Stabilization of Oil-in-Water Pickering Emulsions by Surface-Functionalized Cellulose Hydrogel
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
An amphiphilic cellulose (CLH) hydrogel was synthesized via grafting of quaternary ammonium groups onto cellulose. The structural properties of CLH were characterized via Fourier transform infrared (FTIR)/13C solid-state NMR spectroscopy, elemental (CHN) analysis, particle size distribution (PSD), thermogravimetric analysis (TGA), and wettability was assessed through contact angle measurements. Pickering emulsions of apolar oils in water were prepared using variable weights of the CLH hydrogel as the stabilizing agent, along with different methods of agitation (mechanical shaking and sonication). The characterization results for CLH provide support for the successful grafting of quaternary ammonium groups onto cellulose to produce hydrogels. Different methods of agitation of an oil/water mixture revealed the formation of an oil-in-water (O/W) Pickering emulsion that was stable to coalescence for over 14 days. The resulting emulsions showed variable droplet sizes and stability according to the dosage of CLH in the emulsion and the agitation method, where the emulsion droplet size is related to the particle size of CLH. The addition of methyl orange (MO), a probe to evaluate the phase partitioning of the dye, had minor effects on the emulsion droplet size, and the emulsion prepared with 0.8 wt.% of CLH and agitated via sonication exhibited the smallest droplet size and greatest stability. This study is anticipated to catalyze further research and the development of low-cost and sustainable biopolymer hydrogels as stabilizers for tunable Pickering emulsion. Grafted cellulose materials of this type represent versatile stabilizing agents for foods, agrochemicals, and pharmaceutical products and technologies.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it