The Donor–Supply Electrode Enhances Performance in Colloidal Quantum Dot Solar Cells
Bibliographic record
Abstract
Colloidal quantum dot (CQD) solar cells combine solution-processability with quantum-size-effect tunability for low-cost harvesting of the sun's broad visible and infrared spectrum. The highest-performing colloidal quantum dot solar cells have, to date, relied on a depleted-heterojunction architecture in which an n-type transparent metal oxide such as TiO2 induces a depletion region in the p-type CQD solid. These devices have, until now, been limited by a modest depletion region depth produced in the CQD solid owing to limitations in the doping available in TiO2. Herein we report a new device geometry-one based on a donor-supply electrode (DSE)-that leads to record-performing CQD photovoltaic devices. Only by employing this new charge-extracting approach do we deepen the depletion region in the CQD solid and thereby extract notably more photocarriers, the key element in achieving record photocurrent and device performance. With the use of optoelectronic modeling corroborated by experiment, we develop the guidelines for building a superior CQD solar cell based on the DSE concept. We confirm that using a shallow-work-function terminal electrode is essential to producing improved charge extraction and enhanced performance.
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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 | 0.001 | 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".