Effect of Ligand Density on the Spectral, Physical, and Biological Characteristics of CdSe/ZnS Quantum Dots
Bibliographic record
Abstract
Chemical modification of the surface of CdSe/ZnS quantum dots (QDs) with small molecules or functional ligands often alters the characteristics of these particles. For instance, dopamine conjugation quenches the fluorescence of the QDs, which is a property that can be exploited for sensing applications if the conjugates are taken up into living cells. However, different sizes and/or preparations of mercaptocarboxylic acid solubilized QDs show very different properties when incubated with cells. It is unknown what physical parameters determine a QDs ability to interact with a cell surface, be endocytosed, escape from endosomes, and/or enter the nucleus. In this study, we examine the surface chemistry of QD-dopamine conjugates and present an optimized method for tracking the attachment of small biomolecules to the surface. It is found that the fluorescence intensity, surface charge, colloidal stability, and biological interactions of the QDs vary as a function of the density of dopamine on the surface. Successful targeting of QD-dopamine to dopamine receptor positive PC12 cells correlates with greater homogeneity of particle thiol layer, and a minimum number of ligands required for specific association can be estimated. These results will enable users to develop methods for screening QD conjugates for biological activity before proceeding to experiments with cell lines and animals.
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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 | 0.001 |
| 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".