Ionic gelation of chitosan with sodium tripolyphosphate using a novel combined nebulizer and falling film system
Bibliographic record
Abstract
Abstract Nanoparticle technology has made an essential contribution to the pharmaceutical industry and has received considerable attention in comparison with the other domains. In the present study, ionic gelation via a novel combined system of nebulizer and falling film (IG‐NFF) has been proposed. The purpose of the design of this system is to increase the contact surface between the chitosan and sodium tripolyphosphate (STPP) for the preparation of monodisperse and spherical chitosan nanoparticles, because in both nebulizer and falling film systems, the surface‐to‐volume ratio of liquid increases. The formation of these nanoparticles is based on ionic interactions between the negatively charged phosphate groups in the STPP and positively charged amine groups on the chitosan. In this research, the effects of chitosan concentration, STPP concentration, the distance of the nebulizer from the inclined plate, gas to liquid flow rate ratio, and the initial chitosan solution pH on the z‐average size, polydispersity index (PDI), and morphology of nanoparticles formed by IG‐NFF were studied. The results showed that the IG‐NFF process has the ability to produce spherical particles with highly uniform sizes. The mean size of obtained chitosan nanoparticles was in the range of 80–320 nm, and the PDI value ranged from 0.08–0.30.
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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) | 0.001 | 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 | 0.001 |
| 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".