Field testing of thermoplastic encapsulants in high‐temperature installations
Bibliographic record
Abstract
Abstract Recently there has been increased interest in using thermoplastic encapsulant materials in photovoltaic modules, but concerns have been raised about whether these would be mechanically stable at high temperatures in the field. Recently, this has become a significant topic of discussion in the development of IEC 61730 and IEC 61215. We constructed eight pairs of crystalline‐silicon modules and eight pairs of glass/encapsulation/glass thin‐film mock modules using different encapsulant materials, of which only two were formulated to chemically crosslink. One module set was exposed outdoors with thermal insulation on the back side in Mesa, Arizona, in the summer (hot‐dry), and an identical module set was exposed in environmental chambers. High‐precision creep measurements (±20 μm) and electrical performance measurements indicate that despite many of these polymeric materials operating in the melt or rubbery state during outdoor deployment, no significant creep was seen because of their high viscosity, lower operating temperature at the edges, and/or the formation of chemical crosslinks in many of the encapsulants with age despite the absence of a crosslinking agent. Only an ethylene‐vinyl acetate (EVA) encapsulant formulated without a peroxide crosslinking agent crept significantly. In the case of the crystalline‐silicon modules, the physical restraint of the backsheet reduced creep further and was not detectable even for the EVA without peroxide. Because of the propensity of some polymeric materials to crosslink as they age, typical thermoplastic encapsulants would be unlikely to result in creep in the vast majority of installations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".