Metallothermic Reduction of Silica Nanoparticles to Porous Silicon for Drug Delivery Using New and Existing Reductants
Bibliographic record
Abstract
In this study, the influence of metals (Mg, Al, and Ca) and reaction conditions (time, temperature, and metal grain size) on the metallothermic reduction of Stöber silica nanoparticles (NPs) to form porous Si has been explored. Mg metal was found to be an effective reducing agent even at temperatures below its melting point; however, it also induced a high degree of structural damage and morphology change. Al was effective in reducing silica NPs only at its melting point or above, but the resulting particles retained a higher degree of structural morphology as compared to those reduced using Mg. Ca was found to be ineffective in reducing silica. A new reductant, a mixture of 70 % Mg and 30 % Al, was found to induce the least amount of morphology change, and the reactions proceeded at a temperature (450 °C) lower than those required with Mg or Al individually. Furthermore, porous Si NPs obtained using Mg, Al, and the mixture of 70 % Mg and 30 % Al as reductants have been investigated as carriers for ibuprofen loading and release. Porous Si obtained from reductions with Mg and the Mg/Al mixture showed higher drug loading and a sustained drug release profile, whereas porous Si obtained from Al reduction had lower loading and showed a conventional release profile over 24 h.
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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".