Lignin-containing cellulose nanocrystals from maple leaves: A natural Pickering emulsion stabilizer for food preservation
Bibliographic record
Abstract
Cellulose nanocrystals have been obtained from maple leaves for stabilizing Pickering emulsions, but a chemical modification is required to improve hydrophobicity and the yield is relatively low due to the removal of non-cellulose components. Herein, lignin was retained while isolating cellulose from maple leaves, and the prepared lignin-containing cellulose nanocrystals (LCNCs) were applied as natural Pickering emulsion stabilizers. Rod-like shaped LCNCs with tunable lignin contents had suitable hydrophobicity and high aspect ratios, resulting in long-term stability of LCNC-stabilized Pickering emulsions. The obtained emulsions provided good encapsulation and protection of cinnamaldehyde, and the controlled release of cinnamaldehyde promoted sustained antibacterial efficacy. Both direct-contact and non-contact preservation modes were investigated for shrimp preservation, where headspace release of cinnamaldehyde from emulsions at non-contact mode was more effective in inhibiting bacterial proliferation compared to direct-contact (spray-coating) mode. This work demonstrates the feasibility of developing value-added LCNCs from maple leaves as sustainable Pickering emulsion stabilizers. • Lignin-containing cellulose nanocrystals (LCNCs) were isolated from maple leaves. • LCNCs with tunable lignin content could serve as natural Pickering emulsion stabilizers. • LCNC-stabilized emulsion showed good encapsulation and protection of cinnamaldehyde. • Cinnamaldehyde-loaded Pickering emulsions exhibited long-term antibacterial efficacy. • Both non-contact and contact preservation modes extended shelf life of shrimp.
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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".