Design Enhancements for High Performance Dye-Sensitized Solar Cells
Bibliographic record
Abstract
Due to the abundance of solar energy, solar cells are considered as a renewable source of energy to replace conventional fossil fuels. Compared to the silicon-based photovoltaic (PV) cell, the next generation dye-sensitized solar cell (DSSC) offers the advantages of increased absorption of visible light, high efficiency potential, less energy intensive and lower-cost manufacturing process, colorable design, and lightweight material options. DSSC is a photo-electrochemical system that is based on a photosensitive dye-sensitized semiconductor (mostly titanium dioxide, TiO2) anode and an iodide-based electrolyte. In order to improve the performance of current DSSC systems, we proposed various design improvement schemes through the use of TiO2 nanotube (TONT) arrays and a multistack design of single cells. Through design modifications, approximately 38% improvement in the performance compared to conventional DSSC is reported. Moreover, optical enhancements to increase the amount of incident light on the cell were applied to DSSCs to further improve its performance by application of Fresnel lenses on top of the DSSC and the use of light reflecting material such as Aluminum on the rear side of the cell. The polarization curves for different designs were measured using a potentiostat and the performance of each cell was compared. Optical enhancements improved the power output by 27% compared to normal cells. A semi-empirical DSSC model was also developed based on the experimental results and the change in the performance of different designs was examined. Based on the model, the necessary conditions for maximum performance could be determined.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".