Improvement of biomolecular analysis in thin films using <i>in situ</i> matrix enhanced secondary ion mass spectrometry
Bibliographic record
Abstract
ion beam sputtering, followed by redeposition on a collector carrying the sample to be analyzed. After evaluating the quality of the transfer of six different matrices on bare Si collectors, α-cyano-4-hydroxycinnamic acid (CHCA) was selected for further experiments. The mass spectra and depth profiles obtained from the organic layer prior to and after the sputter-transfer of CHCA were compared, along with those obtained from regular ME-SIMS samples (dried droplets) and, finally, with MALDI data for the same matrix-analyte combinations. Signal amplification factors were calculated by dividing the integrated molecular intensities obtained with or without matrix transfer. While the amplification factors are between 0.5 and 2 for molecules already detected with high intensities in SIMS, such as cholesterol or human angiotensin, other compounds show very large integrated signal amplification, even above two orders of magnitude. This is the case for D-glucose and cardiolipin, for which the molecular ion intensity is low (or very low) under normal SIMS analysis conditions. For such low ionization probability compounds, the beneficial effect of the matrix is unquestionable. Test experiments on mouse brain tissue sections also indicate signal enhancement with the matrix, especially for high mass lipid ions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".