On-off QD switch that memorizes past recovery from quenching by diazonium salts
Bibliographic record
Abstract
The understanding of the interaction of CdSe/ZnS semiconductor quantum dots (QD) with their chemical environment is fundamental, yet far from being fully understood. p-Methylphenyldiazonium tetrafluoroborate has been used to get some insight into the effect of diazonium salts on the spectroscopy of QD. Our study reveals that the surface of CdSe/ZnS quantum dots can be modified by diazonium salts (although not functionalized), showing and on-off fluorescence behaviour that memorizes past quenching recoveries. Facile modification of the surface confers protection against quenching by new molecules of diazonium salt and other known quenchers such as 4-amino-TEMPO. The reaction mechanism has been explored in detail by using different spectroscopic techniques. At the first time after addition of diazonium salt over QD the fluorescent is turned off with Stern-Volmer behaviour; the fluorescence recovers following irradiation. Subsequent additions of diazonium salts do not cause the same degree of quenching. We have noted that the third addition (following two cycles of addition and irradiation) is unable to quench the fluorescence. Monitoring the process using NMR techniques reveals the formation of p-difluoroborane toluene as a result of the irradiation of diazonium-treated QD; the treatment leads to the fluorination of the QD surface.
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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) | 0.002 | 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".