Synthesis of magnetite-silica-carbon quantum dot nanocomposites for melatonin drug delivery
Bibliographic record
Abstract
In targeted drug delivery, the drug is released at a specific and desired point and condition. In this research, magnetite cores (high saturation magnetization property (emu.g-159) were used to target the drug system. First, magnetite nanoparticles were synthesized by coprecipitation method from divalent and trivalent chloride salts of iron (FeCl2 and FeCl3), then mesoporous silicas (with a pore diameter of 13 nm) were formed by Stöber's method from the tetraethylorthosilicate (TEOS) silica source on magnetite cores in spheres form. After that, the carbon quantum dots were synthesized by hydrothermal method from citric acid and their surface was immobilized by dimethylamine which were placed in silica cavities by physical adsorption method. The effective drug melatonin (6.46 mg of melatonin per 100 mg of the drug system) was also loaded on this system by physical absorption method and the release of this drug was carefully determined by the release from the dialysis bag in the simulated environment of blood and cancer tissue. the quantum gain of the system was determined to be about 40%. The results showed that the loading of melatonin drug and carbon quantum dots was done well on silica nanoparticles with magnetite cores, and this system releases 30% of the drug even under temperature conditions.
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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".