2020
DOI: 10.1080/00268976.2020.1767308
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A 3D non-local density functional theory for any pore geometry

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Cited by 11 publications
(4 citation statements)
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“…The NLDFT method provides accurate estimation of pore size range based on the pore geometry of the material. This method tends to be more accurate for pores having sizes smaller than 10 nm. , For >10 nm pores, the Barrett, Joyner, and Halenda (BJH) method could be implemented to give a more accurate description of the mesoporosity of the adsorbent …”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The NLDFT method provides accurate estimation of pore size range based on the pore geometry of the material. This method tends to be more accurate for pores having sizes smaller than 10 nm. , For >10 nm pores, the Barrett, Joyner, and Halenda (BJH) method could be implemented to give a more accurate description of the mesoporosity of the adsorbent …”
Section: Resultsmentioning
confidence: 99%
“…This method tends to be more accurate for pores having sizes smaller than 10 nm. 28,29 For >10 nm pores, the Barrett, Joyner, and Halenda (BJH) method could be implemented to give a more accurate description of the mesoporosity of the adsorbent. 29 From Table 1, the reduction in the surface area was found to be proportional to the percentage of Fe 2 O 3 added to the paste, since the materials were physically mixed and the surface area of Fe 2 O 3 was only ∼13, 17, and 21 m 2 /g for 5 μm, 100 nm, and 30 nm, respectively, while 13X exhibited a surface area of ∼553 m 2 /g.…”
Section: Materials Characterizationmentioning
confidence: 99%
“…Using non-local density functional theory (NLDFT), the pore width distributions were calculated (Figure c) . During carbonization, the carbon matrices of all PCFs and CFs developed micropores in trimodal peaks centered at ∼0.58, 0.86, and 1.2 nm (Figure c) .…”
Section: Resultsmentioning
confidence: 99%
“…In recent years, advancements in cDFT, particularly in the development of three-dimensional (3D) cDFT, have enhanced its capacity to model density profiles without relying on symmetry assumptions. These improvements have broadened the applicability to a wider range of crystalline porous materials, including MOFs, while maintaining its computational efficiency (Fu et al, 2015b,a;Kessler et al, 2021;Bernet et al, 2020;Sang et al, 2021). On the other hand, GCMC simulations have remained a popular choice for researchers due to their flexibility in modeling fluid behavior, enabling them to capture complex interactions between gases such as methane, nitrogen, and carbon dioxide inside crystalline nanoporous materials like MOFs (Teo et al, 2017;Tao et al, 2022;Li et al, 2022).…”
Section: Introductionmentioning
confidence: 99%