Time‐of‐flight secondary ion mass spectrometry analysis of paint craters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it