Colloidal quantum dot solar cell power conversion efficiency optimization using analysis of current‐voltage characteristics and electrode contact imaging by lock‐in carrierography
Bibliographic record
Abstract
Abstract Although the power conversion efficiency (PCE) of colloidal quantum dot solar cells (CQDSCs) has increased sharply, researchers are struggling with the lack of comprehensive device efficiency optimization strategies, which retards significant progress in CQDSC improvement. This paper addresses this critical issue through analyzing the impact of colloidal quantum dot (CQD) carrier hopping mobility, bandgap energy, illumination intensity, and electrode/CQD interface on device performance to develop a guiding criterion for CQDSC PCE optimization. This general strategy has been used for the successful fabrication of high‐efficiency CQDSCs yielding certified PCEs as high as 11.28 %. A major experimental finding of this work is that the widely used constant photocurrent density (Jph) assumption is invalid as Jph is external‐voltage dependent due to the low carrier hopping mobility. Furthermore, the theoretical model developed herein predicts the nonmonotonic dependence of CQDSC PCE on carrier hopping mobility and bandgap energy, which were also demonstrated with the high‐efficiency CQDSCs. These results constitute a revision basis of the widespread belief that higher mobility and lower bandgap energy correspond to a higher CQDSC efficiency. Furthermore, electrode/CQD interface‐dependent surface recombination velocities were investigated in the framework of our abovementioned theoretical model using lock‐in carrierography, a contactless, large‐area frequency‐domain photocarrier diffusion‐wave imaging methodology that elucidated the carrier collection process at the electrodes through open‐circuit voltage distribution imaging. Lock‐in carrierography eliminates the limitations of today's widely used small‐spot (<0.1 cm2) testing methods which, however, raise questionable overall solar cell performance and stability estimations.
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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".