Engineering matrix‐free laser desorption ionization mass spectrometry using glancing angle deposition films
Bibliographic record
Abstract
RATIONALE: Thin, nanoporous films fabricated using Glancing Angle Deposition (GLAD) technology are demonstrated for solid matrix laser desorption/ionization mass spectrometry (SMALDI-MS). GLAD allows facile engineering of nanoporosity, film thickness, post alignment, and material composition, as demonstrated here by the fabrication of Co-GLAD and Si-GLAD films for SMALDI, and by exploration of the SMALDI performance as a function of thickness, post density, and angle of the post relative to surface normal. METHODS: GLAD films were prepared by electron beam evaporation onto silicon substrates, using steep angles of incidence for the vacuum deposition, with computer controlled substrate rotation. LDI from the GLAD films was evaluated using an MDS-Sciex time-of-flight (TOF) MALDI mass spectrometer. RESULTS: Co-GLAD films give a limit of quantitation of 6 fmol for complex carbohydrate derivatives, and slanted-post Si-GLAD films show up to three times higher sensitivity than vertical post structures. Reproducibility of both Si and Co films is much higher than conventional MALDI methods for m/z below at least 2100 Da. Both reproducibility and detection limits are comparable to or better than other nano-structured materials. Co-GLAD films are significantly better in performance than Co powders or Co thin films on silicon substrates previously evaluated. CONCLUSIONS: The flexibility of GLAD for thin film fabrication of LDI materials is demonstrated by the range of nanoporous materials that can be grown, and the fine control over structural conformation, thickness and porosity. 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".