Visualizing Quantum Dots in Biological Samples Using Silver Staining
Bibliographic record
Abstract
Quantum dot (QD) based contrast agents are currently being developed as probes for bioimaging and as vehicles for drug delivery. The ability to detect QDs, regardless of fluorescence brightness, in cells, tissues, and organs is imperative to their development. Traditional methods used to visualize the distribution of QDs in biological samples mainly rely on fluorescence imaging, which does not account for optically degenerate QDs as a result of oxidative quenching within the biological environment. Here, we demonstrate the use of silver staining for directly visualizing the distribution of QDs within biological samples under bright field microscopy. This strategy involves silver deposition onto the surface of QDs upon reduction by hydroquinone, effectively amplifying the size of QDs until visible for detection. The method can be used to detect non-fluorescent QDs and is fast, simple, and inexpensive.
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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) | 0.001 | 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".