Tuning Surface Properties of PLA for Capturing Nonpolar Compounds from Water
Bibliographic record
Abstract
In the available literature, the surface wettability of polylactic acid (PLA), a compostable polymer, has been tuned for oil capture by employing multistep processes and incorporating non-biodegradable materials. Such processes are complicated to scale up for industrial production, and the addition of multiple components reduces the compostability of PLA. In this work, we report on the surface wettability tuning of PLA by an easily scalable, solvent-induced recrystallization process, termed Dip-Dip-Dry (DDD), without adding any other materials into PLA. The increase in crystallinity by DDD treatment increases the intramolecular coupling interaction of C=O and C–O groups in PLA, thus reducing the polar component of surface energy to zero, rendering it nonpolar. Surface-modified PLA selectively captures non-polar compounds from water mixtures: discs uptake 0.11 g of oil/g of PLA and 0.06 g of diesel/g of PLA and powders uptake 2.6 g of oil/g of PLA. The selective oil capture capacity of surface-modified PLA is also confirmed in real world conditions by testing them in acidic, basic, and salt water–oil mixtures. The oil-saturated PLA can be regenerated by washing in isopropanol. The durability of this material was tested by exposing it to DI water and simulated seawater for more than 30 days. Hence, this work proposes DDD treated PLA as an industrially scalable sustainable material for non-polar compound capture that prevents secondary pollution.
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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) | 0.001 | 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 | 0.001 |
| 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".