Investigation of imaging ToF‐SIMS as a means to study coatings on wood
Bibliographic record
Abstract
Abstract Measurement of the penetration of coatings into wood may be performed by SEM analysis with OsO 4 or other markers used for post‐treatment labeling. However, there are indications that the Os only binds with the uncured components of the resin. Since these are mainly located at the surface of the coating, the use of Os could thus misinterpret the actual penetration of the coatings into the wood surface. Time‐of‐Flight Secondary Ion Mass Spectrometry (ToF‐SIMS) provides a unique means by which the penetration depths of the various components of the coating (resin + pigments) into the wood microstructure can be visualized. This arises from both the high spatial resolution (<100 nm) and high mass resolution achievable with the technique (albeit not at the same time). The latter feature is of importance to resolve different molecular fragments of similar molecular weight which could be needed as markers for the various components in the complex hydrocarbon systems constituting wood and the coating materials, whilst the former is required to ascertain differences in penetration depth of the components. In this work, imaging ToF‐SIMS spectra are obtained on cross‐sections of coated wood samples using a Bi cluster ion source. Coating distribution is followed by identifying fragments associated more with the coating and with the wood respectively. Sample preparation techniques are of importance and thus results are compared for cross‐sections prepared both by microtoming, as well as by mounting in epoxy resin and polishing. Copyright © 2010 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".