Study of change in heat capacity of carbon nanotubes based ionanofluid prepared from a series of imidazolium ionic liquids

0560297 - ÚCHP 2023 RIV GB eng J - Článek v odborném periodiku
Parmar, Nirmal - Bendová, Magdalena - Wagner, Zdeněk - Jacquemin, J.
Study of change in heat capacity of carbon nanotubes based ionanofluid prepared from a series of imidazolium ionic liquids.
Physical Chemistry Chemical Physics. Roč. 24, č. 36 (2022), s. 22181-22190. ISSN 1463-9076. E-ISSN 1463-9084
Grant CEP: GA MŠMT(CZ) 8J19FR033
Grant ostatní: AV ČR(CZ) StrategieAV21/3; PHC Barrande 2019(FR) 42771VL
Program: StrategieAV
Institucionální podpora: RVO:67985858
Klíčová slova: theermodynamic properties * thermophysical properties * nanofluids
Obor OECD: Physical chemistry
Impakt faktor: 3.3, rok: 2022
Způsob publikování: Omezený přístup

Ionanofluids (INFs), nanoparticles dispersed into a base fluid, e.g. an ionic liquid, are a novel class of an alternative heat transfer fluids. An addition of nanoparticles into base ionic liquid is the prime reason for an enhancement in thermophysical properties of ionanofluids. However, due to very limited research on ionanofluids, further studies are required to understand change in isobaric heat capacity of ionanofluids as a function of the size of cation of base ionic liquids structure and concentration of nanoparticles. Herein, isobaric heat capacity was measured as a function of temperature for the prepared ionanofluids samples from a series of imidazolium ionic liquids and multi wall carbon nanotubes (MWCNT). Moreover, the influence of the size of cation on the isobaric heat capacity enhancement mechanism and stability of ionanofluid samples were studied. Furthermore, experimental isobaric heat capacity data were assessed by a novel non-statistical data analysis method named as mathematical gnostics (MG). MG marginal analysis was used to evaluate most probable values from the measured data set. A robust linear regression along a gnostic influence function was also used to find the best fit to correlate measured data.
Trvalý link: https://hdl.handle.net/11104/0333285