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International Conference on Dynamic Data Driven Application Systems

DDDAS 2020: Dynamic Data Driven Applications Systems pp 132–139Cite as

Dynamic Data-Driven Distribution Tracking of Nanoparticle Morphology

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12312))

Abstract

We present a data-driven distribution tracking system that is capable of tracking the process quality in a chemical synthesis process for nanoparticles. In the process, the process quality is defined as a distribution of particle sizes and shapes, which influence the functionalities of nanoparticles. A system of tracking the distribution of nanoparticle sizes and shapes consists of three components: (a) in situ measurement system, (b) a mathematical model to represent nanoparticle sizes and shapes, their distributions and the temporal changes in the distributions, and (c) a statistical algorithm to estimate the model with in situ measurements. We will review the state-of-the-art approaches to tracking the time-varying distribution of particle sizes and shapes. The advance of the distribution tracking by combining complementary in situ instruments based on the DDDAS paradigm is discussed.

We acknowledge support for this work from the AFOSR (FA9550-13-1-0075, FA9550-16-1-0110, FA9550-18-1-0144).

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Author information

Authors and Affiliations

  1. Florida State University, Tallahassee, FL, 32310, USA

    Chiwoo Park

  2. Texas A&M University, College Station, TX, 77840, USA

    Yu Ding

Authors
  1. Chiwoo Park
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  2. Yu Ding
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Corresponding author

Correspondence to Chiwoo Park .

Editor information

Editors and Affiliations

  1. InfoSybiotic Systems Society, Bethesda, MD, USA

    Frederica Darema

  2. Air Force Office of Scientific Research, Arlington, VA, USA

    Erik Blasch

  3. Massachusetts Institute of Technology, Cambridge, MA, USA

    Sai Ravela

  4. Air Force Research Laboratories, Rome, NY, USA

    Alex Aved

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Park, C., Ding, Y. (2020). Dynamic Data-Driven Distribution Tracking of Nanoparticle Morphology. In: Darema, F., Blasch, E., Ravela, S., Aved, A. (eds) Dynamic Data Driven Applications Systems. DDDAS 2020. Lecture Notes in Computer Science(), vol 12312. Springer, Cham. https://doi.org/10.1007/978-3-030-61725-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-61725-7_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61724-0

  • Online ISBN: 978-3-030-61725-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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