000119852 001__ 119852
000119852 005__ 20221124114025.0
000119852 0247_ $$2doi$$a10.1039/D0CP00468E
000119852 0248_ $$2sideral$$a130491
000119852 037__ $$aART-2020-130491
000119852 041__ $$aeng
000119852 100__ $$0(orcid)0000-0002-3134-8566$$aAnsón-Casaos, A.
000119852 245__ $$aThe viscosity of dilute carbon nanotube (1D) and graphene oxide (2D) nanofluids
000119852 260__ $$c2020
000119852 5060_ $$aAccess copy available to the general public$$fUnrestricted
000119852 5203_ $$aControlling the physicochemical properties of nanoparticles in fluids directly impacts on their liquid phase processing and applications in nanofluidics, thermal engineering, biomedicine and printed electronics. In this work, the temperature dependent viscosity of various aqueous nanofluids containing carbon nanotubes (CNTs) or graphene oxide (GO), i.e. 1D and 2D nanoparticles with extreme aspect ratios, is analyzed by empirical and predictive physical models. The focus is to understand how the nanoparticle shape, concentration, motion degrees and surface chemistry affect the viscosity of diluted dispersions. To this end, experimental results from capillary viscosimeters are first examined in terms of the energy of viscous flow and the maximum packing fraction applying the Maron–Pierce model. Next, a comparison of the experimental data with predictive physical models is carried out in terms of nanoparticle characteristics that affect the viscosity of the fluid, mostly their aspect ratio. The analysis of intrinsic viscosity data leads to a general understanding of motion modes for carbon nanoparticles, including those with extreme aspect ratios, in a flowing liquid. The resulting universal curve might be extended to the prediction of the viscosity for any kind of 1D and 2D nanoparticles in dilute suspensions.
000119852 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T03-20R$$9info:eu-repo/grantAgreement/ES/MICINN/Juan de la Cierva Program-IJCI-2016-27789$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2014-068727$$9info:eu-repo/grantAgreement/ES/MINECO-FEDER/ENE2016-79282-C5-1-R
000119852 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000119852 590__ $$a3.676$$b2020
000119852 591__ $$aPHYSICS, ATOMIC, MOLECULAR & CHEMICAL$$b8 / 37 = 0.216$$c2020$$dQ1$$eT1
000119852 591__ $$aCHEMISTRY, PHYSICAL$$b77 / 162 = 0.475$$c2020$$dQ2$$eT2
000119852 592__ $$a1.052$$b2020
000119852 593__ $$aPhysics and Astronomy (miscellaneous)$$c2020$$dQ1
000119852 593__ $$aPhysical and Theoretical Chemistry$$c2020$$dQ1
000119852 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000119852 700__ $$0(orcid)0000-0002-0048-3036$$aCiria, J.C.$$uUniversidad de Zaragoza
000119852 700__ $$0(orcid)0000-0001-9460-7206$$aSanahuja-Parejo, O.
000119852 700__ $$aVíctor-Román, Sandra
000119852 700__ $$aGonzález-Domínguez, J.M.
000119852 700__ $$0(orcid)0000-0001-8158-1270$$aGarcía-Bordejé, E.
000119852 700__ $$0(orcid)0000-0002-8654-7386$$aBenito, Ana M.
000119852 700__ $$aMaser, W.K.
000119852 7102_ $$15007$$2075$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ciencia Comput.Intelig.Ar
000119852 773__ $$g22, 20 (2020), 11474-11484$$pPhys. chem. chem. phys.$$tPHYSICAL CHEMISTRY CHEMICAL PHYSICS$$x1463-9076
000119852 8564_ $$s3241260$$uhttps://zaguan.unizar.es/record/119852/files/texto_completo.pdf$$yVersión publicada
000119852 8564_ $$s2795032$$uhttps://zaguan.unizar.es/record/119852/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000119852 909CO $$ooai:zaguan.unizar.es:119852$$particulos$$pdriver
000119852 951__ $$a2022-11-24-09:14:17
000119852 980__ $$aARTICLE