Temperature induced phase transition in fluorescence active zein nanoparticles
Bibliographic record
Abstract
Stable colloidal zein nanoparticles (NPs) were synthesized by using controlled precipitation method. They were made fluorescence active by incorporating a small amount of fluorescence quinolinium surfactant. The incorporation of fluorescence surfactant provided both the colloidal stability and the fluorescence ability to determine the phase transition in zein NPs under the effect of temperature variation. Maintaining colloidal stability under the effect of temperature variation is an essential aspect of zein NPs applicability as a source of vegetarian protein supplement in different food suspensions. Different techniques such as fluorescence, DLS size, zeta potential, and FTIR measurements were applied to determine the influence of temperature on the colloidal stability of zein NPs. Zein NPs undergo phase transition well above room temperature while maintaining their size in nanometer range, and the phase transition temperature decreased with the amount of zein used in the synthesis of zein NPs. The results highlighted the potential use of zein NPs as a vegetarian supplement protein in different food products.
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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".