Dextran-Functionalized Super-nanoparticle Assemblies of Quantum Dots for Enhanced Cellular Immunolabeling and Imaging
Bibliographic record
Abstract
Colloidal semiconductor quantum dots (QDs) are a popular material for applications in bioanalysis and imaging. Although individual QDs are bright, some applications benefit from the use of even brighter materials. One approach to achieve higher brightness is to form super-nanoparticle (super-NP) assemblies of many QDs. Here, we present the preparation, characterization, and utility of dextran-functionalized super-NP assemblies of QDs. Amphiphilic dextran was synthesized and used to encapsulate many hydrophobic QDs via a simple emulsion-based method. The resulting super-NP assemblies or “super-QDs” had hydrodynamic diameters of ca. 90–160 nm, were characterized at the ensemble and single-particle levels, had orders-of-magnitude superior brightness compared to individual QDs, and were non-blinking. Additionally, binary mixtures of red, green, and blue (RGB) colors of QDs were used to prepare super-QDs, including colors difficult to obtain from individual QDs ( e.g., magenta). Tetrameric antibody complexes (TACs) enabled simple antibody conjugation for selective cellular immunolabeling and imaging with both an epifluorescence microscope and a smartphone-based platform. The technical limitations of the latter platform were overcome by the increased per-particle brightness of the super-QDs, and the super-QDs outperformed individual QDs in both cases. Overall, the super-QDs are a very promising material for bioanalysis and imaging applications where brightness is paramount.
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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) | 0.000 | 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 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".