Advanced Analysis of Isobaric Heat Capacities by Mathematical Gnostics

Wagner, Z.; Bendová, Magdalena; Rotrekl, Jan; Velíšek, Petr; Storch, Jan; Uchytil, Petr; Setničková, Kateřina; Řezníčková, Jiřina

doi:10.1007/s10953-017-0676-x

Cited by 13 publications

(9 citation statements)

References 19 publications

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“…The measured heat capacity data of the pure IL was then compared with literature data, , which reported pure IL heat capacity data with uncertainty estimated from statistics. To critically assess our data in comparison with the literature values, MG marginal analysis was carried out on our experimental data alone and on the combined data sets of the experimental and literature data . The DI and TI values significantly increased in comparison with the present measured data sample.…”

Section: Results and Discussionmentioning

confidence: 91%

“…MG is an advanced non-statistical approach toward data uncertainty. Generally, statistics uses an a priori assumption on the type of error distribution function to estimate the measurement uncertainty . On the other hand, MG theory was derived considering the properties of every single measurement, based on fundamental laws of nature.…”

Section: Methodsmentioning

confidence: 99%

“…The measured data were then analyzed by a novel, non-statistical advanced data analysis method based on mathematical gnostics (MG). A robust liner regression along a gnostic influence function was used to optimize the parameters of equations used to describe the experimental data. , …”

Section: Introductionmentioning

confidence: 99%

“…A robust liner regression along a gnostic influence function was used to optimize the parameters of equations used to describe the experimental data. 36,37 ■ EXPERIMENTAL SECTION Materials. The INF samples of 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl) imide ([C 2 mim] [Tf 2 N]), used as the base IL, and MWCNTs from Sigma-Aldrich were prepared by mixing of the two components (see Table 1 for the ,MWCNT and base IL details).…”

Section: ■ Introductionmentioning

confidence: 99%

“…Generally, statistics uses an a priori assumption on the type of error distribution function to estimate the measurement uncertainty. 36 On the other hand, MG theory was derived considering the properties of every single measurement, based on fundamental laws of nature. The properties of data samples are then obtained by aggregation of properties of individual measurements.…”

Section: ■ Introductionmentioning

confidence: 99%

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Carbon Nanotube-Based Ionanofluids for Efficient Energy Storage: Thermophysical Properties’ Determination and Advanced Data Analysis

Parmar

Bendová

Wagner

et al. 2021

Ind. Eng. Chem. Res.

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Ionanofluids (INFs), ionic liquids (ILs) containing dispersed nanoparticles, show fascinating thermophysical properties. The amount of dispersed nanoparticles influences the properties of the base IL, allowing for a fine-tuning of the properties of the resulting INF. Even though INFs have been studied for more than a decade now, sufficient and reliable data are still lacking. The aim of this work is to report on the heat capacity, viscosity, melting temperature, and electrical conductivity of INFs based on 1-ethyl-3-methylimidazolium bis-(trifluoromethylsulfonyl)imide as a function of multi-wall carbon nanotube compositions. In this work, mathematical gnostics has been also used to estimate the uncertainty in each measured property. Moreover, a robust linear regression along a gnostic influence function was used to find the best curve fit for the measured data.

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Section: Results and Discussionmentioning

confidence: 91%

Section: Methodsmentioning

confidence: 99%