Recent progress in flexible dye solar cells
Bibliographic record
Abstract
Flexible dye‐sensitized solar cells are an intriguing photovoltaic technology, especially from the point of view of integration of photovoltaics into everyday objects, owing to these solar cells conforming easily to different nonplanar geometries and low‐intensity lighting conditions. However, the commercialization of these devices has not yet fully taken off due to few persisting gaps in the optimization of employed materials and processes. Herein, we focus on the recent progress on flexible dye sensitized solar cells, and how obstacles to larger‐scale production have been removed. There have been major advancements in diverse roll‐to‐roll compatible preparation methods of various cell layers, as well as in understanding the corrosion of metal electrodes in liquid electrolyte. We also pinpoint the remaining challenges for full commercialization of these technologies, one of which is reaching long‐term stability in which case sealing of the flexible device plays a major role. Furthermore, environmental considerations such as the life cycle assessment and the use of more sustainable materials in solar cell preparation are discussed. This article is categorized under: Energy Research & Innovation > Science and Materials Photovoltaics > Science and Materials
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".