Adaptive Structured Pickering Emulsions and Porous Materials Based on Cellulose Nanocrystal Surfactants
Bibliographic record
Abstract
Taking advantage of the formation and assembly of cellulose nanocrystal surfactants (CNCSs) at the water-oil interface, where polar cellulose nanocrystals (CNCs) and end-functionalized polymer chains interact, the preparation and stability of emulsions prepared with CNCSs were investigated. The packing density of CNCSs at the interface can be adjusted by tuning parameters such as pH, ionic strength, and concentration/molecular weight of the end-functionalized polymer ligands. Stable non-spherical emulsions are obtained during homogenization, as a result of the interfacial jamming of CNCSs, with pH-triggered reconfigurability. Porous materials are prepared by freeze-drying creamed, CNCS-stabilized emulsions. The cells of the porous materials have a controlled pore size and shape that are commensurate with the droplets in the emulsion and are responsive to pH. The behavior of the adaptive, reconfigurable supracolloidal system is coupled to its internal and surrounding environment.
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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".