Effect of ultraviolet‐A radiation on surface structure, thermal, and mechanical and electrical properties of liquid silicone rubber
Bibliographic record
Abstract
ABSTRACT Liquid silicone rubber (LSR) products outdoors are often subjected to high intensity of ultraviolet (UV)‐A radiation. However, the influence of UV‐A radiation on LSR is rarely studied. To study the influence of UV‐A radiation on LSR, the surface of LSR with UV‐A aging time was characterized by tests at different detection depths [X‐ray photoelectron spectroscopy (XPS), energy dispersive X‐ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FTIR)]. The operation properties (thermal stability and mechanical and electrical properties) for outdoor insulators were also analyzed. It was found that the porous surface and the loose layer of LSR increased, but the growth rate of them decreased as UV‐A aging time increased. SiO 2 fillers were lost in the surface. CH bonds in CH 3 and CH 3 in SiCH 3 increased with aging time. The increased crosslinking density increased the thermal stability, hardness, dielectric loss tangent, relative dielectric constant, and volume resistivity and decreased elongation‐at‐break. The mechanical strength initially increased and then decreased. Based on the proposed aging mechanism, the UV‐A resistance of LSR can be improved by reducing the amount of hydrogen‐containing silicone oil and by increasing the molecular weight of raw rubber. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136 , 47652.
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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".