Correlation Between Cryogenic Parameters and Physico-Chemical Properties of Porous Gelatin Cryogels
Bibliographic record
Abstract
In the present work, we have performed an in-depth physico-chemical and bio-physical evaluation of a series of previously described porous gelatin scaffolds (S. VanVlierberghe, V. Cnudde, P. Dubruel, B. Masschaele, A. Cosijns, I. DePaepe, P.J.S. Jacobs, L. VanHoorebeke, J.P. Remon and E. Schacht, Biomacromolecules 8, 331 (2007)). All scaffolds were prepared by a cryogenic treatment and subsequent freeze-drying. Three types of scaffolds were prepared by using different gelatin concentrations and cooling protocols. Type-I hydrogels were composed of cone-like pores with decreasing diameter from top (330 microm) to bottom (20-30 microm). Type-II and type-III scaffolds contained spherical pores with an average diameter of 135 (type II) and 65 microm (type III), respectively. The physico-chemical and bio-physical properties studied include the water uptake capacity and kinetics, the mechanical properties and the enzyme-mediated degradation. We can conclude that the pore geometry affects the water uptake capacity, the mechanical properties and the degradation profile of the hydrogels. Type-I hydrogels possess the highest water uptake, the lowest compression modulus and the fastest enzyme mediated degradation, indicating a clear effect of the pore morphology (elongated channels for type I versus spherical pores for types II and III) on the physico-chemical and bio-physical properties of the materials. In contrast to the effect of the pore geometry (channel-like versus spherical), the pore size does not significantly affect the water uptake, the mechanical properties and the enzyme mediated degradation in the investigated pore size range (65-135 microm). To the best of our knowledge, this is the first report in which the effects of a cryogenic treatment on the hydrogel network properties are investigated in such detail.
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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 | 0.001 |
| 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".