Although polycrystalline metal‐organic framework (MOF) membranes offer several advantages over other nanoporous membranes, thus far they have not yielded good CO2 separation performance, crucial for energy‐efficient carbon capture. ZIF‐8, one of the most popular MOFs, has a crystallographically determined pore aperture of 0.34 nm, ideal for CO2/N2 and CO2/CH4 separation; however, its flexible lattice restricts the corresponding separation selectivities to below 5. A novel postsynthetic rapid heat treatment (RHT), implemented in a few seconds at 360 °C, which drastically improves the carbon capture performance of the ZIF‐8 membranes, is reported. Lattice stiffening is confirmed by the appearance of a temperature‐activated transport, attributed to a stronger interaction of gas molecules with the pore aperture, with activation energy increasing with the molecular size (CH4 > CO2 > H2). Unprecedented CO2/CH4, CO2/N2, and H2/CH4 selectivities exceeding 30, 30, and 175, respectively, and complete blockage of C3H6, are achieved. Spectroscopic and X‐ray diffraction studies confirm that while the coordination environment and crystallinity are unaffected, lattice distortion and strain are incorporated in the ZIF‐8 lattice, increasing the lattice stiffness. Overall, RHT treatment is a facile and versatile technique that can vastly improve the gas‐separation performance of the MOF membranes.

Poly(triazine imide) (PTI), a crystalline g-C3N4, hosting two-dimensional nanoporous structure with an electron density gap of 0.34 nm, is highly promising for high-temperature hydrogen sieving because of its high chemical and thermal robustness. Currently, layered PTI is synthesized in potentially unsafe vacuum ampules in milligram quantities. Here, we demonstrate a scalable and safe ambient pressure synthesis route leading to several grams of layered PTI platelets in a single batch with 70% yield with respect to the precursor. Solvent exfoliation under anhydrous conditions led to single-layer PTI nanosheets evidenced by the observation of triangular g-C3N4 nanopores. Gas permeation studies confirm that PTI nanopores can sieve He and H2 from larger molecules. Last, high-temperature H2 sieving from PTI nanosheet–based membranes, prepared by the scalable filter coating technique, is demonstrated with H2 permeance reaching 1500 gas permeation units, with H2/CO2, H2/N2, and H2/CH4 selectivities reaching 10, 50, and 60, respectively, at 250°C.

Etching single-layer graphene to incorporate a high pore density with sub-angstrom precision in molecular differentiation is critical to realize the promising high-flux separation of similar-sized gas molecules, e.g., CO2 from N2. However, rapid etching kinetics needed to achieve the high pore density is challenging to control for such precision. Here, we report a millisecond carbon gasification chemistry incorporating high density (>1012 cm−2) of functional oxygen clusters that then evolve in CO2-sieving vacancy defects under controlled and predictable gasification conditions. A statistical distribution of nanopore lattice isomers is observed, in good agreement with the theoretical solution to the isomer cataloging problem. The gasification technique is scalable, and a centimeter-scale membrane is demonstrated. Last, molecular cutoff could be adjusted by 0.1 Å by in situ expansion of the vacancy defects in an O2 atmosphere. Large CO2 and O2 permeances (>10,000 and 1000 GPU, respectively) are demonstrated accompanying attractive CO2/N2 and O2/N2 selectivities.

High-flux nanoporous single-layer graphene membranes are highly promising for energy-efficient gas separation. Herein, in the context of carbon capture, a remarkable enhancement in the CO 2 selectivity is demonstrated by uniquely masking nanoporous single-layer graphene with polymer with intrinsic microporosity (PIM-1). In the process, a major bottleneck of the state-of-theart pore-incorporation techniques in graphene has been overcome, where in addition to the molecular sieving nanopores, larger nonselective nanopores are also incorporated, which so far, has restricted the realization of CO 2sieving from graphene membranes. Overall, much higher CO 2 /N 2 selectivity (33) is achieved from the composite film than that from the standalone nanoporous graphene (NG) (10) and the PIM-1 membranes (15), crossing the selectivity target (20) for postcombustion carbon capture. The selectivity enhancement is explained by an analytical gas transport model for NG, which shows that the transport of the stronger-adsorbing CO 2 is dominated by the adsorbed phase transport pathway whereas the transport of N 2 benefits significantly from the direct gas-phase transport pathway. Further, slow positron annihilation Doppler broadening spectroscopy reveals that the interactions with graphene reduce the free volume of interfacial PIM-1 chains which is expected to contribute to the selectivity. Overall, this approach brings graphene membrane a step closer to industrial deployment.

Generating pores in graphene by decoupled nucleation and expansion is desired to achieve a fine control over the porosity, and is desired to advance several applications. Herein, epoxidation is introduced, which is the formation of nanosized epoxy clusters on the graphitic lattice as nucleation sites without forming pores. In situ gasification of clusters inside a transmission electron microscope shows that pores are generated precisely at the site of the clusters by surpassing an energy barrier of 1.3 eV. Binding energy predictions using ab initio calculations combined with the cluster nucleation theory reveal the structure of the epoxy clusters and indicate that the critical cluster is an epoxy dimer. Finally, it is shown that the cluster gasification can be manipulated to form Å‐scale pores which then effectively sieve gas molecules based on their size. This decoupled cluster nucleation and pore formation will likely pave the way for an independent control of pore size and density.

Oxidation of graphitic materials has been studied for more than a century to synthesize materials such as graphene oxide, nanoporous graphene, and to cut or unzip carbon nanotubes. However, the understanding of the early stages of oxidation is limited to theoretical studies, and experimental validation has been elusive. This is due to (i) challenging sample preparation for characterization because of the presence of highly mobile and reactive epoxy groups formed during oxidation, and (ii) gasification of the functional groups during imaging with atomic resolution, e.g., by transmission electron microscopy. Herein, we utilize a low-temperature scanning tunneling microscope (LT-STM) operating at 4 K to solve the structure of epoxy clusters form upon oxidation. Three distinct nanostructures corresponding to three stages of evolution of vacancy defects are found by quantitatively verifying the experimental data by the van der Waals density functional theory. The smallest cluster is a cyclic epoxy trimer. Their observation validates the theoretical prediction that epoxy trimers minimize the energy in the cyclic structure. The trimers grow into honeycomb superstructures to form larger clusters (1–3 nm). Vacancy defects evolve only in the larger clusters (2–3 nm) in the middle of the cluster, highlighting the role of lattice strain in the generation of vacancies. Semiquinone groups are also present and are assigned at the carbon edge in the vacancy defects. Upon heating to 800 °C, we observe cluster-free vacancy defects resulting from the loss of the entire epoxy population, indicating a reversible functionalization of epoxy groups.

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.

Poly(triazine imide) or PTI is a promising material for molecular sieving membranes, thanks to its atom-thick ordered lattice with an extremely high density (1.6 × 1014 pores/cm2) of triangular-shaped nanopores...