Water-Soluble CdSe Quantum Dots Passivated by a Multidentate Diblock Copolymer
Bibliographic record
Abstract
We describe a process for transferring trioctylphosphine oxide-stabilized CdSe semiconductor nanocrystals (CdSe/TOPO quantum dots, QDs) from toluene into water through a ligand exchange process with a poly(ethylene glycol- b -2- N,N -dimethylaminoethyl methacrylate) (PEG- b -PDMA) diblock copolymer. In this polymer, the DMA units serve as multidentate ligands for the QD. While we expected the protruding PEG layer to enhance the water-solubility of the polymer/QD adduct, it was not sufficient, upon initial exposure to the QDs, to impart water solubility. Residual TOPO groups remained at the particle surface. Multiple exchanges with the polymer accommodated the slow dissociation of TOPO groups from the CdSe surface, and allowed the particles to achieve water solubility (dispersibility). Transfer to water was accompanied by a 10-fold decrease in the quantum yield of photoluminescence (PL), but this intensity could be recovered by photoactivation. Before photoactivation, the QDs in water showed a small decrease in PL intensity as the solutions were warmed from 25 to 55 °C, which was fully reversible when the solutions were cooled. After photoactivation, the QDs showed a similar decrease in PL intensity upon warming, but only a fraction of this loss of intensity was recovered when the sample was cooled to room temperature. At high ionic strength (0.2−1.0 M NaCl) the PL emission intensity decreased, accompanied by an increase in trap emission at longer wavelength.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 source (direct Gemma or distilled Codex), 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".