The Role of Carbon Allotrope-Based Charge Transport Layers in Enhancing the Performance of Perovskite Solar Cells
Bibliographic record
Abstract
Third generation solar cells, such as organic photovoltaics, dye-sensitized solar cells, and most recently perovskite solar cells (PSCs), have emerged as low-cost solutions compared with commercial silicon-based technologies. The main drawbacks toward the commercialization of PSCs are the long-term stability of the devices, and the use of expensive materials, such as noble metals, and polymers that limit scale-up. Some commonly used charge transport materials have a detrimental effect on the perovskite layer, which increase the degradation of the perovskite under UV radiation, thermal stress, or in the presence of moisture. To improve performance and reduce cost, the incorporation of new materials and processing techniques are being actively pursued. Carbonaceous materials have been proposed for such purposes, owing to their exceptional electrical, optical, thermal, and mechanical properties. The synergy between the properties of metal halide perovskite semiconductors and carbon allotropes has recently been revealed and has contributed toward the realization of PSCs with impressive efficiencies and operational stability. In this chapter, we first briefly introduce the different structures of carbon allotropes, then we describe how these materials can be integrated in different charge transport layers used in PSCs, highlighting their roles in enhancing performance and stability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".