2D Composite Materials for Electrodes in Dye-Sensitized Solar Cells─An Overview
Bibliographic record
Abstract
Dye-sensitized solar cells (DSSCs) are anticipated to become economical, efficient, and commercially viable due to their simple fabrication, environmental friendliness, low-light performance, and flexibility for product integration. However, inherent voltage loss during the sensitizing dye regeneration process, uneven titanium layer deposition, electrolyte filling, and electrical interconnections contribute to the lower efficiency of DSSCs compared to conventional silicon solar cells. Researchers have focused on optimizing material and structural properties to address these issues, achieving record-setting efficiencies of up to 14%. Recently, incorporating 2D materials, such as graphene and transition metal carbides or nitrides (MXenes), has been studied to improve DSSC performance and stability. 2D materials can enhance the photoanode's diffuse reflectance, increasing light utilization efficiency. This review provides an overview of recent progress in DSSC research toward developing new materials (2D) for electrodes, focusing on applying 2D composite materials. We discuss how each of these materials has been utilized as hole-transporting layers or as dopants in electrodes to improve the photovoltaic performance and long-term stability of DSSCs. Furthermore, we outline the features that must be optimized for the highest efficiency in DSSCs and provide a perspective on future directions in DSSC research.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".