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Journal articles on the topic 'Dynamic SIMS'

Author: Grafiati

Published: 28 June 2021

Last updated: 1 February 2022

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1

JUNKER, E., K. P. WIRTH, and F. W. RÖLLGEN. "DYNAMIC SIMS OF SUPERSATURATED SOLUTIONS." Le Journal de Physique Colloques 50, no. C2 (February 1989): C2–53—C2–58. http://dx.doi.org/10.1051/jphyscol:1989210.

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2

Garrison, Barbara J., Zachary J. Schiffer, Paul E. Kennedy, and Zbigniew Postawa. "Modeling dynamic cluster SIMS experiments." Surface and Interface Analysis 45, no. 1 (March 1, 2012): 14–17. http://dx.doi.org/10.1002/sia.4905.

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3

Lakens, Daniël, and Kirsten I. Ruys. "The dynamic interaction of conceptual and embodied knowledge." Behavioral and Brain Sciences 33, no. 6 (December 2010): 449–50. http://dx.doi.org/10.1017/s0140525x10001329.

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AbstractWe propose the SIMS model can be strengthened by detailing the dynamic interaction between sensorimotor activation and contextual conceptual information. Rapidly activated evaluations and contextual knowledge can guide and constrain embodied simulations. In addition, we stress the potential importance of extending the SIMS model to dynamic social interactions that go beyond the passive observer.

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4

Davis, AN, P. Peres, A. Merkulov, F. Desse, S.-Y. Choi, and M. Schuhmacher. "Dynamic SIMS Applications for Photovoltaic Technology Development." Microscopy and Microanalysis 16, S2 (July 2010): 1392–93. http://dx.doi.org/10.1017/s1431927610062975.

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5

Peres, P., A. Merkulov, S. Y. Choi, F. Desse, and M. Schuhmacher. "Characterization of LED materials using dynamic SIMS." Surface and Interface Analysis 45, no. 1 (May 15, 2012): 437–40. http://dx.doi.org/10.1002/sia.4952.

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6

Linton, Richard W. "Direct Imaging of Trace Elements, Isotopes, and Molecules Using Mass Spectrometry." Microscopy and Microanalysis 4, S2 (July 1998): 124–25. http://dx.doi.org/10.1017/s1431927600020742.

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Secondary ion mass spectrometry (SIMS) is based upon the energetic ion bombardment of surfaces resulting in in the emission of sputtered particles, including both atomic and molecular ions. The use of mass spectrometric detection provides a highly versatile and sensitive tool for surface and thin film microanalysis. The scope of the technique includes a diversity of analysis modes including:1.Elemental Depth Profiling (dynamic SIMS),2.Laterally Resolved Imaging (ion microprobe or ion microscope analysis),3.Image Depth Profiling (combination of modes 1 and 2 providing 3-D images),4.Molecular Monolayer Analysis and Imaging (static SIMS),5.Sputtered Neutral Mass Spectrometry (post-ionization).Much of the early work in dynamic SIMS centered on depth profiling and imaging techniques, with an emphasis on applications to electronic materials. SIMS has made extensive contributions to semiconductor materials science since the 1960's, including the development of new devices and processes, and in failure analysis.

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7

Linton, Richard W. "Secondary ion mass spectroscopy in the biological and materials sciences." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 498–99. http://dx.doi.org/10.1017/s0424820100148320.

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Secondary ion mass spectrometry (SIMS) is based upon energetic ion bombardment of surfaces resulting in in the emission of sputtered particles, including both atomic and molecular ions. The use of mass spectrometric detection provides a highly versatile and sensitive tool for surface and thin film chemical analysis. In recent years, the scope of the technique has broadened to include a variety of analysis modes including:1.Elemental Depth Profiling (dynamic SIMS),2.Laterally Resolved Imaging (ion microprobe or ion microscope analysis),3.Image Depth Profiling (combination of modes 1 and 2 providing 3-D images),4.Molecular Monolayer Analysis (static SIMS),5.Sputtered Neutral Mass Spectrometry (post-ionization).Much of the early work in dynamic SIMS centered on the development of depth profiling and imaging techniques, with an emphasis on applications to electronic materials. SIMS has made extensive contributions to semiconductor materials science since the 1960's, including the development of new devices and processes, and in failure analysis.

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8

Salaita, Ghaleb N., and Gar B. Hoflund. "Dynamic SIMS study of Cr3C2, Cr7C3 and Cr23C6." Applied Surface Science 134, no. 1-4 (September 1998): 194–96. http://dx.doi.org/10.1016/s0169-4332(98)00246-3.

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9

Peres, Paula, Seo-Youn Choi, François Desse, Philippe Bienvenu, Ingrid Roure, Yves Pipon, Clotilde Gaillard, Nathalie Moncoffre, Lola Sarrasin, and Denis Mangin. "Dynamic SIMS for materials analysis in nuclear science." Journal of Vacuum Science & Technology B, Nanotechnology and Microelectronics: Materials, Processing, Measurement, and Phenomena 36, no. 3 (May 2018): 03F117. http://dx.doi.org/10.1116/1.5017027.

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10

Fichtner, M., J. Goschnick, and H. J. Ache. "Identification of nitrates and sulphates with dynamic SIMS." Fresenius' Journal of Analytical Chemistry 348, no. 3 (1994): 201–4. http://dx.doi.org/10.1007/bf00325360.

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11

Fahey, A. J. "Isotopic Measurements of Inorganic Material by Time-Of-Flight SIMS." Microscopy and Microanalysis 4, S2 (July 1998): 412–13. http://dx.doi.org/10.1017/s1431927600022182.

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Isotopic measurements via Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) have generally not been considered as practical because of the low duty cycle at which ToF-SIMS instruments operate and the corresponding low data rate. The recent discovery of pre-solar material in meteorites has shown that large variations in isotopic ratios (several orders of magnitude for some elements) exist in small (∼1 μm), refractory meteoritic grains. These grains are ideal candidates for ToF-SIMS, which consumes little sample material, compared to dynamic, magneticsector SIMS. ToF-SIMS also allows for parallel detection of all species present in the sample; thus, multiple isotopic systems can be studied in one measurement. As a prerequisite to studying the isotopic composition of meteoritic materials, preliminary determinations of ratios for a number of elements have been made on materials of known isotopic composition. This allows us to investigate problems that may be unique to ToF-SIMS for the measurement of isotopic ratios.

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12

Li-Fatou, A. V., and M. Douglas. "Metal implant standards for surface analysis by TOF-SIMS and dynamic SIMS: comparison with TRIM simulation." Applied Surface Science 203-204 (January 2003): 290–93. http://dx.doi.org/10.1016/s0169-4332(02)00660-8.

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13

Stevie, F. A., and D. P. Griffis. "Quantification in dynamic SIMS: Current status and future needs." Applied Surface Science 255, no. 4 (December 2008): 1364–67. http://dx.doi.org/10.1016/j.apsusc.2008.05.041.

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14

Lodding, A. "SIMS of Biomineralized Tissues: Present Trends and Potentials." Advances in Dental Research 11, no. 4 (November 1997): 364–79. http://dx.doi.org/10.1177/08959374970110040101.

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The technique of dynamic secondary ion mass spectrometry (SIMS) has, during the 1980s, become a firmly established tool in the microanalytical and microstructural characterization of dental hard tissues. SIMS has proved to be outstandingly suited for charting the distributions of most elements, even at extremely low concentrations, in tooth and bone materials. In-depth concentration profiles as well as surface distribution maps of elements have been recorded with excellent (sub-micron) morphologic resolution. In spite of documented success, only relatively few teams, in a handful of countries, are presently engaged, to any significant extent, in conducting tooth or bone research by the application of SIMS. For dental-medical-surgical laboratories, a partial reason for non-communication is a lack of information about SIMS and its particular assets. Another reason may be connected with an essentially groundless reputation, among non-specialists, of SIMS being an exclusive and expensive technique. Among SIMS laboratories, on the other hand, the inertia in tackling biomineralization is partly due to some particular artifacts of analysis, hitherto not generally known and controlled. The present paper briefly sketches the chief principles of modem SIMS, emphasizing factors of special relevance in the characterization of biomineralized tissues. Examples of recent applications are provided. Present procedures and their limitations are discussed, especially with regard to elemental quantification and imaging. Suggestions for relatively simple modifications to existing routines are offered with the aim of enhancing the ease and availability of SIMS in odontological and surgical research.

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15

Wu, Yuewei, Danian Tian, Jesús Ferrando-Soria, Joan Cano, Lei Yin, Zhongwen Ouyang, Zhenxing Wang, Shuchang Luo, Xiangyu Liu, and Emilio Pardo. "Modulation of the magnetic anisotropy of octahedral cobalt(ii) single-ion magnets by fine-tuning the axial coordination microenvironment." Inorganic Chemistry Frontiers 6, no. 3 (2019): 848–56. http://dx.doi.org/10.1039/c8qi01373j.

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16

Lee, J. J., J. L. Hunter, W. J. Lin, and R. W. Linton. "Three-Dimensional Display of Secondary Ion Images." Proceedings, annual meeting, Electron Microscopy Society of America 48, no. 2 (August 12, 1990): 344–45. http://dx.doi.org/10.1017/s0424820100135320.

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Since the sample surface region is continuously sputtered in dynamic secondary ion mass spectrometry (SIMS), three dimensional (3D) chemical maps can be obtained by acquiring a series of two dimensional (2D) images. Owing to the limitations of the ion beam sputtering technique, SIMS analysis artifacts resulting from factors such as surface roughness, matrix effects, and atomic mixing are present in the 3D volume data. One potential advantage of using 3D display is to provide visual feedback regarding the elimination of artifacts by utilizing correction algorithms as well as correlative information obtained from other surface imaging techniques. In this paper, SIMS 3D maps are displayed by a volume rendering technique in which more information is retained in the image processing steps.Various 3D display methods have been proposed for SIMS and other microscopic imaging techniques. Some of the previous methods can not display the complete 3D spatial distribution of mass-selected ion intensities.

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17

Forni, Mario, and Marco Lippi. "THE GENERALIZED DYNAMIC FACTOR MODEL: REPRESENTATION THEORY." Econometric Theory 17, no. 6 (December 2001): 1113–41. http://dx.doi.org/10.1017/s0266466601176048.

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This paper, along with the companion paper Forni, Hallin, Lippi, and Reichlin (2000, Review of Economics and Statistics 82, 540–554), introduces a new model—the generalized dynamic factor model—for the empirical analysis of financial and macroeconomic data sets characterized by a large number of observations both cross section and over time. This model provides a generalization of the static approximate factor model of Chamberlain (1983, Econometrica 51, 1181–1304) and Chamberlain and Rothschild (1983, Econometrica 51, 1305–1324) by allowing serial correlation within and across individual processes and of the dynamic factor model of Sargent and Sims (1977, in C.A. Sims (ed.), New Methods in Business Cycle Research, pp. 45–109) and Geweke (1977, in D.J. Aigner & A.S. Goldberger (eds.), Latent Variables in Socio-Economic Models, pp. 365–383) by allowing for nonorthogonal idiosyncratic terms. Whereas the companion paper concentrates on identification and estimation, here we give a full characterization of the generalized dynamic factor model in terms of observable spectral density matrices, thus laying a firm basis for empirical implementation of the model. Moreover, the common factors are obtained as limits of linear combinations of dynamic principal components. Thus the paper reconciles two seemingly unrelated statistical constructions.

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18

Biersack, Jochen. "TRIM-DYNAMIC applied to marker broadening and SIMS depth profiling." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 153, no. 1-4 (June 1999): 398–409. http://dx.doi.org/10.1016/s0168-583x(98)01029-5.

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19

Chandra, Subhash, Duane R. Smith, and George H. Morrison. "Peer Reviewed: A Subcellular Imaging by Dynamic SIMS Ion Microscopy." Analytical Chemistry 72, no. 3 (February 2000): 104 A—114 A. http://dx.doi.org/10.1021/ac002716i.

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20

Gui, D., Y. N. Hua, Z. X. Xing, and S. P. Zhao. "Investigation of Potassium Contamination in SOI Wafer Using Dynamic SIMS." IEEE Transactions on Device and Materials Reliability 7, no. 2 (June 2007): 369–72. http://dx.doi.org/10.1109/tdmr.2007.901279.

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21

Legent, G., A. Delaune, V. Norris, A. Delcorte, D. Gibouin, F. Lefebvre, G. Misevic, M. Thellier, and C. Ripoll. "Method for Macromolecular Colocalization Using Atomic Recombination in Dynamic SIMS." Journal of Physical Chemistry B 112, no. 17 (May 2008): 5534–46. http://dx.doi.org/10.1021/jp7100489.

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22

Toujou, F., K. Tsukamoto, and K. Matsuoka. "Characterization of lubricants for fluid dynamic bearing by TOF-SIMS." Applied Surface Science 203-204 (January 2003): 590–95. http://dx.doi.org/10.1016/s0169-4332(02)00772-9.

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23

Ebihara, T., M. Nojima, T. Kondo, and M. Yuasa. "Dynamic SIMS Analysis of PEMFC Catalyst Layer/Solid Electrolyte Interfaces." ECS Transactions 50, no. 2 (March 15, 2013): 385–92. http://dx.doi.org/10.1149/05002.0385ecst.

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24

Gillen, Greg, Christopher Szakal, and Tim M. Brewer. "Useful yields of organic molecules under dynamic SIMS cluster bombardment." Surface and Interface Analysis 43, no. 1-2 (July 9, 2010): 376–79. http://dx.doi.org/10.1002/sia.3484.

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25

Suzuki, Masato, Masashi Nojima, Makiko Fujii, Toshio Seki, and Jiro Matsuo. "Mass analysis by Ar-GCIB-dynamic SIMS for organic materials." Surface and Interface Analysis 46, no. 12-13 (November 25, 2014): 1212–14. http://dx.doi.org/10.1002/sia.5696.

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26

Hibbert, S., N. Baba-Ali, and I. Harrison. "A dynamic SIMS study of interdiffusion in GaAs/AIAs heterostructures." Euro III-Vs Review 3, no. 5 (September 1990): 16–17. http://dx.doi.org/10.1016/0959-3527(90)90198-3.

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27

Dickinson, M., P. J. Heard, J. H. A. Barker, A. C. Lewis, D. Mallard, and G. C. Allen. "Dynamic SIMS analysis of cryo-prepared biological and geological specimens." Applied Surface Science 252, no. 19 (July 2006): 6793–96. http://dx.doi.org/10.1016/j.apsusc.2006.02.236.

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28

Pang, Wei Kong, It Meng Low, and J. V. Hanna. "Detection of Amorphous Silica in Air-Oxidized Ti₃SiC₂ at 500–1000°C by NMR and SIMS." Key Engineering Materials 434-435 (March 2010): 169–72. http://dx.doi.org/10.4028/www.scientific.net/kem.434-435.169.

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The use of secondary-ion mass spectrometry (SIMS), nuclear magnetic resonance (NMR) and transmission electron microscopy (TEM) to detect the existence of amorphous silica in Ti3SiC2 oxidised at 500–1000°C is described. The formation of an amorphous SiO2 layer and its growth in thickness with temperature was monitored using dynamic SIMS. Results of NMR and TEM verify for the first time the direct evidence of amorphous silica formation during the oxidation of Ti3SiC2 at 1000°C.

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29

Xu, Duo, Xin Hua, Shao-Chuang Liu, Hong-Wei Qiao, Hua-Gui Yang, Yi-Tao Long, and He Tian. "In situ and real-time ToF-SIMS analysis of light-induced chemical changes in perovskite CH3NH3PbI3." Chemical Communications 54, no. 43 (2018): 5434–37. http://dx.doi.org/10.1039/c8cc01606b.

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30

Nojima, M., M. Suzuki, T. Adachi, S. Hotta, M. Fujii, T. Seki, and J. Matsuo. "Development of Au-GCIB Dynamic SIMS and Cluster Size Filtering System." Microscopy and Microanalysis 20, S3 (August 2014): 1152–53. http://dx.doi.org/10.1017/s1431927614007491.

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31

Ngo, K. Q., P. Philipp, Y. Jin, S. E. Morris, M. Shtein, J. Kieffer, and T. Wirtz. "Analysis and fragmentation of organic samples by (low-energy) dynamic SIMS." Surface and Interface Analysis 43, no. 1-2 (June 8, 2010): 88–91. http://dx.doi.org/10.1002/sia.3533.

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32

Suzuki, Masato, Masashi Nojima, Makiko Fujii, Toshio Seki, and Jiro Matsuo. "Retracted: Mass analysis by Ar-GCIB-dynamic SIMS for organic materials." Surface and Interface Analysis 47, no. 2 (December 18, 2014): 295–97. http://dx.doi.org/10.1002/sia.5705.

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33

Suzuki, Masato, Masashi Nojima, Makiko Fujii, Toshio Seki, and Jiro Matsuo. "Retracted: Mass analysis by Ar-GCIB-dynamic SIMS for organic materials." Surface and Interface Analysis 47, no. 2 (December 18, 2014): 298–300. http://dx.doi.org/10.1002/sia.5707.

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34

Mowat, Ian A., Xue-Feng Lin, Thomas Fister, Marius Kendall, Gordon Chao, and Ming Hong Yang. "A study of dynamic SIMS analysis of low-k dielectric materials." Applied Surface Science 252, no. 19 (July 2006): 7182–85. http://dx.doi.org/10.1016/j.apsusc.2006.02.222.

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35

Fahey, Albert J., Greg Gillen, Peter Chi, and Christine M. Mahoney. "Performance of a C60+ ion source on a dynamic SIMS instrument." Applied Surface Science 252, no. 19 (July 2006): 7312–14. http://dx.doi.org/10.1016/j.apsusc.2006.02.263.

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36

Berghmans, B., B. Van Daele, L. Geenen, T. Conard, A. Franquet, and W. Vandervorst. "Cesium near-surface concentration in low energy, negative mode dynamic SIMS." Applied Surface Science 255, no. 4 (December 2008): 1316–19. http://dx.doi.org/10.1016/j.apsusc.2008.05.020.

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37

McIntyre, N. S., D. M. Kingston, P. A. W. van der Heide, M. L. Wagter, M. B. Stanley, and A. H. Clarke. "Volumetric rendering of 3D SIMS depth profiles." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 1050–51. http://dx.doi.org/10.1017/s0424820100167718.

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Dynamic secondary ion mass spectrometry (SIMS) can be used to uncover unique information about interfaces and whole structures which are “buried” within a solid. During a depth profile of a solid, a sequence of SIMS images is acquired for each element under study The sequence is correlated into a vertical “stack” which contains digitised three dimensional (3D) elemental distributions. Although such distributions are distorted by surface roughness, preferential sputtering and SIMS matrix effects, there is still considerable structural information contained in the volume and much potential for further information retrieval as the above-mentioned distorting effects are addressedOne of the major tools used to assess distributional information within the 3D volume has been visual rendering software. Using Sunvision software (Sun Microsystems Inc.) and a small workstation, images of the volume can be constructed which display pixels either in a “maximum value” perspective or in perspectives where the density of each phase can be adjusted to maximise structural detail.Several examples of the technique will be shown.

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38

Newbury, Dale E. "Ion microscope and microprobe studies of surfaces and interfaces." Proceedings, annual meeting, Electron Microscopy Society of America 51 (August 1, 1993): 856–57. http://dx.doi.org/10.1017/s0424820100150113.

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Secondary ion mass spectrometry (SIMS) in its spatially-resolved forms, the ion microscope and ion microprobe, offers elemental, isotopic, and molecular detection, wide dynamic intensity range spanning major to trace concentrations in the part per million (ppm) range or lower, high lateral spatial resolution in the micrometer to sub-micrometer range, shallow sampling depths to the nanometer range, and the possibility of "microanalytical tomography", the reconstruction of three-dimensional distributions. With this broad range of capabilities, SIMS has special advantages for the characterization of surfaces and interfaces that complement the measurement capabilities of other microanalysis/surface analysis techniques such as electron probe x-ray microanalysis (EPMA), analytical electron microscopy (AEM), Raman and infrared microscopy, scanning Auger electron microanalysis (SAM/AES), and spatially-resolved x-ray photoelectron spectroscopy (XPS). Examples of applications will highlight the special contributions of SIMS to surface/interface characterization studies.1. Surface studiesFigure 1 shows an example of characterization with extreme surface sensitivity. Changes in surface chemistry induced on a passivated silicon surface by scanning tunneling microscopy in air are revealed by time-of-flight secondary ion mass spectrometry (TOF-SIMS).

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39

Paruch, Robert J., Zbigniew Postawa, Andreas Wucher, and Barbara J. Garrison. "Steady-State Statistical Sputtering Model for Extracting Depth Profiles from Molecular Dynamics Simulations of Dynamic SIMS." Journal of Physical Chemistry C 116, no. 1 (December 21, 2011): 1042–51. http://dx.doi.org/10.1021/jp2098075.

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40

McMahon, G., M. W. Phaneuf, and L. Weaver. "SIMS depth profiling and direct ion imaging of a 16-megabit DRAM." Proceedings, annual meeting, Electron Microscopy Society of America 54 (August 11, 1996): 962–63. http://dx.doi.org/10.1017/s0424820100167275.

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The use of secondary ion mass spectrometry (SIMS) has long been established as a powerful tool forthe analysis of microelectronic devices. However, as newer fabrication technologies emerge, and device dimensions shrink, increasing demands are placed upon the SIMS analyst.The present work illustrates how the depth profiling and direct ion imaging capabilities of a Cameca ims 4f SIMS can be exploited to determine the structure and dopant chemistry of a state-of-the-art16 megabit DRAM (dynamic random access memory) device. The optical micrograph shown in Fig. 1 is an overview of a portion of the sense amplifiers located between two array blocks. In the centre of this area, p-type devices sitting in an n-well, surrounded on either side by n-type devices, all in a p-type substrate can be observed. The object of the study was to determine if a low concentration p-type implant is present below the n-type devices.

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41

Kung, Camy C. H., Mandar T. Naik, Szu-Huan Wang, Hsiu-Ming Shih, Che-Chang Chang, Li-Ying Lin, Chia-Lin Chen, Che Ma, Chi-Fon Chang, and Tai-Huang Huang. "Structural analysis of poly-SUMO chain recognition by the RNF4-SIMs domain." Biochemical Journal 462, no. 1 (July 24, 2014): 53–65. http://dx.doi.org/10.1042/bj20140521.

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Using a multi-faceted approach we unveil the dynamic nature and elucidate the molecular basis of avidity of the poly-SUMO-SIMs domain interaction. A knowledge-based HADDOCK model of the complex reveals a helical structure that serves as a framework for subsequent functional understanding.

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42

Linton, Richard W. "Three-dimensional compositional mapping using ion microscopy and volume-rendering techniques." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (August 1992): 1614–15. http://dx.doi.org/10.1017/s0424820100132704.

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Secondary ion mass spectrometry (SIMS), using ion microprobe or microscope instrumentation, couples lateral imaging and dynamic ion beam sputtering to provide 3-D compositional maps (image depth profiles). A data set acquired with an ion microscope may involve more than 100 massresolved ion images, each containing at least 64,000 pixels, with typical lateral and depth resolutions of 1 μm and 10 nm, respectively. The vast majority of prior quantitative surface analysis studies have addressed depth profiling, thin film, or overlayer measurements without the additional feature of laterally resolved imaging. The ability to create 3-D compositional maps using SIMS creates enormous challenges for quantification. In principle, each volume element requires individual calibration reflecting the combined effects of spatial resolution, sample heterogeneity, and variations in instrumental response. An overview of analytical considerations will be presented involving aspects of data acquisition, display, and processing, with a special emphasis on sector field mass spectrometers that provide high dynamic range image depth profiles.

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43

Li, Quan-Wen, Jun-Liang Liu, Jian-Hua Jia, Yan-Cong Chen, Jiang Liu, Long-Fei Wang, and Ming-Liang Tong. "“Half-sandwich” YbIII single-ion magnets with metallacrowns." Chemical Communications 51, no. 51 (2015): 10291–94. http://dx.doi.org/10.1039/c5cc03389f.

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Two “half-sandwich” Yb^III-SIMs are presented bearing metallacrowns. The central ytterbium ion is coordinated by YbO₈ geometry in D_4d symmetry. The analysis of static, dynamic magnetism and emission spectrum offers an insight into the magneto-optical correlation.

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44

Kang, H. J., W. S. Kim, D. W. Moon, H. Y. Lee, S. T. Kang, and R. Shimizu. "Dynamic Monte Carlo simulation for SIMS depth profiling of delta-doped layer." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 153, no. 1-4 (June 1999): 429–35. http://dx.doi.org/10.1016/s0168-583x(98)01021-0.

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45

von Criegern, R., L. Weitzel, H. Zeininger, and R. Lange-Gieseler. "Optimization of the dynamic range of SIMS depth profiles by sample preparation." Surface and Interface Analysis 15, no. 7 (July 1990): 415–21. http://dx.doi.org/10.1002/sia.740150704.

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46

Mahoney, Christine M., Sonya Roberson, and Greg Gillen. "Dynamic SIMS utilizing SF5+ polyatomic primary ion beams for drug delivery applications." Applied Surface Science 231-232 (June 2004): 174–78. http://dx.doi.org/10.1016/j.apsusc.2004.03.109.

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47

Shibatani, Masaya, Tsutomu Asakawa, Toshiharu Enomae, and Akira Isogai. "Approach for detecting localization of inkjet ink components using dynamic-SIMS analysis." Colloids and Surfaces A: Physicochemical and Engineering Aspects 326, no. 1-2 (August 2008): 61–66. http://dx.doi.org/10.1016/j.colsurfa.2008.05.033.

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48

Tian, Hua, Andreas Wucher, and Nicholas Winograd. "Reducing the Matrix Effect in Organic Cluster SIMS Using Dynamic Reactive Ionization." Journal of The American Society for Mass Spectrometry 27, no. 12 (September 22, 2016): 2014–24. http://dx.doi.org/10.1007/s13361-016-1492-z.

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49

Williams, John T. "Dynamic Change, Specification Uncertainty, and Bayesian Vector Autoregression Analysis." Political Analysis 4 (1992): 97–125. http://dx.doi.org/10.1093/pan/4.1.97.

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The analysis of time-series data is fraught with problems of specification uncertainty and dynamic instability. Vector autoregression (VAR) is one attempt to overcome specification problems in time-series analysis, but this methodology has been criticized for being unparsimonious and potentially unstable through time.1 This article describes an important extension of VAR, one using Bayesian methods and allowing for time-varying parameters. These extensions improve VAR, making analysis less vulnerable to these criticisms. These VAR methods, developed by Doan, Litterman, and Sims (1984), provide a reasonable method for dealing with general time variation when theory does not provide useful a priori specification restrictions.

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Liu, Yu, Qiu-Li Li, Guo-Qiang Tang, Xian-Hua Li, and Qing-Zhu Yin. "Towards higher precision SIMS U–Pb zircon geochronology via dynamic multi-collector analysis." Journal of Analytical Atomic Spectrometry 30, no. 4 (2015): 979–85. http://dx.doi.org/10.1039/c4ja00459k.

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