Effects of Multifunctional Interlayers on the Performance of Perovskite Solar Cells
Bibliographic record
Abstract
The development of perovskite solar cell (PSC) is one of the major advances in photovoltaic technology in recent years due to its lower cost, simple fabrication process, lightweight, sustainability, and being environmentally friendly. However, the power conversion efficiency (PCE) of the conventional PSC is still lower for commercialization. The efficiency of PSC has increased from 5% to 25% within the past few years, where introducing interlayer shows a significant improvement in PCE. In this paper, we numerically investigate the effects of different combinations of interlayers on the performance of PSC using gpvdm simulation software. Three different PCEs of 28.08%, 17.89%and 25.62%are found for three different interlayer combinations i.e., (i) Model A (TIO2/PMMA), (ii) Model B (V2O5/PEDOTPSS) and (iii) Model C (P3HT/ spiroMeOTAD). Both optical and electrical characteristics of different interlayers-based PSCs are investigated by using the properties of different interlayer materials in the simulation. Additionally, the photon distribution, their absorption, and consequent charge carrier generation are investigated while modifying the layer thickness of each unit. This study could be helpful for optimizing the electron and hole transport materials-based interlayers of the PSC.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".