Liquid Metal‐Exfoliated SnO<sub>2</sub>‐Based Mixed‐Dimensional Heterostructures for Visible‐to‐Near‐Infrared Photodetection
Bibliographic record
Abstract
Abstract Ultra‐thin 2D materials have gain significant attention for making next‐generation optoelectronic devices. Here, a large‐area heterojunction photodetector is fabricated using a liquid metal‐printed 2D SnO 2 layer transferred onto CdTe thin films. The resulting device demonstrates efficient broadband light sensing from visible to near‐infrared wavelengths, with enhanced detectivity and faster photo response. Significantly, the device shows a ≈10 5 ‐fold increase in current than the dark current level when illuminated with a 780 nm laser and achieves a specific detectivity of ≈10 12 Jones, nearly two orders of magnitude higher than that of a standalone CdTe device. Additionally, temperature‐dependent optoelectronic testing shows that the device maintains a stable response up to 140 °C and generates distinctive photocurrent at temperatures up to 80 °C, demonstrating its thermal stability. Through band structure analysis, DFT calculations, and photocurrent mapping, the formation of a p‐n junction is confirmed, which enhances carrier separation via the built‐in potential, significantly boosting photoresponse. These results highlight the potential of liquid metal‐derived 2D materials in heterostructure integration, paving the way for advanced optoelectronic applications.
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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".