High-throughput Screening of Real Metal-organic Frameworks for Adsorption Separation of C4 Olefins

Accelerated screening and assembly of promising MOFs with open Cu sites for isobutene/isobutane separation using a data-driven approach

As the by-products of catalytic cracking or alkane dehydrogenation, isobutene (2-methyl-propylene) and isobutane (2-methyl-propane) are important chemical feedstocks, but the separation of their mixture is a challenging issue in the petrochemical industry. Herein, we report the first example of large-scale computational screening of metal-organic frameworks (MOFs) with copper open metal sites (Cu-OMS) on the adsorptive separation of isobutene/isobutane using configuration-bias Monte Carlo (CBMC) simulations and machine learning among >330 000 MOFs data. We discovered that the optimal structural features governing the MOFs-based separation of isobutene/isobutane were density (ρ) and porosity (φ), with ranges of 0.2-0.5 g cm^-3 and 0.8-0.9, respectively. Furthermore, the key genes (metal nodes or linkers of frameworks) contributing to such adsorptive separation were data-mined by feature engineering of ML. These genes were cross-assembled into novel frameworks using a material-genomics strategy. The screened AVAKEP, XAHPON, HUNCIE, Cu₂O₈-mof177-TDPAT_No730 and assembled Cu₂O₈-BTC_B-core-4_No1 possessed high isobutene uptake and isobutene/isobutane selectivity of >19.5 mmol g^-1 and 4.7, with high thermal stability (as validated by molecular-dynamics simulations) overcoming the critical "trade-off" problem to some extent. The macroporous structures (pore-limiting diameter >12 Å) of these five promising frameworks with multi-layer adsorption on isobutene resulted in high isobutene loading, as validated by adsorption isotherms and CBMC simulations. The higher adsorption energy and heat of adsorption of isobutene than those of isobutane indicated that the thermodynamic equilibrium drove their selective adsorption. Generalized charge decomposition analysis and localized orbit locator calculations based on density functional theory wavefunctions suggested that high selectivity was due to complexation of feedback π bonds between isobutene and Cu-OMS, but also the strong π-π stacking interaction induced by the CC bond of isobutene with the multiple aromatic rings and unsaturated bonds of frameworks. Our theoretical results and data-driven approach may provide insights into the development of efficient MOF materials for the separation of isobutene/isobutane and other mixtures.

High-throughput computational screening of hypothetical metal-organic frameworks with open copper sites for CO2/H2 separation

It is challenging to identify the optimal metal-organic framework (MOF) adsorbents for gas adsorption and membrane-based separation from the large-scale material databases. The high-throughput computational screening (HTCS) method was adopted to discover the optimal materials for CO₂/H₂ separation from thousands of MOFs. First, a hierarchical strategy was used to select 1092 MOFs from 13 512 MOFs, and their adsorption capacity towards the equimolar CO₂/H₂ mixture at 298 K and 10 bar was further calculated using the grand canonical Monte Carlo (GCMC) simulations. The results show that those MOFs with lvtb topology and organic linker 1,2,4,5-tetrazine are conducive to exhibiting high performance CO₂/H₂ adsorption separation among top-100 MOFs with high performance. The MOFs with pore limited diameter (PLD), largest cavity diameter (LCD), gravimetrical surface area (GSA), and void fraction in the range of 4-12 Å, 5-12 Å, 5500-6500 m² g^-1 and 0.80-0.85, respectively, have high adsorption capacity towards CO₂. Second, the dynamic adsorption properties of the top-4 MOFs were simulated by the breakthrough curves of the binary (CO₂/H₂) and quinary (CO₂/H₂/CH₄/CO/N₂) mixtures in the fixed adsorption bed. MOF-4641 exhibits a high breakthrough time of 130 for the quinary mixture. Finally, the adsorption mechanism of CO₂ in the top-4 MOFs was investigated by the radial distribution function (RDF), the mass center probability density distribution, etc. The atomic insights from HTCS and breakthrough curve predictions in this work will be helpful in developing novel porous materials and obtaining superior CO₂ separation performance.

Research on Ethanol Coupling to Prepare C4 Olefins Based on BP Neural Network and Cluster Analysis

Ethanol, as a clean energy source, is an ideal raw material for the preparation of C4 olefins, but there are few studies on the preparation of C4 olefins by the coupling of ethanol. This research is based on the data from Question B of the 2021 Chinese Contemporary Undergraduate Mathematical Contest in Modeling. The researchers firstly perform data interpolation and visualization processing on the original data. Secondly, based on the two-dimensional visualization analysis, we cluster the different catalyst combinations; divide the influencing factors into temperature, Co/SiO2 and HAP loading ratio, Co loading, and ethanol concentration; and construct a quaternary linear regression equation that affects ethanol conversion rate and C4 olefin selectivity. Finally, according to the three-dimensional spatial visualization analysis and using the BP neural network model training data, we obtain that under the conditions of using loading method I, catalyst combination type 200 mg 0.5 wt% Co/SiO2-200 mg HAP-ethanol concentration 0.9 ml/min and temperature 450°C, the yield of C4 olefins can reach the maximum. This study provides new research ideas and methods for the preparation of C4 olefins from ethanol.