Liquid‐Metal Synthesized Ultrathin SnS Layers for High‐Performance Broadband Photodetectors
Bibliographic record
Abstract
Abstract Atomically thin materials face an ongoing challenge of scalability, hampering practical deployment despite their fascinating properties. Tin monosulfide (SnS), a low‐cost, naturally abundant layered material with a tunable bandgap, displays properties of superior carrier mobility and large absorption coefficient at atomic thicknesses, making it attractive for electronics and optoelectronics. However, the lack of successful synthesis techniques to prepare large‐area and stoichiometric atomically thin SnS layers (mainly due to the strong interlayer interactions) has prevented exploration of these properties for versatile applications. Here, SnS layers are printed with thicknesses varying from a single unit cell (0.8 nm) to multiple stacked unit cells (≈1.8 nm) synthesized from metallic liquid tin, with lateral dimensions on the millimeter scale. It is reveal that these large‐area SnS layers exhibit a broadband spectral response ranging from deep‐ultraviolet (UV) to near‐infrared (NIR) wavelengths (i.e., 280–850 nm) with fast photodetection capabilities. For single‐unit‐cell‐thick layered SnS, the photodetectors show upto three orders of magnitude higher responsivity (927 A W −1 ) than commercial photodetectors at a room‐temperature operating wavelength of 660 nm. This study opens a new pathway to synthesize reproduceable nanosheets of large lateral sizes for broadband, high‐performance photodetectors. It also provides important technological implications for scalable applications in integrated optoelectronic circuits, sensing, and biomedical imaging.
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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".