Thermally sensitive and tunable water-soluble polymer molds for the preparation of porous hydrogels
Bibliographic record
Abstract
ABSTRACT Porous hydrogels are highly sought-after biomaterials for regenerative medicine and 3D cell culture media. For such purposes, direct contact between the hydrogels and organic solvents during the synthesis step must be avoided as much as possible. In this work, porous water-soluble molds, composed of a thermosensitive and melt-processable, i.e. thermoplastic, polyvinyl alcohol (PVA), are used to prepare porous hydrogels. The process offers a high level of control over the porosity features, including full pore interconnectivity and tunable average pore size from nearly 75 μm to 200 μm – the typically targeted range for cell growth in a 3D scaffold. The porous PVA molds are prepared by the sequential process of (1) melt-extrusion of a polystyrene (PS)/PVA co-continuous blend, (2) quiescent annealing to tune the morphology, (3) and selective extraction of the PS phase. The gelling solution is injected in the PVA molds at low temperature – i.e. < 50 °C when the molds are insoluble. Once the solution has gelled, the PVA molds are extracted in hot water – i.e. > 50 °C, over which the molds are fully water soluble, yielding porous hydrogels with nearly comparable macroscopic and microscopic features to the PVA molds, as revealed by electron microscopy and microcomputed tomography. The approach is demonstrated herein with sodium alginate hydrogels, and could be extended to other types of gel chemistries.
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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".