Bibliographic record
Abstract
Biodegradable blend membranes based on polylactide and chitosan with various compositions were prepared via a two-step processing pathway. In the first step, solutions of each component were properly mixed and cast into a gelatinous membrane, and in the second step, the obtained membrane was immersed into a mixed solution for the solvent extraction followed by a drying procedure to finally generate a well-blended membrane. An acetic acid-acetone solvent system was selected for poly(DL-lactide)/chitosan membranes, and another solvent system for poly(L-lactide)/chitosan membranes consisted of acetic acid and dimethyl sulfoxide. Some processing parameters, such as the concentration of component solutions and the composition ratio of mixed solvents and extraction solvents, were optimized by primarily considering whether the directly visible phase separation occurred during the processing procedures. Morphologies of these blend membranes were viewed using SEM. It was found that the processing parameters exerted quite notable impacts on the morphology of the membranes. The hydrophilicity of membranes was examined by measuring their water contact angle and swelling index. These blend membranes were also investigated for their miscibility using IR spectra, X-ray diffractograms, TG, DSC, and dynamic mechanical analysis methods. Although the presence of phase separation at a microscopic level was detected for these membranes, pronounced interactions between components were confirmed. The obtained results shown that some membranes prepared under optimized processing conditions had a partially miscible structure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".