Primary ion fluence dependence in time-of-flight SIMS of a self-assembled monolayer of octadecylphosphonic acid molecules on mica discussion of static limit
Bibliographic record
Abstract
By using a self-assembled monolayer of octadecylphosphonic acid molecules, CH3(CH2)17PO(OH)2, on mica as a model of the “soft” materials, such as self-assembled monolayers (SAMs) and multilayers in many biological systems as well as artificially engineered molecular electronic systems, we have examined the effects of primary ion fluence on time-of-flight secondary ion mass spectrometry (TOF-SIMS) of the technologically important model. Our measurements clearly show that although the intensity per unit primary ion fluence of most atomic ions and many small fragment ions do not vary by more than 10% for the fluence range of 1010–1013 cm–2, the intensity of the parent molecular ion can drop by two orders of magnitude in this fluence range. While the changes are different for the primary ion beams of Bi3+ (25 keV, 45°), Bi+ (25 keV, 45°), and Ar+ (8 keV, 45°), they are all substantial, with the damage cross section induced by the Bi3+ beam being the largest (6 000 Å2). Since different secondary ions have quite different intensity changes, the analytical results derived from TOF-SIMS can vary significantly by the time and duration of the measurements in the TOF-SIMS experiment. Therefore, our results suggest that for TOF-SIMS of molecular layers such as SAMs, the primary ion fluence condition should be recorded and reported. In general, the validity of the static condition becomes questionable when the cumulative primary ion fluence exceeds 1 × 1011 cm–2.Key words: SIMS, static SIMS, TOF-SIMS, soft materials, self-assembled monolayer, bilayer, surface of biological materials.
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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".