Control of Ultrasmall Sub-10 nm Ligand-Functionalized Fluorescent Core–Shell Silica Nanoparticle Growth in Water
Bibliographic record
Abstract
Ultrasmall fluorescent silica nanoparticles (SNPs) and core–shell SNPs surface functionalized with polyethylene glycol (PEG), specific surface ligands, and overall SNP size in the regime below 10 nm are of rapidly increasing interest for clinical applications, because of their favorable biodistribution and safety profiles. Here, we present an aqueous synthesis methodology for the preparation of narrowly size-dispersed SNPs and core–shell SNPs with size control below 1 nm, i.e., at the level of a single atomic layer. Different types of fluorophores, including near-infrared (NIR) emitters, can be covalently encapsulated. Brightness can be enhanced via addition of extra silica shells. This methodology further enables synthesis of <10 nm sized fluorescent core and core–shell SNPs with previously unknown compositions. In particular, the addition of an aluminum sol gel precursor leads to fluorescent aluminosilicate nanoparticles (ASNPs) and core–shell ASNPs. Encapsulation efficiency and brightness of highly negatively charged NIR fluorophores is enhanced, relative to the corresponding SNPs without aluminum. Resulting particles show quantum yields of ∼0.8, i.e., starting to approach the theoretical brightness limit. All particles can be PEGylated providing steric stability. Finally, heterobifunctional PEGs can be employed to introduce ligands onto the PEGylated particle surface of fluorescent SNPs, core–shell SNPS, and their aluminum-containing analogues, producing ligand-functionalized <10 nm NIR fluorescent nanoprobes. In order to distinguish these water-based-synthesis-derived materials from the earlier alcohol-based modified Stöber process derived fluorescent core–shell SNPs referred to as Cornell dots or C dots, the SNPs and ASNPs described here and synthesized in water will be referred to as Cornell prime dots or C′ dots and AlC′ dots. These organic–inorganic hybrid nanomaterials may find applications in nanomedicine, including cancer diagnostics and therapy (theranostics).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.006 | 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 teacher head, 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".