Ion density of positive and negative ions at ambient pressure in air at 12-136 mm from 4.9 kV soft x-ray source

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 x 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 x 10(8) and 3.8 x 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. (C) 2021 Author(s).Peer reviewe

Parametric Sensitivity in a Generalized Model for Atmospheric Pressure Chemical Ionization Reactions

Gas phase reactions between hydrated protons H+(H2O)(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+(H2O)(n)], rate constants k(1) to form protonated monomer (MH+(H2O)(x)) and k(2) to form proton bound dimer (M2H+(H2O)(z)), and diffusion constants. At 1 x 10(10) cm(-3) (0.4 ppb) for [H+(H2O)(n)] and vapor concentrations for M from 10 ppb to 10 ppm, a maximum signal was reached at 4.5 mu s to 4.6 ms for MH+(H2O)(x) and 7.8 mu s to 46 ms for M2H+(H2O)(z). Maximum yield for protonated monomer for a reaction time of 1 ms was similar to 40% for k(1) from 10(-11) to 10(-8) cm(3).s(-1), for k(2)/k(1) = 0.8, and specific values of [M]. This model demonstrates that ion distributions could be shifted from [M2H+(H2O)(z)] to [MH+(H2O)(x)] using excessive levels of [H+(H2O)(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 ofPeer reviewe

Clustering of Alpha Curves in Differential Mobility Spectrometry Data

Volatile organic compounds (VOC) can be analyzed and classified based on dispersion plots yielded by Differential Mobility Spectrometry (DMS). These dispersion plots contain traces, also known as alpha curves that indicate the presence of VOCs with specific charge and cross-sectional area. However, often the dispersion plots are analyzed without utilizing this information but rather using the pixel values independently or analyzing values at a specific high electric field level. This paper proposes a technique for extracting and clustering alpha curves from dispersion plots. The cluster information could then be used inside existing detection and classification algorithms to potentially improve their accuracyacceptedVersionPeer reviewe

Characterization of signal kinetics in real time surgical tissue classification system

Effective surgical margin assessment is paramount for good oncological outcomes and new methods are in active development. One emerging approach is the analysis of the chemical composition of surgical smoke from tissues. Surgical smoke is typically removed with a smoke evacuator to protect the operating room staff from its harmful effects to the respiratory system. Thus, analysis of the evacuated smoke without disturbing the operation is a feasible approach. Smoke transportation is subject to lags that affect system usability. We analyzed the smoke transportation delay and evaluated its effects to tissue classification with differential mobility spectrometry in a simulated setting using porcine tissues. With a typical smoke evacuator setting, the front of the surgical plume reaches the analysis system in 380 ms and the sensor within one second. For a typical surgical incision (duration 1.5 s), the measured signal reaches its maximum in 2.3 s and declines to under 10% of the maximum in 8.6 s from the start of the incision. Two-class tissue classification was tested with 2, 3, 5, and 11 s repetition rates resulting in no significant differences in classification accuracy, implicating that signal retention from previous samples is mitigated by the classification algorithm.publishedVersionPeer reviewe

Real Time Tissue Identification from Diathermy Smoke by Differential Mobility Spectrometry

Current methods for intraoperative surgical margin assessment are inadequate in terms of diagnostic accuracy, ease-of-use, and speed of analysis. Molecular analysis of tissues could potentially overcome these issues. A system based on differential ion mobility spectrometry (DMS) analysis of surgical smoke has been proposed as one potential method, but to date, it has been able to function in a relatively slow and heavily controlled manner that is inadequate for clinical use. In this study, we present an integrated sensor system that can measure a surgical smoke sample in seconds and relay the information of the tissue type to the user in near real time in simulated surgical use. The system was validated by operating porcine adipose tissue and muscle tissue. The differentiation of these tissues based on their surgical smoke profile with a cross-validated linear discriminant analysis model produced a classification accuracy of 93.1% (N = 1059). The measurements were also classified with a convolutional neural network model, resulting in a classification accuracy of 93.2%. These results indicate that the DMS-based smoke analysis system is capable of rapid tissue identification from surgical smoke produced in freehand surgery.acceptedVersionPeer reviewe

Computational analysis of an electrostatic separator design for removal of volatile organic compounds from indoor air

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.Peer reviewe