Surface properties of poly(lactic/glycolic acid)–pluronic® blend films
Bibliographic record
Abstract
Abstract Poly(dl‐lactide) (PLA) and two of its random copolymers with glycolic acid, poly(dl‐lactide‐co‐glycolide) (PLGA) with 75/25 and 50/50 component ratios of lactide/glycolide were blended with poly(ethylene oxide)/poly(propylene oxide)/poly(ethylene oxide) (PEO–PPO–PEO) triblock non‐ionic surfactants, known by the Pluronic® trade names of PE6100, PE6400 and PE6800. The surface chemical compositions of the blended films were identified by X‐ray photoelectron spectroscopy (XPS). Based on the component of the carbon signal assigned to the ether carbon of the Pluronic® molecule, quantification of the surface accumulation of the Pluronic® additive, compared to its bulk concentration, was performed. The data demonstrated that PEO‐containing surfaces were prepared by the blending process. A significant surface hydrophilization, characterized by wettability measurements, was obtained by applying the Pluronics® at a concentration of 1.0–9.1 wt% in the blends. The composition of the surface layer and, in accordance with this, the wettability of the film were found to be dependent on the type of Pluronic® and on the composition of the unmodified polymer. Protein adsorption on the polymer films was measured by the FT‐IR ATR spectroscopic technique. The adsorbed amount of bovine serum albumin onto PLA was highly reduced when the polymer was blended with a Pluronic®. The increased hydrophilicity and the reduced protein adsorption properties of the PLA and PLGA obtained by blending with PEO compounds might contribute to their applications as drug carrier systems with great potential. Copyright © 2003 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".