Establishing Predictive Models for Solvatochromic Parameters of Ionic Liquids

Ionic liquid electrolyte selection for high voltage supercapacitors in high-temperature applications

Systematic analyses of electrolyte physicochemical properties are important to screen ionic liquids (ILs) and understand the electrochemical performance of supercapacitor electrolytes. This study harmonizes the evaluation of electrochemical performance and transport properties of eight shortlisted ILs from 22 commercially available hydrophobic ILs toward achieving a ≥ 5 V supercapacitor capable of high-temperature operation (up to 353.15 K). The eight ILs are N-Propyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide ([Pyr 1, 3] [TFSI], N-Pentyl-N-methylpyrrolidinium bis(trifluoromethanesulfonyl)imide ([Pyr 1, 5] [TFSI]), N-Propyl-N-methylpyrrolidinium bis(fluorosulfonyl)imide ([Pyr 1, 3] [FSI]), 1-Methyl-1-(2-methoxyethyl)pyrrolidinium Bis(trifluoromethanesulfonyl)imide ([Pyr 1, 102] [TFSI]), 1-Methyl-1-propylpiperidinium bis(trifluoromethanesulfonyl)imide ([Pip 1, 3] [TFSI]), 1-Methyl-1-propylpiperidinium bis(fluorosulfonyl)imide ([Pip 1, 3] [FSI]), N-Trimethyl-N-propylammonium bis(trifluoromethanesulfonyl)imide ([N 111, 3] [TFSI]), N-Trimethyl-N-hexylammonium bis(trifluoromethanesulfonyl)imide ([N 111, 6] [TFSI]). The density, viscosity, and ionic conductivity of the eight ILs were measured between 278.15 and 373.15 K to confirm the effects of temperature and ion structure before electrochemical characterization. The [FSI]-based ILs ([Pip 1, 3] [FSI] and [Pyr 1, 3] [FSI]) showed lower densities and viscosities compared to other ILs among the eight based on [TFSI]. Consequently, the highest conductivity was obtained for [Pyr 1, 3] [FSI]. Cyclic voltammetry and impedance spectroscopy was performed on supercapacitors assembled with the eight ILs as electrolytes between 298.15-353.15 K. Conclusion from the two-electrode supercapacitors using multi-walled carbon nanotubes showed the 6 most-applicable ILs towards the targeted ≥ 5 V SC at high temperature are [Pip 1, 3] [TFSI] (5.4 V), [Pip 1, 3] [FSI] (5 V), [N 111, 3] [TFSI] (5.1 V), [N 111, 6] [TFSI] (5.2 V), [Pyr 1, 102] [TFSI] (5.2 V), and [Pyr 1, 5] [TFSI] (5.2 V).

Closed-Loop Electrolyte Design for Lithium-Mediated Ammonia Synthesis

Novel methods for producing ammonia, a large-scale industrial chemical, are necessary for reducing the environmental impact of its production. Lithium-mediated electrochemical nitrogen reduction is one attractive alternative method for producing ammonia. In this work, we experimentally tested several classes of proton donors for activity in the lithium-mediated approach. From these data, an interpretable data-driven classification model is constructed to distinguish between active and inactive proton donors; solvatochromic Kamlet-Taft parameters emerged to be the key descriptors for predicting nitrogen reduction activity. A deep learning model is trained to predict these parameters using experimental data from the literature. The combination of the classification and deep learning models provides a predictive mapping from proton donor structure to activity for nitrogen reduction. We demonstrate that the two-model approach is superior to a purely mechanistic or a data-driven approach in accuracy and experimental data efficiency.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Highly efficient cellulose dissolution by alkaline ionic liquids

Alkaline ionic liquids (ILs) with unconventional organic anions were prepared and used for cellulose dissolution studies. High concentrations of cellulose were dissolved at room temperature in the phenolate based imidazolium IL [C₂mim][OPh], combined with organic solvent, and up to 45 wt-% cellulose dissolution (wt-% MCC of weight IL) was readily achieved at 100 ºC. No functionalization of the regenerated cellulose was observed during the dissolution process (FTIR). Characteristic cellulose II XRD diffraction pattern was observed after IL dissolution and regeneration of MCC. The crystallinity index (CI) of the pretreated MCC was reduced from 93.2 % to 31 %. Inert conditions were not required for the cellulose dissolution experiments. This study indicates that the IL H-bond basicity is not the only key parameter determining their cellulose dissolution ability. The alkaline ILs represent an energy efficient and sustainable approach for cellulose dissolution.