2022
DOI: 10.2139/ssrn.4150348
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Towards a Robust Evaluation of Nanoporous Materials for Carbon Capture Applications

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Cited by 4 publications
(7 citation statements)
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References 35 publications
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“…[18][19][20] There are numerous existing materials and millions of hypothetical ones that can be generated in silico to be explored for DAC applications. [108][109][110][111][112] Their properties (e.g., density, solubility, permeability, volatility, viscosity, porosity, heat of absorption, heat of adsorption, thermal conductivity) cover a broad design space, making their exploration challenging (#1). Further materials research could help reduce the energy consumption or increase CO 2 removal efficiency (#2).…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…[18][19][20] There are numerous existing materials and millions of hypothetical ones that can be generated in silico to be explored for DAC applications. [108][109][110][111][112] Their properties (e.g., density, solubility, permeability, volatility, viscosity, porosity, heat of absorption, heat of adsorption, thermal conductivity) cover a broad design space, making their exploration challenging (#1). Further materials research could help reduce the energy consumption or increase CO 2 removal efficiency (#2).…”
Section: Methodsmentioning
confidence: 99%
“…Studies have shown that a lack of such a harmonization can lead to expensive delays in identifying optimally tailored materials. [108][109][110][111][112]114 Also, no framework exists for advising on optimal material and process characteristics needed catalyze large-scale implementation of DAC. This obstacle is observed among all DAC techniques, even those currently at higher TRL.…”
Section: Process Designmentioning
confidence: 99%
“…Therefore, we have developed a workflow that gives us the optimal IAST predictions without having to fit the data. This workflow is based on the PyIAST program developed by Simon et al [21] and is described in detail by Moubarak et al [22]. From a process simulation point of view, predicting mixture isotherms using IAST requires more CPU time compared to using an analytical expression, such as DSL isotherms.…”
Section: The Power Of An Integrated Platform For Materials Discoverymentioning
confidence: 99%
“…Therefore, we have developed a workflow that gives us the optimal IAST predictions without having to fit the data. This workflow is based on the PyIAST program developed by Simon et al 21 and is described in detail by Moubarak et al 22.…”
Section: The Power Of An Integrated Platform For Materials Discoverymentioning
confidence: 99%
“…All the details of the simulations (input files, structures, pure component isotherms, and mixture simulations) can be found on the Materials Cloud. 38…”
Section: Supporting Information Availablementioning
confidence: 99%