Van der Waals Multi‐Heterostructures (PN, PIN, and NPN) for Dynamic Rectification in 2D Materials
Bibliographic record
Abstract
Abstract Here, van der Waals multi‐heterojunctions (PN, NP, PIN, and NPN) are fabricated by stacking of MoTe 2 , hexagonal boron nitride (h‐BN), and MoSe 2 nanoflakes using a mechanical‐exfoliation technique where the dynamic rectification is examined. Low‐resistance metal contacts Al/Au and Pt/Au are applied to MoSe 2 and MoTe 2 , respectively, and gate‐dependent rectifying behavior is achieved, with a rectification ratio of up to 10 5 in PN devices. It is found that the performance of the device is enhanced by placing an interfacial layer h‐BN between two opposite layers of 2D materials where the rectification ratio is found to be >10 6 with the ideality factor ≈1.3 in the PIN devices. Also, using the conventional Richardson's plot, the barrier heights of PN and PIN diodes are calculated to be 260 and 490 meV at zero gate bias, respectively. As well, the devices exhibit good performance with a built‐in electric field observed in both PN and PIN diodes, which gives rise to an open‐circuit voltage ( V oc ) and short‐circuit current ( I sc ) under zero external bias. Remarkably, it is found that the performance of the devices also gets better by forming double heterojunction (NPN) layer than PN or NP layers. The device is also tested for a rectification application, and it successfully rectifies an input alternating‐current signal. These findings are important for the development of nano‐ and optoelectronics devices.
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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".