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 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 | 0.001 |
| 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 teacher head, 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".