Stable Aqueous Foams from Cellulose Nanocrystals and Methyl Cellulose
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it