Mapping the Exciton Diffusion in Semiconductor Nanocrystal Solids
Bibliographic record
Abstract
Colloidal nanocrystal solids represent an emerging class of functional materials that hold strong promise for device applications. The macroscopic properties of these disordered assemblies are determined by complex trajectories of exciton diffusion processes, which are still poorly understood. Owing to the lack of theoretical insight, experimental strategies for probing the exciton dynamics in quantum dot solids are in great demand. Here, we develop an experimental technique for mapping the motion of excitons in semiconductor nanocrystal films with a subdiffraction spatial sensitivity and a picosecond temporal resolution. This was accomplished by doping PbS nanocrystal solids with metal nanoparticles that force the exciton dissociation at known distances from their birth. The optical signature of the exciton motion was then inferred from the changes in the emission lifetime, which was mapped to the location of exciton quenching sites. By correlating the metal-metal interparticle distance in the film with corresponding changes in the emission lifetime, we could obtain important transport characteristics, including the exciton diffusion length, the number of predissociation hops, the rate of interparticle energy transfer, and the exciton diffusivity. The benefits of this approach to device applications were demonstrated through the use of two representative film morphologies featuring weak and strong interparticle coupling.
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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".