Surface areas of metal−organic frameworks (MOFs) have been commonly characterized using the Brunauer−Emmett−Teller (BET) method based on adsorption isotherms of nonreactive nitrogen or argon. Recently, some discrepancies between surface areas computed from the BET method and those from geometric methods were reported in the literature. In this study, we systematically evaluated the BET and geometric surface areas of over 200 geometrically diverse real MOFs as well as CNTs with varying pore sizes as model systems to achieve a comprehensive understanding of the limitations of the BET and geometric methods. We compared the BET and geometric surface areas to the true monolayer area, which is determined by directly counting the number of molecules included in the monolayer of the surface from molecular simulation snapshots. Furthermore, we found that the excess sorption work (ESW) method or a combination of ESW and BET methods can potentially help facilitate a more accurate estimation of the surface area, particularly in cases where the structures show relatively less complex isotherms having distinct steps.
Water adsorption in porous materials has recently drawn considerable attention for its tremendous potential in environmental applications such as water harvesting from air. A key step to the deployment of such technologies is the selection of optimal adsorbent materials as material performance directly impacts their efficiency. Owing to the vast materials space, computational studies may play a critically important role in facilitating the selection of the best material. The current state-ofthe-art method to predict adsorption behavior of porous materials, the grand canonical Monte Carlo (GCMC) simulations, however, can converge very slowly due to its inefficient sampling of the phase space accessible to the system and may yield unreliable results. To this end, we have demonstrated a method from a class of techniques known as flat histogram methods, which can sample the accessible states of the system much more efficiently. Further, a so-called C-map method is proposed herein to efficiently determine the applicability of a material in water adsorption applications. These methods can offer unprecedented insights into the behaviors of the GCMC-predicted isotherms and, more importantly, are promising for large-scale computational screening of porous materials for water adsorption.
Technologies based on water adsorption such as water harvesting from air have tremendous potential in mitigating important global crises such as water scarcity. An important challenge to the deployment of such technologies is finding optimal adsorbent materials. Given the large materials space of available adsorbents, large-scale computational screening can be extremely helpful for this task. This work explores
Surface areas of porous materials such as metal−organic frameworks (MOFs) are commonly characterized using the Brunauer− Emmett−Teller (BET) method. However, it has been shown that the BET method does not always provide an accurate surface area estimation, especially for large-surface area MOFs. In this work, we propose, for the first time, a data-driven approach to accurately predict the surface area of MOFs. Machine learning is employed to train models based on adsorption isotherm features of more than 300 diverse structures to predict a benchmark measure of the surface area known as the true monolayer area. We demonstrate that the ML-based methods can predict true monolayer areas significantly better than the BET method, showing great promise for their potential as a more accurate alternative to the BET method in the structural characterization of porous materials.
Polyimide (C22H10N2O5, PMDA-ODA, Kapton-H) samples were doped with phosphorous or boron and fluorine using the radiation assisted diffusion technique, with Co-60 gamma-rays over the dose range ∼64–384 kGy, at room temperature. The diffusion of phosphorus and fluorine was confirmed by the RBS technique and that of boron by the neutron depth profiling technique. The elemental concentration on the surface was studied by the XPS technique. The relative concentration of phosphorus, fluorine and boron increased with increasing dose of gamma-rays. The dielectric constant, ε′, of the polyimide increased by ∼43% after phosphorus doping but decreased by ∼33% after boron and fluorine doping. The increase in ε′ is attributed to the radiation induced chemical coupling of the phosphorus atoms across the intra-molecular polyimide chains. The down shift in ε′ is attributed to the decrease in the degree of electronic polarization and to the increase in the free volume due to the diffused boron or fluorine atoms. For all the doped samples the dielectric constant, ε′, decreased very slowly with increasing frequency, over the range 100 Hz–7 MHz. AFM results reveal that the surface morphology and the roughness of the doped polyimide are appreciably different than that of virgin polyimide.
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