Carbon capture, storage, and utilization (CCSU) represents an opportunity to mitigate carbon emissions that drive global anthropogenic climate change. Promising materials for CCSU through gas adsorption have been developed by leveraging the porosity, stability, and tunability of extended crystalline coordination polymers called metal−organic frameworks (MOFs). While the development of these frameworks has yielded highly effective CO 2 sorbents, an in-depth understanding of the properties of MOF pores that lead to the most efficient uptake during sorption would benefit the rational design of more efficient CCSU materials. Though previous investigations of gas−pore interactions often assumed that the internal pore environment was static, discovery of more dynamic behavior represents an opportunity for precise sorbent engineering. Herein, we report a multifaceted in situ analysis following the adsorption of CO 2 in MOF-808 variants with different capping agents (formate, acetate, and trifluoroacetate: FA, AA, and TFA, respectively). In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analysis paired with multivariate analysis tools and in situ powder X-ray diffraction revealed unexpected CO 2 interactions at the node associated with dynamic behavior of node-capping modulators in the pores of MOF-808, which had previously been assumed to be static. MOF-808-TFA displays two binding modes, resulting in higher binding affinity for CO 2 . Computational analyses further support these dynamic observations. The beneficial role of these structural dynamics could play an essential role in building a deeper understanding of CO 2 binding in MOFs.

This paper proposes an optimization framework for sustainable post-disaster building reconstruction. Based on mathematical optimization, it is intended to provide decision makers with a versatile tool to optimize building designs and to explore the trade-off between costs and environmental impact (represented by embodied energy) of alternative building materials. The mixed-integer nonlinear optimization model includes an analytical building model that considers structural and safety constraints and incorporates regional building codes. Using multi-objective optimization concepts, Pareto-optimal designs are computed that represent the best trade-off designs from which a decision maker can choose when they take additional criteria into consideration. As a case study, we consider the design of a multi-room one-story masonry building in Nepal. We demonstrate how the framework can be employed to answer a variety of questions, such as the optimal building design and material selection, the sensitivity of the decision to the prices, and the impact of regional safety regulation thresholds.

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