Samar K. Guharay

Ion Mobility Spectrometry: Ion Source Development and Applications in Physical and Biological Sciences

The utility of ion mobility spectrometry (IMS) has been steadily growing, and it cuts across diverse areas in physical and biological sciences. The development of ion sources, particularly in the context of IMS, is described. IMS ion sources operate efficiently in ambient environment and yield ions for a wide range of complex molecules including biological materials. While significant progress has been made in this area through the development of a variety of ion sources, further research to address several key issues, namely, ionization processes, reaction chemistry, and overall system miniaturization for field deployment of IMS, is the primary focus of current activities. Aside from reviewing the present state of the art of ion sources for IMS, this paper has discussed the wide range of applications and current trends of research in the field.

Modular Ion Mobility Spectrometer for Explosives Detection Using Corona Ionization

Ion mobility spectrometry (IMS) has become the most widely used technology for trace explosives detection. A key task in designing IMS systems is to balance the explosives detection performance with size, weight, cost, and safety of the instrument. Commercial instruments are, by and large, equipped with radioactive (63)Ni ionization sources which pose inherent problems for transportation, safety, and waste disposal regulation. An alternative to a radioactive source is a corona discharge ionization source, which offers the benefits of simplicity, stability, and sensitivity without the regulatory problems. An IMS system was designed and built based on modeling and simulation with the goal to achieve a lightweight modular design that offered high performance for the detection of trace explosives using a corona ionization source. Modeling and simulations were used to investigate design alternatives and optimize parameters. Simulated spectra were obtained for 2,4,6-trinitrotoluene (TNT) and cyclo-1,3,5-trimethylene-2,4,6-trinitramine (RDX) and showed good agreement with experimentally measured spectra using a corona ionization source. The reduced mobilities for TNT and RDX obtained with corona ionization were 1.53 and 1.46 cm(2)/(V s), respectively, and this agreed well with literature values.

Effect of space charge on resolving power and ion loss in ion mobility spectrometry.

The effect of space charge on the performance of an Ion Mobility Spectrometry (IMS) system becomes more important as the system is made smaller. We use the SIMION software package with the Statistical Diffusion Simulation (SDS) module and SIMIONs new capability to solve the Poisson equation to study the effect of space charge on ion loss and resolving power in IMS systems. We consider IMS systems ranging in length from 50 mm to 150 mm and in diameter from 8.33 mm to 50 mm with a fixed electric field of 50 V/mm. We also examine a system with a length of 50 mm, a diameter of 16.7 mm, and an electric field of 16.7 V/mm. We assume that any charge density can be injected into the IMS system, and we have obtained expressions that predict the ion loss and resolving power of IMS systems as a function of input charge density and drift tube aspect ratio (length/diameter).

Predicting Raman Spectra Using Density Functional Theory

Accurately computing molecular Raman spectra would enable rapid development of inexpensive and extensive Raman libraries. This is especially beneficial for chemicals that are regulated, toxic, or otherwise difficult to handle. Numerous quantum mechanical methods have been developed that enable computation of Raman spectra. Here, we study the B3LYP exchange correlation functional with various combinations of basis sets, polarization functions, and diffuse functions to determine which combination best computes the Raman spectra for explosive and nonexplosive molecules. In comparing spectra, three metrics were utilized: the root mean square error, the earth movers distance, and the weighted cross-correlation average. The earth movers distance and weighted cross-correlation metrics are shown to have significantly greater power at detecting spectral similarities and differences than the root mean square error. Across all methods and molecules examined, B3LYP/6-311++G(d,p) was found to provide the best match between measured and computed Raman spectra. Spectra generated at the B3LYP/6-311++G(d,p) level were found to be accurate enough to correctly identify each molecule out of a set of measured molecular spectra.

A Structured Approach for Rapidly Mapping Multilevel System Measures via Simulation Metamodeling

With increasing complexity of real-world systems, especially for continuously evolving scenarios, systems analysts encounter a major challenge with the modeling techniques that capture detailed system characteristics defining input-output relationships. The models become very complex and require long time of execution. In this situation, techniques to construct approximations of the simulation model by metamodeling alleviate long run times and the need for large computational resources; it also provides a means to aggregate a simulations multiple outputs of interest and derives a single decision-making metric. The method described here leverages simulation metamodeling to map the three basic SE metrics, namely, measures of performance to measures of effectiveness to a single figure of merit. This enables using metamodels to map multilevel system measures supports rapid decision making. The results from a case study demonstrate the merit of the method. Several metamodeling techniques are compared and bootstrap error analysis and predicted residual sums of squares statistic are discussed to evaluate the standard error and error due to bias.

Ionization, transport, separation, and detection of ions in non-electrolyte containing liquids.

Liquid phase ion mobility spectrometry (LPIMS) has the potential to be miniaturized such that it can be incorporated into chip based technology, providing higher performance in terms of both detection sensitivity and resolving power than is currently available by other separation technologies such as gas phase IMS, chromatography, or electrophoresis. This work presents modeling, simulation, and experimental investigations to characterize the mobility of ions in a liquid phase. This study included the ionization, transfer, separation, and detection of ions in non-electrolyte liquids. Using a resistive glass tube, mobility spectra were obtained by pulsed ionization for several different analytes, namely, tetramethylammonium chloride, tetrabutylammonium chloride, and dimethyl methylphosphonate (DMMP). Ion separation was demonstrated by separating solvent ions from the ions generated from the test compounds. Simulation and theoretical resolving power calculations were made to validate the experimental mobility measurements. A parametric study on the dependence of IMS resolving power on drift length, voltage across drift cell, and pulse width determined the requirements for designing a miniaturized IMS system, approximately the centimeter scale, with high performance, resolving power approaching 100 or higher. Mobility spectra are used for the first time to determine the diffusion coefficients of ions in a liquid.

Configuration of a standoff detection system via rapid, model-based systems engineering

This paper presents a model-based systems engineering approach developed for rapid analysis of complex systems, not requiring the use of high computational resources. The basis of the approach involves the mapping of three basic systems engineering metrics, namely, Measures of Performance (MOP) to Measures of Effectiveness (MOE) to a single Figure of Merit (FOM), through metamodeling. Through this approach, analysts can leverage validated metamodels to map system measures, from component level MOPs to the overall system FOM in real-time to support decisions under constrained time-frames. Through metamodeling we achieve approximations of the simulation model in mathematical form, which alleviates long run times and the need for large computational resources. The metamodels also provide an effective means to aggregate a simulations multiple outputs of interest via a preference function. These two approaches together form the foundation of this rapid, model-based systems engineering approach. The effectiveness of this model-based approach is demonstrated on configuring a standoff detection system.

Critical infrastructure network analysis enabled by simulation metamodeling

This paper presents an application of simulation metamodeling to improve the analysis capabilities within a decision support tool for Critical Infrastructure network evaluation. Simulation metamodeling enables timeliness of analysis, which was not achievable by the original large-scale network simulation due to long set-up times and slow run times. We show through a case study that the behavior of a large-scale simulation for Critical Infrastructure analysis can be effectively captured by Neural Network metamodels and Stochastic Kriging metamodels. Within the case study, metamodeling is integrated into the second step of a two-step analysis process for vulnerability assessment of the network. This consists first of an algorithmic exploration of a power grid network to locate the most susceptible links leading to cascading failures. These links represent the riskiest links in the network and were used by the metamodels to visualize how their failure probabilities affect global network performance measures.

A case study examining the impact of factor screening for neural network metamodels

Metamodeling of large-scale simulations consisting of a large number of input parameters can be very challenging. Neural Networks have shown great promise in fitting these large-scale simulations even without performing factor screening. However, factor screening is an effective method for logically reducing the dimensionality of an input space and thus enabling more feasible metamodel calibration. Applying factor screening methods before calibrating Neural Network metamodels or any metamodel can have both positive and negative effects. The critical assumption for factor screening under investigation involves the prevalence of two-way interactions that contain a variable without a significant main effect by itself. In a simulation with a large parameter space, the prevalence of two-way interactions and their contribution to the total variability in the model output is far from transparent. Important questions therefore arise regarding factor screening and Neural Network metamodels: (a) is this a process worth doing with todays more powerful computing processors, which provide a larger library of runs to do metamodeling; and (b), does erroneously screening these buried interaction terms critically impact the level of metamodel fidelity that one can achieve. In this paper we examine these questions through the construction of a case study on a large-scale simulation. This study projects regional homelessness levels per county of interest based on a large array of budget decisions and resource allocations that expand out to hundreds of input parameters.

Metamodeling of simulations consisting of time series inputs and outputs

Long run times of a simulation can be a hindrance when an analyst is attempting to use the model for timely system analysis and optimization. In this situation, techniques such as simulation metamodeling should be considered to expedite the end users intended analysis procedure. A difficult problem arises in the application of metamodeling when the simulation inputs and outputs are not of a single value, but constitute a time series, a phenomenon that is seen repeatedly in the area of financial simulations and many naturally occurring events. This paper provides a method to develop a mapping between multiple time series inputs of a simulation and a single Figure of Merit (FoM) of the system across a given time period of interest. In addition, this paper discusses a means for an end user to define a tailored FoM with respect to their own specific system beliefs and objectives in the case of multiple simulation outputs.