Matrix‐free laser desorption/ionization mass spectrometry using silicon glancing angle deposition (GLAD) films
Bibliographic record
Abstract
Glancing angle deposition (GLAD) was used to fabricate nanostructured silicon (Si) thin films with highly controlled morphology for use in laser desorption/ionization mass spectrometry (DIOS-MS). Peptides, drugs and metabolites in the mass range of 150-2500 Da were readily analyzed. The best performance was obtained with 500 nm thick films deposited at a deposition angle of 85 degrees . Low background mass spectra and attomole detection limits were observed with DIOS-MS for various peptides. Films used after three months of dry storage in ambient conditions produced mass spectra with negligible low-mass noise following a 15 min UV-ozone treatment. The performance of the Si GLAD films was as good as or better than that reported for electrochemically etched porous silicon and related materials, and was superior to matrix-assisted laser desorption/ionization (MALDI)-MS for analysis of mixtures of small molecules between 150-2500 Da in terms of background chemical noise, detection limits and spot-to-spot reproducibility. The spot-to-spot reproducibility of signal intensities (100 shots/spectrum) from 21 different Si GLAD film targets was +/-13% relative standard deviation (RSD). The single shot-to-shot reproducibility of signals on a single target was +/-19% RSD (n = 7), with no indication of sweet spots or mute spots.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".