Nano-patterning on multilayer MoS2 via block copolymer lithography for highly sensitive and responsive phototransistors
Bibliographic record
Abstract
Abstract Indirect bandgap of multilayer molybdenum disulfide has been recognized as a major hindrance to high responsivity of MoS 2 phototransistors. Here, to overcome this fundamental limitation, we propose a structural engineering of MoS 2 via nano-patterning using block copolymer lithography. The fabricated nanoporous MoS 2 , consisting of periodic hexagonal arrays of hexagon nanoholes, includes abundant edges having a zigzag configuration of atomic columns with molybdenum and sulfur atoms. These exposed zigzag edges are responsible for multiple trap states in the bandgap region, as confirmed by photo-excited charge-collection spectroscopy measurements on multilayer nanoporous MoS 2 phototransistors, showing that in-gap states only near the valence band can result in a photogating effect. The effect of nano-patterning is therefore to significantly enhance the responsivity of multilayer nanoporous MoS 2 phototransistors, exhibiting an ultra-high photoresponsivity of 622.2 A W −1 . Our nano-patterning of MoS 2 for photosensing application paves a route to structural engineering of two-dimensional materials for highly sensitive and responsive optoelectronic 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".