A Practical Approach for Internal Energy Tuning in LDI-MS: Porous Silicon Substrates as a Case Study
Bibliographic record
Abstract
This study presents a methodical procedure for optimizing laser desorption/ionization mass spectrometry (LDI-MS) supports using porous silicon (PSi) substrates. The approach involves the use of substituted benzyl-pyridinium salts (thermometer ions) to obtain one metric that assesses analyte fragmentation (the effective temperature of vibration). Porous silicon substrates were synthesized via electrochemical etching of p-type silicon wafers (10–20 mΩ·cm), with etching parameters adjusted to vary porosity while maintaining a layer thickness between 700 and 1200 nm. The results revealed that PSi substrates with 40–60% porosity achieved the lowest fragmentation levels. This finding was validated through the analysis of N-acetyl glucosamine, a carbohydrate, which confirmed the effective temperature trend. Further analysis involving peptides, specifically P14R and a peptide mix (Peptide Calibration Standard II, Bruker), demonstrated that the optimized PSi substrates enabled the desorption and ionization of peptides with a maximum mass at m / z 2465, corresponding to ACTH clip 1–17. These results highlight the critical role of substrate porosity in minimizing analyte fragmentation and enhancing LDI-MS performance.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".