Formation and Stability of Pea Proteins Nanoparticles Using Ethanol-Induced Desolvation
Bibliographic record
Abstract
Protein nanoparticles have recently found a lot of interests due to their unique physicochemical properties and structure-functionality compared to the conventional proteins. The aim of this research was to synthesize pea protein nanoparticles (PPN) using ethanol-induced desolvation, to determine the changes in secondary structures and the particle stability in an aqueous dispersion. The nanoparticles were prepared by diluting 3.0 wt% pea protein solutions in 1-5 times ethanol at pH 3 and 10 at different temperatures. Higher ratios of ethanol caused greater extent of desolvation and larger sizes of PPN. After homogenization at 5000 psi for 5 min, PPN displayed uniform size distribution with a smaller size and higher zeta potential at pH 10 compared to pH 3. PPN prepared from a preliminary thermal treatment at 95 °C revealed a smaller size than those synthesized at 25 °C. Electron microscopy showed roughly spherical shape and extensively aggregated state of the nanoparticles. Addition of ethanol caused a reduction in β-sheets and an increase in α-helices and random coil structures of the proteins. When PPN were separated from ethanol and re-dispersed in deionized water (pH 7), they were stable over four weeks, although some solubilization of proteins leading to a loss in particle size was observed.
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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) | 0.000 | 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 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".