Carbon Electrodes for Perovskite Photovoltaics: Interfacial Properties, Meta‐analysis, and Prospects
Bibliographic record
Abstract
Carbon electrodes have gained significant attention as a cost‐effective, sustainable, stable, and scalable replacement for metal electrodes in perovskite solar cells (PSCs). However, traditional carbon‐electrode‐based PSCs (C‐PSCs) lack a hole‐selective layer (HSL) due to their incompatibility with the most effective organic HSLs employed in the PSC literature. In turn, the absence of an HSL has been identified as one of the main factors hindering the performance of C‐PSCs. Consequently, numerous studies have recognized the pivotal significance of the region between the perovskite absorber and the carbon electrode in C‐PSCs, proposing various interfacial engineering strategies to improve the performance of these solar cells. Given the rapid evolution of this field, an up‐to‐date and comprehensive review of C‐PSCs is in order. Key areas of focus of this review include the shift from high‐temperature to low‐temperature carbon electrodes, strategies to improve energetic alignment at the interface, novel approaches such as hole‐selective bilayers, and alternative carbon deposition methods to reduce solvent damage. Additionally, this review presents a comprehensive meta‐analysis—the first of its kind in the C‐PSC literature—to assess how various interfacial modifications impact critical C‐PSC performance metrics, offering valuable insights for future advancements in the field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".