4.6 Article

NLDFT Pore Size Distribution in Amorphous Microporous Materials

期刊

LANGMUIR
卷 33, 期 42, 页码 11138-11145

出版社

AMER CHEMICAL SOC
DOI: 10.1021/acs.langmuir.7b01961

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资金

  1. National Science Foundation [DMR1604376]
  2. Division Of Materials Research
  3. Direct For Mathematical & Physical Scien [1604376] Funding Source: National Science Foundation

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The pore size distribution (PSD) is one of the most important properties when characterizing and designing materials for gas storage and separation applications. Experimentally, one of the current standards for determining microscopic PSD is using indirect molecular adsorption methods such as nonlocal density functional theory (NLDFT) and N-2 isotherms at 77 K. Because determining the PSD from NLDFT is an indirect method, the validation can be a nontrivial task for amorphous microporous materials. This is especially crucial since this method is known to produce artifacts. In this work, the accuracy of NLDFT PSD was compared against the exact geometric PSD for 11 different simulated amorphous microporous materials. The geometric surface area and micropore volumes of these materials were between 5 and 1698 m(2)/g and 0.039 and 0.55 cm(3)/g, respectively. N-2 isotherms at 77 K were constructed using Gibbs ensemble Monte Carlo (GEMC) simulations. Our results show that the discrepancies between NLDFT and geometric PSD are significant. NLDFT PSD produced several artificial gaps and peaks that were further confirmed by the coordinates of inserted particles of a specific size. We found that dominant peaks from NLDFT typically reported in the literature do not necessarily represent the truly dominant pore size within the system. The confirmation provides concrete evidence for artifacts that arise from the NLDFT method. Furthermore, a sensitivity analysis was performed to show the high dependency of PSD as a function of the regularization parameter, lambda. A higher value of lambda produced a broader and smoother PSD that closely resembles geometric PSD. As an alternative, a new criterion for choosing lambda, called here the smooth-shift method (SSNLDFT), is proposed that tuned the NLDFT PSD to better match the true geometric PSD. Using the geometric pore size distribution as our reference, the smooth-shift method reduced the root-mean-square deviation by similar to 70% when the geometric surface area of the material is greater than 100 m(2)/g.

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