Fully Self-Assembled Silica Nanoparticle–Semiconductor Quantum Dot Supra-Nanoparticles and Immunoconjugates for Enhanced Cellular Imaging by Microscopy and Smartphone Camera
Bibliographic record
Abstract
There is a growing need for brighter luminescent materials to improve the detection and imaging of biomarkers. Relevant contexts include low-abundance biomarkers and technology-limited applications, where an example of the latter is the emerging use of smartphones and other nonoptimal but low-cost and portable devices for point-of-care diagnostics. One approach to achieving brighter luminescent materials is incorporating multiple copies of a luminescent material into a larger supra-nanoparticle (supra-NP) assembly. Here, we present a facile method for the preparation and immunoconjugation of supra-NP assemblies (SiO 2 @QDs) that comprised many quantum dots (QDs) around a central silica nanoparticle (SiO 2 NP). The assembly was entirely driven by spontaneous affinity interactions between the constituent materials, which included imidazoline-functionalized silica nanoparticles, ligand-coated QDs, imidazole-functionalized dextran, and tetrameric antibody complexes (TACs). The physical and optical properties of the SiO 2 @QDs were characterized at both the ensemble and single-particle levels. Notably, the optical properties of the QDs were preserved upon assembly into supra-NPs, and single SiO 2 @QDs were approximately an order of magnitude brighter than single QDs and nonblinking. In proof-of-concept applications, including selective immunolabeling of breast cancer cells, the SiO 2 @QDs provided higher sensitivity and superior signal-to-background ratios whether using research-grade fluorescence microscopy or smartphone-based imaging. Overall, the SiO 2 @QDs are promising materials for enhanced bioanalysis and imaging.
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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".