Carboxylated cellulose nanocrystal cryogel monoliths: a multi-tool study of morphology and porosity of pure and magnetite nanoparticle-decorated CNC scaffolds
Bibliographic record
Abstract
Tissue scaffolds are known to benefit from incorporation of nanoscale bio-additives like cellulose nanocrystals (CNC), which can affect porosity as an important tunable design parameter for bio-based scaffolds. In this paper, we probe how freeze-casting of carboxylated cellulose nanocrystals and CNC derivatized with magnetite nanoparticles yields macroscale cryogel scaffold monoliths. Cryogel topographical features and macropore morphologies depend on the conditions under which ice formation takes place, and on exposure to static magnetic fields. We examine porosity over several length scales with scanning electron microscopy (SEM) coupled with Local Thickness Euclidean distance image processing, small angle X-ray scattering (SAXS), and dynamic vapor sorption (DVS). SAXS data fitted with a mass fractal model and power law suggest that CNC particles aggregate to form well-defined compact walls in the range of 96.7–27.3 nm for all samples, while inclusion of Fe 3 O 4 nanoparticles disrupts this compactness in the range of 27.3–4.8 nm. Analysis of DVS reveals that nanoparticles directly impact water uptake by the cryogel scaffolds and can reduce water sorption in mesopores with a radius of 5–6 nm.
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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".