Effects of RbI doping on perovskite film and photovoltaic performance
Bibliographic record
Abstract
Perovskite solar cells are brought into sharp focus by their high-power conversion efficiencies and low costs. However, their photovoltaic performances are severely limited by the defects of perovskite films. In this study, we composed perovskite films with a two-step method and doped RbI into the precursor solution. The morphologies of perovskite films were measured with scanning electron microscope (SEM), ultraviolet visible absorption spectroscopy (UV-Vis), Xray diffraction (XRD) and photoluminescence spectroscopy (PL), and photovoltaic properties were determined with solar simulator. The effects of different RbI concentrations on film morphology and photovoltaic performance of the perovskite films were investigated in this work. Results show that doping with RbI of low-concentration is instrumental to the grain size and film morphology and enhances the absorbance of 400-580 nm spectrum. However, high-concentration doping is detrimental to the perovskite film, resulting in more holes on surface and diminished absorbance. The best doping concentration of RbI is 10 mg/mL. Comparing with undoped perovskite film, the grain size increased from 500-1000 nm to 1-2 μm, and short-circuit current density is increased from 23.48 mA/cm2 to 23.73 mA/cm2, with 10 mg/mL RbI doped into the precursor solution. This study not only helps optimize the perovskite film morphology, but also help improve photovoltaic performance of perovskite 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 | 0.000 |
| 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) | 0.001 | 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".