Time‐of‐flight secondary ion mass spectrometry analysis of paint craters
Bibliographic record
Abstract
Localized sudden changes in the surface energy of a surface to be painted are commonly held responsible for causing paint craters. However, it is not necessarily an easy task to identify the material(s) that produced the defects. Automotive paint cratering, when it happens, often requires immediate identification of its causes because the product line may have to be shut down until the problem is solved. For the past 18 years, Surface Science Western has applied time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), among other techniques, to help its clients in the automotive industry identify the chemicals responsible for producing paint craters. In this article, we demonstrate that ToF‐SIMS is a unique and powerful technique in identifying the chemicals such as siloxane, fluorocarbons and fatty acids that are responsible for causing paint craters. We further show that the chemicals can be foreign contaminants, as well as segregation of additives in the paint systems, and even from contaminated solvents used in the paint. When the chemicals causing the paint craters can be identified by ToF‐SIMS analysis, the automotive company can often track down the primary source and remove the root cause. As such, surface analysis and in particular ToF‐SIMS is invaluable in understanding paint cratering for both the surface analysis research community and paint manufacturers and users. Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".