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Global access to drinking water shrinks yearly, yet the atmosphere—our largest sustainable water source—remains largely untapped. Metal–organic frameworks (MOFs), a tunable class of crystalline porous materials, are promising candidates for atmospheric water harvesting. The channel-pore MOF STA-16(Co) stands out due to its robust phosphonate-based structure, which provides high stability and excellent water uptake. However, STA-16(Co) suffers from slow water uptake kinetics. To address this limitation, we introduced defects into STA-16(Co) by selectively removing linkers through treatment with nitrilotriacetic acid, significantly improving water diffusion kinetics. The defective MOFs demonstrate markedly faster water saturation rates—delivering ~50% more water in a 40-minute cycle—while maintaining the same uptake capacity and isothermal behavior as pristine STA-16(Co). Solid-state nuclear magnetic resonance analysis confirms that localized defects enhance efficiency without altering the overall pore geometry. This study presents a straightforward and generalizable strategy to optimize water sorption in channel-based MOFs. 

Mechanical bonds arise between molecules that contain interlocked subunits, such as one macrocycle threaded through another. Within polymers, these linkages will confer distinctive mechanical properties and other emergent behaviors, but polymerizations that form mechanical bonds efficiently and use simple monomeric building blocks are rare. In this work, we introduce a solid-state polymerization in which one monomer infiltrates crystals of another to form a macrocycle and mechanical bond at each repeat unit of a two-dimensional (2D) polymer. This mechanically interlocked 2D polymer is formed as a layered solid that is readily exfoliated in common organic solvents, enabling spectroscopic characterization and atomic-resolution imaging using advanced electron microscopy techniques. The 2D mechanically interlocked polymer is easily prepared on multigram scales, which, along with its solution processibility, enables the facile fabrication of composite fibers with Ultem that exhibit enhanced stiffness and strength. 

Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m²/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm²of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H₂SO₄. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements. 

Abstract Solid‐state NMR measurements coupled with density functional theory (DFT) calculations demonstrate how hydrogen positions can be refined in a crystalline system. The precision afforded by rotational‐echo double‐resonance (REDOR) NMR to interrogate¹³C–¹H distances is exploited along with DFT determinations of the¹³C tensor of carbonates (CO₃²⁻). Nearby¹H nuclei perturb the axial symmetry of the carbonate sites in the hydrated carbonate mineral, hydromagnesite [4 MgCO₃⋅Mg(OH)₂⋅4 H₂O]. A match between the calculated structure and solid‐state NMR was found by testing multiple semi‐local and dispersion‐corrected DFT functionals and applying them to optimize atom positions, starting from X‐ray diffraction (XRD)‐determined atomic coordinates. This was validated by comparing calculated to experimental¹³C{¹H} REDOR and¹³C chemical shift anisotropy (CSA) tensor values. The results show that the combination of solid‐state NMR, XRD, and DFT can improve structure refinement for hydrated materials. 

Abstract This is the first report of a multifunctional separator for potassium‐metal batteries (KMBs). Double‐coated tape‐cast microscale AlF₃on polypropylene (AlF₃@PP) yields state‐of‐the‐art electrochemical performance: symmetric cells are stable after 1000 cycles (2000 h) at 0.5 mA cm⁻²and 0.5 mAh cm⁻², with 0.042 V overpotential. Stability is maintained at 5.0 mA cm⁻²for 600 cycles (240 h), with 0.138 V overpotential. Postcycled plated surface is dendrite‐free, while stripped surface contains smooth solid electrolyte interphase (SEI). Conventional PP cells fail rapidly, with dendrites at plating, and “dead metal” and SEI clumps at stripping. Potassium hexacyanoferrate(III) cathode KMBs with AlF₃@PP display enhanced capacity retention (91% at 100 cycles vs 58%). AlF₃partially reacts with K to form an artificial SEI containing KF, AlF₃, and Al₂O₃phases. The AlF₃@PP promotes complete electrolyte wetting and enhances uptake, improves ion conductivity, and increases ion transference number. The higher of K⁺transference number is ascribed to the strong interaction between AlF₃and FSI⁻anions, as revealed through¹⁹F NMR. The enhancement in wetting and performance is general, being demonstrated with ester‐ and ether‐based solvents, with K‐, Na‐, or Li‐ salts, and with different commercial separators. In full batteries, AlF₃prevents Fe crossover and cycling‐induced cathode pulverization.