Ligand Exchange Functionalization of CIS Quantum Dots for CIS/ZnO Film Heterojunctions
Bibliographic record
Abstract
Colloidal quantum dots (QD) are rapidly making their way into several optoelectronic applications. While Cd- and Pb-based QDs are promising for high-performance devices, toxicity remains a concern. In this regard, Cu-In-S (CIS) QDs provide an alternative option for scalable, commercial production. A low-temperature, high-throughput process was used to synthesize CIS QDs with 1-dodecanethiol (DDT) ligands. While QD synthesis with a DDT ligand is facile, there are drawbacks when it comes to device implementation and interfacing with electron transport films such as ZnO. A ligand exchange process was employed to replace the DDT ligands in the as-synthesized QDs with 3-mercaptopropionic acid (MPA); owing to the short and bifunctional nature of the MPA molecules, this process improved the surface adhesion and carrier transport to ZnO. I-V measurements on planar structures showed the significance of ligand exchange in CIS QDs for heterojunction device implementation.
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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".