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Title: Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach

Authors:
 [1]; ORCiD logo [2];  [3];  [4];  [1];  [3]
  1. Department of Mathematics Colorado State University Fort Collins Colorado USA
  2. Department of Mathematics Colorado State University Fort Collins Colorado USA, Department of Geosciences Colorado State University Fort Collins Colorado USA
  3. Department of Chemistry Colorado State University Fort Collins Colorado USA
  4. Department of Chemistry Colorado State University Fort Collins Colorado USA, Department of Chemistry University of Basel Basel Switzerland
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1826851
Grant/Contract Number:  
SE‐FG402‐02ER15453
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Computational Chemistry
Additional Journal Information:
Journal Name: Journal of Computational Chemistry Journal Volume: 43 Journal Issue: 1; Journal ID: ISSN 0192-8651
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Long, Danny K., Bangerth, Wolfgang, Handwerk, Derek R., Whitehead, Christopher B., Shipman, Patrick D., and Finke, Richard G. Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach. United States: N. p., 2021. Web. doi:10.1002/jcc.26770.
Long, Danny K., Bangerth, Wolfgang, Handwerk, Derek R., Whitehead, Christopher B., Shipman, Patrick D., & Finke, Richard G. Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach. United States. https://doi.org/10.1002/jcc.26770
Long, Danny K., Bangerth, Wolfgang, Handwerk, Derek R., Whitehead, Christopher B., Shipman, Patrick D., and Finke, Richard G. Thu . "Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach". United States. https://doi.org/10.1002/jcc.26770.
@article{osti_1826851,
title = {Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach},
author = {Long, Danny K. and Bangerth, Wolfgang and Handwerk, Derek R. and Whitehead, Christopher B. and Shipman, Patrick D. and Finke, Richard G.},
abstractNote = {},
doi = {10.1002/jcc.26770},
journal = {Journal of Computational Chemistry},
number = 1,
volume = 43,
place = {United States},
year = {2021},
month = {10}
}

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