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.

Enhancing Gas Solubility in Nanopores: A Combined Study Using Classical Density Functional Theory and Machine Learning

Geometrical confinement has a large impact on gas solubilities in nanoscale pores. This phenomenon is closely associated with heterogeneous catalysis, shale gas extraction, phase separation, etc. Whereas several experimental and theoretical studies have been conducted that provide meaningful insights into the over-solubility and under-solubility of different gases in confined solvents, the microscopic mechanism for regulating the gas solubility remains unclear. Here, we report a hybrid theoretical study for unraveling the regulation mechanism by combining classical density functional theory (CDFT) with machine learning (ML). Specifically, CDFT is employed to predict the solubility of argon in various solvents confined in nanopores of different types and pore widths, and these case studies then supply a valid training set to ML for further investigation. Finally, the dominant parameters that affect the gas solubility are identified, and a criterion is obtained to determine whether a confined gas-solvent system is enhance-beneficial or reduce-beneficial. Our findings provide theoretical guidance for predicting and regulating gas solubilities in nanopores. In addition, the hybrid method proposed in this work sets up a feasible platform for investigating complex interfacial systems with multiple controlling parameters.

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.

A 3D metal–organic framework with dual-aerial-octahedral trinucleate building units: synthesis, structure and fluorescence sensing properties

JLNU-2 can be used to detect nitrobenzene with high selectivity, sensitivity, anti-interference ability and recyclability through tracing the fluorescence quenching behaviour.

Efficient Molecular Approach to Quantifying Solvent-Mediated Interactions

The solvent-mediated interaction, or equivalently the depletion force, play a pivotal role in the processes, by which two objects in solution such as lock and key particles, antibody and antigen, macromolecule and substrate, are attracted to each other. The quantification of this interaction is important yet challenging since it depends on the microscopic solvent structure in the surrounding. Here, we report an efficient molecular approach for predicting the solvent-mediated interaction by combining the classical density functional theory with a reversible solvation thermodynamic circle. For demonstration, the solvent-mediated interactions between two nanoparticles and between a nanoparticle and a rough wall are examined, and good agreements compared with the simulation results are illustrated. This approach is thereafter employed to interpret the reported self-assembly phenomena of lock and key colloidal particles. We show that the binding probability between the lock and key colloids can be successfully characterized at different depletant concentrations and system temperatures. This approach provides a potential route for identifying the coarse-graining interaction between two objects in fluid systems.

Fluids in porous media. IV. Quench effect on chemical potential

It appears to be a common sense to measure the crowdedness of a fluid system by the densities of the species constituting it. In the present work, we show that this ceases to be valid for confined fluids under some conditions. A quite thorough investigation is made for a hard sphere (HS) fluid adsorbed in a hard sphere matrix (a quench-annealed system) and its corresponding equilibrium binary mixture. When fluid particles are larger than matrix particles, the quench-annealed system can appear much more crowded than its corresponding equilibrium binary mixture, i.e., having a much higher fluid chemical potential, even when the density of each species is strictly the same in both systems, respectively. We believe that the insight gained from this study should be useful for the design of functionalized porous materials.

Tunable integration of absorption-membrane-adsorption for efficiently separating low boiling gas mixtures near normal temperature

Separation of low boiling gas mixtures is widely concerned in process industries. Now their separations heavily rely upon energy-intensive cryogenic processes. Here, we report a pseudo-absorption process for separating low boiling gas mixtures near normal temperature. In this process, absorption-membrane-adsorption is integrated by suspending suitable porous ZIF material in suitable solvent and forming selectively permeable liquid membrane around ZIF particles. Green solvents like water and glycol were used to form ZIF-8 slurry and tune the permeability of liquid membrane surrounding ZIF-8 particles. We found glycol molecules form tighter membrane while water molecules form looser membrane because of the hydrophobicity of ZIF-8. When using mixing solvents composed of glycol and water, the permeability of liquid membrane becomes tunable. It is shown that ZIF-8/water slurry always manifests remarkable higher separation selectivity than solid ZIF-8 and it could be tuned to further enhance the capture of light hydrocarbons by adding suitable quantity of glycol to water. Because of its lower viscosity and higher sorption/desorption rate, tunable ZIF-8/water-glycol slurry could be readily used as liquid absorbent to separate different kinds of low boiling gas mixtures by applying a multistage separation process in one traditional absorption tower, especially for the capture of light hydrocarbons.

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.