RESUMEN
Knowledge of the atomic structure of layer-stacked two-dimensional conjugated metal-organic frameworks (2D c-MOFs) is an essential prerequisite for establishing their structure-property correlation. For this, atomic resolution imaging is often the method of choice. In this paper, we gain a better understanding of the main properties contributing to the electron beam resilience and the achievable resolution in the high-resolution TEM images of 2D c-MOFs, which include chemical composition, density, and conductivity of the c-MOF structures. As a result, sub-angstrom resolution of 0.95 Å has been achieved for the most stable 2D c-MOF of the considered structures, Cu3(BHT) (BHT = benzenehexathiol), at an accelerating voltage of 80 kV in a spherical and chromatic aberration-corrected TEM. Complex damage mechanisms induced in Cu3(BHT) by the elastic interactions with the e-beam have been explained using detailed ab initio molecular dynamics calculations. Experimental and calculated knock-on damage thresholds are in good agreement.
RESUMEN
Breakthroughs in efficient use of biogas fuel depend on successful separation of carbon dioxide/methane streams and identification of appropriate separation materials. In this work, machine learning models are trained to predict biogas separation properties of metal-organic frameworks (MOFs). Training data are obtained using grand canonical Monte Carlo simulations of experimental MOFs which have been carefully curated to ensure data quality and structural viability. The models show excellent performance in predicting gas uptake and classifying MOFs according to the trade-off between gas uptake and selectivity, with R2 values consistently above 0.9 for the validation set. We make prospective predictions on an independent external set of hypothetical MOFs, and examine these predictions in comparison to the results of grand canonical Monte Carlo calculations. The best-performing trained models correctly filter out over 90% of low-performing unseen MOFs, illustrating their applicability to other MOF datasets.