An investigation of the beam damage effect on <i>in situ</i> liquid secondary ion mass spectrometry analysis
Bibliographic record
Abstract
Rationale During in situ liquid secondary ion mass spectrometry (SIMS) analysis, the primary ion beam is normally scanned on a very small area to collect signals with high ion doses (10 14 to 10 16 ions/cm 2 ). As a result, beam damage may become a concern when compared with the static limit of SIMS analysis, in which the dose is normally less than 10 12 ions/cm 2 . Therefore, a comparison of ion yields in in situ liquid SIMS analysis versus traditional static SIMS analysis of corresponding dry samples is of great interest. Methods In this study, a dipalmitoylphosphatidylcholine (DPPC) liposome solution was used as a model system. Both liquid sample and dry sample were examined. Secondary ion yields using three primary ion species (Bi + , Bi 3 + and Bi 3 ++ ) with various beam currents were investigated. Results Usable ion yields for both positive and negative characteristic signals (including molecular ions and characteristic fragment ions) were achievable based on optimized experimental conditions for in situ liquid SIMS analysis. The ion yield of the key DPPC molecular ion was comparable to that of traditional static SIMS, and unexpected low fragmentation was observed. The flexible structure of the liquid plays an important role for these observations. Conclusions Therefore, beam damage may not be a concern in in situ liquid SIMS analysis if proper experimental conditions are used.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".