Screening of desulfurization adsorbent in metal–organic frameworks: A classical density functional approach

Predicting Gas Adsorption without the Knowledge of Pore Structures: A Machine Learning Method Based on Classical Density Functional Theory

Predicting gas adsorption from the pore structure is an intuitive and widely used methodology in adsorption. However, in real-world systems, the structural information is usually very complicated and can only be approximately obtained from the characterization data. In this work, we developed a machine learning (ML) method to predict gas adsorption form the raw characterization data of N2 adsorption. The ML method is modeled by a convolutional neural network and trained by a large number of data that are generated from a classical density functional theory, and the model gives a very accurate prediction of Ar adsorption. Though the training set is limited to modeling slit pores, the model can be applied to three-dimensional structured pores and real-world materials. The good agreements suggest that there is a universal relationship among adsorption isotherms of different adsorbates, which could be captured by the ML model.

Gas adsorption and separation in metal-organic frameworks by PC-SAFT based density functional theory

In this work, we examine the theoretical performance of perturbed-chain statistical associating fluid theory based density functional theory (DFT) in predicting gas adsorption and separation in metal-organic frameworks by using simulation and experimental data as the benchmark. Adsorption isotherms of methane and ethane in pure gas and mixtures and selectivities for ethane/methane mixtures are calculated. The predicted isotherms by DFT are in excellent agreement with simulation and experimental data for pure methane and ethane, whereas for the mixture, DFT is in semi-quantitative accordance with simulation results. For fast and high-throughput screening of material purpose, three algorithms including Picard iteration with line search, Anderson mixing, and Picard-Anderson-hybrid algorithm are proposed to calculate the three dimensional density distribution of confined gases. The advantages and limitations of the three algorithms under various conditions are discussed.

Synthesis and characterization of AFe2O4 (A: Ni, Co, Mg)-silica nanocomposites and their application for the removal of dibenzothiophene (DBT) by an adsorption process: kinetics, isotherms and experimental design

The kinetics, equilibrium, and statistical aspects of the sulfur removal process from hydrocarbon fuels by AFe₂O₄–silica nanocomposites (A: Ni, Mg, and Co) have been investigated in the present study. Nanocomposites were prepared via the auto-combustion sol–gel method and then employed in the adsorptive desulfurization (ADS) process. Next, the prepared samples were characterized by different analytical methods including XRD, SEM, TEM, FT-IR, TGA, and BET. The contributions of conventional parameters including adsorbent dosage and contact time were then studied by central composite design (CCD) under response surface methodology (RSM). Based on the statistical investigations, optimum conditions for ADS were an adsorbent dosage of 7.82 g per 50 ml of the model fuel and a contact time of 32 min. The adsorption amounts reached 38.6 mg g⁻¹ for DBT. The quadratic model was applied for the analysis of variance. Based on the experimental data, the pseudo-first-order (PFO) model could explain the adsorption kinetics of the compounds. Furthermore, the Langmuir isotherm demonstrated considerable agreement with the experimental equilibrium data. According to the results, the NiFe₂O₄–SiO₂ nanocomposite showed the best performance compared to other compounds. The sulfur removal efficiency increased from 63 to 94% upon increasing the NiFe₂O₄–SiO₂ dosage from 3 to 9 g per 50 ml of the model fuel.

Among the methods for adsorptive desulfurization (ADS) represents a promising alternative method of removing sulfur by adsorption.

A DFT Screening of M-HKUST-1 MOFs for Nitrogen-Containing Compounds Adsorption

To develop promising adsorbent candidates for adsorptive denitrogenation, we screened the adsorption of NO, NO₂, and NH₃ in 19 M-HKUST-1 (M = Be, Fe, Ni, Cr, Co, Cu, V, Zn, Mo, Mn, W, Sn, Ti, Cd, Mg, Sc, Ca, Sr, and Ba) systematically using first-principle calculations. Of these, four variants of M-HKUST-1 (M = Ni, Co, V, and Sc) yield more negative adsorption Gibbs free energy ΔG_ads than the original Cu-HKUST-1 for three adsorbates, suggesting stronger adsorbate binding. Ti-HKUST-1, Sc-HKUST-1, and Be-HKUST-1 are predicted to have the largest NO, NO₂, and NH₃ adsorption energies within the screened M-HKUST-1 series, respectively. With the one exception of NO₂ dissociation on V-HKUST-1, dissociative adsorption of NO, NO₂, and NH₃ molecules on the other considered M-HKUST-1 is energetically less favorable than molecular adsorption thermodynamically. The barrier calculations show that the dissociation is difficult to occur on Cu-HKUST-1 kinetically due to the very large dissociation barrier. Electronic analysis is provided to explain the bond nature between the adsorbates and M-HKUST-1. Note that the isostructural substitution of Cu to the other metals is a major simplification of the system, representing the ideal situation; however, the present study provides interesting targets for experimental synthesis and testing.

Fast screening of porous materials for noble gas adsorption and separation: a classical density functional approach

The design and screening of porous materials for noble gas adsorption and separation are an important issue in the production and utilization of gases. The conventional method to do this is via molecular simulation. In this work, we introduced a classical density functional theory (CDFT) to replace molecular simulation because CDFT is more efficient. A molecular dynamics (MD)/CDFT combined method was proposed to consider the flexibility of the adsorbent. The theory was first examined by comparing it to reported experiments and simulations. Then, the theory was applied to determine the most favorable adsorbents for noble gas adsorption/separation from 4764 real adsorbents and 1200 hypothetical adsorbents. A series of favorable adsorbents was identified, and some of them seemed promising. The macroscopic adsorption isotherms and microscopic density profiles of the most favorable adsorbents were examined, and the adsorption mechanisms were revealed. The specific separation of Kr/Xe was examined, and two of the adsorbents showed higher adsorption efficiency than shown in previously reported data.

Development of 3-dimensional time-dependent density functional theory and its application to gas diffusion in nanoporous materials

An entropy scaling based TDDFT has been proposed and applied to diffusion in a nanoporous material.