For a given carbon dioxide capture process, the choice of the best capture material, ultimately, depends on the trade-off between productivity and energy cost. For our select group of MOF structures, we plot in Figure energy cost as function of (c) CO 2 capture productivity simulated at process-level with a material-independent, constant K eff (left) as well as with a material-dependent K eff that accounts for the microscopic diffusion coefficient of each MOF (right). Open symbols represent materials for which the process-level simulation did not yield a CO 2 purity of at least 95%. productivity for a representative TSA process. We compare side-by-side the results obtained by assuming a constant and a material-dependent K eff . We note that the distribution of energy costs spans four orders of magnitude while the productivity distribution is relatively narrow. For our in-depth analysis, we define a region of interest in the lower right corner of the plots, with CO 2 productivity higher than 1 kg day.L and energy cost lower than 5 MJ/kg of CO 2 . In Figure , left panel, we plot energy cost as function of productivity assuming a constant, material-independent K eff . productivity for a crystallite diameter R µ = 10 µm and a 95%-purity threshold assuming a constant K eff (left) and a material-dependent K eff (right) that accounts for microscopic diffusivity. The respective CO 2 diffusion coefficients are indicated by color. Open symbols mark the positions of excluded materials for which the process-level simulation did not yield at least 95% purity. The dashed rectangle delineates the region of interest. (b-d) Effect of microscopic diffusivity in K eff on process performance for crystallite sizes 1, 10 and 100 µm, respectively. Open symbols mark the positions of excluded materials for which the process-level simulation did not yield at least 95% purity. The arrows indicate the changes undergone by MOF structures for which the inclusion of molecular diffusivity has led to significant process performance drop.