Holistic Design of Charge Transfer Layers for Highly Efficient and Stable AgBiS<sub>2</sub> Quantum Dot Photodetectors
Bibliographic record
Abstract
Abstract Developing highly efficient and stable photodetectors based on eco‐friendly AgBiS 2 quantum dots (QDs) has garnered significant attention. However, optimizing charge transfer layers (CTLs) to enhance device performance and stability remains a critical challenge. Here, the study presents the development of highly efficient, stable, fully inorganic, self‐powered AgBiS 2 QD‐based photodetectors through the holistic design of CTLs, consisting of zinc‐copper‐indium‐sulfide QDs blended with black phosphorus nanosheets as hole‐transport layers, and unzipped carbon nanotubes doped with ZnO nanoparticles as electron‐transport layers. The rationally designed CTLs exhibit well‐matched energy‐level alignment with the AgBiS 2 QDs layer and balanced charge mobility, resulting in a robust and efficient charge transfer system. The optimized device exhibits a responsivity of 20 mA/W and a detectivity of 1.9 × 10 10 Jones at 1000 nm, among the best performance for heavy metal‐free QD‐based photodetectors. The all‐inorganic nature of the devices demonstrates excellent stability for over 2 months in air, with minimal degradation in performance. Furthermore, these enhanced self‐powered AgBiS 2 QD‐based photodetectors are used as light sensors in the receiver terminal of a near‐infrared optical communication system. This work presents a comprehensive approach to the holistic design of CTLs in AgBiS 2 QD‐based photodetectors for achieving superior device performance and long‐term stability.
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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) | 0.000 | 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".