Transient Measurements and Simulations Correlate Exchange Ligand Concentration and Trap States in Colloidal Quantum Dot Photodetectors
Bibliographic record
Abstract
Colloidal quantum dot (CQD) photodetectors (PDs) can detect wavelengths longer than the 1100 nm limit of silicon because of their highly tunable bandgaps. CQD PDs are acutely affected by the ligands that separate adjacent dots in a CQD-solid. Optimizing the exchange solution ligand concentration in the processing steps is crucial to achieving high photodetector performance. However, the complex mix of chemistry and optoelectronics involved in CQD PDs means that the effects of the exchange solution ligand concentration on device physics are poorly understood. Here we report direct correspondence between simulated and experimental transient photocurrent responses in CQD PDs. For both deficient and excess conditions, our model demonstrated the experimental changes to the transient photocurrent aligned with changes in trap state density. Combining transient photoluminescence, absorption, and photocurrent with this simulation model, we revealed that different mechanisms are responsible for the increased trap density induced by excess and deficient active layer ligand concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".