Dye‐sensitized solar cells as promising candidates for underwater photovoltaic applications
Bibliographic record
Abstract
Abstract Harvesting solar energy using photovoltaic (PV) cells is the simplest, efficient, and reliable approach to power marine electronics. Installing PV above or under water provides cooling and cleaning to sustain the power conversion efficiency. Previous work on commercially available silicon‐based PV quantified the performance of PV with different submerged environments and showed promising results in harvesting available underwater solar energy. Subsequent, theoretical studies point to enormous potential of using wide‐band‐gap PV in underwater conditions. With this motivation, herein for the first time, a dye‐sensitized solar cells (DSSCs) employing wide‐bandgap ruthenium sensitizers (1.8 eV) have been tested under submerged conditions. The DSSCs were characterized under submerged conditions up to 20 cm. Four replicates provided data detailing DSSCs potential for underwater PV applications when compared with the previously collected data for monocrystalline, polycrystalline, and amorphous silicon PV. Although the light intensity under water decreases with an increase in depths, the rate of decrease in power output for DSSCs was only 40.68%, which was less than the traditional monocrystalline and polycrystalline silicon PV by approximately 20–25%. Also, compared with amorphous silicon PV, DSSCs showed a slightly better performance by 2–3%, clearly displaying the capability of DSSCs to harvest indirect/diffused lights in comparison with the conventional PVs. Compared with the conventional PVs, indigenously fabricated DSSCs showed tremendous relative increase in performance in underwater conditions. Further work is underway to further optimize DSSCs even though it can be concluded that with added advantages of simple fabrication process and cost‐effectiveness, DSSCs have enormous future potential for underwater PV applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".