Oxygen Driven Defect Engineering of Monolayer MoS<sub>2</sub> for Tunable Electronic, Optoelectronic, and Electrochemical Devices
Bibliographic record
Abstract
Abstract Molybdenum disulfide (MoS 2 ), a two‐dimensional (2D) semiconducting material harbors intrinsic defects that can be harnessed to achieve tuneable electronic, optoelectronic, and electrochemical devices. However, achieving precise control over defect formation within monolayer MoS 2 , remains a notable challenge. Here, an in‐situ defect engineering approach for monolayer MoS 2 using a pressure‐dependent chemical vapor deposition (CVD) process is presented. Monolayer MoS 2 grown in a low pressure CVD conditions (LP‐MoS 2 ) produces sulfur vacancy ( V s ) induced defect‐rich crystals primarily attributed to the oxygen‐deficient growth conditions. Conversely, atmospheric pressure CVD‐grown MoS 2 (AP‐MoS 2 ) passivates these defects with oxygen from ambient conditions. This disparity in defect profiles profoundly impacts crucial functional properties and device performance. AP‐MoS 2 shows a drastically enhanced photoluminescence, which is significantly quenched in LP‐MoS 2 attributed to in‐gap electron donor states induced by the V s defects. However, the n‐doping induced in LP‐MoS 2 generates enhanced photoresponsivity and detectivity in fabricated photodetectors compared to the AP‐MoS 2 ‐based devices. Defect‐rich LP‐MoS 2 outperforms AP‐MoS 2 as channel layers of field‐effect transistors (FETs), as well as electrocatalytic material for hydrogen evolution reaction (HER). This work presents a single‐step CVD approach for in situ defect engineering in monolayer MoS 2 and presents a pathway to control defects in other monolayer 2D materials.
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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".