Prediction of Xe/Kr Separation in Metal-Organic Frameworks by a Precursor-Based Neural Network Synergistic with a Polarizable Adsorbate Model

Adsorption and separation of Xe/Kr are significant for making high-density nuclear energy environmentally friendly and for meeting the requirements of the gas industry. Enhancing the accuracy of the adsorbate model for describing the adsorption behaviors of Xe and Kr in MOFs and the efficiency of the model for predicting the separation potential (SP) value of Xe/Kr separation in MOFs helps in searching for promising MOFs for Xe/Kr adsorption and separation within a short time and at a low cost. In this work, polarizable and transferable models for mimic Xe and Kr adsorption behaviors in MOFs were constructed. Using these models, SP values of 38 MOFs at various temperatures and pressures were calculated. An optimal neural network model called BPNN-SP was designed to predict SP value based on physical parameters of metal center (electronegativity and radius) and organic linker (three-dimensional size and polarizability) combined with temperature and pressure. The regression coefficient value of the BPNN-SP model for each data set is higher than 0.995. MAE, MBE, and RMSE of BPNN-SP are only 0.331, −0.002, and 0.505 mmol/g, respectively. Finally, BPNN-SP was validated by experiment data from six MOFs. The transferable adsorbate model combined with the BPNN-SP model would highly improve the efficiency for designing MOFs with high performance for Xe/Kr adsorption and separation.

[1]  Dingguo Xu,et al.  High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption , 2023, The Journal of Physical Chemistry C.

[2]  W. Gong,et al.  Programmed Polarizability Engineering in a Cyclen-Based Cubic Zr(IV) Metal-Organic Framework to Boost Xe/Kr Separation. , 2023, Journal of the American Chemical Society.

[3]  Madhab C. Das,et al.  Potential of a pH-Stable Microporous MOF for C2H2/C2H4 and C2H2/CO2 Gas Separations under Ambient Conditions. , 2022, Inorganic chemistry.

[4]  Kexin Bi,et al.  Finding the Optimal CO2 Adsorption Material: Prediction of Multi-properties of Metal Organic Frameworks (MOFs) Based on DeepFM , 2022, Separation and Purification Technology.

[5]  Kexin Bi,et al.  High-throughput screening of metal–organic frameworks for hydrogen purification , 2022, Chemical Engineering Journal.

[6]  Yu-Ling Wang,et al.  Microporous Metal–Organic Framework with Cage-within-Cage Structures for Xenon/Krypton Separation , 2022, Industrial & Engineering Chemistry Research.

[7]  W. Gong,et al.  Creating Optimal Pockets in a Clathrochelate-Based Metal-Organic Framework for Gas Adsorption and Separation: Experimental and Computational Studies. , 2022, Journal of the American Chemical Society.

[8]  Dongchu Chen,et al.  Recent advances in the synthesis of monolithic metal-organic frameworks , 2021, Science China Materials.

[9]  Jayant K. Singh,et al.  High-Throughput Screening of Metal–Organic Frameworks for Ethane–Ethylene Separation Using the Machine Learning Technique , 2020 .

[10]  B. Saad,et al.  Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies , 2020, RSC advances.

[11]  R. Krishna,et al.  Boosting Selective Adsorption of Xe over Kr by Double-Accessible Open-Metal Site in Metal-Organic Framework: Experimental and Theoretical Research. , 2020, Inorganic chemistry.

[12]  Ying-gen Ouyang,et al.  New Discovery of Metal–Organic Framework UTSA-280: Ultrahigh Adsorption Selectivity of Krypton over Xenon , 2020 .

[13]  S. Xiong,et al.  Methyl functionalized Zr-Fum MOF with enhanced Xenon adsorption and separation , 2020 .

[14]  C. Wade,et al.  Insights into CO2 Adsorption in M–OH Functionalized MOFs , 2020 .

[15]  Peng Zhao,et al.  Robust Bimetallic Ultramicroporous Metal-Organic Framework for Separation and Purification of Noble Gases. , 2020, Inorganic chemistry.

[16]  Da-Ren Chen,et al.  Bimetallic metal-organic frameworks (MOFs) synthesized using the spray method for tunable CO2 adsorption , 2020 .

[17]  Zhiwei Qiao,et al.  Combining large-scale screening and machine learning to predict the metal-organic frameworks for organosulfurs removal from high-sour natural gas , 2019, APL Materials.

[18]  S. Calero,et al.  High-throughput screening of metal – Organic frameworks for CO2 and CH4 separation in the presence of water , 2019 .

[19]  Qilong Ren,et al.  A Robust Squarate-Based Metal-Organic Framework Demonstrates Record-High Affinity and Selectivity for Xenon over Krypton. , 2019, Journal of the American Chemical Society.

[20]  Seda Keskin,et al.  An extensive comparative analysis of two MOF databases: high-throughput screening of computation-ready MOFs for CH4 and H2 adsorption , 2019, Journal of Materials Chemistry A.

[21]  Shuo Ma,et al.  Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption , 2019, International Journal of Hydrogen Energy.

[22]  Weiquan Cai,et al.  Computational design of tetrazolate-based metal-organic frameworks for CH4 storage. , 2018, Physical chemistry chemical physics : PCCP.

[23]  T. Vlugt,et al.  Potential of polarizable force fields for predicting the separation performance of small hydrocarbons in M-MOF-74. , 2018, Physical chemistry chemical physics : PCCP.

[24]  High-Throughput Screening of MOF Adsorbents and Membranes for H2 Purification and CO2 Capture , 2018, ACS applied materials & interfaces.

[25]  Hongxia Xi,et al.  New functionalized IRMOF-10 with strong affinity for methanol: A simulation study , 2018 .

[26]  Seda Keskin,et al.  Database for CO2 Separation Performances of MOFs Based on Computational Materials Screening , 2018, ACS applied materials & interfaces.

[27]  Rui Li,et al.  A new framework of global sensitivity analysis for the chemical kinetic model using PSO-BPNN , 2018, Comput. Chem. Eng..

[28]  Banglin Chen,et al.  A microporous metal–organic framework with commensurate adsorption and highly selective separation of xenon , 2018 .

[29]  Zhiqiang Deng,et al.  Development of artificial intelligence approach to forecasting oyster norovirus outbreaks along Gulf of Mexico coast. , 2018, Environment international.

[30]  Seda Keskin,et al.  High-Throughput Computational Screening of the Metal Organic Framework Database for CH4/H2 Separations , 2018, ACS applied materials & interfaces.

[31]  M. Jahanshahi,et al.  Using Artificial Neural Network and Ideal Adsorbed Solution Theory for Predicting the CO2/CH4 Selectivities of Metal–Organic Frameworks: A Comparative Study , 2017 .

[32]  S. Calero,et al.  Adsorptive separation of ethane and ethylene using IsoReticular Metal-Organic Frameworks , 2017 .

[33]  R. Krishna.  Screening metal–organic frameworks for mixture separations in fixed-bed adsorbers using a combined selectivity/capacity metric , 2017 .

[34]  Xiao-Na Guo,et al.  Artificial neural network - Genetic algorithm to optimize wheat germ fermentation condition: Application to the production of two anti-tumor benzoquinones. , 2017, Food chemistry.

[35]  Seda Keskin,et al.  Molecular simulations of MOF adsorbents and membranes for noble gas separations , 2017 .

[36]  Y. Hwang,et al.  Adsorptive separation of xenon/krypton mixtures using a zirconium-based metal-organic framework with high hydrothermal and radioactive stabilities. , 2016, Journal of hazardous materials.

[37]  Tony Pham,et al.  Hybrid Ultra-Microporous Materials for Selective Xenon Adsorption and Separation. , 2016, Angewandte Chemie.

[38]  Maciej Haranczyk,et al.  Metal–organic framework with optimally selective xenon adsorption and separation , 2016, Nature Communications.

[39]  O. Farha,et al.  CO2 Adsorption in M-IRMOF-10 (M = Mg, Ca, Fe, Cu, Zn, Ge, Sr, Cd, Sn, Ba) , 2016 .

[40]  T. Ghanty,et al.  Computational Modeling of Adsorption of Xe and Kr in M-MOF-74 Metal Organic Frame Works with Different Metal Atoms , 2016 .

[41]  R. Snurr,et al.  RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials , 2016 .

[42]  Hongxia Xi,et al.  Adsorptive Separation of Methanol-Acetone on Isostructural Series of Metal-Organic Frameworks M-BTC (M = Ti, Fe, Cu, Co, Ru, Mo): A Computational Study of Adsorption Mechanisms and Metal-Substitution Impacts. , 2015, ACS applied materials & interfaces.

[43]  Chad L. Staiger,et al.  DFT-based force field development for noble gas adsorption in metal organic frameworks , 2015 .

[44]  Maciej Haranczyk,et al.  What Are the Best Materials To Separate a Xenon/Krypton Mixture? , 2015 .

[45]  M. Hirscher,et al.  Understanding the adsorption mechanism of noble gases Kr and Xe in CPO-27-Ni, CPO-27-Mg, and ZIF-8. , 2014, Physical chemistry chemical physics : PCCP.

[46]  A. Cooper,et al.  Separation of rare gases and chiral molecules by selective binding in porous organic cages. , 2014, Nature materials.

[47]  M. Allendorf,et al.  Noble Gas Adsorption in Metal–Organic Frameworks Containing Open Metal Sites , 2014 .

[48]  Guanghua Li,et al.  An N-rich metal-organic framework with an rht topology: high CO2 and C2 hydrocarbons uptake and selective capture from CH4. , 2014, Chemical communications.

[49]  M. Allendorf,et al.  Effects of Polarizability on the Adsorption of Noble Gases at Low Pressures in Monohalogenated Isoreticular Metal–Organic Frameworks , 2012 .

[50]  C. Wilmer,et al.  Thermodynamic analysis of Xe/Kr selectivity in over 137 000 hypothetical metal–organic frameworks , 2012 .

[51]  Tian Lu,et al.  Multiwfn: A multifunctional wavefunction analyzer , 2012, J. Comput. Chem..

[52]  K. Föhr,et al.  Intranasal Application of Xenon Reduces Opioid Requirement and Postoperative Pain in Patients Undergoing Major Abdominal Surgery: A Randomized Controlled Trial , 2011, Anesthesiology.

[53]  H. Sartorius,et al.  Ten Years of Development of Equipment for Measurement of Atmospheric Radioactive Xenon for the Verification of the CTBT , 2010 .

[54]  R. Snurr,et al.  Assessment of Isoreticular Metal−Organic Frameworks for Adsorption Separations: A Molecular Simulation Study of Methane/n-Butane Mixtures , 2004 .

[55]  A. Myers,et al.  Molecular simulation of adsorption: Gibbs dividing surface and comparison with experiment , 2001 .

[56]  W. Goddard,et al.  UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations , 1992 .

[57]  S. L. Mayo,et al.  DREIDING: A generic force field for molecular simulations , 1990 .

[58]  J. Barker,et al.  What is "liquid"? Understanding the states of matter , 1976 .

[59]  P. Hoff,et al.  Observations of stimulated emission from high‐pressure krypton and argon/xenon mixtures , 1973 .

[60]  Guoping Yang,et al.  Porous functional metal–organic frameworks (MOFs) constructed from different N-heterocyclic carboxylic ligands for gas adsorption/separation , 2023, CrystEngComm.

[61]  Zhang Kai,et al.  Predicting adsorption and separation performance indicators of Xe/Kr in metal-organic frameworks via a precursor-based neural network model , 2021 .

[62]  B. Oboirien,et al.  Performance evaluation of gasification system efficiency using artificial neural network , 2020 .

[63]  Alan L. Myers,et al.  Thermodynamics of mixed‐gas adsorption , 1965 .

[64]  W. A. Goddard,et al.  a Full Periodic Table Force Field for Molecular Mechanics and Molecular Dynamics Simulations , 2022 .