Ion Migration as a New Paradigm to Boost Self‐Driven Perovskite Narrowband Photodetectors
Bibliographic record
Abstract
Abstract Halide perovskite narrowband photodetectors based on a charge collection narrowing mechanism have emerged as a new class of optoelectronic devices for monochromatic imaging. However, improving the figures‐of‐merit of such narrowband photodetectors remains challenging due to the inability to manipulate the major material players in the elusive photoresponse process. Here, a novel approach of manipulating ion migration to enhance the narrowband photoresponse of self‐driven p‐i‐n type photodetectors is taken by intentionally adding mobile ions into the formamidine and methylamine mixed cation perovskite layer. The excess mobile ions reduce the activation energy of ion migration, and this facilitated migration orchestrates the ions in the perovskite layer to re‐engineer the energy band, and thus modulates the charge separation and collection energetics and kinetics, leading to an unprecedented boost of the narrowband photoresponse. The photodetector based on this approach achieved a peak responsivity of as high as 112.41 mA W−1 at 820 nm at zero bias with a full‐width at half maximum of only 22 nm and an over 3‐fold improvement in the spectral rejection ratio, making it highly promising for the next‐generation color imaging devices.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".