Arresting Ion Migration from the ETL Increases Stability in Infrared Light Detectors Based on III‐V Colloidal Quantum Dots
Bibliographic record
Abstract
Abstract III‐V colloidal quantum dots (CQDs) are of interest in infrared photodetection, and recent developments in CQDs synthesis and surface engineering have improved performance. Here this work investigates photodetector stability, finding that the diffusion of zinc ions from charge transport layers (CTLs) into the CQDs active layer increases trap density therein, leading to rapid and irreversible performance loss during operation. In an effort to prevent this, this work introduces organic blocking layers between the CQDs and ZnO layers; but these negatively impact device performance. The device is then, allowing to use a C60:BCP as top electron‐transport layer (ETL) for good morphology and process compatibility, and selecting NiO X as the bottom hole‐transport layer (HTL). The first round of NiO X ‐based devices show efficient light response but suffer from high leakage current and a low open‐circuit voltage (Voc) due to pinholes. This work introduces poly[bis(4‐phenyl) (2,4,6‐trimethylphenyl)amine] (PTAA) with NiO X NC to form a hybrid HTL, an addition that reduces pinhole formation, interfacial trap density, and bimolecular recombination, enhancing carrier harvesting. The photodetectors achieve 53% external quantum efficiency (EQE) at 970 nm at 1 V applied bias, and they maintain 95% of initial performance after 19 h of continuous illuminated operation. The photodetectors retain over 80% of performance after 80 days of shelf storage.
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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".