Further studies on the signal enhancement effect in laser diode thermal desorption‐triple quadrupole mass spectrometry using microwell surface coatings
Bibliographic record
Abstract
The laser diode thermal desorption (LDTD) ionization source allows ultrafast and sensitive analysis of small molecules by mass spectrometry. Signal enhancement in LDTD has been observed when coating the surface of sample microwells with a solution of ethylenediaminetetraacetic acid (EDTA) or nitrilotriacetic acid. Here we present a quantitative analysis of signal enhancement using solutions of diverse commercial proteins (lysozyme, immunoglobulin G, albumin, and fibrinogen) as coatings. Results showed that compounds with polar chemical functions such as carboxylic acid, sulfonyl, and nitro had signal enhancement factors, in most cases higher than 10, when using any of the tested proteins as coating agent. Analysis of variance revealed that immunoglobulin G and fibrinogen gave the best results. However, the signal enhancement factors obtained with these proteins were not superior to those observed with EDTA. To explain the signal enhancement effect of proteins, analysis by scanning electron microscopy of dried samples on the microwell sample plates was carried out. Images showed that salicylic acid, one of the compounds with the highest observed signal enhancement, formed a thick layer when applied directly on the uncoated surface, but it formed small crystals (<1 μm) in the presence of protein or EDTA coatings. Further crystallographic studies using powder X-ray diffraction showed that the crystalline form of salicylic acid is modified in the presence of EDTA. Salicylic acid when mixed with EDTA had a higher percentage of amorphous phase (38.1%) than without EDTA (23.1%). These results appear to confirm that the diminution of crystal size of analytes and the increase of amorphous phase are implicated in signal enhancement effect observed in LDTD using microwell surface coatings. To design better coatings and completely elucidate the signal enhancement effect in LDTD, more studies are necessary to understand the effects of coatings on the ionization of analytes.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".