Stimuli-Responsive Cellulose Nanocrystals for Surfactant-Free Oil Harvesting
Bibliographic record
Abstract
Cellulose nanocrystals with grafted binary polymer brushes (CNC-BPB), poly(oligoethylene glycol) methacrylate (POEGMA) and poly(methacrylic acid) (PMAA), were prepared by cerium-mediated polymerization in aqueous solution. The physical properties of CNC-BPB can be controlled by external triggers, such as temperature and pH, which can be utilized to stabilize and destabilize oil-water emulsions. By virtue of the modifications, these bifunctionalized CNCs diffused to the oil-water interface and stabilized the oil droplets at high pHs. When the pH was lowered to 2, strong hydrogen bonding between POEGMA and PMAA chains grafted on the CNC induced the coalescence of the emulsion droplets, resulting in the phase separation of oil and water. For emulsions stabilized by CNC-POEGMA and free PMAA mixtures, instantaneous coalescence was not observed at low pHs. Successive stabilization-destabilization over 5 cycles was demonstrated by modulating the pH with the addition of acid or base without any loss in efficiency. This work demonstrates that functional sustainable nanomaterials can be used for small scale oil-water separations, particularly for oil droplet transportation and harvesting of lipophilic compounds.
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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".