Effect of Reverse Bias on Dye-Sensitized Technology: Lessons for Application in PV-Integrated Textile Fabric Designs Useable in Wajir, Vihiga, Kitui and Kajiado Counties in Kenya
Bibliographic record
Abstract
This paper reports on the effect of reverse bias (RB) on dye-sensitized solar cells (DSSCs) that were investigated outdoor in Wajir (1o44’50’’ North, 40o 4’ 8’’ East), Vihiga (35o0’ East, 0o15’ North), Kitui (3o 0’ South, 37o 50’ East) and Kajiado (360o 5’ East, 30o 0’ South) in Kenya. The DSSCs’ J-V characteristics, namely, Voc, Jsc, FF and ?, were studied under varied RB potentials. This was achieved through partial, as well as complete shading of the DSSCs during their operation in the study sites, using a thick piece of black cloth, and measuring the obtaining J-V characteristics. Findings of the study reveal that subjecting the DSSC module that was investigated in Wajir to RB of between 1V and 4V triggered between 25.53% and 23.53% drop in the module’s efficiency (?), followed by its total breakdown thereafter. The modules studied in Vihiga, Kitui and Kajiado exhibited a similar trend, but with variations in ? under the different RB regimes. The DSSCs’ breakdown under RB regimes of over half their voltage ratings could be attributed to the damaging of their dye constituents. These findings are important for context-informed DSSC dye choices, as well as DSSC-integrated designs that appeal to local cultural textile fabrics, like shawls, kanzu (long robes) and light coats that women and men dress in, respectively, in Wajir, and blankets that both men and women wrap around their shoulders in Kajiado, as well as in local architectures. The findings underscore the existence of vast prospects for localized industries that innovate in DSSC-integrated designs for local espousal. They could form foundations for programs that mentor people, especially children and youths at local levels to engage in climate change-mitigating enterprises.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".