Inverse Pickering Emulsions Stabilized by Cinnamate Modified Cellulose Nanocrystals as Templates To Prepare Silica Colloidosomes
Bibliographic record
Abstract
A facile method to prepare colloidosomes at room temperature from inverse water-in-oil (w/o) Pickering emulsions containing silica precursors and stabilized by cinnamate modified cellulose nanocrystals (Cin-CNC) is proposed. Cin-CNC Pickering emulsifiers were prepared by acylation with an excess of cinnamoyl chloride. The Cin-CNC surface displayed partial wettability with both toluene and water, which allowed the stabilization of w/o inverse Pickering emulsions. The Cin-CNC particles around the droplets were subsequently locked by cross-linking TEOS or TBOS silica precursors at the water/toluene interface, leading to an intricate network of polysiloxane within the Cin-CNC shell. In optimized conditions, the Cin-CNC/silica colloidosomes obtained displayed a robust and compact shell, which ensured a long-term encapsulation of rhodamine B or biological molecules such as fluorescent deoxyribonucleic acid (DNA).
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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".