Innovative Optimization of LDI-MS Porous Silicon Substrates Using Thermometer ions

24 October 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

This study presents a methodical procedure for optimizing laser desorption/ionization mass spectrometry (LDI-MS) supports using porous silicon (PSi) substrates. The approach involves the use of substituted benzyl-pyridinium salts (thermometer ions) to obtain one metric that assesses analyte fragmentation (the effective temperature of vibration). Porous silicon substrates were synthesized via electrochemical etching of p-type silicon wafers (10-20 mΩ⋅cm), with etching pa-rameters adjusted to vary porosity while maintaining a layer thickness between 700 and 1200 nm. The results revealed that PSi substrates with 40-60% porosity achieved the lowest fragmentation levels. This finding was validated through the analysis of N-Acetyl glucosamine, a carbohydrate, which confirmed the effective temperature trend. Further analysis involving peptides, specifically P14R and a peptide mix (Peptide Calibration Standard II, Bruker), demonstrated that the optimized PSi substrates enabled the desorption and ionization of peptides with a maximum mass at m/z 2465, corresponding to ACTH clip 1-17. These results highlight the critical role of substrate porosity in minimizing analyte fragmentation and enhancing LDI-MS performance.

Keywords

Porous Silicon
Internal energy distribution
Laser Desorption/Ionization Mass Spectrometry (LDI-MS)
Analyte Fragmentation
Effective Temperature of Vibration

Supplementary materials

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Title
Supporting Information for Innovative Optimization of LDI-MS Porous Silicon Substrates Using Thermometer ions: Computational Details and Data Analysis
Description
This supporting information file includes detailed descriptions of the computational and fitting procedures used in the study, including density functional theory (DFT) computations, geometry optimizations of fragments, the integration of the Boltzmann distribution, fitting methodologies, error propagation analysis, and a comparison of results. It provides additional insights into the calculations and methods applied to ensure accurate analysis and validation of experimental findings.
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