11. Solar Juice - Creating Green and Cost-Effective Dye Sensitized Solar Cells
Bibliographic record
Abstract
The goal of this project was to address the current need for alternative energy sources by investigating in dye-sensitized solar cells (DSSCs). This experiment explored ways to prolong the operational lifespan of DSSCs and increase their current production. Firstly, to prolong the effectiveness of the dye, benzoic acid was added to the solar cells as a preservative. Secondly, to prevent the evaporation of the liquid electrolyte, several sealing agents were used to seal the edges of the cells. Thirdly, to increase current production, different types of fruit dyes were mixed based on spectroscopy analysis, and produced solar cells with higher current readings than the control cells. A wider range of wavelength was absorbed by the combined dye. Citric acid in the form of lemon juice was also added to the dyes in order to increase current production as well as increase lifespan. The optimal pHs for current production of several dye molecules were determined. Factoring in carbon footprint and cost analysis, the solar cells are fabricated in the most efficient and environmentally‐friendly manner. Through this experiment, it has been revealed that DSSCs may eventually become a viable future technology with additional research and experimentation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".