resumo
The tracer diffusion coefficients of six ketones (propanone, butanone, pentan-2-one, pentan-3-one, hexan-2-one, hexan-3-one) and six aldehydes (methanal, ethanal, propanal, butanal, pentanal and hexanal) in liquid ethanol were computed by classical molecular dynamics (MD) simulations over 303.15- 333.15 K and 1-150 bar. The calculated tracer diffusion coefficients, DMD12 , compared very satisfactorily with experimental data from the literature, with average absolute relative deviations (AARD) between 9.48 % and 12.18 % for ketones, and between 6.30 % and 9.11 % for aldehydes. The trends of DMD 12 with solute size and temperature were accurately simulated in all cases, while the weaker influence of pressure was not rigorously reached in all cases when jumping from 1 to 75 bar and then to 150 bar. Furthermore, a temperature-based correction to DMD12 was introduced, which decreased the AARD values of ketones to the range 1.52-5.16 % and aldehydes to 2.94-3.45 %. The analyses of the spatial distribution functions and coordination numbers show that ethanol has more affinity with ketones than with aldehydes, though such affinity difference is not always translated to the computed DMD12 of ketone-aldehyde isomers. Nevertheless, the experimental diffusivities of both families of compounds are only ca. 7 % different, hence within the uncertainties associated with the calculated results. (c) 2022 The Author(s). Published by Elsevier B.V.This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
palavras-chave
BINARY DIFFUSION-COEFFICIENTS; TRANSPORT-PROPERTIES; SUPERCRITICAL CO2; DENSITY; FLUIDS; MODEL; EQUATIONS; ALCOHOLS
categoria
Chemistry; Physics
autores
Zêzere, B; Portugal, I; Silva, CM; Gomes, JRB
nossos autores
Bruno Miguel Martins Zêzere
Estudante de doutoramento
Carlos Manuel Silva
Professor associado
Inês Portugal
Professor Auxiliar
José R. B. Gomes
Investigador PrincipalGrupos
G4 - Materiais Renováveis e Economia Circular
G6 - Materiais Virtuais e Inteligência Artificial
agradecimentos
This work was developed within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020 and LA/P/0006/20 20, financed by national funds through the FCT/MEC (PIDDAC). Bruno Zezere thanks FCT for the PhD grant SFRH/BD/137751/2018.