the amount of pore volume generated within the polymer is maximized. The highest pore volume achieved in this study was accomplished by using THF as a solvent and water as a coagulant to treat PLA with 9±1% crystallinity, resulting in porous layers 117±8 μm thick and with a porosity of 35%. Additionally, it was shown that it is possible to use DDD on more crystalline forms of PLA, including sheets (34±3% crystallinity) and drawn fibers (48±2% crystallinity). Tensile strength of PLA sheets was reduced after treatment with a 68% decrease in the force required to break PLA sheets, 43% decrease in the force required to break monofilament fibers and an 84% decrease in the force required to break multifilament fibers. Other significant conclusions are that DDD makes the surface of PLA hydrophobic (water contact angle 140±8°) and, by changing solvents, DDD can be applied to other polymers like low-density polyethylene (LDPE) for the same purpose.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".