Molecular‐Additive‐Assisted Tellurium Homogenization in ZnSeTe Quantum Dots
Bibliographic record
Abstract
Abstract Addition of aqueous hydrohalic acids during the synthesis of colloidal quantum dots (QDs) is widely employed to achieve high‐quality QDs. However, this reliance on the use of aqueous solutions is incompatible with oxygen‐ and water‐sensitive precursors such as those used in the synthesis of Te‐alloyed ZnSe QDs. Herein, it is shown that this incompatibility leads to phase segregation into Te‐rich and Te‐poor regions, causing spectral broadening and luminescence peak shifting under high laser irradiation and applied electrical bias. Here, a synthetic strategy to produce anhydrous‐HF in situ by using benzenecarbonyl fluoride (BF) as a chemical additive is reported. Through in situ 19 F NMR spectroscopy, it is found that BF reacts with surfactants in tandem, ultimately producing intermediary F···H···trioctylamine adducts. These act as a pseudo‐HF source that releases anhydrous HF. The controlled release of HF during nucleation and growth steps homogenizes Te distribution in ZnSeTe lattice, leading to spectrally stable blue‐emitting QDs under increasing laser flux from ≈3 µW to ≈12 mW and applied bias from 2.6 to 10 V. Single‐dot photoluminescence (PL) spectroscopy and analyses of the absorption, PL and transient absorption spectra together with density functional theory point to the role of anhydrous HF as a Te homogenizer.
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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) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".