Physicochemical properties of piperidinium, ammonium, pyrrolidinium and morpholinium cations based ionic liquids paired with bis(trifluoromethylsulfonyl)imide anion

Ibrahim, Mohammed Sani; Hayyan, Maan; Hashim, Mohd Ali; Hayyan, Adeeb; Hadj-Kali, Mohamed K.

doi:10.1016/j.fluid.2016.06.028

Cited by 33 publications

(28 citation statements)

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“…These data are used to regressed the contribution parameters of each invovled group in the proposed model. Experimental value [78]: 0.539 𝑆 • 𝑚 −1 RD(%): 1.67%…”

Section: Supplementary Materialsmentioning

confidence: 99%

A group contribution-based prediction method for the electrical conductivity of ionic liquids

Chen

Cai

Thomsen

et al. 2020

Fluid Phase Equilibria

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“…These data are used to regressed the contribution parameters of each invovled group in the proposed model. Experimental value [78]: 0.539 𝑆 • 𝑚 −1 RD(%): 1.67%…”

Section: Supplementary Materialsmentioning

confidence: 99%

A group contribution-based prediction method for the electrical conductivity of ionic liquids

Chen

Cai

Thomsen

et al. 2020

Fluid Phase Equilibria

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“…No less important was the length of alkyl chain present in the cation of ILs. On the other hand, TFSI is an organic anion with a weak coordinating ability that has a significant impact on the properties of ILs such as solvent abilities, interaction abilities (e.g., hydrogen bonding), miscibility and melting point [ 42 ]. These properties of ILs can affect their activity in the elastomer composites.…”

Section: Introductionmentioning

confidence: 99%

Bromide and Chloride Ionic Liquids Applied to Enhance the Vulcanization and Performance of Natural Rubber Biocomposites Filled with Nanosized Silica

Maciejewska

Sowińska‐Baranowska

2022

Nanomaterials

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In this study, the possibility of using ionic liquids (ILs) as auxiliary substances improving the vulcanization and physicochemical properties of natural rubber (NR) biocomposites filled with nanosized silica was investigated. Hence, the influence of ILs with bromide and chloride anions and various cations, i.e., alkylimidazolium, alkylpyrrolidinium and alkylpiperidinium cation, on the curing characteristics and crosslink density of NR compounds was determined. Furthermore, the effect of nanosized silica and ILs on the functional properties of the obtained vulcanizates, including mechanical properties under static and dynamic conditions, hardness, thermal stability and resistance to thermo-oxidative aging, were explored. Applying nanosized silica improved the processing safety of NR compounds but significantly increased the optimal vulcanization time compared to the unfilled rubber. ILs significantly improved the cure characteristics of NR compounds by increasing the rate of vulcanization and the crosslink density of NR biocomposites. Consequently, the tensile strength and hardness of the vulcanizates significantly increased compared to that without ILs. Moreover, the use of nanosized silica and ILs had a favorable impact on the thermal stability of the vulcanizates and their resistance to prolonged thermo-oxidation.

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“…Similar to pyridinium cations, piperidinium cations have slower dynamics than pyrrolidinium cations because of the bulky nature of the cation. As such, there are relatively few studies that have explored the possibility of piperidinium cations as electrolytes for battery application [33][34][35] . Besides cyclic and aromatic cations, other central atom-based cations such as tetralkylphosphonium, tetraalkylammonium, and trialkylsulfonium, are also extensively studied for various applications [36][37][38] .…”

Section: Introductionmentioning

confidence: 99%

“…The other common cation type is sulfonium-based ionic liquids which have favorable properties compared to phosphonium-based cations because of the small volume occupied by the core sulfur atom leading to lower viscosity and high ionic conduc-tivity [41][42][43] . In addition to the commonly studied cations, there are several other cation types such as morpholinium 33,35,44 , pyrazolium 45,46 , oxazolidinium 47 which might offer desirable properties for battery application but there is very limited information on the physicochemical properties of these ionic liquids in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Generalized Machine Learning Model for Predicting Ionic Conductivity for Ionic Liquids

Dhakal

Shah

2022

Preprint

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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, 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 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

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Physicochemical properties of piperidinium, ammonium, pyrrolidinium and morpholinium cations based ionic liquids paired with bis(trifluoromethylsulfonyl)imide anion

Cited by 33 publications

References 50 publications

A group contribution-based prediction method for the electrical conductivity of ionic liquids

A group contribution-based prediction method for the electrical conductivity of ionic liquids

Bromide and Chloride Ionic Liquids Applied to Enhance the Vulcanization and Performance of Natural Rubber Biocomposites Filled with Nanosized Silica

A Generalized Machine Learning Model for Predicting Ionic Conductivity for Ionic Liquids

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