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Ionic Liquids as Thermal Energy Storage Materials: On the Importance of Reliable Data Analysis in Assessing Thermodynamic Data.

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    0511975 - ÚCHP 2020 RIV US eng J - Článek v odborném periodiku
    Bendová, Magdalena - Čanji, Maja - Wagner, Zdeněk - Bogdanov, M.G.
    Ionic Liquids as Thermal Energy Storage Materials: On the Importance of Reliable Data Analysis in Assessing Thermodynamic Data.
    Journal of Solution Chemistry. Roč. 48, č. 7 (2019), s. 949-961. ISSN 0095-9782.
    [International Symposium on Solubility Phenomena and Related Equilibrium Processes (ISSP) /18./. Tours, 15.07.2018-20.07.2018]
    Grant CEP: GA ČR(CZ) GA17-08218S
    Institucionální podpora: RVO:67985858
    Klíčová slova: ionic liquids * thermophysical properties * differential scanning calorimetry
    Kód oboru RIV: CF - Fyzikální chemie a teoretická chemie
    Obor OECD: Physical chemistry
    Impakt faktor: 1.273, rok: 2019

    In spite of many statements on the application potential of ionic liquids, these organic salts present both advantages and drawbacks for their possible use in real processes. Nevertheless, they are still an undeniably fascinating class of compounds, both from the fundamental point of view and as promising task-specific materials. For instance, reliable thermal property data seem to be significantly lacking for pure ionic liquids. In addition, to assess the application potential of any material or process, a reliable analysis of experimental data is of key importance, not only to obtain recommended data, but also to be able to identify patterns in structure–property relationships, even if those may not seem evident at first sight. The aim of this work is to assess the potential application of a series of 1-alkyl-3-methylimidazolium saccharinate ionic liquids (alkyl standing for butyl, hexyl, octyl, and decyl) in thermal energy storage. To this end, heat capacity and energy density were determined experimentally by means of differential scanning calorimetry (DSC) and oscillating-tube densitometry. The experimental data were then analyzed by means of advanced data analysis methods based on mathematical gnostics. Based on the thermodynamic data and theory of measurement, mathematical gnostics is a novel non-statistical approach towards data uncertainty. As such it enables us to evaluate measurement uncertainty of statistically non-significant data sets containing as few as four data points. Also, using robust regression algorithms along a gnostic influence function, functional dependencies and structure–property patterns can be reliably determined.
    Trvalý link: http://hdl.handle.net/11104/0302207
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