Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/37893
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dc.contributor.authorZêzere, Brunopt_PT
dc.contributor.authorRios, William Q.pt_PT
dc.contributor.authorPortugal, Inêspt_PT
dc.contributor.authorSilva, Carlos M.pt_PT
dc.contributor.authorGomes, José R.B.pt_PT
dc.date.accessioned2023-05-25T14:58:02Z-
dc.date.issued2023-05-16-
dc.identifier.issn0167-7322pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/37893-
dc.description.abstractA computational strategy based on molecular dynamics (MD) simulations is proposed for the 14 prediction of tracer diffusion coefficients of bioactive compounds in CO2 ( ) and CO 𝐷12 2 modified 15 with ethanol ( ), in both supercritical and subcritical regions. Three systems were used for its 𝐷m,i 16 validation, namely, β-carotene and ibuprofen in supercritical CO2, and gallic acid in CO2 modified 17 with 16 mol.% of ethanol. The MD diffusivities were assessed with experimental data, with 18 average absolute relative deviations (AARD) between 3.82 % and 7.36 %, and average relative 19 deviations (ARD) between -6.18 % and 5.75 %. The dependency of the diffusion coefficient on 20 temperature, pressure, and Stoke-Einstein coordinate ( ) was also evaluated. All data followed 𝑇 𝜇 21 the expected trends apart from gallic acid in CO2 modified with 16 mol.% of ethanol, for which 22 the computed showed a weak linear dependency with . 𝐷m,i 𝑇 𝜇 23 The validated MD strategy was then employed to calculate diffusivities of eucalyptol in 24 sub/supercritical CO2, and in sub/supercritical CO2 modified with 8 mol.% and 16 mol.% of 25 ethanol. The computational strategy was re-evaluated upon comparison with experimental 26 diffusivities of eucalyptol in sub/supercritical mixtures of CO2 with 8 mol.% ethanol. The 27 expected dependencies on pressure, temperature, and Stoke-Einstein coordinate were obeyed, and 28 it was also found that the diffusion coefficient decreased with increasing ethanol content. Four 29 phenomenological models from the literature were adopted to represent MD data, with AARD 30 values between 1.21 % and 4.40 %, suggesting they can be used for diffusivity estimations over 31 wider ranges of operating conditions.pt_PT
dc.language.isoengpt_PT
dc.publisherElsevierpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50011%2F2020/PTpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50011%2F2020/PTpt_PT
dc.relationLA/P/0006/2020pt_PT
dc.relationPOCI-01-0247-FEDER-070302pt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F137751%2F2018/PTpt_PT
dc.rightsembargoedAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectMolecular dynamicspt_PT
dc.subjectDiffusion coefficientpt_PT
dc.subjectSupercritical mixturept_PT
dc.subjectEthanolpt_PT
dc.subjectCarbon dioxidept_PT
dc.subjectBioactive compoundspt_PT
dc.titleDiffusion coefficients of bioactive compounds in sub/supercritical CO2 and CO2/ethanol mixtures from molecular dynamics simulationspt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.titleJournal of Molecular Liquidspt_PT
dc.date.embargo2025-05-16-
dc.identifier.doi10.1016/j.molliq.2023.122096pt_PT
dc.identifier.essn1873-3166pt_PT
dc.identifier.articlenumber122096pt_PT
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