Massive plastic pollution and grand scale emission of CO2 into the atmosphere represent two major and deeply connected societal challenges, which can have adverse impacts on climate, human health, and marine ecosystems. In particular, the COVID-19 pandemic led to substantially increased production, use, and discarding of disposable masks, a problem that requires urgent and effective technological solutions to mitigate their negative environmental impacts. Furthermore, over the years significant research efforts have sought to address the challenges of plastic waste and CO2 emission, such as development of chemical upcycling methods and low-cost CO2 capture sorbents at scale, respectively. In this work, we introduce a simple and scalable method for directly converting surgical polypropylene mask waste into sulfur-doped carbon fibers, which can exhibit a high CO2 sorption capacity of ≤3.11 mmol/g and high selectivity (>45) against N2 gas. This excellent performance is attributed to the high affinity between sulfur heteroatoms in the carbon framework and CO2 gas molecules, confirmed by combined experimental and simulation investigations. This work provides an industrially viable approach for upcycling plastic waste into carbon-based products with increased value, which can then be employed to address the environmental challenges of CO2 remediation.

Machine learning and data mining coupled with molecular modeling have become powerful tools for materials discovery. Metal–organic frameworks (MOFs) are a rich area for this due to their modular construction and numerous applications. Here, we make data from several previous large-scale studies in MOFs and zeolites from our groups (and new data for N2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over three million simulated adsorption data points for H2, CH4, CO2, Xe, Kr, Ar, and N2 in over 160 000 MOFs and 286 zeolites, textural properties like pore sizes and surface areas, and the structure file for each material. We include metadata about the Monte Carlo simulations to enable reproducibility. The database is searchable by MOF properties, and the data are stored in a standardized JavaScript Object Notation format that is interoperable with the NIST adsorption database. We also identify several MOFs that meet high performance targets for multiple applications, such as high storage capacity for both hydrogen and methane or high CO2 capacity plus good Xe/Kr selectivity. By providing this data publicly, we hope to facilitate machine learning studies on these materials, leading to new insights on adsorption in MOFs and zeolites.

Organophosphorus nerve agents are among the most toxic chemicals known and remain threats to humans due to their continued use despite international bans. Metal−organic frameworks (MOFs) have emerged as a class of heterogeneous catalysts with tunable structures that are capable of rapidly detoxifying these chemicals via hydrolysis at Lewis acidic active sites on the metal nodes. To date, the majority of studies in this field have focused on zirconium-based MOFs (Zr-MOFs) that contain hexanuclear Zr(IV) clusters, despite the large toolbox of Lewis acidic transition metal ions that are available to construct MOFs with similar catalytic properties. In particular, very few reports have disclosed the use of a Ti-based MOF (Ti-MOF) as a catalyst for this transformation even though Ti(IV) is a stronger Lewis acid than Zr(IV). In this work, we explored five Ti-MOFs (Ti-MFU-4l, NU-1012-NDC, MIL-125, Ti-MIL-101, MIL-177(LT), and MIL-177(HT)) that each contains Ti(IV) ions in unique coordination environments, including monometallic, bimetallic, octanuclear, triangular clusters, and extended chains, as catalysts to explore how both different node structures and different linkers (e.g., azolate and carboxylate) influence the binding and subsequent hydrolysis of an organophosphorus nerve agent simulant at Ti(IV)-based active sites in basic aqueous solutions. Experimental and theoretical studies confirm that Ti-MFU-4l, which contains monometallic Ti(IV)−OH species, exhibits the best catalytic performance among this series with a half-life of roughly 2 min. This places Ti-MFU-4l as one of the best nerve agent hydrolysis catalysts of any MOF reported to date.