Estimating reaction parameters in mechanism‐enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach
- Authors:
-
- Department of Mathematics Colorado State University Fort Collins Colorado USA
- Department of Mathematics Colorado State University Fort Collins Colorado USA, Department of Geosciences Colorado State University Fort Collins Colorado USA
- Department of Chemistry Colorado State University Fort Collins Colorado USA
- 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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