2015
DOI: 10.1063/1.4923057
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Design of nanoporous materials with optimal sorption capacity

Abstract: Modern technological advances have enabled one to manufacture nanoporous materials with a prescribed pore structure. This raises a possibility of using controllable pore-scale parameters (e.g., pore size and connectivity) to design materials with desired macroscopic properties (e.g., diffusion coefficient and adsorption capacity). By relating these two scales, the homogenization theory (or other upscaling techniques) provides a means of guiding the experimental design. To demonstrate this approach, we consider… Show more

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Cited by 9 publications
(15 citation statements)
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References 35 publications
(41 reference statements)
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“…Generalizing the homogenization via multiple-scale expansions in Ref. 11 to account for spatial variability of D, it is easy to show that the Darcy-scale solute concentration C(x,t) satisfies a reaction-diffusion equation…”
Section: B Darcy-scale Modelmentioning
confidence: 99%
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“…Generalizing the homogenization via multiple-scale expansions in Ref. 11 to account for spatial variability of D, it is easy to show that the Darcy-scale solute concentration C(x,t) satisfies a reaction-diffusion equation…”
Section: B Darcy-scale Modelmentioning
confidence: 99%
“…We present our global sensitivity analysis (GSA) and uncertainty quantification (UQ) for the longitudinal diffusion coefficient D 11 eff D L ðpÞ. All the other functions of p in (8) are treated identically.…”
Section: Global Sensitivity Analysis (Gsa) and Uncertainty Quantmentioning
confidence: 99%
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“…Here, · denotes the volume of a domain and φ := P / V = P / V is the material porosity. Using homogenization via multiple-scale expansions [52], one can show that u satisfies a reaction-diffusion equation…”
Section: 2mentioning
confidence: 99%
“…Understanding statistical and causal relations between properties/model parameters at various scales is essential for science-based predictions in general and for forecasts of transport phenomena in porous media in particular. For example, the design of materials for energy storage devices aims to optimize macroscopic material properties (quantities of interest or QoIs), such as effective diffusion coefficient and capacitance, through engineered pore structures [52,51]. Quantification of both uncertainty in predictions of these macroscopic QoIs and their sensitivity to variability and uncertainty in microscopic features are crucial for informing such decision tasks as optimal experimental design and reliability engineering.…”
Section: Introductionmentioning
confidence: 99%