Modulation of photocarrier relaxation dynamics in two-dimensional semiconductors
Bibliographic record
Abstract
Due to strong Coulomb interactions, two-dimensional (2D) semiconductors can support excitons with large binding energies and complex many-particle states. Their strong light-matter coupling and emerging excitonic phenomena make them potential candidates for next-generation optoelectronic and valleytronic devices. The relaxation dynamics of optically excited states are a key ingredient of excitonic physics and directly impact the quantum efficiency and operating bandwidth of most photonic devices. Here, we summarize recent efforts in probing and modulating the photocarrier relaxation dynamics in 2D semiconductors. We classify these results according to the relaxation pathways or mechanisms they are associated with. The approaches discussed include both tailoring sample properties, such as the defect distribution and band structure, and applying external stimuli such as electric fields and mechanical strain. Particular emphasis is placed on discussing how the unique features of 2D semiconductors, including enhanced Coulomb interactions, sensitivity to the surrounding environment, flexible van der Waals (vdW) heterostructure construction, and non-degenerate valley/spin index of 2D transition metal dichalcogenides (TMDs), manifest themselves during photocarrier relaxation and how they can be manipulated. The extensive physical mechanisms that can be used to modulate photocarrier relaxation dynamics are instrumental for understanding and utilizing excitonic states in 2D semiconductors.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".