Stabilizing β-carotene-loaded Pickering emulsion with chitin nanoparticles extracted from insect shells using deep eutectic solvents
Bibliographic record
Abstract
• Deep eutectic solvents were used for chitin and chitin nanofibers (CNFs) preparation. • CNFs were used for stabilizing Pickering emulsions (PEs) up to 180 days. • PEs was used to encapsulate β-carotene with good stability and high embedding rate. • This work provides an approach for sustainable utilization of insect waste. With the rapid growth of artificial insect breeding, the management of insect shell waste has emerged as a pressing concern. In this study, insect shells were treated with environmentally friendly deep eutectic solvents (DESs) to yield chitin nanofibers (CNFs) for stabilizing Pickering emulsion. The purity of chitin extracted from Tenebrio molitor shells was 94.89 %, and CNFs with lengths between 100 and 300 nm were produced. This Pickering emulsion was stabilized using CNFs, and its stability was enhanced by optimizing environmental conditions, such as NaCl and pH, allowing for stable storage over a period of 180 days. At a β-carotene of 1.5 mg/g and an oil phase mass fraction of 45 %, the emulsion achieved an encapsulation efficiency of 98.47 ± 0.12 %, with no significant changes observed after 30 days of storage. These findings demonstrate that insect shells can be effectively utilized to stabilize Pickering emulsions and encapsulate bioactive compounds, offering a sustainable strategy for valorizing waste resources.
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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 | 0.001 |
| 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".