Tandem differential mobility spectrometry with chemical modification of ions

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification

Differential mobility spectrometry (DMS)-based detectors are being widely studied to detect chemical warfare agents, explosives, chemicals, drugs and analyze volatile organic compounds (VOCs). The dispersion plots from DMS devices are complex to effectively analyze through visual inspection. In the current work, we adopted machine learning to differentiate pure chemicals and identify chemicals in a mixture. In particular, we observed the convolutional neural network algorithm exhibits excellent accuracy in differentiating chemicals in their pure forms while also identifying chemicals in a mixture. In addition, we propose and validate the magnitude-squared coherence (msc) between the DMS data of known chemical composition and that of an unknown sample can be sufficient to inspect the chemical composition of the unknown sample. We have shown that the msc-based chemical identification requires the least amount of experimental data as opposed to the machine learning approach.

Differential mobility spectrometry/mass spectrometry history, theory, design optimization, simulations, and applications

This review of differential mobility spectrometry focuses primarily on mass spectrometry coupling, starting with the history of the development of this technique in the Soviet Union. Fundamental principles of the separation process are covered, in addition to efforts related to design optimization and advancements in computer simulations. The flexibility of differential mobility spectrometry design features is explored in detail, particularly with regards to separation capability, speed, and ion transmission. 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:687-737, 2016.

Tandem differential mobility spectrometry in purified air for high-speed selective vapor detection

A tandem ion mobility instrument based on differential mobility spectrometry (DMS) was used to demonstrate selectivity in response through differences in field dependence of mobility for ions in purified air at ambient pressure. The concept of chemical selectivity solely from characteristic dispersion curves or from field dependence of ion mobility was experimentally demonstrated in three steps with mixtures of increasing complexity. In a mixture of four alcohols with carbon numbers four and below, distinct pairs of separation voltage and compensation voltage, applied to the first and second DMS stages, permitted isolation of ions from individual substances without detectable levels of other substances. In a three-component mixture of a ketone, alcohol, and organophosphorus compound, the same level of ion isolation was observed using specific and characteristic separation and compensation voltages on each DMS stage. In the last experiment, the isolation of product ions of individual substances from a mixture of 23 volatile organic compounds from four chemical groups was incomplete though the improvement in the ratio of analyte signal to chemical noise was calculated as 31 for DMMP and 106 for 1-hexanol. These findings demonstrate that chemical information available in dispersion curves can be accessed in response times below 100 ms through a tandem DMS measurement.