Volume 25: 2012

Peer Reviewed Papers
September 19, 2012

Application of Fluidic Control within a Plano-Convex Singlet Lens
Eric Gutierrez, Nickolaos Savidis, James Schwiegerling
May 14, 2012

Transient Antihydrogen Production in a Paul Trap
G. Geyer, R. Blümel
February 7, 2012

Measurement of the Speed and Energy Distribution of Cosmic Ray Muons
Grant Remmen, Elwood McCreary
January 25, 2012

A Geometrical Interpretation of Bell's Inequalities
Taylor Firman
January 17, 2012

Effects of Catalyst Components on Carbon Nanotubes Grown by Chemical Vapor Deposition
Tasha Adams

SDSS-II: Determination of Shape and Color Parameter Coefficients for SALT-II Model
L. Dojcsak

Anharmonic Oscillator Potentials:Exact and Perturbation Results
Floyd, Ludes, Moua, Ostle, and Varkony
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Application of Fluidic Control within a Plano-Convex Singlet Lens

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Eric Gutierrez1, Nickolaos Savidis2, James Schwiegerling2,3
Dept. of Mechanical Engineering1, University of California, Riverside, Riverside, CA 92521; College of Optical Sciences2, Dept. of Ophthalmology & Vision Science3, University of Arizona, Tucson, AZ 85721

This paper focuses on designing a computer code for the purpose of developing a systematic method for continuous control of the focal length of a plano-convex fluidic lens and utilizing the code for studying the optical behavior of a plano-convex fluidic lens. A syringe, which was controlled by a syringe pump controller, was utilized for fluid control. The code decreased the operation time for the syringe pump controller by replacing the manual push of buttons with a few clicks on a computer. By utilizing the code, the focal length of the fluidic lens was measured as a function of the curvature of the lens’ flexible membrane. This was accomplished for three lasers of differing wavelengths (red: 633 nm; green: 543 nm; blue: 488 nm). A graphical relationship was found for the three wavelengths: as the lens curvature increased, the focal length decreased. In addition, as expected, the longer wavelength outputted a longer focal length per lens curvature.

Fluidic Lenses, Focal Length, Wavelength, Fluidic Control

1. S. P. Casey, "Liquid Lens: Advances in Adaptive Optics," (paper presented at the Asia Communications and Photonics Conference and Exhibition, Shanghai, China, November 2, 2009), <http://www.opticsinfobase.org/abstract.cfm?URI=ACP-2009-FE3>.
2. R. Marks, D.L. Mathine, G. Peyman, J. Schwiegerling, N. Peyghambarian, “Adjustable Adaptive Compact Fluidic Phoropter With No Mechanical Translation of Lenses,” Optics Letters 35, no. 5 (2010): 739-741.
3. W. Zhang, P. Liu, X. Wei, S. Zhuang, B. Yang, “The Analysis of the Wave Front Aberration Caused by the Gravity of the Tunable-Focus Liquid-Filled Membrane Lens,” Proceedings of the SPIE 7849 (2010): 78491W, accessed June, 13, 2011, doi: 10.1117/12.869866.
4. H. Ren, D. Fox, P.A. Anderson, B. Wu, S.T. Wu, “Tunable-Focus Liquid Lens Controlled Using a Servo Motor,” Optics Express 14, no. 18 (2006): 8031-8036, accessed June, 13, 2011, doi: 10.1364/OE.14.008031.

Transient Antihydrogen Production in a Paul Trap

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G.Geyer and R. Blümel
Department of Physics, Wesleyan University,
Middletown, Connecticut, 06459.

Although positrons and antiprotons have vastly different masses, we show that it is possible to store both particle species simultaneously in a Paul trap, using the space charge of the positron cloud as a trap for the antiprotons. Computer simulations confirm the validity of this new trapping mechanism. In addition, the simulations show transient antihydrogen production that manifests itself in the intermittent production of bound positron-antiproton Rydberg states. Since realistic trapping parameters are used in the simulations, (i) simultaneous positron-antiproton trapping and (ii) transient antihydrogen formation should be experimentally observable in a Paul trap. Strategies are suggested to lengthen the lifetime of antihydrogen in the Paul trap.

Paul trap, antihydrogen, positron, antiproton

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Measurement of the Speed and Energy Distribution of Cosmic Ray Muons

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Grant Remmen and Elwood McCreary
Department of Physics, School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN, 55455

The time of flight distribution of cosmic ray muons was measured for various spacings of detectors filled with plastic scintillator, allowing for a determination of the mean speed, as well as constraint of the energy spectrum below 0.95c. The use of a time-to-amplitude converter allowed for precise timing measurements and resolution of the shape of the timing distribution for each spacing, necessary for constraining the energy spectrum. The mean speed of cosmic ray muons was found to be (2.978 ± 0.007) 108 ms-1 = (0.993±0.002)c. The energy spectrum below 0.34 GeV was found to be consistent with a flat distribution and was parameterized with a power law of the form n(E) dE ∝ EdE, with α best fit by (-7.9 ± 9.1)*10-4.

cosmic rays, elementary particles: muons, energy spectrum, relativistic velocity measurement,
scintillation detector, nanosecond timing measurement, astroparticle physics, special relativity

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A Geometrical Interpretation of Bell’s Inequalities

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Taylor Firman
University of Puget Sound

Bell’s Inequalities express constraints on the correlations of three random, binary variables and they can be applied to the interpretation of quantum mechanics. In an analysis of the theory behind these inequalities, this paper suggests a geometrical interpretation of Bell’s Inequality in the form of a tetrahedron in “correlation space” to which correlation measurements are restricted. Using correlated photons produced through spontaneous parametric downconversion, we were able to experimentally demonstrate a set of measurements lying outside of this tetrahedron, negating the binary nature of hidden-variable theories.

Bell's Inequality, hidden variables, EPR paradox, correlation space

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Effects of Catalyst Components on Carbon Nanotubes Grown by Chemical Vapor Deposition

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Tasha Adams*, Binh Duong, and Supapan Seraphin
*Department of Optical Engineering, Norfolk State University

In this paper, we study the role of each of the four chemical components of a catalyst system used in growing carbon nanotubes (CNTs). Our goal is to be able to grow desirable carbon nanotubes by chemical vapor deposition (CVD), which is believed to be the most practical growth method for CNTs. The catalyst used in our process is known for inducing Y-junction CNTs. To the best of our knowledge, there has not been a thorough investigation on the importance of each chemical component used during the CNT growth. To identify the impact that each component has on CNT growth, we prepared nine samples by either excluding or doubling the amount of each component (iron nitrate, aluminum oxide, and molybdenum). We used one of three different solvents including methanol, DI water, or ethanol for the catalyst solutions. We found that 1) all three catalyst components are needed in effective growth of CNTs; 2) molybdenum has a significant role in CNT growth and y-junctions in our system; 3) the solvent has noteworthy effect on the degree of CNT crystallinity of the nanotubes and 4) the growth of multiple samples at the same time may cause interaction from one sample to the next.

Y-Junction, Iron Nitrate Nonahydrate, Aluminum Oxide, and Molybdenum Acetylacetonate

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SDSS-II: Determination of Shape and Color Parameter Coefficients for SALT-II Model

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L. Dojcsak*, J. Marriner
*Lawrence Technological University, 21000 West Ten Mile Rd., Southfield, MI, 48075

In this study we look at the SALT-II model of Type IA supernova analysis, which determines the distance moduli based on the known absolute standard candle magnitude of the Type IA supernovae. We take a look at the determination of the shape and color parameter coefficients, α and β respectively, in the SALTII model with the intrinsic error that is determined from the data. Using the SNANA software package provided for the analysis of Type IA supernovae, we use a standard Monte Carlo simulation to generate data with known parameters to use as a tool for analyzing the trends in the model based on certain assumptions about the intrinsic error. In order to find the best standard candle model, we try to minimize the residuals on the Hubble diagram by calculating the correct shape and color parameter coefficients. We can estimate the magnitude of the intrinsic errors required to obtain results with χ2/degree of freedom = 1. We can use the simulation to estimate the amount of color smearing as indicated by the data for our model. We find that the color smearing model works as a general estimate of the color smearing, and that we are able to use the RMS distribution in the variables as one method of estimating the correct intrinsic errors needed by the data to obtain the correct results for α and β. We then apply the resultant intrinsic error matrix to the real data and show our results.

Sloan Digital Sky Survey, SALT-II, Supernova Analysis

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Anharmonic Oscillator Potentials: Exact and Perturbation Results

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Benjamin T. Floyd, Amanda M. Ludes, Chia Moua, Allan A. Ostle, and Oren B. Varkony
Department of Physics, University of Nebraska at Omaha, Omaha, Nebraska, 68182

In order to determine the first four energy levels of our anharmonic potential, we will compute the first four eigenvalues of the anharmonic oscillator potential with a quartic term using Heun polynomials and Maple software packages. We will then compare them to the results obtained from the conventional perturbation method, treating the quartic term as perturbation up to the third order. Through this it can be shown that generally the two methods agree well with each other when the perturbing potential is weak. Nevertheless, the perturbation results will start to deviate from those of the exact solutions at stronger perturbation potentials and higher excited states.

Perturbation Method, Anharmonic Potential, Anharmonic Oscillator

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3. D. J. Griffiths, “Introduction to Quantum Mechanics” (Upper Saddle River, NJ, Pearson Preston Hall, 2005, second edition) Chapter 6, and the references cited therein, especially Footnote 5 on P. 256.
4. ibid, Chapter 2, Section 2.3.
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