Emission and Decay Lifetime Tunability in Less-Toxic Quaternary ZnCuInS Quantum Dots
Bibliographic record
Abstract
Due to the increasing need for the use of safer nanomaterials in the electronic and optoelectronic sectors, there is a growing emphasis on the development of less-toxic quantum dots (QD) as replacements for traditional cadmium or lead-based QD technologies. Although the ternary Cu-In-S (CIS) QDs have gained prominence due to their advantageous optical properties and unique tunability, their intrinsically defective structures present some drawbacks for device applications. A one-pot, high-throughput process was developed to synthesize quaternary Zn-Cu-In-S (ZCIS) QDs, which show higher structural crystallinity and lattice stability. Stoichiometric variation was explored to determine its impact on QD optical and structural properties. It was found that the trial-cationic structure of the quaternary ZCIS QDs allowed further variation and flexibility of QD properties. Induction of Cu deficiency via In- or Zn-excess precursor ratios improved photoluminescence (PL) intensity and structural stability. ZnS overcoating was investigated, but it was found that the Zn-containing ZCIS cores, which are more stable than core CIS QDs, benefitted only minimally from Zn-passivation. The highly emissive quaternary QDs produced through this scalable synthesis procedure are widely tunable and functionalizable, capable of emission from 564-650 nm, and with a high lifetime up to 159 ns, making them more suitable for a variety of device applications.
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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) | 0.000 | 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 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".