Ultrasensitive Multilayer MoS<sub>2</sub>‐Based Photodetector with Permanently Grounded Gate Effect
Bibliographic record
Abstract
Abstract 2D materials, specifically MoS2 semiconductors, have received tremendous attention for photo‐sensing applications due to their tunable bandgap and low noise levels. A unique photodetector using multilayer MoS2 as the semiconductor channel, in which the gate electrode of the device is permanently connected to the grounded source electrode to introduce rectification, is reported. The proposed grounded‐gate photodiode exhibits high photoresponsivity of 1.031 A W−1, excellent photodetectivity (>6 × 1010 jones), and highly stable rise/fall time response (100–200 ms) under illumination of visible light (at the wavelengths of 405, 532, and 638 nm). Numerical device simulations using quantum transport methods and photoconductive effects are used to explain the device operation. It is also suggested that the gate metal work function can be carefully chosen to increase the sensitivity of the grounded‐gate photodetector by suppressing the dark current. The grounded‐gate device proposed, owing to the properties of rectifying behavior, low contact resistance, consistent photoresponsivity, and linear sensitivity, provides a new platform for next‐generation applications in the field of electronics and optoelectronics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".