Static SIMS studies of fatty alcohols, amines and esters on gold and aluminium–magnesium alloy surfaces
Bibliographic record
Abstract
Abstract Static secondary ion mass spectrometry was used to study the chemical reactions and lateral distributions of fatty amines, alcohols and esters spin coated onto gold surfaces and commercial aluminium–magnesium (Al–Mg) alloy surfaces, cleaned using UV–ozone. The aim of this study is to develop an understanding of the interactions of model lubricants with metal surfaces, such as gold and aluminium. This static SIMS study of organic thin films has been able to identify specific reaction products on the aluminium surface for each functional group. This work demonstrates that organic molecules with alcohol, ester and amine functional groups undergo specific chemical reactions with oxidized Al–Mg alloy surfaces. For example, films composed of the fatty alcohol dodecanol were observed to emit monomers, dimers and trimers with discrete distributions. In addition, negative secondary ion mass spectra indicate that a surface carboxylate is formed from the alcohol. The formation of carboxylate reaction products was confirmed by Fourier transform infrared spectroscopy. On Al–Mg alloy surfaces, a direct interaction with the amine and aluminium oxide surface is observed by the detection of a molecular ion that corresponds to the mass of dodecylamine and AlO − , characteristic of aluminium oxide. Ethyl laurate was shown to eliminate the ethyl group, leaving the laurate anion. This study demonstrates the ability of time‐of‐flight (ToF) SIMS to discriminate and detect chemical reaction products formed between model lubricant molecules and metal surfaces. As a result of this study, the use of ToF‐SIMS to identify reaction products of model lubricants can be extended to provide a better understanding of the interactions of lubricants and metal surfaces at high temperatures and pressures that more closely resemble the conditions encountered in industrial rolling processes. Copyright © 2005 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 | 0.000 | 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) | 0.002 | 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".