Field Induced Fragmentation (Fif) Spectra of Oxygen Containing Volatile Organic Compounds with Reactive Stage Tandem Ion Mobility Spectrometry and Functional Group Classification by Neural Network Analysis

Detection of Triacetone Triperoxide by High Kinetic Energy Ion Mobility Spectrometry

High Kinetic Energy Ion Mobility Spectrometry (HiKE-IMS) is a versatile technique for the detection of gaseous target molecules that is particularly useful in complex chemical environments, while the instrumental effort is low. Operating HiKE-IMS at reduced pressures from 10 to 60 mbar results in fewer ion-neutral collisions than at ambient pressure, reducing chemical cross-sensitivities and eliminating the need for a preceding separation dimension, e.g., by gas chromatography. In addition, HiKE-IMS allows operation over a wide range of reduced electric field strengths E/N up to 120 Td, allowing separation of ions by low-field ion mobility and exploiting the field dependence of ion mobility, potentially allowing separation of ion species at high E/N despite similar low-field ion mobilities. Given these advantages, HiKE-IMS can be a useful tool for trace gas analysis such as triacetone triperoxide (TATP) detection. In this study, we employed HiKE-IMS to detect TATP. We explore the ionization of TATP and the field-dependent ion mobilities, providing a database of the ion mobilities depending on E/N. Confirming the literature results, ionization of TATP by proton transfer with H3O+ in HiKE-IMS generates fragments, but using NH4+ as the primary reactant ion leads to the TATP·NH4+ adduct. This adduct fragments at high E/N, which could provide additional information for reliable detection of TATP. Thus, operating HiKE-IMS at variable E/N in the drift region generates a unique fingerprint of TATP made of all ion species related to TATP and their ion mobilities depending on E/N, potentially reducing the rate of false positives.

Neural network classification of mobility spectra for volatile organic compounds using tandem differential mobility spectrometry with field induced fragmentation

A spectral library of field induced fragmentation (FIF) spectra for 45 oxygen-containing volatile organic compounds from 5 chemical classes was obtained using tandem differential mobility spectrometry (DMS). Protonated monomers were mobility isolated in a first DMS stage, fragmented with electric fields >10,000 V/cm in a middle (or reactive) stage, and mobility characterized in a second DMS stage. Other spectral libraries were obtained for protonated monomers and for complete mobility spectra from a single DMS stage. Neural networks from Python/Tensorflow software, prepared in-house, and from commercial NeuralWorks Professional II/PLUS were trained to assign spectra into a chemical class. The success at classification was determined for familiar and unfamiliar spectra from these three libraries. Classification test scores were best with FIF spectra with >0.99 for familiar compounds and 0.52 for unfamiliar compounds and were consistent with neural network learning of structural information from fragment ions when compared to other spectral libraries. Radar charts are introduced as measures of classification and as a tool to explore mis-classification. This work shows that ion fragmentation with multi-stage tandem DMS portends molecular identification with the portability and robustness of ambient pressure ion mobility analyzers.

Highly Efficient Ion Manipulator for Tandem Ion Mobility Spectrometry: Exploring a Versatile Technique by a Study of Primary Alcohols

In this work, we present a tandem ion mobility spectrometer (IMS) utilizing a highly efficient ion manipulator allowing to store, manipulate, and analyze ions under high electric field strengths and controlled ion-neutral reactions at ambient conditions. The arrangement of tandem drift regions and an ion manipulator in a single drift tube allows a sequence of mobility selection of precursor ions, followed by storage and analysis, mobility separation, and detection of the resulting product ions. In this article, we present a journey exploring the capabilities of the present instrument by a study of eight different primary alcohols characterized at reduced electric field strengths E/N of up to 120 Td with a water vapor concentration ranging from 40 to 540 ppb. Under these conditions, protonated alcohol monomers up to a carbon number of nine could be dissociated, resulting in 18 different fragmented product ions in total. The fragmentation patterns revealed regularities, which can be used for assignment to the chemical class and improved classification of unknown substances. Furthermore, both the time spent in high electrical field strengths and the reaction time with water vapor can be tuned precisely, allowing the fragment distribution to be influenced. Thus, further information regarding the relations of the product ions can be gathered in a standalone drift tube IMS for the first time.

Geometric Feature Extraction of Point Cloud of Chemical Reactor Based on Dynamic Graph Convolution Neural Network

Geometric features are an important factor for the classification of drugs and other transport objects in chemical reactors. The moving speed of drugs and other transport objects in chemical reactors is fast, and it is difficult to obtain their features by imaging and other methods. In order to avoid the mistaken and missed distribution of drugs and other objects, a method of extracting geometric features of the drug's point cloud in a chemical reactor based on a dynamic graph convolution neural network (DGCNN) is proposed. In this study, we first use MATLAB R2019a to add a random number of noise points in each point cloud file and label the point cloud. Second, k-nearest neighbor (KNN) is used to construct the adjacency relationship of all nodes, and the effect of DGCNN under different k values and the confusion matrix under the optimal k value are analyzed. Finally, we compare the effect of DGCNN with PointNet and PointNet++. The experimental results show that when k is 20, the accuracy, precision, recall, and F1 score of DGCNN are higher than those of other k values, while the training time is much shorter than that of k = 25, 30, and 35; in addition, the effect of DGCNN in extracting geometric features of the point cloud is better than that of PointNet and PointNet++. The results show that it is feasible to use DGCNN to analyze the geometric characteristics of drug point clouds in a chemical reactor. This study fills the gap of the end-to-end extraction method for a point cloud's corresponding geometric features without a data set. In addition, this study promotes the institutionalization, standardization, and intelligent design of safe production and management of drugs and other objects in the chemical reactor, and it has positive significance for the production cost and resource utilization of the whole pharmaceutical process. At the same time, it provides a new method for the intelligent processing of point cloud data.

Field induced displacement reactions with proton bound dimers of organophosphorus compounds in a tandem differential mobility spectrometer

Endothermic displacement reactions between proton bound dimers of organophosphorus compounds (OPCs) and isopropanol (IPA) were enabled in air at ambient pressure with tandem differential mobility spectrometry (DMS). Proton bound dimers (M2H+) were mobility isolated in purified air with a first DMS stage, mixed with IPA at ≥100 ppm in a middle reactive stage at 106 to 160 Td from a symmetrical 4 MHz waveform, and mobility analyzed in a second DMS stage. Although the enthalpy for displacement of M by IPA in M2H+ is unfavorable by +44 to 50 kJ mol-1, formation of the heterogenous proton bound dimer, MH+(IPA) arises from field induced dissociation of M2H+ to MH+ and addition of IPA. While peak dispersion for M2H+ of OPCs is limited to -2.25 to -0.5 V compensation voltage, peaks for MH+(IPA) were located at -10.5 to -8.25 V through a combination of ion transformation and mobility-based vapor modification. This inaugural use of ion reactions in air at ambient pressure demonstrates that multi-stage sequential processing of ions can improve significantly the analytical performance in a mobility spectrometer.

Quantitative response to nitrite from field-induced decomposition of the chloride adduct of RDX by reactive stage tandem ion mobility spectrometry

An additional dimension of selectivity for the determination of RDX by ion mobility spectrometry (IMS) was introduced through field-induced decomposition of RDX·Cl^- to NO₂^- on a spectral baseline free of interfering peaks. In this variant of reactive stage tandem IMS, the explosive ion is decomposed selectively in the presence of an interferent and from significantly convolved peaks which were mobility isolated within a narrow range of drift times using dual ion shutters. Field-induced decomposition at 170 °C and field strength of 112 Td (∼16 kV cm^-1) provided 15% decomposition yield and RDX, amid interferent, was detected decisively even when peaks differed in reduced mobility coefficients (K_o) by only 0.02 cm² V^-1 s^-1. A nitrite peak with S/N of 8.5 was observed with vapour concentrations of 54 ppb for RDX and 329 ppb for Interferent A in the ionization volume corresponding to 2 ng of RDX and 100 ng of Interferent A deposited on sample traps in the thermal desorption inlet. Findings on quantitative response suggest the presence of excessive amounts of interferent caused ionization suppression of RDX. Still, RDX was determined quantitatively using sequential processing of ions by mobility isolation, selective field induced decomposition, and mobility analysis in a second drift region.

Field induced fragmentation spectra from reactive stage-tandem differential mobility spectrometry

A planar tandem differential mobility spectrometer was integrated with a middle reactive stage to fragment ions which were mobility selected in a first analyzer stage using characteristic compensation and separation fields.