Efficacy of Rotten and Fresh Fruit Extracts as the Photosensitive Dye for Dye-Sensitized Solar Cells
Bibliographic record
Abstract
Global demand for energy is increasing exponentially, developing the need for renewable energy sources such as solar cells. Dye-sensitized solar cells (DSSC) use dye as the photoactive material, which capture the incoming photon of light and use the energy to excite electrons. These excited electrons then travel to a titanium dioxide (TiO2) layer, while the electrolyte in the cell closes the circuit by accepting the electrons and recycling them to the dye. Research in DSSCs has centered around improving the efficacy of photosensitive dyes. A fruit's color is defined by a unique set of molecules, known as a pigment profile, which changes as a fruit progresses from ripe to rotten. This project investigates the use of fresh and rotten fruit extracts as the photoactive dye in a DSSC. Dyes were extracted from cherries, plums, nectarines, peaches, kiwis, avocados, blueberries, and blackberries, both fresh and rotten. TiO2 coated electrodes were soaked overnight in the dyes and assembled into a DSSC using a graphite-coated counter electrode and an iodide-triiodide (3I-/I3-) electrolyte solution. The dye efficacy was determined by measuring the electric potential (voltage) with a multimeter. In fresh fruits, blackberries and blueberries produced the greatest potential. In most colors, the fresh dyes produced a greater potential than the rotten dyes. However, in kiwi, the rotten dye produced a greater potential than the fresh dye. In fruit crops, wastage levels are high due to quick rotting and market standards — the use of fruit-based dyes in DSSCs can convert this wastage to useful energy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".