Effect of CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub>thickness on device efficiency in planar heterojunction perovskite solar cells
Bibliographic record
Abstract
Recent advances in the development of perovskite solar cells based on CH3NH3PbI3 have produced devices with power conversion efficiencies of >15%. While initial work in this area assumed that the perovskite-based cells required a mesoporous TiO2 support, many recent reports have instead focused on the development of planar heterojunction structures. A better understanding of how both cell architecture and various design parameters (e.g., perovskite thickness and morphology) affect cell performance is needed. Here, we report the fabrication of perovskite solar cells based on a ZnO nanoparticle electron transport layer, CH3NH3PbI3 light absorber, and poly(3-hexylthiophene) (P3HT) hole transport layer. We show that vapor-phase deposition of the PbI2 precursor film produces devices with performances equivalent to those prepared using entirely solution-based techniques, but with very precise control over the thickness and morphology of the CH3NH3PbI3 layer. Optimization of the layer thickness yielded devices with efficiencies of up to 11.3%. The results further demonstrate that a delicate balance between light absorption and carrier transport is required in these planar heterojunction devices, with the thickest perovskite films producing only very low power conversion efficiencies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".