Filterless narrowband photodetectors enabled by controllable band modulation through ion migration: The case of halide perovskites
Bibliographic record
Abstract
Abstract Narrowband photodetectors conventionally rely on optical structure design or bandpass filters to achieve the narrowband regime. Recently, a strategy for filterless narrowband photoresponse based on the charge collection narrowing (CCN) mechanism was reported. However, the CCN strategy requires an electrically and optically "thick" photoactive layer, which poses challenges in controlling the narrowband photoresponse. Here we propose a novel strategy for constructing narrowband photodetectors by leveraging the inherent ion migration in perovskites, which we term "band modulation narrowing" (BMN). By manipulating the ion migration with external stimuli such as illumination, temperature, and bias voltage, we can regulate in situ the energy‐band structure of perovskite photodetectors (PPDs) and hence their spectral response. Combining the Fermi energy levels obtained by the Kelvin probe force microscopy, the internal potential profiles from solar cell capacitance simulator simulation, and the anion accumulation revealed by the transient ion‐drift technique, we discover two critical mechanisms behind our BMN strategy: the extension of an optically active but electronically dead region proximal to the top electrode and the down‐bending energy bands near the electron transport layer. Our findings offer a case for harnessing the often‐annoying ion migration for developing advanced narrowband PPDs. image
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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".