High Operating Temperature and Low Power Consumption Boron Nitride Nanosheets Based Broadband UV Photodetector
Bibliographic record
Abstract
We extend our work on the use of digitally controlled pulsed laser plasma deposition (PLPD) technique to synthesize high quality, 2-dimensional single crystalline boron nitride nanosheets (BNNSs) at a low substrate temperature for applications in high-performance deep UV photodetectors. The obtained sample consists of a large amount of BNNSs partially overlapping one another with random orientations. Each sheet is composed of a few (from 2 to 10) stacked atomic layers exhibiting high transparency due to its highly ordered hBN crystallinity. Deep UV detectors based on the obtained BNNSs were designed, fabricated, and tested. The bias and temperature effects on the photocurrent strength and the signal-to-noise ratio have been carefully characterized and discussed. A significant shift in the cut off wavelength of the BNNSs based photodetectors was observed suggesting a band gap reduction as a result of the BNNSs' collective structure. The newly designed photodetector presented exceptional properties: a high sensitivity to weak intensities of radiation in both UVC and UVB range while remaining visible-blind, and a high signal-to-noise ratio operation even at temperatures as high as 400 °C. In addition, the BNNSs based photodetector exhibited potential for self-powered operation.
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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".