Towards high-performance dye-sensitized solar cells by utilizing reduced graphene oxide-based composites as potential alternatives to conventional electrodes: A review
Bibliographic record
Abstract
Dye-sensitized solar cells (DSSCs) have recently emerged as one of the most promising new-generation photovoltaic devices due to their facile fabrication protocols, capacity to operate under diffuse light, and low-impact on the environment. However, their low power conversion efficiency (∼15.2%) hinders practical applications. This is primarily owing to ineffective dyes, significant recombination at solid/liquid interfaces, and limitations of TiO 2 , the conventional photoanode material, especially poor light harvesting and electron transport. Moreover, Pt, the traditional counter electrode material, is costly and unstable due to its scarcity and low corrosion resistance to I 3 ˉ, respectively. This increases the device cost and shortens its lifespan. Inspired by this, current research interests have shifted their focus from traditional materials to low-cost alternatives, including metal oxides, metal chalcogenides and perovskites, which offer competitive photovoltaic performance. Nonetheless, these alternative materials exhibit relatively low electrical conductivity, which compromises device performance. Thus, to improve device efficiency and sustainability, these materials have recently been coupled with highly conductive and stable carbon nanomaterials, particularly graphene-based materials. Among them, reduced graphene oxide (rGO) has been more appealing due to its compatibility with low-cost solution processing. Therefore, this review highlights the recent advances in DSSC efficiency and sustainability made over the last five-years (2020–2024) by developing TiO 2 -free photoanodes and Pt-free counter electrodes, in particular, by introducing rGO into metal oxides, metal chalcogenides and perovskites. Challenges and future directions for fabricating TiO 2 - and Pt-free DSSCs are discussed to close the gap between emerging nanomaterials and their traditional counterparts, thereby setting the stage for commercialization. • TiO 2 ineffectively absorbs visible light and is susceptible to high recombination. • Pt is costly and has poor stability due to its low corrosion resistance to I 3 ˉ • Metal oxides, chalcogenides or perovskites exhibit competitive device performance. • Reduced graphene oxide (rGO) increases conductivity and stability of electrodes. • rGO/Metal oxide, chalcogenide or perovskite composites can replace TiO 2 and Pt.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".