Lignin nanoparticles as co-stabilizers and modifiers of nanocellulose-based Pickering emulsions and foams
Bibliographic record
Abstract
Nanocellulose is very hydrophilic, preventing interactions with the oil phase in Pickering emulsions. This limitation is herein addressed by incorporating lignin nanoparticles (LNPs) as co-stabilizers of nanocellulose-based Pickering emulsions. LNP addition decreases the oil droplet size and slows creaming at pH 5 and 8 and with increasing LNP content. Emulsification at pH 3 and LNP cationization lead to droplet flocculation and rapid creaming. LNP application for emulsification, prior or simultaneously with nanocellulose, favors stability given the improved interactions with the oil phase. The Pickering emulsions can be freeze-dried, enabling the recovery of a solid macroporous foam that can act as adsorbent for pharmaceutical pollutants. Overall, the properties of nanocellulose-based Pickering emulsions and foams can be tailored by LNP addition. This strategy offers a unique, green approach to stabilize biphasic systems using bio-based nanomaterials without tedious and costly modification procedures. Supplementary Information: The online version contains supplementary material available at 10.1007/s10570-023-05399-y.
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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".