Abstract:The work presented in this article is aimed to assess the performances of 4763 Metal−Organic Frameworks (MOFs) for separation of hexane isomers using computer simulation methods. These MOFs, taken from the Computational Ready Experimental (CoRE) MOF database, are ranked on the basis of various performance metrics, namely, adsorption selectivity, working capacity, and regenerability. We investigated six binary mixtures, one three-component equimolar mixture, and one five-component equimolar mixture of hexane is… Show more
“…The separation performances of the MOFs were assessed using four metricsadsorption selectivity, working capacity, regenerability, and adsorbent performance score (APS). Adsorption selectivity for a binary mixture is defined as 33,44 S q q f f / / ads 1 2…”
Section: Detailsmentioning
confidence: 99%
“…The separation performances of the MOFs were assessed using four metricsadsorption selectivity, working capacity, regenerability, and adsorbent performance score (APS). Adsorption selectivity for a binary mixture is defined as , where, q i is the loading and f i is the fugacity of the i th component. Working capacity ( C w ) is defined as the adsorption capacity of a MOF at the adsorption pressure (i.e., 1 bar in our case) minus the adsorption capacity at the desorption pressure (i.e., 0.1 bar in our case).…”
Section: Molecular Modeling and Computational Detailsmentioning
confidence: 99%
“…Adsorption selectivity for a binary mixture is defined as , where, q i is the loading and f i is the fugacity of the i th component. Working capacity ( C w ) is defined as the adsorption capacity of a MOF at the adsorption pressure (i.e., 1 bar in our case) minus the adsorption capacity at the desorption pressure (i.e., 0.1 bar in our case). For an adsorbent to be practically deployable in an adsorption–desorption-based cyclic separation system such as pressure swing adsorption, the regeneration of the adsorption sites during the desorption is very crucial for the reuse of the adsorbent.…”
Section: Molecular Modeling and Computational Detailsmentioning
confidence: 99%
“…The advent of the modern technology in high-performance computing can be handy to carry out such a screening. In the past, researchers have employed such high-throughput computational screening of various porous materials to identify the best candidate for a given application of interest. − In this article, we present a high-throughput screening of around 12,000 MOF structures adopted from the Computational Ready Experimental (CoRE) MOF database developed by Chung et al to screen out the best material that can separate methane from ethane and propane at ambient conditions. We have screened these MOFs for the separation of two equimolar binary mixturesethane/methane (50% ethane and 50% methane) and propane/methane (50% propane and 50% methane), and two ternary mixturesequimolar (33.33% methane, 33.33% ethane, and 33.33% propane) and 90:7:3 (90% methane, 7% ethane, and 3% propane).…”
Efficient separation of mixtures
of light hydrocarbons is an industrially
demanding but challenging process. In this study, we present a high-throughput
computational screening of ∼12,000 experimentally realizable
metal–organic framework (MOF) structures in order to identify
the best candidate that can separate methane from ethane and propane
at ambient conditions. We calculated several performance metricsadsorption
selectivity, working capacity, and regenerability to assess the performance
of the MOFs in the database. The MOFs were screened based on high
adsorbent performance score and regenerability >80%. MOFs AZIVAI
and
BEWCUD were found to be performing the best for the separation of
methane from its binary and ternary mixtures with ethane and propane.
We looked at various structure–property correlations of selectivity
and working capacity that reveal a generic trade-off relation between
these two metrics. Selectivity correlates strongly with the heat of
adsorption in a linear fashion, whereas working capacity exhibits
an increasing and then decreasing behavior with the heat of adsorption
complementing the trade-off relation between selectivity and working
capacity. We have also screened out few promising MOFs that are thermally
and chemically stable and discussed their experimental stability conditions
in detail.
“…The separation performances of the MOFs were assessed using four metricsadsorption selectivity, working capacity, regenerability, and adsorbent performance score (APS). Adsorption selectivity for a binary mixture is defined as 33,44 S q q f f / / ads 1 2…”
Section: Detailsmentioning
confidence: 99%
“…The separation performances of the MOFs were assessed using four metricsadsorption selectivity, working capacity, regenerability, and adsorbent performance score (APS). Adsorption selectivity for a binary mixture is defined as , where, q i is the loading and f i is the fugacity of the i th component. Working capacity ( C w ) is defined as the adsorption capacity of a MOF at the adsorption pressure (i.e., 1 bar in our case) minus the adsorption capacity at the desorption pressure (i.e., 0.1 bar in our case).…”
Section: Molecular Modeling and Computational Detailsmentioning
confidence: 99%
“…Adsorption selectivity for a binary mixture is defined as , where, q i is the loading and f i is the fugacity of the i th component. Working capacity ( C w ) is defined as the adsorption capacity of a MOF at the adsorption pressure (i.e., 1 bar in our case) minus the adsorption capacity at the desorption pressure (i.e., 0.1 bar in our case). For an adsorbent to be practically deployable in an adsorption–desorption-based cyclic separation system such as pressure swing adsorption, the regeneration of the adsorption sites during the desorption is very crucial for the reuse of the adsorbent.…”
Section: Molecular Modeling and Computational Detailsmentioning
confidence: 99%
“…The advent of the modern technology in high-performance computing can be handy to carry out such a screening. In the past, researchers have employed such high-throughput computational screening of various porous materials to identify the best candidate for a given application of interest. − In this article, we present a high-throughput screening of around 12,000 MOF structures adopted from the Computational Ready Experimental (CoRE) MOF database developed by Chung et al to screen out the best material that can separate methane from ethane and propane at ambient conditions. We have screened these MOFs for the separation of two equimolar binary mixturesethane/methane (50% ethane and 50% methane) and propane/methane (50% propane and 50% methane), and two ternary mixturesequimolar (33.33% methane, 33.33% ethane, and 33.33% propane) and 90:7:3 (90% methane, 7% ethane, and 3% propane).…”
Efficient separation of mixtures
of light hydrocarbons is an industrially
demanding but challenging process. In this study, we present a high-throughput
computational screening of ∼12,000 experimentally realizable
metal–organic framework (MOF) structures in order to identify
the best candidate that can separate methane from ethane and propane
at ambient conditions. We calculated several performance metricsadsorption
selectivity, working capacity, and regenerability to assess the performance
of the MOFs in the database. The MOFs were screened based on high
adsorbent performance score and regenerability >80%. MOFs AZIVAI
and
BEWCUD were found to be performing the best for the separation of
methane from its binary and ternary mixtures with ethane and propane.
We looked at various structure–property correlations of selectivity
and working capacity that reveal a generic trade-off relation between
these two metrics. Selectivity correlates strongly with the heat of
adsorption in a linear fashion, whereas working capacity exhibits
an increasing and then decreasing behavior with the heat of adsorption
complementing the trade-off relation between selectivity and working
capacity. We have also screened out few promising MOFs that are thermally
and chemically stable and discussed their experimental stability conditions
in detail.
“…[31][32][33][34][35] Regarding the separation of hexane isomers, several MOFs have been considered to date. [36][37][38][39][40][41][42][43][44][45][46] For instance ZIF-8, featuring a sodalite zeolite type structure, can completely remove nhexane from branched isomers with higher adsorption capacity than zeolite 5A. [40][41][42] Fe 2 (BDP) 3 , consisting in a framework of onedimensional triangular channels that can accommodate all hexane isomers, separates them by the degree of branching.…”
A series of isoreticular Zr carboxylate MOFs, MIL-140A, B and C, exhibiting 1D microporous triangular shaped channels and based on different aromatic dicarboxylate ligands (1,4-BDC, 2,6-NDC and 4,4’-BPDC, respectively), were...
The isolation of di-branched alkanes from their isomers is vital in gasoline upgrading to achieve high octane numbers but is significantly challenging and energy-intensive. Here, we report the highly efficient separation of hexane isomers by combing molecular recognition and size-sieving in a bismuth-based MOF, UU-200. The unique auxetic structure with reentrant honeycomb-like pore cavities connected by narrow pore windows endows UU-200 with a complete rejection of di-branched alkanes and high capacities for linear and mono-branched isomers. The molecular sieving effect, unprecedented separation selectivities, and excellent efficiencies are proved via adsorption isotherms and breakthrough experiments with high research octane numbers obtained (> 96), indicating a benchmark for alkane separation under ambient conditions. The molecular recognition mechanism was unveiled by theoretical simulation and in situ Fourier-transform infrared spectroscopy.
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