UV photodetector based on energy bandgap shifted hexagonal boron nitride nanosheets for high-temperature environments
Bibliographic record
Abstract
Abstract We extend our investigations in the use of boron nitride nanosheets (BNNSs) as sensing material for UV photodetectors by exploring the energy bandgap shift in a new BNNSs arrangement on silicon substrate produced by a pulsed laser plasma deposition (PLPD) technique. Characterizations by XRD and Raman spectrum analysis indicate that the material is composed of high purity hexagonal boron nitride ( h BN). SEM and AFM images confirm this particular arrangement of BNNSs is made of randomly orientated h BN nanosheets. The BNNS on silicon substrate material exhibits higher conductivity and photosensitivity in deep UV region than previously investigated BNNS thin films. The material is also sensitive to the UVB region, indicative of having a lower band gap width than that of bulk or thin films, while remaining visible-blind. The observed decrease in cut-off frequency was a direct result of the structural arrangement of the BNNSs in the film. This has the advantage of avoiding doping in order to tune bandgap width, which can compromise intrinsic desirable properties of h BN. Additionally, the material performed extremely well as a UV photodetector even at temperatures as high as 400 °C, making this particular arrangement of BNNSs an ideal candidate for applications where UV sensing in high-temperature environments is required.
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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".