Nashrieh Shimi va Mohandesi Shimi Iran

Nashrieh Shimi va Mohandesi Shimi Iran

Evaluation of a Narrow Neural Network for Predicting Xenon Adsorption Capacity in Metal-Organic Frameworks

Document Type : Research Article

Authors
Seyed Rohollah Ghorbani Khoshkroodi 1
Seyed Mohammadali Mousavian 1
Abolfazl Dastbaz 1
Javad Karimi Sabet 2
1 School of Chemical Engineering, College of Engineering, University of Tehran, Tehran, IR. IRAN.
2 Nuclear Fuel Cycle Research School, Nuclear Science and Technology Research Institute (NSTRI), Tehran, IR. IRAN
Abstract
Metal–Organic Frameworks, as advanced porous materials, have wide applications in gas adsorption and separation. In this study, a Narrow Neural Network was utilized to predict the xenon adsorption capacity in hypothetical MOFs. The developed model was trained using six structural features, including Void Fraction, gravimetric and volumetric Surface Area, Pore Limiting Diameter, Largest Cavity Diameter, and Pressure. Data analysis indicated that the model provided optimal performance with an R² value of 0.80 and low RMSE (0.96) and MAE (0.66) values. To validate the model, a HKUST-1 type MOF was synthesized and evaluated. XRD and SEM analyses confirmed its cubic crystalline structure and uniform morphology. The xenon adsorption capacity of HKUST-1 was measured at 25 °C and 1 bar, yielding 1.91 mol/kg, which was in agreement with the model's prediction of 1.53 mol/kg.
Keywords
Metal-Organic Frameworks (MOFs)
Xe adsorption
Narrow neural network
Model prediction
[1] Chen X., Plonka A.M., Banerjee D., Krishna R., Schaef H.T., Ghose S., Thallapally P.K., Parise J.B., Direct Observation of Xe and Kr Adsorption in a Xe-selective Microporous Metal Organic Framework. Journal of the American Chemical Society, 137: 22 (2015).
[2] Millet A., Courtney C., Gossard A., Couchaux G., Mascunan P., Tuel A., Farrusseng D., Desilicated ZSM-5 Zeolites for Optimized Xenon Adsorption at Very Low Pressure. Microporous and Mesoporous Materials, 360: 112686 (2023).
[3] Luo L., Wang Y., Tong J., Li L., Zhu Y., Jin M., Xenon Postconditioning Attenuates Neuronal Injury After Spinal Cord Ischemia/Reperfusion Injury by Targeting Endoplasmic Reticulum Stress-Associated Apoptosis. Neurosurgical Review, 46: 213 (2023).
[4] Patil R.S., Banerjee D., Simon C.M., Atwood J.L., Thallapally P.K., Noria: A Highly Xe-Selective Nanoporous Organic Solid, Chemistry - A European Journal, 22: 12618–12623 (2016).
[5] Wang Q., Wang H., Peng S., Peng X., Cao D., Adsorption and Separation of Xe in Metal-Organic Frameworks and Covalent-Organic Materials. Journal of Physical Chemistry C, 118: 10221–10229 (2014).
[6] Lee S.J., Kim S., Kim E.J., Kim M., Bae Y.S., Adsorptive Separation of Xenon/Krypton Mixtures using Ligand Controls in a Zirconium-Based Metal-Organic Framework. Chemical Engineering Journal, 335: 345–351 (2018).
[7] Liu J., Thallapally P.K., Strachan D., Metal-Organic Frameworks for Removal of XE and KR from Nuclear Fuel Reprocessing Plants. Langmuir, 28: 11584–11589 (2012).
[8] Lalinia M., Hassanzadeh Nemati N., Karimi sabet J., Sadrnezhaad S.K., Synthesis and Comparative Study of ZIF8 and ZIF7 Metal Organic Frameworks as Carrier for Controlled Release of Doxorubicin in Cancer Treatment. Iranian Journal of Chemistry and Chemical Engineering, 43(11): 3863-3878 (2024).
[9] Zhong S., Wang Q., Cao D., ZIF-Derived Nitrogen-Doped Porous Carbons for Xe Adsorption and Separation. scientific reports, 6: 21295 (2016).
[10] Wu X., Xiang S., Su J., Cai W., Understanding Quantitative Relationship between Methane Storage Capacities and Characteristic Properties of Metal–Organic Frameworks Based on Machine Learning. The Journal of Physical Chemistry C, 123: 8550–8559 (2019).
[11] Yañez-Aulestia A., Trejos V.M., Esparza-Schulz J.M., Ibarra I.A., Sánchez-González E., Chemically Modified HKUST-1(Cu) for Gas Adsorption and Separation: Mixed-Metal and Hierarchical Porosity. ACS Appl Mater Interfaces, 16: 65581–65591 (2024).
[12] Burner J., Schwiedrzik L., Krykunov M., Luo J., Boyd P.G., Woo T.K., High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO2 Adsorption Properties of Metal−Organic Frameworks. Journal of Physical Chemistry C, 124: 27996–28005 (2020).
[13] Batra R., Chen C., Evans T.G., Walton K.S., Ramprasad R., Prediction of Water Stability of Metal–Organic Frameworks Using Machine Learning. nature machine intelligence, 2: 704–710 (2020).
[14] He Y., Zhou W., Krishna R., Chen B., Microporous Metal–Organic Frameworks for Storage and Separation of Small Hydrocarbons. Chemical Communications, 48: 11813–11831 (2012).
[15] Lalinia M., Nemati N.H., Mofazali P., Gross J.D., Samadi A., Recent Advances in Metal-Organic Framework Capabilities with Machine Learning Innovations for Enhanced Drug Release Systems. Mater Today Chem, 45: 102640 (2025).
[16] لعلی‌نیا، مینوش.، حسن‌زاده نعمتی، ناهید.، کریمی‌ثابت، جواد.، صدرنژاد، خطیب‌الاسلام.، چارچوب های فلز-آلی و بررسی کاربرد آنها در داروهای ضدسرطانی به روش یادگیری ماشین. نشریه شیمی و مهندسی شیمی ایران، 43(4): 99 تا 115 (1403).
[17] Niknam Shahrak M., Esfandyari M., Karimi M., Efficient Prediction of Water Vapor Adsorption Capacity in Porous Metal–Organic Framework Materials: ANN and ANFIS Modeling. Journal of the Iranian Chemical Society, 16: 11–20  (2019).
[18] Jani F., Hosseini S., Sepahi A., Afzali S.K., Torabi F., Ghorbani R., Houshmandmoayed S., Enhancing Quality Control of Polyethylene in Industrial Polymerization Plants Through Predictive Multivariate Data-Driven Soft Sensors. Canadian Journal of Chemical Engineering 103(5): 2234-2250 (2024).
[19] Pai K.N., Prasad V., Rajendran A., Generalized, Adsorbent-Agnostic, Artificial Neural Network Framework for Rapid Simulation, Optimization, and Adsorbent Screening of Adsorption Processes. Industrial & Engineering Chemistry Research, 59: 16730–16740 (2020).
[20] Khurana M., Farooq S., Simulation and Optimization of a 6-step Dual-Reflux VSA Cycle for Post-Combustion CO2 Capture. Chemical Engineering Science, 152: 507–515 (2016).
[21] Subraveti S.G., Li Z., Prasad V., Rajendran A., Machine Learning-Based Multiobjective Optimization of Pressure Swing Adsorption. Industrial & Engineering Chemistry Research, 58: 20412–20422 (2019).
[22] Leperi K.T., Yancy-Caballero D., Snurr R.Q., You F., 110th Anniversary: Surrogate Models Based on Artificial Neural Networks To Simulate and Optimize Pressure Swing Adsorption Cycles for CO2 Capture. Industrial & Engineering Chemistry Research, 58: 18241–18252 (2019).
[23] Pai K.N., Prasad V., Rajendran A., Experimentally Validated Machine Learning Frameworks for Accelerated Prediction of Cyclic Steady State and Optimization of Pressure Swing Adsorption Processes. Separation and Purification Technology, 241: 116651 (2020).
[24] Wang Z., Zhou Y., Zhou T., Sundmacher K., Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive Modeling. Computers & Chemical Engineering, 160: 107739 (2022).
[25] Liang H., Jiang K., Yan T.A., Chen G.H., XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr. ACS Omega, 6: 9066–9076 (2021).
[26] Bashiri R., Lawson P.S., He S., Nanayakkara S., Kim K., Barnett N.S., Stavila V., Gabaly F. El, Lee J., Ayars E., So M.C., Discovery of Dual Ion-Electron Conductivity of Metal–Organic Frameworks via Machine Learning-Guided Experimentation. Chemistry of Materials, 37: 1143–1153 (2025).
[27] Guan Y., Huang X., Xu F., Wang W., Li H., Gong L., Zhao Y., Guo S., Liang H., Qiao Z., Data-Driven and Machine Learning to Screen Metal–Organic Frameworks for the Efficient Separation of Methane. Nanomaterials, 14: 1074 (2024).
[28] Sikora B.J., Wilmer C.E., Greenfield M.L., Snurr R.Q., Thermodynamic Analysis of Xe/Kr Selectivity in Over 137000 Hypothetical Metal-Organic Frameworks. Chemical Science, 3: 2217–2223 (2012).
[29] Yang Y., Tu C., Guo L., Wang L., Cheng F., Luo F., Metal-Organic Frameworks for Xenon and Krypton Separation. Cell Rep Phys Sci, 4: 101694 (2023).
[30] Liu Q., Gong Y., Liu B., Xiong S., Wen H.M., Wang X., Dense Packing of Xenon in an Ultra-Microporous Metal–Organic Framework for Benchmark Xenon Capture and Separation. Chemical Engineering Journal, 453: 139849 (2023).
[31] Esfandiari K., Ghoreyshi A.A., Jahanshahi M., Using Artificial Neural Network and Ideal Adsorbed Solution Theory for Predicting the CO2/CH4 Selectivities of Metal-Organic Frameworks: A Comparative Study. Industrial & Engineering Chemistry Research, 56: 14610–14622 (2017).
[32] Gupta K., Joshi P., Gusain R., Khatri O.P., Recent Advances in Adsorptive Removal of Heavy Metal and Metalloid Ions by Metal Oxide-Based Nanomaterials. Coordination Chemistry Reviews, 445: 214100 (2021).
[33] Song X., Huang Q., Liu J., Xie H., Idrees K.B., Hou S., Yu L., Wang X., Liu F., Qiao Z., Wang H., Chen Y., Li Z., Farha O.K., Reticular Chemistry in Pore Engineering of a Y-Based Metal-Organic Framework for Xenon/Krypton Separation. ACS Appl Mater Interfaces, 15: 18229–18235 (2023).
[34] Burner J., Schwiedrzik L., Krykunov M., Luo J., Boyd P.G., Woo T.K., High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO2 Adsorption Properties of Metal−Organic Frameworks. Journal of Physical Chemistry C, 124: 27996–28005 (2020).
[35] Teo H.W.B., Ng M.S., Lee B.J., Chakraborty A., Xenon Storage Density and Its Energy Flow Through Adsorption on Metal–Organic Frameworks. Journal of Industrial and Engineering Chemistry, 129: 235–242 (2024).
[36] Kapelewski M.T., Oktawiec J., Runčevski T., Gonzalez M.I., Long J.R., Separation of Xenon and Krypton in the Metal–Organic Frameworks M2(m-dobdc) (M=Co, Ni). Israel Journal of Chemistry, 58: 1138–1143 (2018).
[37] Howarth A.J., Liu Y., Li P., Li Z., Wang T.C., Hupp J.T., Farha O.K., Chemical, Thermal and Mechanical Stabilities of Metal-Organic Frameworks. nature reviews materials, 1: 15018 (2016).