Controlling Growth of Ultrasmall Sub-10 nm Fluorescent Mesoporous Silica Nanoparticles
Bibliographic record
Abstract
Mesoporous silica nanoparticles (MSNs) have recently attracted a lot of interest for future nanotheranostic applications because of their large surface-area and high biocompatibility. However, studies to date of MSNs are confined to >10 nm particle sizes which may result in unfavorable biodistribution characteristics for in vivo experiments and hence limit their clinical applications. Here we provide a full account of a synthesis approach to ultrasmall sub-10 nm mesoporous silica nanoparticles with narrow size distributions and homogeneous porous particle morphology. Key features enabling this structure control are (i) fast hydrolysis, (ii) slow condensation, and (iii) capping of particle growth by addition of a PEG-silane at different time-points of the synthesis. Variation of synthesis conditions including monomer/catalyst concentrations, temperature, and time-point of PEG-silane addition leads to synthesis condition-particle structure correlations as mapped out by a combination of results from data analysis of dynamic light scattering (DLS) and transmission electron microscopy (TEM) measurements. Results establish precise control over average particle diameter from 6 to 15 nm with increments below 1 nm. Solid state nuclear magnetic resonance (NMR) measurements, zeta-potential measurements, and thermogravimetric analysis (TGA) were conducted to reveal details of the particle surface structure. Long-term particle stability tests in deionized (DI) water and phosphate buffered saline (PBS) 1X buffer solution were performed using DLS demonstrating that the PEGylated particles are stable in physiological environments for months. Fluorescent single pore silica nanoparticles (mC dots) encapsulating blue (DEAC) and green (TMR) dyes were synthesized and characterized by a combination of DLS, TEM, static optical spectroscopy, and fluorescence correlation spectroscopy (FCS) establishing probes for multicolor fluorescence imaging applications. The ultraprecise particle size control demonstrated here in particular for sizes around and below 10 nm may render these particles an interesting subject for further fundamental studies of porous silica particle formation mechanisms as well as for sensing, drug delivery, and theranostic applications.
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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".