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A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime

Nanoparticle agglomeration in the transition regime (e.g. at high pressures or low temperatures) is commonly simulated by population balance models for volume-equivalent spheres or agglomerates with a constant fractal-like structure. However, neglecting the fractal-like morphology of agglomerates or...

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Autores principales: Kelesidis, Georgios A., Kholghy, M. Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306586/
https://www.ncbi.nlm.nih.gov/pubmed/34300803
http://dx.doi.org/10.3390/ma14143882
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author Kelesidis, Georgios A.
Kholghy, M. Reza
author_facet Kelesidis, Georgios A.
Kholghy, M. Reza
author_sort Kelesidis, Georgios A.
collection PubMed
description Nanoparticle agglomeration in the transition regime (e.g. at high pressures or low temperatures) is commonly simulated by population balance models for volume-equivalent spheres or agglomerates with a constant fractal-like structure. However, neglecting the fractal-like morphology of agglomerates or their evolving structure during coagulation results in an underestimation or overestimation of the mean mobility diameter, [Formula: see text] , by up to 93 or 49%, repectively. Here, a monodisperse population balance model (MPBM) is interfaced with robust relations derived by mesoscale discrete element modeling (DEM) that account for the realistic agglomerate structure and size distribution during coagulation in the transition regime. For example, the DEM-derived collision frequency, [Formula: see text] , for polydisperse agglomerates is 82 ± 35% larger than that of monodisperse ones and in excellent agreement with measurements of flame-made TiO(2) nanoparticles. Therefore, the number density, [Formula: see text] , mean, [Formula: see text] and volume-equivalent diameter, [Formula: see text] , estimated here by coupling the MPBM with this [Formula: see text] and power laws for the evolving agglomerate morphology are on par with those obtained by DEM during the coagulation of monodisperse and polydisperse primary particles at pressures between 1 and 5 bar. Most importantly, the MPBM-derived [Formula: see text] , [Formula: see text] , and [Formula: see text] are in excellent agreement with the data for soot coagulation during low temperature sampling. As a result, the computationally affordable MPBM derived here accounting for the realistic nanoparticle agglomerate structure can be readily interfaced with computational fluid dynamics in order to accurately simulate nanoparticle agglomeration at high pressures or low temperatures that are present in engines or during sampling and atmospheric aging.
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spelling pubmed-83065862021-07-25 A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime Kelesidis, Georgios A. Kholghy, M. Reza Materials (Basel) Article Nanoparticle agglomeration in the transition regime (e.g. at high pressures or low temperatures) is commonly simulated by population balance models for volume-equivalent spheres or agglomerates with a constant fractal-like structure. However, neglecting the fractal-like morphology of agglomerates or their evolving structure during coagulation results in an underestimation or overestimation of the mean mobility diameter, [Formula: see text] , by up to 93 or 49%, repectively. Here, a monodisperse population balance model (MPBM) is interfaced with robust relations derived by mesoscale discrete element modeling (DEM) that account for the realistic agglomerate structure and size distribution during coagulation in the transition regime. For example, the DEM-derived collision frequency, [Formula: see text] , for polydisperse agglomerates is 82 ± 35% larger than that of monodisperse ones and in excellent agreement with measurements of flame-made TiO(2) nanoparticles. Therefore, the number density, [Formula: see text] , mean, [Formula: see text] and volume-equivalent diameter, [Formula: see text] , estimated here by coupling the MPBM with this [Formula: see text] and power laws for the evolving agglomerate morphology are on par with those obtained by DEM during the coagulation of monodisperse and polydisperse primary particles at pressures between 1 and 5 bar. Most importantly, the MPBM-derived [Formula: see text] , [Formula: see text] , and [Formula: see text] are in excellent agreement with the data for soot coagulation during low temperature sampling. As a result, the computationally affordable MPBM derived here accounting for the realistic nanoparticle agglomerate structure can be readily interfaced with computational fluid dynamics in order to accurately simulate nanoparticle agglomeration at high pressures or low temperatures that are present in engines or during sampling and atmospheric aging. MDPI 2021-07-12 /pmc/articles/PMC8306586/ /pubmed/34300803 http://dx.doi.org/10.3390/ma14143882 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kelesidis, Georgios A.
Kholghy, M. Reza
A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime
title A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime
title_full A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime
title_fullStr A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime
title_full_unstemmed A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime
title_short A Monodisperse Population Balance Model for Nanoparticle Agglomeration in the Transition Regime
title_sort monodisperse population balance model for nanoparticle agglomeration in the transition regime
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8306586/
https://www.ncbi.nlm.nih.gov/pubmed/34300803
http://dx.doi.org/10.3390/ma14143882
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