Pickering/Non‐Pickering Emulsions of Nanostructured Sulfonated Lignin Derivatives
Bibliographic record
Abstract
Abstract Sulfoethylated lignin (SEKL) polymeric surfactant and sulfoethylated lignin nanoparticles (N‐SEKL) with a size of 750±50 nm are produced by using a facile green process involving a solvent‐free reaction and acidification‐based fractionation. SEKL forms a liquid‐like conventional emulsion with low viscosity that has temporary stability (5 h) at pH 7. However, N‐SEKL forms a gel‐like, motionless, and ultra‐stable Pickering emulsion through a network of interactions between N‐SEKL particles, which creates steric hindrance among the oil droplets at pH 3. The deposition of SEKL and N‐SEKL on the oil surface is monitored by a using a quartz crystal microbalance. Experimentally, the formation of emulsions at pH 7 is found to be reversible owing to the low adsorption energy Δ E of SEKL on the oil droplet (Δ E ≈15 k B T ), which is determined with the help of three‐phase contact‐angle measurements. However, the high desorption energy (Δ E ≈6.0×10 5 k B T ) of N‐SEKL makes it irreversibly adsorb on the oil droplets. SEKL is too hydrophilic to attach to the oil interface (Δ E ≈0) and thus does not facilitate emulsion formation at pH 11. Therefore, it is feasible to apply SEKL for the formulation of Pickering or non‐Pickering emulsions in the form of nanoparticles or polymeric surfactants, depending on the targeted application.
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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".