Anomalous Solute Diffusivity in Ionic Liquids: Label-Free Visualization and Physical Origins

Bayles, Alexandra V.; Valentine, Connor S.; Überrück, Till; Danielsen, Scott P. O.; Han, Songi; Helgeson, Matthew E.; Squires, Todd M.

doi:10.1103/physrevx.9.011048

Cited by 8 publications

(20 citation statements)

References 85 publications

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“…The experimental and analytic details of μFPI are similar to those reported previously 38 with a few minor distinctions detailed below. Briefly, μFPI devices (Figure 1a) consist of a single, 90 μm layer of double-sided tape (permanent doublesided tape, Scotch) sandwiched between semireflective slides.…”

Section: ■ Introductionsupporting

confidence: 60%

“…This function is consistent with previous measurements of water diffusivity within a series of increasingly electronegative halide alkylmethylimidazolium ILs. 38 The measured parameters D 0 and α scale as anticipated with temperature via eqs 5 and 6 and also exhibit opposing trends with increasingly electronegative (or stronger binding) anions. In particular, the dry diffusivity D 0 decreases with tighter-binding anions, whereas the waterdependence α increases with electronegativity.…”

Section: ■ Introductionmentioning

confidence: 65%

“…Measurements conducted across a broad composition space revealed that the diffusivity of water molecules in water/methylimidazolium halide IL blends increases exponentially with water content. 38 A simple activated hopping model captures qualitative and quantitative features of these measurements. Specifically, we proposed that water molecules diffuse through methylimidazolium halide ILs by hopping between cation "head" groups that are segregated from their apolar "tails."…”

Section: ■ Introductionmentioning

confidence: 99%

“…43−48 The H2 proton's hydrogen bond susceptibility is reflected in its extreme downfield 1 H NMR chemical shift. Notably, the 1 H NMR chemical shift of H2 moves upfield with water content in H 2 O-methylimidazolium halide mixtures, 38,43 suggesting that increasing water content weakens the average strength of potential hydrogen bonds. Moreover, the 1 H NMR chemical shift of H2 shifts upfield for less electronegative anions, again indicating a decrease in strength of potential hydrogen bonds.…”

Section: ■ Introductionmentioning

confidence: 99%

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Hydrogen Bonding Strength Determines Water Diffusivity in Polymer Ionogels

et al. 2021

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Polymeric ionogels, cross-linked gels swollen by ionic liquids (ILs), are useful vehicles for the release and storage of molecular solutes in separation, delivery, and other applications. Although rapid solute diffusion is often critical for performance, it remains challenging to predict diffusivities across multidimensional composition spaces. Recently, we showed that water (a neutral solute) diffuses through alkyl-methylimidazolium halide ILs by hopping between hydrogen bonding sites on relatively immobile cations. Here, we expand on this activated hopping mechanism in two significant ways. First, we demonstrate that water diffuses through poly(ethylene glycol)diacrylate ionogels via the same mechanism at a reduced rate. Second, we hypothesize that the activation energy barrier can be determined from relatively simple 1 H NMR chemical shift measurements of the proton responsible for H-bonding. This relationship enables water's diffusivity in ionogels of this class to be predicted quantitatively, requiring only (1) the composition-dependent diffusivity and Arrhenius behavior of a single IL and (2) 1 H NMR spectra of the ionogels of interest. High-throughput microfluidic Fabry−Perot interferometry measurements verify prediction accuracy across a broad formulation space (four ILs, 0 ≤ x H 2 O ≤ 0.7, 0 ≤ ϕ PEGDA ≤ 0.66). The predictive model may expedite IL-material screening; moreover, it intimates a powerful connection between solute mobility and hydrogen bonding and suggests targets for rational design.

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Section: ■ Introductionsupporting

confidence: 60%

Section: ■ Introductionmentioning

confidence: 65%

Section: ■ Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

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Hydrogen Bonding Strength Determines Water Diffusivity in Polymer Ionogels

et al. 2021

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“…In this Letter we demonstrate the possibility of learning constitutive relations directly from experimental images, which has become increasingly relevant with the growing volume and quality of in situ and in operando images [23][24][25][26][27]. Our approach extends the inversion problem based on still images [28][29][30] and low-dimensional representation of images [31,32] and makes use of all the pixels in 2D video data to infer the physics of pattern formation.…”

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confidence: 97%

Learning the Physics of Pattern Formation from Images

et al. 2020

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Using a framework of partial differential equation-constrained optimization, we demonstrate that multiple constitutive relations can be extracted simultaneously from a small set of images of pattern formation. Examples include state-dependent properties in phase-field models, such as the diffusivity, kinetic prefactor, free energy, and direct correlation function, given only the general form of the Cahn-Hilliard equation, Allen-Cahn equation, or dynamical density functional theory (phase-field crystal model). Constraints can be added based on physical arguments to accelerate convergence and avoid spurious results. Reconstruction of the free energy functional, which contains nonlinear dependence on the state variable and differential or convolutional operators, opens the possibility of learning nonequilibrium thermodynamics from only a few snapshots of the dynamics.

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