Lignin-Based Nanoparticles Stabilized Pickering Emulsion for Stability Improvement and Thermal-Controlled Release of <i>trans</i>-Resveratrol
Bibliographic record
Abstract
In this work, we designed a novel multifunctional Pickering emulsion stabilized by lignin-based nanoparticles. We utilized the industrial waste lignin to prepare thermoresponsive lignin copolymer by grafting poly( N -isopropylacrylamide) (PNIPAM) onto lignin via atom transfer radical polymerization (ATRP) and then formed self-assembled nanoparticles (AL- g -PNIPAM NPs). AL- g -PNIPAM NPs well stabilized trans -resveratrol ( trans -RSV)-containing palm oil emulsion droplets in water. Thanks to the abundant UV chromophoric groups of lignin, the light stability of trans -RSV was significantly improved by the protecting of the AL- g -PNIPAM NPs layer. Moreover, the emulsion properties and release behavior strongly depend on the temperature and nanoparticles size: decreasing temperature induced deformation of AL- g -PNIPAM NPs at the interface, an increase in droplet size, and the accelerated release of trans -RSV. These results showed the great potential of this approach of a green functional lignin-based nanoparticles stabilized Pickering emulsion for storage and thermal-controlled release of light-unstable and poorly water-soluble drugs.
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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".