Alexandre A. Shvartsburg

Bio: Alexandre A. Shvartsburg is an academic researcher from Wichita State University. The author has contributed to research in topics: Ion-mobility spectrometry & Mass spectrometry. The author has an hindex of 48, co-authored 129 publications receiving 8304 citations. Previous affiliations of Alexandre A. Shvartsburg include Northwestern University & University of Nevada, Reno.

Structural Information from Ion Mobility Measurements: Effects of the Long-Range Potential

Abstract: In a number of recent studies, information about the structure of large polyatomic ions has been deduced from gas phase ion mobility measurements by comparing mobilities measured in helium to those estimated for assumed geometries using a hard sphere projection approximation. To examine the validity of this approach, we have compared mobilities calculated using the hard sphere projection approximation for a range of fullerenes (C20−C240) to those determined from trajectory calculations with a more realistic He−fullerene potential. The He−fullerene potential we have employed, a sum of two-body 6-12 interactions plus a sum of ion-induced dipole interactions, was calibrated using the measured mobility of C60+ in helium over an 80−380 K temperature range. For the systems studied, the long-range interactions between the ion and buffer gas have a small, less than 10%, effect on the calculated mobility at room temperature. However, the effects are not insignificant, and in many cases it will be necessary to cons...

Structures of medium-sized silicon clusters

Abstract: Silicon is the most important semiconducting material in the microelectronics industry. If current miniaturization trends continue, minimum device features will soon approach the size of atomic clusters. In this size regime, the structure and properties of materials often differ dramatically from those of the bulk. An enormous effort has been devoted to determining the structures of free silicon clusters1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22. Although progress has been made for Sin with n < 8, theoretical predictions for larger clusters are contradictory2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22 and none enjoy any compelling experimental support. Here we report geometries calculated for medium-sized silicon clusters using an unbiased global search with a genetic algorithm. Ion mobilities23 determined for these geometries by trajectory calculations are in excellent agreement with the values that we measure experimentally. The cluster geometries that we obtain do not correspond to fragments of the bulk. For n = 12–18 they are built on a structural motif consisting of a stack of Si9 tricapped trigonal prisms. For n ⩾ 19, our calculations predict that near-spherical cage structures become the most stable. The transition to these more spherical geometries occurs in the measured mobilities for slightly larger clusters than in the calculations, possibly because of entropic effects.

Fundamentals of Traveling Wave Ion Mobility Spectrometry

Abstract: Traveling wave ion mobility spectrometry (TW IMS) is a new IMS method implemented in the Synapt IMS/mass spectrometry system (Waters). Despite its wide adoption, the foundations of TW IMS were only qualitatively understood and factors governing the ion transit time (the separation parameter) and resolution remained murky. Here we develop the theory of TW IMS using derivations and ion dynamics simulations. The key parameter is the ratio (c) of ion drift velocity at the steepest wave slope to wave speed. At low c, the ion transit velocity is proportional to the squares of mobility (K) and electric field intensity (E), as opposed to linear scaling in drift tube (DT) IMS and differential mobility analyzers. At higher c, the scaling deviates from quadratic in a way controlled by the waveform profile, becoming more gradual with the ideal triangular profile but first steeper and then more gradual for realistic profiles with variable E. At highest c, the transit velocity asymptotically approaches the wave speed. ...

Recommendations for reporting ion mobility Mass Spectrometry measurements

Abstract: Here we present a guide to ion mobility mass spectrometry experiments, which covers both linear and nonlinear methods: what is measured, how the measurements are done, and how to report the results, including the uncertainties of mobility and collision cross section values. The guide aims to clarify some possibly confusing concepts, and the reporting recommendations should help researchers, authors and reviewers to contribute comprehensive reports, so that the ion mobility data can be reused more confidently. Starting from the concept of the definition of the measurand, we emphasize that (i) mobility values (K0) depend intrinsically on ion structure, the nature of the bath gas, temperature, and E/N; (ii) ion mobility does not measure molecular surfaces directly, but collision cross section (CCS) values are derived from mobility values using a physical model; (iii) methods relying on calibration are empirical (and thus may provide method‐dependent results) only if the gas nature, temperature or E/N cannot match those of the primary method. Our analysis highlights the urgency of a community effort toward establishing primary standards and reference materials for ion mobility, and provides recommendations to do so.

Boundary Layer Theory

Abstract: The boundary layer equations for plane, incompressible, and steady flow are $$\matrix{ {u{{\partial u} \over {\partial x}} + v{{\partial u} \over {\partial y}} = - {1 \over \varrho }{{\partial p} \over {\partial x}} + v{{{\partial ^2}u} \over {\partial {y^2}}},} \cr {0 = {{\partial p} \over {\partial y}},} \cr {{{\partial u} \over {\partial x}} + {{\partial v} \over {\partial y}} = 0.} \cr }$$

Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields

Abstract: : The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

Structural properties of nanoclusters: Energetic, thermodynamic, and kinetic effects

Abstract: The structural properties of free nanoclusters are reviewed. Special attention is paid to the interplay of energetic, thermodynamic, and kinetic factors in the explanation of cluster structures that are actually observed in experiments. The review starts with a brief summary of the experimental methods for the production of free nanoclusters and then considers theoretical and simulation issues, always discussed in close connection with the experimental results. The energetic properties are treated first, along with methods for modeling elementary constituent interactions and for global optimization on the cluster potential-energy surface. After that, a section on cluster thermodynamics follows. The discussion includes the analysis of solid-solid structural transitions and of melting, with its size dependence. The last section is devoted to the growth kinetics of free nanoclusters and treats the growth of isolated clusters and their coalescence. Several specific systems are analyzed.

Structural relaxation made simple.

Abstract: We introduce a simple local atomic structure optimization algorithm which is significantly faster than standard implementations of the conjugate gradient method and often competitive with more sophisticated quasi-Newton schemes typically used in ab initio calculations. It is based on conventional molecular dynamics with additional velocity modifications and adaptive time steps. The surprising efficiency and especially the robustness and versatility of the method is illustrated using a variety of test cases from nanoscience, solid state physics, materials research, and biochemistry.

Protein Analysis by Shotgun/Bottom-up Proteomics

Abstract: According to Genome Sequencing Project statistics (http://www.ncbi.nlm.nih.gov/genomes/static/gpstat.html), as of Feb 16, 2012, complete gene sequences have become available for 2816 viruses, 1117 prokaryotes, and 36 eukaryotes.1–2 The availability of full genome sequences has greatly facilitated biological research in many fields, and has greatly contributed to the growth of proteomics. Proteins are important because they are the direct bio-functional molecules in the living organisms. The term “proteomics” was coined from merging “protein” and “genomics” in the 1990s.3–4 As a post-genomic discipline, proteomics encompasses efforts to identify and quantify all the proteins of a proteome, including expression, cellular localization, interactions, post-translational modifications (PTMs), and turnover as a function of time, space and cell type, thus making the full investigation of a proteome more challenging than sequencing a genome. There are possibly 100,000 protein forms encoded by the approximate 20,235 genes of the human genome,5 and determining the explicit function of each form will be a challenge. The progress of proteomics has been driven by the development of new technologies for peptide/protein separation, mass spectrometry analysis, isotope labeling for quantification, and bioinformatics data analysis. Mass spectrometry has emerged as a core tool for large-scale protein analysis. In the past decade, there has been a rapid advance in the resolution, mass accuracy, sensitivity and scan rate of mass spectrometers used to analyze proteins. In addition, hybrid mass analyzers have been introduced recently (e.g. Linear Ion Trap-Orbitrap series6–7) which have significantly improved proteomic analysis. “Bottom-up” protein analysis refers to the characterization of proteins by analysis of peptides released from the protein through proteolysis. When bottom-up is performed on a mixture of proteins it is called shotgun proteomics,8–10 a name coined by the Yates lab because of its analogy to shotgun genomic sequencing.11 Shotgun proteomics provides an indirect measurement of proteins through peptides derived from proteolytic digestion of intact proteins. In a typical shotgun proteomics experiment, the peptide mixture is fractionated and subjected to LC-MS/MS analysis. Peptide identification is achieved by comparing the tandem mass spectra derived from peptide fragmentation with theoretical tandem mass spectra generated from in silico digestion of a protein database. Protein inference is accomplished by assigning peptide sequences to proteins. Because peptides can be either uniquely assigned to a single protein or shared by more than one protein, the identified proteins may be further scored and grouped based on their peptides. In contrast, another strategy, termed ‘top-down’ proteomics, is used to characterize intact proteins (Figure 1). The top-down approach has some potential advantages for PTM and protein isoform determination and has achieved notable success. Intact proteins have been measured up to 200 kDa,12 and a large scale study has identified more than 1,000 proteins by multi-dimensional separations from complex samples.13 However, the top-down method has significant limitations compared with shotgun proteomics due to difficulties with protein fractionation, protein ionization and fragmentation in the gas phase. By relying on the analysis of peptides, which are more easily fractionated, ionized and fragmented, shotgun proteomics can be more universally adopted for protein analysis. In fact, a hybrid of bottom-up and top-down methodologies and instrumentation has been introduced as middle-down proteomics.14 Essentially, middle-down proteomics analyzes larger peptide fragments than bottom-up proteomics, minimizing peptide redundancy between proteins. Additionally the large peptide fragments yield similar advantages as top-down proteomics, such as gaining further insight into post-translational modifications, without the analytical challenges of analyzing intact proteins. Shotgun proteomics has become a workhorse for the analysis of proteins and their modifications and will be increasingly combined with top-down methods in the future. Figure 1 Proteomic strategies: bottom-up vs. top-down vs. middle-down. The bottom-up approach analyzes proteolytic peptides. The top-down method measures the intact proteins. The middle-down strategy analyzes larger peptides resulted from limited digestion or ... In the past decade shotgun proteomics has been widely used by biologists for many different research experiments, advancing biological discoveries. Some applications include, but are not limited to, proteome profiling, protein quantification, protein modification, and protein-protein interaction. There have been several reviews nicely summarizing mass spectrometry history,15 protein quantification with mass spectrometry,16 its biological applications,5,17–26 and many recent advances in methodology.27–32 In this review, we try to provide a full and updated survey of shotgun proteomics, including the fundamental techniques and applications that laid the foundation along with those developed and greatly improved in the past several years.