دورية أكاديمية
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Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach.

التفاصيل البيبلوغرافية
العنوان: Estimating reaction parameters in mechanism-enabled population balance models of nanoparticle size distributions: A Bayesian inverse problem approach.
المؤلفون: Long DK; Department of Mathematics, Colorado State University, Fort Collins, Colorado, USA., Bangerth W; Department of Mathematics, Colorado State University, Fort Collins, Colorado, USA.; Department of Geosciences, Colorado State University, Fort Collins, Colorado, USA., Handwerk DR; Department of Chemistry, Colorado State University, Fort Collins, Colorado, USA., Whitehead CB; Department of Chemistry, Colorado State University, Fort Collins, Colorado, USA.; Department of Chemistry, University of Basel, Basel, Switzerland., Shipman PD; Department of Mathematics, Colorado State University, Fort Collins, Colorado, USA., Finke RG; Department of Chemistry, Colorado State University, Fort Collins, Colorado, USA.
المصدر: Journal of computational chemistry [J Comput Chem] 2022 Jan 05; Vol. 43 (1), pp. 43-56. Date of Electronic Publication: 2021 Oct 21.
نوع المنشور: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
اللغة: English
بيانات الدورية: Publisher: Wiley Country of Publication: United States NLM ID: 9878362 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1096-987X (Electronic) Linking ISSN: 01928651 NLM ISO Abbreviation: J Comput Chem Subsets: PubMed not MEDLINE; MEDLINE
أسماء مطبوعة: Original Publication: New York : Wiley,
مستخلص: In order to quantitatively predict nano- as well as other particle-size distributions, one needs to have both a mathematical model and estimates of the parameters that appear in these models. Here, we show how one can use Bayesian inversion to obtain statistical estimates for the parameters that appear in recently derived mechanism-enabled population balance models (ME-PBM) of nanoparticle growth. The Bayesian approach addresses the question of "how well do we know our parameters, along with their uncertainties?." The results reveal that Bayesian inversion statistical analysis on an example, prototype Ir 0 n nanoparticle formation system allows one to estimate not just the most likely rate constants and other parameter values, but also their SDs, confidence intervals, and other statistical information. Moreover, knowing the reliability of the mechanistic model's parameters in turn helps inform one about the reliability of the proposed mechanism, as well as the reliability of its predictions. The paper can also be seen as a tutorial with the additional goal of achieving a "Gold Standard" Bayesian inversion ME-PBM benchmark that others can use as a control to check their own use of this methodology for other systems of interest throughout nature. Overall, the results provide strong support for the hypothesis that there is substantial value in using a Bayesian inversion methodology for parameter estimation in particle formation systems.
(© 2021 Wiley Periodicals LLC.)
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فهرسة مساهمة: Keywords: Bayesian inversion; kinetics and mechanism; nanoparticles; nucleation and growth; particle size distribution; population balance modeling
تواريخ الأحداث: Date Created: 20211021 Date Completed: 20220207 Latest Revision: 20220207
رمز التحديث: 20231215
DOI: 10.1002/jcc.26770
PMID: 34672375
قاعدة البيانات: MEDLINE
الوصف
تدمد:1096-987X
DOI:10.1002/jcc.26770