skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 2

Search for: All records

Creators/Authors contains: "Moore, William"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ; ; ; ; (, Soft Matter)
    This work elucidates a route to mesoporous magnetic materials with co-continuous morphologies from block copolymer self-assembly. The co-continuous structure impacts the magnetic behavior compared to non-structured chemically-identical materials. 
    more » « less
    Full Text Available 
  2. ; ; ; ; ; ; (, ACS Applied Materials & Interfaces)
    Full Text Available 
  3. ; ; ; ; ; (, ACS Applied Nano Materials)
    Full Text Available 
  4. ; ; ; ; ; (, Inorganic Chemistry)
    Full Text Available 
  5. ; ; ; ; (, Inorganic Chemistry)
    Full Text Available 
  6. ; ; ; ; ; ; ; ; (, Physical Review Materials)
  7. ; ; ; (, Current Opinion in Chemical Biology)
    Full Text Available 
  8. ; ; (, Organometallics)
    Full Text Available 
  9. ; ; ; ; (, Theoretical and computational fluid dynamics)
    Fluid flow around a random distribution of stationary spherical particles is a problem of substantial importance in the study of dispersed multiphase flows. In this paper we present a machine learning methodology using Generative Adversarial Network framework and Convolutional Neural Network architecture to recreate particle-resolved fluid flow around a random distribution of monodispersed particles. The model was applied to various Reynolds number and particle volume fraction combinations spanning over a range of [2.69, 172.96] and [0.11, 0.45] respectively. Test performance of the model for the studied cases is very promising. 
    more » « less
    Accepted Manuscript Full Text Available
  10. ; ; ; ; (, Polyhedron)
    Full Text Available