Synthesis of CIS Quantum Dots in Low-Temperature Regime: Effects of Precursor Composition and Temperature Ramps
Bibliographic record
Abstract
Copper Indium Sulfide (CIS) based quantum dots (QDs) have strong potential for future large-scale applications in photovoltaic, display, and optoelectronic sectors, partly due to their minimal toxicity. CIS QDs are usually grown at temperatures over 200 °C. In this work, we present a gram-scale, noninjection synthesis of CIS-based QDs in a low temperature regime and report, through systematic analyses, on the influence of temperature ramping profiles and precursor compositions on the properties of core CIS and core/shell CIS/ZnS QDs. It is established that the temperature ramp method is as important as the peak temperature itself. Nonabrupt temperature ramping results in core CIS QDs with more ligand coverage and fewer defects, and makes ZnS overgrowth possible even with lower Zn:S molar ratios. Precursor level molar ratios of 1:2 for Cu:In and 8:1 for Zn:S resulted in improved efficiency in CIS and CIS/ZnS QDs with strong and long-lived emissions in the 85-245 ns range. Implementing a dropwise addition of the ZnS precursor leads to prolonged QD extraction and shorter emission wavelengths. The different processes showed a wide range of tunability in the visible-to-IR range along with intense photoluminescence. The ZnS shell growth dependence on temperature ramping mode is explained through a mechanism for sulfur consumption, from the core or from the ligands' thiol groups. The low temperature regime processes, tunability for wide range of emissions, and identified pathways for long-lived emissions make these less-toxic, CIS-based QDs amenable to large area, scaled-up processing for 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) | 0.001 | 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".