Self‐assembled monolayers of octadecylphosphonic acid and polymer films: Surface chemistry and chemical structures studied by time‐of‐flight secondary ion mass spectrometry
Bibliographic record
Abstract
With the ever‐decreasing thickness of functional organic thin films, surface sensitive analytical techniques are required to probe surface/interface chemistry and structural changes of ultra‐thin organic films such as self‐assembled monolayers (SAMs) of amphiphilic molecules and polymeric coatings. Time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) fits this requirement because it is extremely surface sensitive and provides rich chemical information. In this article prepared for celebrating the 35th anniversary of our lab as a surface analysis service provider and a surface science research center, we highlight our TOF‐SIMS studies on exploring the surface chemistry of SAMs of octadecylphosphonic acid. Due to our contribution to developing the fast growth method of delivering octadecylphosphonic acid SAMs via the use of solvents having a dielectric constant of 3 to 5, we will review the formation mechanisms of SAMs. We will also review our recent results that demonstrated the feasibility of using the ion intensity ratio between C 6 H − and C 4 H − to differentiate the chemical structures of several polymers and depth profiling the cross‐linking degree of a polymer. In this article, we show results from principal component analysis on numerous C n H − intensity data from multiple spectra obtained from polyethylene, polypropylene, polyisoprene, and polystyrene. This multivariate analysis method allowed us to better understand the relationships between the polymers and between C n H − , as well as verify the rationale for the selection of C 4 H − as the reference ion for normalization. We demonstrate that together with principal component analysis, TOF‐SIMS is unique in differentiating chemical structures of polymers. 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 | 0.000 |
| 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".