Increasing sensitivity and decreasing spot size using an inexpensive, removable hydrophobic coating for matrix‐assisted laser desorption/ionisation plates
Bibliographic record
Abstract
Spot size reduction and increased detection sensitivity in matrix-assisted laser desorption/ionisation (MALDI) of small molecules are accomplished by using an inexpensive and removable hydrophobic coating for MALDI targets, based on 3M Scotch Gard surface treatment. Several variations in sample preparation were explored, such as surface coating technique, identity of the matrix, solvent composition, and the type of metal support plate used. These were investigated on both uncoated and coated surfaces and their impact on spot size, crystal coverage, and sensitivity is presented here. Additionally, crystallisation behaviour obtained on coated plates is compared with that on uncoated plates using scanning electron microscope analysis. To demonstrate the potential of the new coating technique, erythromycin A and valinomycin are studied to determine the increase in detection sensitivity of coated plates in comparison to uncoated plates, and to reveal the suitability of the plates for application in combined high-performance liquid chromatography/MALDI (HPLC/MALDI), where widely varying solvent compositions and droplet volumes are observed. It is shown that enhancements in detection sensitivities correlate very well with the achieved spot size reduction. The versatility of the coated plates is also exhibited by the ease of removing the surface layer, after which the plates can be rigorously cleaned without worry about damaging the hydrophobic surface, followed by a quick reapplication of new hydrophobic coating material. This makes the non-polar coating superior to more expensive commercial hydrophobic-coated targets, which are much more delicate to clean. Furthermore, cleaning and reapplication eliminate potential carry-over effects and the easy application procedure also makes the fabrication of inexpensive, disposable MALDI targets readily possible.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".