Laser desorption ionization mass spectrometry of peptides on a hybrid CHCA organic–inorganic matrix
Bibliographic record
Abstract
We report applications of new hybrid organic-inorganic silica based materials as laser desorption/ionization (LDI)-promoting surfaces for high-throughput identification of peptides. The driving force of our work was to design a new material composed of a conventional MALDI matrix covalently attached to silica with a high organic/inorganic ratio in order to improve the UV absorption by such LDI hybrid matrices. Amorphous CHCA-functionalized silica presenting an organic content up to 1.3 mmol g(-1) (around 40% in weight from TGA and elementary analysis measurements) gave very interesting LDI performances in terms of detection sensitivity as well as relative ionization discrepancy (spectral suppression) through the analyses of small synthetic peptide mixtures (550-1300 Da) taking CHCA and amorphous silica as model matrices for control experiments.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".