An atomistic model of the nanoparticle size Silicon Carbide Derived Carbon (SiC-CDC) is constructed using the Hybrid Reverse Monte Carlo (HRMC) simulation technique through a two-step modeling procedure. Pore volume and three-membered ring constraints are utilized in addition to the commonly used structure factor and energy constraints in the HRMC modeling to overcome the challenges arising from uncertainties involved in determining the structure. The final model is characterized for its important structural features including pore volume, surface area, pore size distribution, physical pore accessibility, and structural defects. It is shown that the microporous structure of SiC-CDC 800 possesses a high pore volume and surface area, making it potentially a good candidate for gas adsorption applications. The HRMC model reveals the SiC-CDC 800 structure to be highly amorphous, largely comprising twisted graphene sheets. It is found that these distorted graphene-like carbon sheets comprising the carbon structure present a higher value for the solid–fluid potential strength compared to that of graphite, which is crucial in correct interpretation of experimental adsorption data. Furthermore, the constructed model is validated by comparing predictions of Ar, CO2 and CH4 adsorption against experimental data over a wide range of temperatures and pressures. It is demonstrated that our model is able to predict the experimental isotherms of different simple gases over various thermodynamic conditions with acceptable accuracy. The model also suggests the presence of ultramicroporosity that is accessible to CO2 but only partially accessible to CH4.

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Multiscale material screening strategies combine molecular simulations and process modeling to identify the best performing adsorbents for a particular application, such as carbon capture. The idea to go from the properties of a single crystal to the prediction of material performance in a real process is both powerful and appealing; however, it is yet to be established how to implement it consistently. In this article, we focus on the challenges associated with the interface between molecular and process levels of description. We explore how predictions of the material performance in the actual process depend on the accuracy of molecular simulations, on the procedures to feed the equilibrium adsorption data into the process simulator, and on the structural characteristics of the pellets, which are not available from molecular simulations and should be treated as optimization parameters. The presented analysis paves the way for more consistent and robust multiscale material screening strategies.

Investigation of adsorbents maximum theoretical performance, computational efficiency of multiscale screening workflows, and consistency of materials rankings for CO2 capture.