Gas aggregated Ag NPs as a matrix for small molecules: a study on natural amino acids
Source abstract
Abstract
The use of nanomaterials as a matrix for soft ionization mass spectrometry has been investigated and reported various times. Under certain conditions, measurements of samples containing small molecules enhanced by nanomaterials have better sensitivity compared with other methods. Nanoscaled Ag prepared by various methods was reported as a substrate for surface-assisted LDI MS. Higher desorption and (in some cases) charge transfer through adduct formation with silver ion was reported. But primary ionization mechanism was through attachment of proton or alkali ion. Our approach is to overcoat the sample by Ag nanoparticles (the so-called nanoparticle-assisted LDI MS or nanoPALDI MS). As nanoparticles having adsorption maxima close to the laser wavelength, we expect an improvement in desorption enhancement, release of silver ions and (as a result of latter) increased adduct formation of [analyte+Ag]+ ions. The nanoparticles are produced by gas aggregation under low-pressure, making the process homogeneous over relatively large areas, free from impurities, and absence of capping agent often presented in colloidal nanoparticles. This work reports the detection of natural amino acids with Ag nanoparticles as a matrix. We successfully detected all amino acids (as [analyte+Ag]+ ion), including in the mixtures of few amino acids. Moreover, a formation of other adducts, particularly [analyte+Ag3]+ and [analyte2+Ag]+ is reported. The differences in ion intensity ratios depending on amino acid types are discussed. The obtained results showed that laser ablation is more efficient for amino acids with hydrophobic, unique, and neutral side chains.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Gold and Silver Nanoparticles Synthesis and Applications · Nanocluster Synthesis and Applications · Molecular Junctions and Nanostructures
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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