Enzymatic degradation of dimensionally constrained polyhydroxybutyrate films
Bibliographic record
Abstract
The effect of dimensional constraint, imparted by a variation in film thickness, on the enzymatic degradation of polyhydroxybutyrate (PHB) is reported. The characterization of the crystalline structure and the surface topography of solvent-cast PHB thin films revealed strong correlations between film thickness and both crystallinity and crystal anisotropy, with the polymer film becoming more amorphous with decreasing thickness. The enzymatic degradation of the PHB films was characterized using a high precision diffraction metrology, which enabled the visualization of small variations in the degradation behavior. The results show that the degradation rate increases with decreasing thickness due to the corresponding decrease in crystallinity. However, in a nanoscopic ultra-thin PHB specimen, produced by μ-transfer molding, enzymatic degradation was impeded. The enzymatic degradation rate of the PHB films therefore was found to exhibit a discontinuous trend with respect to film thickness: initially increasing as film thickness was reduced, and then decreasing dramatically once the thickness was reduced to tens of nanometers. In this regime, enzymatic degradation was hindered by the absence of crystalline regions in the films. These results show that a nano-dimensional constraint on PHB films can result in specimens with a tunable response to extracellular enzymes.
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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".