Elucidating the mechanisms of Paraffin-Olefin separations using nanoporous adsorbents: An overview
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The recent introduction of nano-porous ion emitters (nano-PIEs) formed from metal organic frameworks (MOFs) has demonstrated the potential to enhance sensitivity for thermal ionization mass spectrometry (TIMS). Nano-PIEs take advantage of the parent MOF’s chemical and structural tunability to form scaffolds for ion emitters. A study by Barpaga et al. 2023 on MOF-74 as the parent material found that with high volatility metals in their framework uranium sample utilization efficiency (SUE) increases by up to nine-fold (e.g., Zn-MOF-74) compared to that of the analyte on a bare filament (~0.05%). Here, in this study, we investigate the performance of Zn-MOF-74 to maximize uranium efficiencies at the trace level (= 10 -12 g) by altering the parent MOF morphology and chemistry (i.e., MOF crystal sizes and thermal degradation) and optimizing its integration with TIMS (i.e., MOF mass on a filament and ramp conditions). We observed improvement in SUE up to 20 times (≤1.0%) that of a bare filament load when nano-PIEs derived from nanocrystals of Zn-MOF-74 were heated under a specific current ramp condition. This demonstrates that rates of nano-PIE structural collapse during TIMS analysis and the subsequently formed nanomaterials (and their features) can be tuned to control analyte ionization.
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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 N 2 and Ar adsorption in MOFs) easily accessible in one place. The database includes over 3 million simulated adsorption data points for H 2 , CH 4 , CO 2 , Xe, Kr, Ar, and N 2 in over 160 000 MOFs and 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 JSON format that 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 CO 2 capacity plus good Xe/Kr selectivity. Here, 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.
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While there is no known fundamental lower limit to the thermal conductivity of a material, the lowest thermal conductivities are typically found in amorphous and strongly disordered materials, not highly crystalline materials. Here, we demonstrate a surprising nanostructuring route to ultralow thermal conductivity in a large-unit-cell oxide crystal (Fe 3 O 4 ) containing close-packed nanoscale pores. The electrical conductivity of this material reduces by a factor of 5 relative to dense v, independent of pore size. In contrast, thermal conductivity has a strong dependence on pore size with a factor of 40 of suppression relative to dense Fe 3 O 4 for 40 nm pores vs a factor of 5 for 500 nm pores. The matrix thermal conductivity of Fe 3 O 4 containing 40 nm pores falls below the predicted minimum thermal conductivity by a factor of 3. Finally, we attribute this to strong acoustic phonon scattering and intrinsically limited contributions to thermal conductivity from optical phonons with small dispersion.