Anti-soiling coatings for Sun Simba concentrated photovoltaic (CPV) modules
Bibliographic record
Abstract
Soiling is a major source of power degradation in photovoltaic (PV) modules, with variable impact as a function of environmental conditions. For concentrated photovoltaic (CPV) modules the impact is greater due to requirements in preserving the optical path within the module. In an effort to reduce soiling a number of self-cleaning coatings have been developed and implemented in solar modules. Self-cleaning films are generally based on high (hydrophilic) or low (hydrophobic) surface energy materials that leverage extreme wetting behavior to reduce contamination, and/or photocatalytic materials that decompose contaminants with ultraviolet (UV) activation. In this study we explore a number of third-party coating materials to reduce soiling losses in Morgan Solar’s Sun Simba CPV modules. The primary objective is to identify coating solutions that are mechanically robust and effective in anti-soiling, as well as compatible with our materials and optical concentration architecture. In preliminary testing two products were compared with extreme wetting characteristics. The material 1 coating reduces soiling rates to 0.8-1.4%/month, whereas the material 2 coating failed mechanically and the module soiled at a rate of 6.6%/month as a consequence – the control module soiled at a rate of 4.1%/month for comparison. We followed-up with the deployment of a long-term study that will compare 4 self-cleaning/easy-to-clean products. The materials have been selected for their extreme wetting characteristics and suitable optical properties. Deposition parameters were optimized and the coated modules have been mounted for evaluation in the Mojave Desert, California.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".