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Spot on: A Laser Micromachining-Based Approach to Improve Dried Matrix Spot Preparation with Proof-of-Principle
Daniel O Reddy1, Malek Hassan1, Jonathan O Graham2
1Department of Chemistry, Queen's University, Kingston, ON K7L 3N6, Canada.
Researchers developed a new laser micromachining method for creating smaller dried matrix spots (DMSs). This technique improves sample targeting and analysis using mass spectrometry, enhancing the efficiency of analytical workflows.
Area of Science:
- Analytical Chemistry
- Mass Spectrometry
- Materials Science
Background:
- Dried matrix spots (DMSs) are increasingly used for sample storage and analysis, especially with ambient ionization mass spectrometry.
- Existing methods face challenges in precise liquid confinement and sample targeting.
- Advancements are needed to improve the efficiency and accuracy of DMS-based analyses.
Purpose of the Study:
- To present a novel laser micromachining approach for preparing targeted DMS substrates.
- To integrate these substrates with direct mass spectrometric analysis techniques.
- To address the limitations of liquid confinement and sample targeting in DMS preparation.
Main Methods:
- Laser micromachining was used to create hydrophobic paper substrates with surface energy traps (SETs) and visual reticles.
- DMSs were prepared on these SETs, significantly reducing spot size (down to 0.55 mm diameter).
- The targeted DMS substrates were analyzed using liquid microjunction-surface sampling probe-mass spectrometry (LMJ-SSP-MS) and flowing atmospheric-pressure afterglow-mass spectrometry (FAPA-MS).
Main Results:
- A laser-based method successfully produced DMSs on SETs with integrated visual reticles.
- The prepared DMSs were approximately 12 times smaller than traditional preparations.
- Caffeine was successfully detected in aqueous and artificial urine solutions using LMJ-SSP-MS and FAPA-MS, demonstrating the method's efficacy.
Conclusions:
- The laser micromachining approach offers precise targeting and reduced sample volume for DMS analysis.
- This method enhances visualization and analytical performance in mass spectrometry.
- Potential for automation using computer vision and robotics exists to further optimize analytical workflows.
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