Synthesis and Physicochemical Characterization of Mesoporous SiO<sub>2</sub> Nanoparticles
Bibliographic record
Abstract
There exists a knowledge gap in understanding potential toxicity of mesoporous silica nanoparticles. A critical step in assessing toxicity of these particles is to have a wide size range with different chemistries and physicochemical properties. There are several challenges when synthesizing mesoporous silica nanoparticles over a wide range of sizes including (1) nonuniform synthesis protocols using the same starting materials, (2) the low material yield in a single batch synthesis (especially for particles below 60–70 nm), and (3) morphological instability during surfactant removal process and surface modifications. In this study, we synthesized a library of mesoporous silica nanoparticles with approximate particle sizes of 25, 70, 100, 170, and 600 nm. Surfaces of the silica nanoparticles were modified with hydrophilic‐CH2–(CH2)2–COOH and relatively hydrophobic‐CH2–(CH2)10–COOH functional groups. All silica nanoparticles were analysed for morphology, surface functionality, surface area/pore volume, surface organic content, and dispersion characteristics in liquid media. Our analysis revealed the synthesis of a spectrum of monodisperse bare and surface modified mesoporous silica nanoparticles with a narrow particle size distribution and devoid of cocontaminants critical for toxicity studies. Complete physicochemical characterization of these synthetic mesoporous silica nanoparticles will permit systematic toxicology studies for investigation of structure‐activity relationships.
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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".