Thiol-Ligand-Catalyzed Quenching and Etching in Mixtures of Colloidal Quantum Dots and Silver Nanoparticles
Bibliographic record
Abstract
Plasmonic nanostructures have the potential to enhance the emissive properties of semiconductor quantum dots (QDs). Although gold nanoparticles have been widely used for this purpose, other metals, such as silver, are also of interest and have more desirable plasmonic properties; however, silver nanoparticles suffer from chemical instability that gold nanoparticles do not. We find that this instability has the potential to limit the integration of silver nanoparticles (AgNPs) with QDs. Specifically, the common selection of thiol ligands for colloidal stabilization of QDs is incompatible with AgNPs, whether silver nanospheres or silver nanoplates. Equilibrium desorption of thiol ligands from QDs drives a pseudocatalytic process wherein the AgNPs are etched to produce silver(I)–ligand complexes, which then undergo cation exchange reactions at the QD leading to quenching of its photoluminescence (PL) through the introduction of long-lived trap states. We characterize this process through a combination of morphological, chemical, and steady-state and time-resolved spectroscopic measurements. The latter include extinction and absorption, PL emission intensity and lifetime, and transient absorption. Importantly, the etching and quenching process is avoided with QDs that are coated with an amphiphilic polymer instead of thiol ligands.
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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) | 0.000 | 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".