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Covalent organic framework (COF) aerogels arehierarchically porous polymeric materials with ultrahigh specific surface area, making them attractive for wide applications such as molecular capture, adsorption, and catalysis. Previous COF aerogel studies have focused on varying their chemical structures and linkage chemistries to fine-tune material properties and functionality, most of which have reported relatively unsatisfying performance (e.g., poor mechanical strength and strain tolerance). This study describes the synthesis and characterization of COF nanocomposite aerogels, whose material properties and functionality are effectively engineered through the incorporation of reinforcing fillers/binders or functional additives. Boron nitride (BN) fillers, cross-linked poly(acrylic acid) (XPAA) binders, and gold nanoparticles (AuNps) are incorporated into 1,3,5-tris(aminophenyl)benzene-terephthaldehyde (TAPB-PDA) COF aerogel matrices to form homogeneous nanocomposite aerogels with enhanced mechanical properties and unique photothermal conversion capabilities. Fourier transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, and scanning electron microscopy results confirm the successful filler/additive inclusion into the final COF nanocomposite aerogels. Specifically, BN filler loading at ∼17 wt % relative to final COF mass doubles COF aerogel’s Young’s modulus from 11 to 22 kPa according to mechanical compression tests, with only ∼10% reduction in COF’s accessible mesopores’ surface area according to nitrogen porosimeter analyses. Meanwhile, incorporating ∼7 wt % XPAA relative to final COF mass improves the Young’s modulus to 21 kPa, while increasing the aerogel’s yield strain from 10 to 65% strain, although this leads to a ∼35% reduction in COF’s accessible mesopores’ surface area. Furthermore, photothermal AuNps are incorporated to form functional COF nanocomposite aerogels, whose overall temperature increases by 5.5 °C after 1 sun (AM1.5G, 1000 W m−2) irradiation. Overall, this study demonstrates potential routes to fabricate hierarchically porous COF nanocomposite aerogels with high specific surface area, robust mechanical stability, and unique photothermal functionality, which hold promises for applications in adsorption separation, gas storage, and photocatalysis. 

We report new advancements in the determination and high-resolution structural analysis of beam-sensitive metal organic frameworks (MOFs) using microcrystal electron diffraction (MicroED) coupled with focused ion beam milling at cryogenic temperatures (cryo-FIB). A microcrystal of the beam-sensitive MOF, ZIF-8, was ion-beam milled in a thin lamella approximately 150 nm thick. MicroED data were collected from this thin lamella using an energy filter and a direct electron detector operating in counting mode. Using this approach, we achieved a greatly improved resolution of 0.59 Å with a minimal total exposure of only 0.64 e−/A2. These innovations not only improve model statistics but also further demonstrate that ion-beam milling is compatible with beam-sensitive materials, augmenting the capabilities of electron diffraction in MOF research. 

Projecting climate change is a generalization problem: We extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than model grid size, which have been the main source of model projection uncertainty. Recent machine learning (ML) algorithms hold promise to improve such process representations but tend to extrapolate poorly to climate regimes that they were not trained on. To get the best of the physical and statistical worlds, we propose a framework, termed “climate-invariant” ML, incorporating knowledge of climate processes into ML algorithms, and show that it can maintain high offline accuracy across a wide range of climate conditions and configurations in three distinct atmospheric models. Our results suggest that explicitly incorporating physical knowledge into data-driven models of Earth system processes can improve their consistency, data efficiency, and generalizability across climate regimes.