InP-Quantum-Dot-in-ZnS-Matrix Solids for Thermal and Air Stability
Bibliographic record
Abstract
InP/ZnS core/shell colloidal quantum dots (QDs) are promising candidates as Cd- and Pb-free emitters owing to their high photoluminescence quantum yield, narrow emission linewidth, and color tunability in the visible range. However, the stability of QD solid films remains an issue: they are vulnerable to oxygen, moisture, and heat. Here, we report the encapsulation of InP/ZnS QDs in a lattice-matched ZnS matrix. We do so by developing a biphasic ligand exchange and uniting it with a sol–gel film assembly. Conventional ZnS precursors, which rely on neutral thiourea, fail to stabilize the QDs in highly polar solvents, leading to their agglomeration prior to matrix formation. Here, we substitute thiourea with ammonium thiocyanate—an ionic compound isomer of thiourea—to stabilize InP/ZnS QDs in a ZnS matrix precursor solution. The stabilized QD and precursor solution is then cast and annealed to trigger the formation of a homogeneous and robust ZnS matrix that protects the QDs. The resulting QD solid films show invariant optical properties following annealing at 200 °C for 1 h under ambient conditions, under continuous laser excitation at 60 mW cm –2 for 180 min, and also after a month of storage under ambient conditions. In contrast, the control film exhibits significant degradation of its optical properties after annealing at 200 °C for 1 h, under laser excitation within 20 min, and under ambient conditions within 10 days.
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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 | 0.001 |
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".