The detention performance enhancement of single gallium arsenide nanowire photodetector by nitrogen plasma treatment
Bibliographic record
Abstract
Abstract Sensors based on gallium arsenide (GaAs) nanowires (NWs) have excellent sensitivity and can directly detect individual virus particles and individual DNA molecules. GaAs NW photodetectors have attracted wildly attentions due to their direct band gap, specific surface area and high optical absorption coefficient. However, GaAs NWs suffer the problem of serious surface states, leading to the development of high performance GaAs NW photodetectors. Plasma treatment with inert gas is one of the important methods to reduce the density of surface states. In this paper, the effect of nitrogen plasma treatment on the performance of GaAs NW photodetector is investigated. The results show that the light current of GaAs NW photodetector is obviously increased. Besides, the responsivity, specific detectivity and external quantum efficiency (EQE) are also improved. Under 808 nm laser with the light intensity is 2966.8 mW cm−2, the responsivity changes from 56.4 A W−1 to 117.6 A W−1 and 18.7 A W−1, the specific detectivity changes from 4.7 × 1011 Jones to 9.9 × 1011 Jones and 3.2 × 1011 Jones, the EQE changes from 13 146% to 27 434% and 4359% when the nitrogen plasma treatment time is 0 s, 10 s and 20 s, respectively. This may be related to the defect states density of NWs is changed after nitrogen plasma treatment. This work promotes the further development of GaAs NW photodetectors for biosensing techniques.
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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".