High-Performance Csgebr<sub>3</sub> Perovskite/ WS<sub>2</sub> Nano-Flakes Field-Effect Transistor In Vacuum
Bibliographic record
Abstract
In this research, due to the non-toxic nature of inorganic perovskite CsGeBr3 (CGB), this material is applied to fabricate a CGB/WS2 FET by drop-casting of lead free perovskite CGB on the WS2 NFs FET.Then the electrical characterization of a hybrid lead free CsGeBr3 perovskite/ WS2 Nano-Flakes (NFs) Field-Effect transistor is investigated in ambient air and vacuum conditions with a simple back-gate device structure.The characterization of the fabricated device has been performed under the illumination of a laser source with a wavelength of ~532 nm and power of 9.13 mWcm -2 out and inside the vacuum chamber at a constant pressure of ~3×10 -3 mbar.By comparing devices in and out of vacuum.It is apparent that atmospheric adsorbates on CGB/WS2 NFs FET degrade external quantum efficiency, photo-responsivity, and detectivity doubled and reached 2.9%, 0.012 AW -1 , and 4.15×106 Jones in comparison with the WS2 NFs FET in the atmosphere.The results demonstrate that the observed shift in threshold voltage suggests the Ids of the CGB/WS2 NFs FET in vacuum conditions have increased compared to the WS2-NFs FET in the atmosphere.Most importantly, the vacuum plays a significant role and shows the superior photo-sensing properties of the CGB/WS2 NFs FET in vacuum are very promising to extend the hybrid heterostructure optoelectronic devices with excellent performance, especially in space.
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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 | 0.001 |
| 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".