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1. Flexo-Ionic Effect of Ionic Liquid Crystal Elastomers

   https://doi.org/10.3390/molecules26144234  

   Rajapaksha, C. P.; Gunathilaka, M. D.; Narute, Suresh; Albehaijan, Hamad; Piedrahita, Camilo; Paudel, Pushpa; Feng, Chenrun; Lüssem, Björn; Kyu, Thein; Jákli, Antal (July 2021, Molecules)

   The first study of the flexo-ionic effect, i.e., mechanical deformation-induced electric signal, of the recently discovered ionic liquid crystal elastomers (iLCEs) is reported. The measured flexo-ionic coefficients were found to strongly depend on the director alignment of the iLCE films and can be over 200 µC/m. This value is orders of magnitude higher than the flexo-electric coefficient found in insulating liquid crystals and is comparable to the well-developed ionic polymers (iEAPs). The shortest response times, i.e., the largest bandwidth of the flexo-ionic responses, is achieved in planar alignment, when the director is uniformly parallel to the substrates. These results render high potential for iLCE-based devices for applications in sensors and wearable micropower generators. 

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2. Ion Transport in Polymerized Ionic Liquid–Ionic Liquid Blends

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3. Estimating ionic conductivity of ionic liquids: Nernst–Einstein and Einstein formalisms

   https://doi.org/10.1016/j.jil.2024.100089  

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4. Recent advances in molecular simulations of ionic liquid–ionic liquid mixtures

   https://doi.org/10.1016/j.cogsc.2019.02.009  

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5. A generalized machine learning model for predicting ionic conductivity of ionic liquids

   https://doi.org/10.1039/D2ME00046F  

   Dhakal, Pratik; Shah, Jindal K. (July 2022, Molecular Systems Design & Engineering)

   Ionic liquids are currently being considered as potential electrolyte candidates for next-generation batteries and energy storage devices due to their high thermal and chemical stability. However, high viscosity and low conductivity at lower temperatures have severely hampered their commercial applications. To overcome these challenges, it is necessary to develop structure–property models for ionic liquid transport properties to guide the ionic liquid design. This work expands our previous effort in developing a machine learning model on imidazolium-based ionic liquids to now include ten different cation families, representing structural and chemical diversity. The model dataset contains 2869 ionic conductivity values over a temperature range of 238–472 K collected from the NIST ILThermo database and literature values for 397 unique ionic liquids. The database covers 214 unique cations and 68 unique anions. Three machine learning models, namely multiple linear regression, random forest, and extreme gradient boosting are applied to correlate the ionic liquid conductivity data with cation and anion features. Shapely additive analysis is performed to glean insights into cation and anion features with significant impact on ionic conductivity. Finally, the extreme gradient boosting model is used to predict the ionic conductivity of ionic liquids from all the possible combinations of unique cations and anions to identify ionic liquids crossing the ionic conductivity threshold of 2.0 S m −1 . 

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