Multilayer WSe<sub>2</sub>/ZnO heterojunctions for self-powered, broadband, and high-speed photodetectors
Bibliographic record
Abstract
Abstract Self-powered broadband photodetectors have attracted great interest due to their applications in biomedical imaging, integrated circuits, wireless communication systems, and optical switches. Recently, significant research is being carried out to develop high-performance self-powered photodetectors based on thin 2D materials and their heterostructures due to their unique optoelectronic properties. Herein, a vertical heterostructure based on p-type 2D WSe2 and n-type thin film ZnO is realized for photodetectors with a broadband response in the wavelength range of 300–850 nm. Due to the formation of a built-in electric field at the WSe2/ZnO interface and the photovoltaic effect, this structure exhibits a rectifying behavior with a maximum photoresponsivity and detectivity of ∼131 mA W−1 and ∼3.92 × 1010 Jones, respectively, under an incident light wavelength of λ = 300 nm at zero voltage bias. It also shows a 3-dB cut-off frequency of ∼300 Hz along with a fast response time of ∼496 μs, making it suitable for high-speed self-powered optoelectronic applications. Furthermore, the facilitation of charge collection under reverse voltage bias results in a photoresponsivity as high as ∼7160 mA W−1 and a large detectivity of ∼1.18 × 1011 Jones at a bias voltage of −5 V. Hence, the p-WSe2/n-ZnO heterojunction is proposed as an excellent candidate for high-performance, self-powered, and broadband photodetectors.
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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".