Electron‐Transport Layers Employing Strongly Bound Ligands Enhance Stability in Colloidal Quantum Dot Infrared Photodetectors
Bibliographic record
Abstract
Abstract Solution‐processed photodetectors based on colloidal quantum dots (CQDs) are promising candidates for short‐wavelength infrared light sensing applications. Present‐day CQD photodetectors employ a CQD active layer sandwiched between carrier‐transport layers in which the electron‐transport layer (ETL) is composed of metal oxides. Herein, a new class of ETLs is developed using n‐type CQDs, finding that these benefit from quantum‐size effect tuning of the band energies, as well as from surface ligand engineering. Photodetectors operating at 1450 nm are demonstrated using CQDs with tailored functionalities for each of the transport layers and the active layer. By optimizing the band alignment between the ETL and the active layer, CQD photodetectors that combine a low dark current of ≈1 × 10 −3 mA cm −2 with a high external quantum efficiency of ≈66% at 1 V are reported, outperforming prior reports of CQD photodetectors operating at >1400 nm that rely on metal oxides as ETLs. It is shown that stable CQD photodetectors rely on well‐passivated CQDs: for ETL CQDs, a strongly bound organic ligand trans ‐4‐(trifluoromethyl)cinnamic acid (TFCA) provides improved passivation compared to the weakly bound inorganic ligand tetrabutylammonium iodide (TBAI). TFCA suppresses bias‐induced ion migration inside the ETL and improves the operating stability of photodetectors by 50× compared to TBAI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".