Signal enhancement in laser diode thermal desorption‐triple quadrupole mass spectrometry analysis using microwell surface coatings
Bibliographic record
Abstract
Abstract Laser‐diode thermal desorption (LDTD) is an ionization source usually coupled to triple quadrupole mass spectrometry (QqQMS) and specifically designed for laboratories requiring high‐throughput analysis. It has been observed that surface coatings on LDTD microwell plates can improve the sensitivity of the analysis of small polar molecules. The objective of the present study is to understand and quantify the effect of microwell surface coatings on signal intensity of small organic molecules of clinical, environmental, and forensic interest. Experiments showed that the peak areas of diclofenac, chloramphenicol, salicylic acid, and 11‐nor‐9‐carboxy‐Δ9‐tetrahydrocannabinol obtained by LDTD‐QqQMS increased by up to 3 orders of magnitude when using microwells coated with ethylenediaminetetraacetic acid (EDTA). Tests with different chelating agents and polytetrafluoroethylene as microwell surface coatings showed that nitrilotriacetic acid gave significantly higher peak areas for five out of the nine compounds that showed signal enhancement using chelating agents as coatings. Scanning electron microscopy studies of EDTA‐coated and uncoated microwells showed that analytes deposited in the former formed more uniform and thinner films than in the latter. The enhancement effect of surface coatings in LDTD‐QqQMS was explained mainly by the formation of homogenous and thinner layers of nanocrystals of analytes that are easier to desorb thermally than the layers formed when the analytes dry in direct contact with the bare stainless‐steel surface. Chemisorption of some analytes to the stainless‐steel surface of the microwell plate appeared to be a minor factor. Surface coatings widen the number of compounds analyzable by LDTD‐QqQMS and can also improve sensitivity and limits of detection.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".