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An ion source concept is described where the sample flow is stopped in a confined volume of an ion mobility spectrometer creating time-dependent patterns of ion patterns of signal intensities for ions from mixtures of volatile organic compounds and improved signal-to-noise rate compared to conventional unidirectional drift gas flow. Hydrated protons from a corona discharge were introduced continuously into the confined volume with the sample in air at ambient pressure, and product ions were extracted continuously using an electric field for subsequent mobility analysis. Ion signal intensities for protonated monomers and proton bound dimers were measured and computationally extracted using mobilities from mobility spectra and exhibited distinct times of appearance over 30 s or more after sample injection. Models, and experimental findings with a ternary mixture, suggest that the separation of vapors as ions over time was consistent with differences in the reaction rate for reactions between primary ions from hydrated protons and constituents and from cross-reactions that follow the initial step of ionization. The findings suggest that the concept of stopped flow, introduced here for the first time, may provide a method for the temporal separation of atmospheric pressure ions. This separation relies on ion kinetics and does not require chromatographic technology.

Concentrations of volatile organic compounds (VOCs) in air can be reduced in electrostatic separators where VOCs are ionized using ion-molecule reactions, extracted using electric fields, and eliminated in a waste flow. Embodiments for such separator technology have been explored in only a few studies, despite the possible advantage of purification without adsorbent filters. In one design, based on ionization of VOCs in positive polarity with hydrated protons as reactant ions, efficiencies for removal were measured as 30-40% . The results were fitted to a one-dimensional convective diffusion model requiring an unexpectedly high production rate of reactant ions to match both the model and data. A realistic rate of reactant ion production was used in finite element method simulations (COMSOL) and demonstrated that low removal efficiency could be attributed to non-uniform patterns of sample flow and to incomplete mixing of VOCs with reactant ions. In analysis of complex systems, such as this model, even limited computational modeling can outperform a pure analytical approach and bring insights into limiting factors or system bottlenecks. Implications: In this work, we applied modern computational methods to understand the performance of an air purifier based on electrostatics and ionized volatile organic compounds (VOCs). These were described in the publication early 2000s. The model presented was one-dimensional and did not account for the effects of flow. In our multiphysics finite element models, the efficiency and operation of the filter is better explained by the patterns of flow and flow influences on ion distributions in electric fields. In general, this work helps using and applying computational modelling to understand and improve the performance bottlenecks in air purification system designs.

A model to quantitatively predict ion abundances from atmospheric pressure chemical ionization (APCI) between hydrated protons and a volatile organic compound (VOC) was extended to binary mixtures of VOCs. The model includes differences in vapor concentrations, rate coefficients, and reaction times and is enhanced with cross reactions between neutral vapors and protonated monomers. In this model, two specific VOCs were considered, a ketone, 6-methyl-5-hepten-2-one (M, and an amine, 2,6-di- tert -butyl-pyridine (N), with measured "conditional rate coefficients" (in cm 3 ·s -1 ) of k M = 1.11 × 10 -9 and k N = 9.17 × 10 -10 O) + (H 2 O) x O) + (H 2 O) y was measured as k cr = 1.31 × 10 -12 at 60 °C. Cross reactions showed an impact on ion abundances at t > 30 ms for equal vapor concentrations of 100 ppb for M and N. In contrast, this impact was negligible for vapor concentrations of 1 ppb and did not exceed 5% change in product ion abundance up to 1000 ms reaction times. The model was validated with laboratory measurements to within ∼10% using an ion mobility spectrometer and effective reaction time obtained from computational fitting of experimental findings. This was necessitated by complex flow patterns in the ion source volume and was determined as ∼10.5 ms. The model has interpretative and predictive value for quantitative analysis of responses with ambient pressure ion sources for mass spectrometry and ion mobility spectrometry.

Gas phase reactions between hydrated protons H + (H 2 O) n and a substance M, as seen in atmospheric pressure chemical ionization (APCI) with mass spectrometry (MS) and ion mobility spectrometry (IMS), were modeled computationally using initial amounts of [M] and [H + (H 2 O) n ], rate constants k 1 to form protonated monomer (MH + O) 2 O) x ) and k 2 to form proton bound dimer (M 2 O) + (H 2 O) z ), and diffusion constants. At 1 × 10 10 cm -3 O) + (H 2 O) n O) + (H 2 O) x and 7.8 μs to 46 ms for M 2 O) + (H 2 O) z . Maximum yield for protonated monomer for a reaction time of 1 ms was ∼40% for k 1 from 10 -11 to 10 -8 cm 3 ·s -1 , for k 2 O) k 1 = 0.8, and specific values of [M]. This model demonstrates that ion distributions could be shifted from [M 2 O) + (H 2 O) z O) + (H 2 O) x ] using excessive levels of [H + (H 2 O) n ], even for [M] > 10 ppb, as commonly found in APCI MS and IMS measurements. Ion losses by collisions on surfaces were insignificant with losses of <0.5% for protonated monomer and <0.1% for proton bound dimer of dimethyl methylphosphonate (DMMP) at 5 ms. In this model, ion production in an APCI environment is treated over ranges of parameters important in mass spectrometric measurements. The models establish a foundation for detailed computations on response with mixtures of neutral substances.

The abundance of ions is an essential parameter for ion mobility and mass spectrometry instrument design and for the control or optimization of chemical reactions with reactant ions. This information also advances the study of atmospheric pressure ion kinetics under continuous ionization, which has a role in developing trace level chemical analyzers. In this study, an ionization chamber is described to measure the abundance of ions produced by a 4.9 keV, model L12535, soft x-ray source from Hamamatsu Corporation. Ions of positive and negative polarity were measured independently in an 8 × 30 mm 2 cross section at distances of 12-136 mm at ambient air from an uncollimated beam. Ions were collected using electric fields and 16 sets of plates. The ion current decreased exponentially with distance from the source, and the calculated ion concentration varied between 1.0 × 10 8 and 3.8 × 10 5 ions cm -3 on plates. A 2D-COMSOL model including losses by recombination and diffusion was favorably matched to changes in ion current intensity in the ionization chamber. Although the ionization chamber was built to characterize a commercial ion source, the design may be considered generally applicable to other x-ray sources.

Differential mobility spectrometry (DMS) analysis of electrosurgical smoke can be used to distinguish cancerous and healthy tissues. Mass spectrometry studies of surgical smoke have revealed phospholipids as the key compounds enabling this discrimination. Lecithin is a mixture of phospholipids encountered in tissues. We hypothesized that DMS is capable of detecting and quantifying lecithin from water solution in headspace chamber, paving way for analysis of surgical smoke. We measured different lecithin concentrations in a biologically relevant range considering healthy and cancerous tissues with DMS and trained regression models to predict the analyte concentration. The models were internally cross-validated and externally validated. The best cross-validation results were obtained with convolutional neural networks, with root mean square error (RMSE) = 0.38 mg/ml. This is the first demonstration of estimation of analyte concentration from DMS measurements with neural networks. The best external validation results were acquired with sparse linear regression methods, with RMSE varying from 0.40 mg/ml to 0.41 mg/ml. The results demonstrate that DMS is sufficiently sensitive to detect biologically relevant changes in phospholipid concentration, potentially explaining its ability to detect cancerous tissue. In the future, we aim to reproduce the results by using surgical smoke as the medium. In this scenario, the complex background of surgical smoke will be the main challenge to overcome. Predicting concentration with neural networks also lays the foundation for wider analytical usage of DMS., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Pathologic examination of clinical tissue samples is time consuming and often does not involve the comprehensive analysis of the whole specimen. Automated tissue analysis systems have potential to make the workflow of a pathologist more efficient and to support in clinical decision-making. So far, these systems have been based on application of mass spectrometry imaging (MSI). MSI provides high fidelity and the results in tissue identification are promising. However, the high cost and need for maintenance limit the adoption of MSI in the clinical setting. Thus, there is a need for new innovations in the field of pathological tissue imaging. In this study, we show that differential ion mobility spectrometry (DMS) is a viable option in tissue imaging. We demonstrate that a DMS-driven solution performs with up to 92% accuracy in differentiating between two grossly distinct animal tissues. In addition, our model is able to classify the correct tissue with 81% accuracy in an eight-class setting. The DMS-based system is a significant innovation in a field dominated by mass-spectrometry-based solutions. By developing the presented platform further, DMS technology could be a cost-effective and helpful tool for automated pathological analysis., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Aim: Rapid identification of bacteria would facilitate timely initiation of therapy and improve cost-effectiveness of treatment. Traditional methods (culture, PCR) require reagents, consumables and hours to days to complete the identification. In this study, we examined whether differential mobility spectrometry could classify most common bacterial species, genera and between Gram status within minutes. Materials & methods: Cultured bacterial sample gaseous headspaces were measured with differential mobility spectrometry and data analyzed using k-nearest-neighbor and leave-one-out cross-validation. Results: Differential mobility spectrometry achieved a correct classification rate 70.7% for all bacterial species. For bacterial genera, the rate was 77.6% and between Gram status, 89.1%. Conclusion: Largest difficulties arose in distinguishing bacteria of the same genus. Future improvement of the sensor characteristics may improve the classification accuracy.

Aspiration ion mobility spectrometry (IMS) has been used for the first time to screen 3,3,6,6,9,9-hexamethyl-1,2,4,5,7,8-hexaoxacyclononane explosive, the most commonly known as triacetone triperoxide (TATP). Gaseous TATP was generated from synthesized solid compound, sublimed and directed to a portable chemical detection system comprised of an aspiration-type IMS detector and six semiconductor sensors. Different unknown TATP gas phase concentrations were produced and corresponding IMS and semiconductor responses were measured. The experimental concentrations were determined by gas chromatography-mass spectrometry (GC-MS). The results evidenced that the monitored compound in the gas phase was TATP. In addition, the determined TATP concentrations and corresponding IMS intensities showed that the IMS response values were proportional to the measured TATP concentrations.