Measuring Charge Carrier Diffusion in Coupled Colloidal Quantum Dot Solids
Bibliographic record
Abstract
Colloidal quantum dots (CQDs) are attractive materials for inexpensive, room-temperature-, and solution-processed optoelectronic devices. A high carrier diffusion length is desirable for many CQD device applications. In this work we develop two new experimental methods to investigate charge carrier diffusion in coupled CQD solids under charge-neutral, i.e., undepleted, conditions. The methods take advantage of the quantum-size-effect tunability of our materials, utilizing a smaller-bandgap population of quantum dots as a reporter system. We develop analytical models of diffusion in 1D and 3D structures that allow direct extraction of diffusion length from convenient parametric plots and purely optical measurements. We measure several CQD solids fabricated using a number of distinct methods and having significantly different doping and surface ligand treatments. We find that CQD materials recently reported to achieve a certified power conversion efficiency of 7% with hybrid organic-inorganic passivation have a diffusion length of 80 ± 10 nm. The model further allows us to extract the lifetime, trap density, mobility, and diffusion coefficient independently in each material system. This work will facilitate further progress in extending the diffusion length, ultimately leading to high-quality CQD solid semiconducting materials and improved CQD optoelectronic devices, including CQD solar cells.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".