Field Induced Fragmentation (Fif) Spectra of Oxygen Containing Volatile Organic Compounds with Reactive Stage Tandem...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Chemical Physics

Background:

• Ion mobility spectrometry (IMS) is a powerful technique for analyzing chemical compounds.
• Tandem IMS, incorporating a reactive stage, enhances analytical capabilities.
• Characterizing volatile organic compounds (VOCs) is crucial for environmental and industrial monitoring.

Purpose of the Study:

• To investigate the application of field induced fragmentation (FIF) in tandem IMS for analyzing protonated monomers of oxygen-containing organic compounds.
• To determine the effectiveness of FIF in differentiating chemical classes based on fragmentation patterns.
• To explore the potential of neural networks for classifying spectra obtained through FIF-IMS.

Main Methods:

• Protonated monomers of 42 VOCs were analyzed using tandem IMS with a reactive stage.
• Field induced fragmentation (FIF) was induced using a sinusoidal waveform.
• Fragment ion spectra were analyzed for characteristic drift times and peak intensities.
• Neural networks were trained and tested for spectral categorization by chemical class.

Main Results:

• FIF effectively fragmented alcohols via single bond cleavage and acetates via six-member ring rearrangements.
• Fragmentation was limited for aldehydes, ethers, and ketones due to strained transition states.
• Neural network categorization performance varied by chemical class, with alcohols and acetates showing the best results.
• Steric influences were identified as key factors affecting fragmentation efficiency and categorization rates.

Conclusions:

• FIF in tandem IMS shows promise for VOC analysis, particularly for alcohols and acetates.
• Further advancements in electric field strength or reactive stage design are necessary for broader application to other VOCs.
• The study highlights the potential of FIF-IMS coupled with machine learning for chemical classification.