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 (SiO2@QDs) that comprised many quantum dots (QDs) around a central silica nanoparticle (SiO2 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 SiO2@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 SiO2@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 SiO2@QDs provided higher sensitivity and superior signal-to-background ratios whether using research-grade fluorescence microscopy or smartphone-based imaging. Overall, the SiO2@QDs are promising materials for enhanced bioanalysis and imaging.
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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 | 0.000 | 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 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".