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Research Papers

Computational Monolayer for Tertiary Nanoparticles Using Supercomputer

[+] Author and Article Information
Kyungdeok Jang

Department of Metallurgical and Materials Engineering,
The University of Texas at El Paso,
El Paso, TX 79968
e-mail: playzanggu@gmail.com

Nubia Zuverza

Department of Metallurgical and Materials Engineering,
The University of Texas at El Paso,
El Paso, TX 79968
e-mail: nzuverza@miners.utep.edu

Tae Eui Jeong

Department of Nano Convergence Engineering,
Seokyeong University,
Seoul, 136-704, Korea
e-mail: tejeong@skuniv.ac.kr

Sung Suk Kim

Department of Computer Science,
Seokyeong University,
Seoul 136-704, Korea
e-mail: sskim03@skuniv.ac.kr

Nam Soo Kim

Department of Metallurgical and Materials Engineering,
The University of Texas at El Paso,
El Paso, TX 79968
e-mail: nkim@utep.edu

1Corresponding author.

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received January 5, 2012; final manuscript received December 29, 2012; published online March 26, 2013. Assoc. Editor: Kyoung-sik Moon.

J. Electron. Packag 135(1), 011003 (Mar 26, 2013) (6 pages) Paper No: EP-12-1004; doi: 10.1115/1.4023527 History: Received January 05, 2012; Revised December 29, 2012

Computer simulation is a practical approach for the accurate study of nanosized materials. In order to produce conductive nano-inks for microelectrodes, we need to simulate different nanoparticles (NPs)’ arrangements to maximize their packing. Even though modeling can be performed on desktop computers using binary packing, this is a time consuming process that may not provide optimal results for practical applications. In this study, we developed a simulation program for a supercomputer to obtain precise results from tertiary packing while reducing the simulation time. The simulation of nanoparticles' packing consists of three different sized particles resulting in a high packing factor of 93.44%. Therefore, the optimal sizes and volumes of particles required for nano-inks with various viscosities can be predetermined. Furthermore, a wide range of applications can be derived such as finding ideal ratios of particles or inks for different mixtures.

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References

Figures

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Fig. 5

Comparison of supercomputer and desktop data (repeating count: 1000, number of CPU: desktop 1, supercomputer 2)

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Fig. 4

Comparison of the number of overlapping particles on boundary according to the number of divided space

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Fig. 8

Simulation of tertiary packing

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Fig. 3

Algorithm of supercomputers simulating system

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Fig. 2

Skeleton of computing system of the supercomputer simulator

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Fig. 6

Changes in packing factor according to the repeating count (use 6 CPU)

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Fig. 7

Packing factor according to the number of CPU's

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