Stable Aqueous Foams from Cellulose Nanocrystals and Methyl Cellulose
Bibliographic record
Abstract
The addition of cellulose nanocrystals (CNC) greatly enhanced the properties of methylcellulose (MC) stabilized aqueous foams. CNC addition decreased air bubble size, initial foam densities and drainage rates. Mixtures of 2 wt % CNC + 0.5 wt % MC gave the lowest density foams. This composition sits near the onset of nematic phase formation and also near the overlap concentration of methylcellulose. More than 94% of the added CNC particles remained in the foam phase, not leaving with the draining water. We propose that the nanoscale CNC particles bind to the larger MC coils both in solution and with MC at the air/water interface, forming weak gels that stabilize air bubbles. Wet CNC-MC foams were sufficiently robust to withstand high temperature (70 °C for 6 h) polymerization of water-soluble monomers giving macroporous CNC composite hydrogels based on acrylamide (AM), 2-hydroxyethyl methacrylate (HEMA), or polyethylene glycol diacrylate (PEGDA). At high temperatures, the MC was present as a fibrillar gel phase reinforced by CNC particles, explaining the very high foam stability. Finally, our CNC-MC foams are based on commercially available forms of CNC and MC, already approved for many applications. This is a "shovel-ready" technology.
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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".