Browsing Original publications by Author "Leis, Jaan"

Showing items 1-3 of 3

Käärik, M.; Arulepp, M.; Käärik, M.; Maran, U.; Leis, J. Characterization and prediction of double-layer capacitance of nanoporous carbon materials using the Quantitative nano-Structure-Property Relationship approach based on experimentally determined porosity descriptors. Carbon 2020, 158, 494-504.

Published: Maike Käärik, Mati Arulepp, Meelis Käärik, Uko Maran, Jaan Leis (2019-11-06)

Abstract: The development of nanoporous carbon-based energy storage is a fast-growing area. To assist these developments, it is necessary to establish simple criteria and relationships between electric double-layer (EDL) capacitance and the nature of porous carbon used as an electrode material. Under special attention is carbide-derived carbon (CDC) due to high content ...

Regression
Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., W. E.; Maran, U. Machine learning Quantitative Structure-Property Relationships as a function of ionic liquid cations for the gas-ionic liquid partition coefficient of hydrocarbons. Int. J. Mol. Sci. 2022, 23, 7534.

Published: Karl Marti Toots, Sulev Sild, Jaan Leis, Acree Jr., W. E., Uko Maran (2022-06-22)

Abstract: Ionic liquids (ILs) are known for their unique characteristics as solvents and electrolytes. Therefore new ILs are being developed and adapted as innovative chemical environments for different applications where their properties need to be understood on a molecular level. Computational data driven methods provide means for understanding of properties at molecular ...

Regression DescriptorCalculation
Toots, K. M.; Sild, S.; Leis, J.; Acree Jr., William E.; Maran, U. The Quantitative Structure-Property Relationships for the gas-ionic liquid partition coefficient of a large variety of organic compounds in three ionic liquids. J. Mol. Liq. 2021, 343, 117573.

Published: Karl Marti Toots, Sulev Sild, Jaan Leis, Acree Jr., William E., Uko Maran (2021-09-20)

Abstract: Ionic liquids (ILs) have unique properties as solvents and electrolytes, which need to be studied using innovative machine learning approaches and which allow the identification of a chemical environment that can be adapted to different applications. The gas-ionic liquid partition coefficients of organic compounds is one such application-oriented parameter for ...

Regression DescriptorCalculation

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