Epitaxial Interface‐Driven Photoresponse Enhancement in Monolayer WS <sub>2</sub> –MoS <sub>2</sub> Lateral Heterostructures
Bibliographic record
Abstract
Abstract 2D transition metal dichalcogenides heterostructures are driving advancements in next‐generation optoelectronic technologies. Lateral 2D heterojunctions with atomically seamless interfaces play a vital role in modulating charge separation and carrier dynamics, yet underlying transport mechanisms remain inadequately understood, limiting practical deployment. Here, monolayer WS 2 ‐MoS 2 lateral edge‐epitaxial heterostructures synthesized via chemical vapor deposition (CVD), providing critical insights into heterointerface effects on charge distribution and photoresponse are reported. Photodetector fabricated from this heterostructures exhibit broadband spectral response from ultraviolet to near‐infrared, achieving peak responsivity of 1850 mA W −1 and detectivity of 4.36 × 10 11 Jones under 565 nm illumination. This represents ≈200% enhancement compared to individual monolayer MoS 2 or WS 2 devices, directly demonstrating the synergistic benefits of lateral heterostructure engineering. Spatially resolved surface potential mapping and second‐harmonic generation imaging reveal that enhanced performance originates at the epitaxial interface, confirming the critical role of interfacial electric fields and nonlinear optical effects in charge carrier dynamics. The characterization provides direct experimental evidence linking atomically seamless interface properties to macroscopic device performance enhancements. These findings underscore the significant potential of CVD‐grown WS 2 ‐MoS 2 lateral heterostructures for high‐performance photodetectors and establish interface engineering as a powerful strategy for advancing 2D semiconductor device technologies.
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 | 0.000 | 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".