Spatial Collection in Colloidal Quantum Dot Solar Cells
Bibliographic record
Abstract
Abstract In thin‐film photovoltaic (PV) research and development, it is of interest to determine where the chief losses are occurring within the active layer. Herein, a method is developed and presented by which the spatial distribution of charge collection, operando, is ascertained, and its application in colloidal quantum dot (CQD) solar cells is demonstrated at a wide range of relevant bias conditions. A systematic computational method that relies only on knowledge of measured optical parameters and bias‐dependent external quantum efficiency spectra is implemented. It is found that, in CQD PV devices, the region near the thiol‐treated hole‐transport layer suffers from low collection efficiency, as a result of bad band alignment at this interface. The active layer is not fully depleted at short‐circuit conditions, and this accounts for the limited short‐circuit current of these CQD solar cells. The high collection efficiency outside of the depleted region agrees with a diffusion length on the order of hundreds of nanometers. The method provides a quantitative tool to study the operating principles and the physical origins of losses in CQD solar cells, and can be deployed in thin‐film solar cell device architectures based on perovskites, organics, CQDs, and combinations of these materials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.013 | 0.003 |
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; both teacher heads agree on what is shown here.
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".