Defect-Engineered Competition Between Exciton Annihilation and Trapping in MOCVD WS2
Bibliographic record
Abstract
Exciton dynamics critically influence the optoelectronic performance of two-dimensional transition metal dichalcogenides (TMDCs). In large-scale WS2 monolayers grown via metal–organic chemical vapor deposition (MOCVD), intrinsic sulfur vacancies introduce in-gap states that promote nonradiative recombination through defect trapping (DT). Under elevated excitation conditions, the decay behaviour changes as exciton–exciton annihilation (EEA) emerges as a competing nonradiative process. To investigate these mechanisms across excitation regimes, we combine steady-state quantum efficiency measurements with femtosecond broadband transient absorption spectroscopy on samples with varying defect concentrations. These complementary measurements provide an unprecedented quantitative disentanglement of these decay pathways, a level of analysis not previously reported for MOCVD-grown monolayer WS2. The induced defect states are partially occupied, as first revealed by sub-bandgap excitation, and variations in defect density exert a pronounced influence on the photo-induced band renormalization. After establishing these DT-specific properties, we apply a rate-equation model including both DT and EEA to extract constants of 0.02 cm2/s and 0.1 cm2/s, followed by an in-depth exploration of their fundamentally diffusion-limited behaviour. The competition between DT and EEA can be set by a critical defect-to-exciton density ratio (≈ 3.5), which serves as the threshold for EEA activation. Moreover, at high exciton densities, defect saturation suppresses DT, reshaping the decay landscape. Overall, our findings provide detailed insights into defect-modulated exciton decay mechanisms and establish a quantitative framework for tailoring the optoelectronic properties of TMDCs via controlled defect engineering.
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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) | 0.000 | 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".