Cellulose nanofibrils-stabilized legume protein-based pickering emulsions for capsaicin delivery: Fabrication, characterization, and encapsulation mechanism exploration
Bibliographic record
Abstract
Capsaicin (CAP) faces limitations in its widespread application due to its low bioaccessibility. Pickering emulsions based on legume proteins are efficient for encapsulating bioactive compounds, but poor solubility and environmental sensitivity of proteins undermine emulsion stability. To tackle these challenges, this study developed a novel Pickering emulsion by using cellulose nanofibrils (CNFs) and chickpea protein isolate (CPI) for efficient CAP delivery. The combination of CPI and CNF at a ratio of 20꞉1 ( w/w ) exhibited the highest encapsulation efficiency (70.90% ± 1.66%) and sustained release properties during in vitro digestion, thereby enhancing CAP bioaccessibility from 39.40% ± 2.33% to 81.54% ± 1.95%. Notably, CNF also enhanced emulsion stability through enhanced hydrogen bonding, reduced droplet size (589.51 ± 47.08 nm), and increased hydrophobicity (contact angle: 85.83° ± 1.20°). Comprehensive characterization revealed that the incorporation of CNF significantly improved the colloidal properties of the emulsion, including its rheological behavior and thermal stability. Mechanistic investigations demonstrated that the enhanced encapsulation capability was attributed to the formation of stable hydrogen-bonding networks between CNF and CPI. Moreover, CAP is bound with CPI through synergistic hydrogen bonding and van der Waals interactions, with Arginine-179 identified as the key residue for binding (binding free energy: –10.46 kJ/mol). These findings offer valuable insights into the development of plant-based nanocarrier systems and highlight the potential of CNF-legume protein complexes in the delivery of bioactive compounds.
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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 | 0.001 | 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 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".