請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80723完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 康敦彥(Dun-Yen Kang) | |
| dc.contributor.author | Ting-Hsiang Hung | en |
| dc.contributor.author | 洪鼎翔 | zh_TW |
| dc.contributor.author | f06524078 | |
| dc.date.accessioned | 2022-11-24T03:14:08Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-24T03:14:08Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-27 | |
| dc.identifier.citation | (1) Wang, Q.; Astruc, D. State of the Art and Prospects in Metal–Organic Framework (MOF)-Based and MOF-Derived Nanocatalysis. Chem. Rev. 2020, 120, 1438-1511. (2) Wilmer, C. E.; Farha, O. K.; Bae, Y.-S.; Hupp, J. T.; Snurr, R. Q. Structure–property relationships of porous materials for carbon dioxide separation and capture. Energy Environ. Sci. 2012, 5, 9849-9856. (3) Hönicke, I. M.; Senkovska, I.; Bon, V.; Baburin, I. A.; Bönisch, N.; Raschke, S.; Evans, J. D.; Kaskel, S. Balancing Mechanical Stability and Ultrahigh Porosity in Crystalline Framework Materials. Angew. Chem. Int. Ed. 2018, 57, 13780-13783. (4) Cui, S.; Qin, M.; Marandi, A.; Steggles, V.; Wang, S.; Feng, X.; Nouar, F.; Serre, C. Metal-Organic Frameworks as advanced moisture sorbents for energy-efficient high temperature cooling. Sci. Rep. 2018, 8, 15284. (5) Xu, W.; Yaghi, O. M. Metal–Organic Frameworks for Water Harvesting from Air, Anywhere, Anytime. ACS Cent. Sci. 2020, 6, 1348-1354. (6) Jarai, B. M.; Stillman, Z.; Attia, L.; Decker, G. E.; Bloch, E. D.; Fromen, C. A. Evaluating UiO-66 Metal–Organic Framework Nanoparticles as Acid-Sensitive Carriers for Pulmonary Drug Delivery Applications. ACS Appl. Mater. Interfaces 2020, 12, 38989-39004. (7) Xiang, S.; He, Y.; Zhang, Z.; Wu, H.; Zhou, W.; Krishna, R.; Chen, B. Microporous metal-organic framework with potential for carbon dioxide capture at ambient conditions. Nat. Commun. 2012, 3, 954. (8) Cadiau, A.; Adil, K.; Bhatt, P. M.; Belmabkhout, Y.; Eddaoudi, M. A metal-organic framework-based splitter for separating propylene from propane. Science 2016, 353, 137-40. (9) Breck, D. W. Zeolite Molecular Sieves: Structure, Chemistry, and Use; John Wiley and Sons, Inc., New York, 1974. (10) Haldoupis, E.; Nair, S.; Sholl, D. S. Efficient Calculation of Diffusion Limitations in Metal Organic Framework Materials: A Tool for Identifying Materials for Kinetic Separations. J. Am. Chem. Soc. 2010, 132, 7528-7539. (11) Wijmans, J. G.; Baker, R. W. The solution-diffusion model: a review. J. Membr. Sci. 1995, 107, 1-21. (12) Daglar, H.; Erucar, I.; Keskin, S. Recent advances in simulating gas permeation through MOF membranes. Mater. Adv. 2021, 2, 5300-5317. (13) Friebe, S.; Geppert, B.; Steinbach, F.; Caro, J. Metal–Organic Framework UiO-66 Layer: A Highly Oriented Membrane with Good Selectivity and Hydrogen Permeance. ACS Appl. Mater. Interfaces 2017, 9, 12878-12885. (14) Fairen-Jimenez, D.; Moggach, S. A.; Wharmby, M. T.; Wright, P. A.; Parsons, S.; Düren, T. Opening the Gate: Framework Flexibility in ZIF-8 Explored by Experiments and Simulations. J. Am. Chem. Soc. 2011, 133, 8900-8902. (15) Hobday, C. L.; Woodall, C. H.; Lennox, M. J.; Frost, M.; Kamenev, K.; Düren, T.; Morrison, C. A.; Moggach, S. A. Understanding the adsorption process in ZIF-8 using high pressure crystallography and computational modelling. Nat. Commun. 2018, 9, 1429. (16) Chiou, D.-S.; Yu, H. J.; Hung, T.-H.; Lyu, Q.; Chang, C.-K.; Lee, J. S.; Lin, L.-C.; Kang, D.-Y. Highly CO2 Selective Metal–Organic Framework Membranes with Favorable Coulombic Effect. Adv. Funct. Mater. 2021, 31, 2006924. (17) Liu, D.; Ma, X.; Xi, H.; Lin, J. Gas transport properties and propylene/propane separation characteristics of ZIF-8 membranes. J. Membr. Sci. 2014, 451, 85-93. (18) Chernikova, V.; Shekhah, O.; Belmabkhout, Y.; Eddaoudi, M. Nanoporous Fluorinated Metal–Organic Framework-Based Membranes for CO2 Capture. ACS Appl. Nano Mater. 2020, 3, 6432-6439. (19) Lee, D.-J.; Li, Q.; Kim, H.; Lee, K. Preparation of Ni-MOF-74 membrane for CO2 separation by layer-by-layer seeding technique. Microporous Mesoporous Mater. 2012, 163, 169-177. (20) Serre, C.; Millange, F.; Thouvenot, C.; Noguès, M.; Marsolier, G.; Louër, D.; Férey, G. Very Large Breathing Effect in the First Nanoporous Chromium(III)-Based Solids: MIL-53 or CrIII(OH)·{O2C−C6H4−CO2}·{HO2C−C6H4−CO2H}x·H2Oy. J. Am. Chem. Soc. 2002, 124, 13519-13526. (21) Boutin, A.; Coudert, F.-X.; Springuel-Huet, M.-A.; Neimark, A. V.; Férey, G.; Fuchs, A. H. The Behavior of Flexible MIL-53(Al) upon CH4 and CO2 Adsorption. J. Phys. Chem. C 2010, 114, 22237-22244. (22) Willems, T. F.; Rycroft, C. H.; Kazi, M.; Meza, J. C.; Haranczyk, M. Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials. Microporous Mesoporous Mater. 2012, 149, 134-141. (23) Hou, Q.; Wu, Y.; Zhou, S.; Wei, Y.; Caro, J.; Wang, H. Ultra-Tuning of the Aperture Size in Stiffened ZIF-8_Cm Frameworks with Mixed-Linker Strategy for Enhanced CO2/CH4 Separation. Angew. Chem. Int. Ed. 2019, 58, 327-331. (24) Lin, R.-B.; Li, L.; Alsalme, A.; Chen, B. An Ultramicroporous Metal–Organic Framework for Sieving Separation of Carbon Dioxide from Methane. Small Structures 2020, 1, 2000022. (25) Yin, H.; Wang, J.; Xie, Z.; Yang, J.; Bai, J.; Lu, J.; Zhang, Y.; Yin, D.; Lin, J. Y. S. A highly permeable and selective amino-functionalized MOF CAU-1 membrane for CO2–N2 separation. Chem. Commun. 2014, 50, 3699-3701. (26) Zou, X.; Zhang, F.; Thomas, S.; Zhu, G.; Valtchev, V.; Mintova, S. Co3(HCOO)6 Microporous Metal–Organic Framework Membrane for Separation of CO2/CH4 Mixtures. Chem. Eur. J. 2011, 17, 12076-12083. (27) Altintas, C.; Avci, G.; Daglar, H.; Gulcay, E.; Erucar, I.; Keskin, S. Computer simulations of 4240 MOF membranes for H2/CH4 separations: insights into structure–performance relations. J. Mater. Chem. A 2018, 6, 5836-5847. (28) Azar, A. N. V.; Velioglu, S.; Keskin, S. Large-Scale Computational Screening of Metal Organic Framework (MOF) Membranes and MOF-Based Polymer Membranes for H2/N2 Separations. ACS Sustain. Chem. Eng. 2019, 7, 9525-9536. (29) Yan, T.; Lan, Y.; Liu, D.; Yang, Q.; Zhong, C. Large-Scale Screening and Design of Metal–Organic Frameworks for CH4/N2 Separation. Chem. Asian J. 2019, 14, 3688-3693. (30) Robeson, L. The upper boud revisited. J. Membr. Sci. 2008, 320, 390-400. (31) Krishna, R.; van Baten, J. M. In silico screening of metal–organic frameworks in separation applications. Phys. Chem. Chem. Phys. 2011, 13, 10593-10616. (32) Krishna, R.; van Baten, J. M. Influence of adsorption thermodynamics on guest diffusivities in nanoporous crystalline materials. Phys. Chem. Chem. Phys. 2013, 15, 7994-8016. (33) Daglar, H.; Keskin, S. Computational Screening of Metal–Organic Frameworks for Membrane-Based CO2/N2/H2O Separations: Best Materials for Flue Gas Separation. J. Phys. Chem. C 2018, 122, 17347-17357. (34) Watanabe, T.; Sholl, D. S. Accelerating Applications of Metal–Organic Frameworks for Gas Adsorption and Separation by Computational Screening of Materials. Langmuir 2012, 28, 14114-14128. (35) Qiao, Z.; Xu, Q.; Jiang, J. High-throughput computational screening of metal-organic framework membranes for upgrading of natural gas. J. Membr. Sci. 2018, 551, 47-54. (36) Zheng, C.; Liu, D.; Yang, Q.; Zhong, C.; Mi, J. Computational Study on the Influences of Framework Charges on CO2 Uptake in Metal−Organic Frameworks. Ind. Eng. Chem. Res. 2009, 48, 10479-10484. (37) Altintas, C.; Keskin, S. Role of partial charge assignment methods in high-throughput screening of MOF adsorbents and membranes for CO2/CH4 separation. Mol. Syst. Des. Eng. 2020, 5, 532-543. (38) Dzubak, A. L.; Lin, L.-C.; Kim, J.; Swisher, J. A.; Poloni, R.; Maximoff, S. N.; Smit, B.; Gagliardi, L. Ab initio carbon capture in open-site metal–organic frameworks. Nat. Chem. 2012, 4, 810-816. (39) Lin, L.-C.; Lee, K.; Gagliardi, L.; Neaton, J. B.; Smit, B. Force-Field Development from Electronic Structure Calculations with Periodic Boundary Conditions: Applications to Gaseous Adsorption and Transport in Metal–Organic Frameworks. J. Chem. Theory Comput. 2014, 10, 1477-1488. (40) 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. J. Phys. Chem. C 2019, 123, 8550-8559. (41) Cho, E. H.; Lin, L.-C. Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties. J. Phys. Chem. Lett. 2021, 12, 2279-2285. (42) Krishnapriyan, A. S.; Montoya, J.; Haranczyk, M.; Hummelshøj, J.; Morozov, D. Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks. Sci. Rep. 2021, 11, 8888. (43) Chung, Y. G.; Camp, J.; Haranczyk, M.; Sikora, B. J.; Bury, W.; Krungleviciute, V.; Yildirim, T.; Farha, O. K.; Sholl, D. S.; Snurr, R. Q. Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals. Chem. Mater. 2014, 26, 6185-6192. (44) Chung, Y. G.; Haldoupis, E.; Bucior, B. J.; Haranczyk, M.; Lee, S.; Zhang, H.; Vogiatzis, K. D.; Milisavljevic, M.; Ling, S.; Camp, J. S., et al. Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019. J. Chem. Eng. Data 2019, 64, 5985-5998. (45) Pophale, R.; Cheeseman, P. A.; Deem, M. W. A database of new zeolite-like materials. Phys. Chem. Chem. Phys. 2011, 13, 12407-12412. (46) Deng, X.; Zou, C.; Han, Y.; Lin, L.-C.; Ho, W. S. W. Computational Evaluation of Carriers in Facilitated Transport Membranes for Postcombustion Carbon Capture. J. Phys. Chem. C 2020, 124, 25322-25330. (47) Cho, E. H.; Deng, X.; Zou, C.; Lin, L.-C. Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening. J. Phys. Chem. C 2020, 124, 27580-27591. (48) Mayo, S. L.; Olafson, B. D.; Goddard, W. A. DREIDING: a generic force field for molecular simulations. J. Phys. Chem. 1990, 94, 8897-8909. (49) Rappe, A. K.; Casewit, C. J.; Colwell, K. S.; Goddard, W. A.; Skiff, W. M. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J. Am. Chem. Soc. 1992, 114, 10024-10035. (50) Nazarian, D.; Camp, J. S.; Sholl, D. S. A Comprehensive Set of High-Quality Point Charges for Simulations of Metal–Organic Frameworks. Chem. Mater. 2016, 28, 785-793. (51) Zou, C.; Penley, D. R.; Cho, E. H.; Lin, L.-C. Efficient and Accurate Charge Assignments via a Multilayer Connectivity-Based Atom Contribution (m-CBAC) Approach. J. Phys. Chem. C 2020, 124, 11428-11437. (52) García-Pérez, E.; Parra, J. B.; Ania, C. O.; García-Sánchez, A.; van Baten, J. M.; Krishna, R.; Dubbeldam, D.; Calero, S. A computational study of CO2, N2, and CH4 adsorption in zeolites. Adsorption 2007, 13, 469-476. (53) Potoff, J. J.; Siepmann, J. I. Vapor–liquid equilibria of mixtures containing alkanes, carbon dioxide, and nitrogen. AlChE J. 2001, 47, 1676-1682. (54) Martin, M. G.; Siepmann, J. I. Transferable Potentials for Phase Equilibria. 1. United-Atom Description of n-Alkanes. J. Phys. Chem. B 1998, 102, 2569-2577. (55) van den Berg, A. W. C.; Bromley, S. T.; Wojdel, J. C.; Jansen, J. C. Adsorption isotherms of H2 in microporous materials with the SOD structure: A grand canonical Monte Carlo study. Microporous and Mesoporous Mater. 2006, 87, 235-242. (56) Boato, G.; Casanova, G. A self-consistent set of molecular parameters for neon, argon, krypton and xenon. Physica 1961, 27, 571-589. (57) Dubbeldam, D.; Calero, S.; Ellis, D. E.; Snurr, R. Q. RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials. Mol. Simul. 2016, 42, 81-101. (58) Plimpton, S. Fast Parallel Algorithms for Short-Range Molecular Dynamics. J. Comput. Phys. 1995, 117, 1-19. (59) Abouelnasr, M. K. F.; Smit, B. Diffusion in confinement: kinetic simulations of self- and collective diffusion behavior of adsorbed gases. Phys. Chem. Chem. Phys. 2012, 14, 11600-11609. (60) Mace, A.; Barthel, S.; Smit, B. Automated Multiscale Approach To Predict Self-Diffusion from a Potential Energy Field. J. Chem. Theory Comput. 2019, 15, 2127-2141. (61) Agrawal, M.; Sholl, D. S. Effects of Intrinsic Flexibility on Adsorption Properties of Metal–Organic Frameworks at Dilute and Nondilute Loadings. ACS Appl. Mater. Interfaces 2019, 11, 31060-31068. (62) Parkes, M. V.; Demir, H.; Teich-McGoldrick, S. L.; Sholl, D. S.; Greathouse, J. A.; Allendorf, M. D. Molecular dynamics simulation of framework flexibility effects on noble gas diffusion in HKUST-1 and ZIF-8. Microporous Mesoporous Mater. 2014, 194, 190-199. (63) Verploegh, R. J.; Nair, S.; Sholl, D. S. Temperature and Loading-Dependent Diffusion of Light Hydrocarbons in ZIF-8 as Predicted Through Fully Flexible Molecular Simulations. J. Am. Chem. Soc. 2015, 137, 15760-15771. (64) Witman, M.; Ling, S.; Jawahery, S.; Boyd, P. G.; Haranczyk, M.; Slater, B.; Smit, B. The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials. J. Am. Chem. Soc. 2017, 139, 5547-5557. (65) Hoover, W. G. Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A 1985, 31, 1695-1697. (66) Nosé, S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys. 1984, 81, 511-519. (67) Addicoat, M. A.; Vankova, N.; Akter, I. F.; Heine, T. Extension of the Universal Force Field to Metal–Organic Frameworks. J. Chem. Theory Comput. 2014, 10, 880-891. (68) Delley, B. From molecules to solids with the DMol3 approach. J. Chem. Phys. 2000, 113, 7756-7764. (69) Inada, Y.; Orita, H. Efficiency of numerical basis sets for predicting the binding energies of hydrogen bonded complexes: Evidence of small basis set superposition error compared to Gaussian basis sets. J. Comput. Chem. 2008, 29, 225-232. (70) Delley, B. Hardness conserving semilocal pseudopotentials. Phys. Rev. B 2002, 66, 155125. (71) Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 1998, 86, 2278-2324. (72) Sikora, B.; Winnegar, R.; Proserpio, D.; Snurr, R. Textural properties of a large collection of computationally constructed MOFs and zeolites. Microporous Mesoporous Mater. 2014, 186, 207–213. (73) Li, J.-R.; Ma, Y.; McCarthy, M. C.; Sculley, J.; Yu, J.; Jeong, H.-K.; Balbuena, P. B.; Zhou, H.-C. Carbon dioxide capture-related gas adsorption and separation in metal-organic frameworks. Coord. Chem. Rev. 2011, 255, 1791-1823. (74) Britt, D.; Furukawa, H.; Wang, B.; Glover, T.; Yaghi, O. Highly efficient separation of carbon dioxide by a metal-organic framework replete with open metal sites. Proc. Natl. Acad. Sci. U.S.A. 2009, 106, 20637-20640. (75) Poloni, R.; Lee, K.; Berger, R. F.; Smit, B.; Neaton, J. B. Understanding Trends in CO2 Adsorption in Metal–Organic Frameworks with Open-Metal Sites. J. Phys. Chem. Lett. 2014, 5, 861-865. (76) Dietzel, P.; Besikiotis, V.; Blom, R. Application of metal-organic frameworks with coordinatively unsaturated metal sites in storage and separation of methane and carbon dioxide. J. Mater. Chem. 2009, 19, 7362-7370. (77) Liu, B.; Jiang, Y.-H.; Li, Z.-S.; Hou, L.; Wang, Y.-Y. Selective CO2 adsorption in a microporous metal–organic framework with suitable pore sizes and open metal sites. Inorg. Chem. Front. 2015, 2, 550-557. (78) Avci, G.; Erucar, I.; Keskin, S. Do New MOFs Perform Better for CO2 Capture and H2 Purification? Computational Screening of the Updated MOF Database. ACS Appl. Mater. Interfaces 2020, 12, 41567-41579. (79) Haldoupis, E.; Watanabe, T.; Nair, S.; Sholl, D. S. Quantifying Large Effects of Framework Flexibility on Diffusion in MOFs: CH4 and CO2 in ZIF-8. ChemPhysChem 2012, 13, 3449-3452. (80) Deng, X.; Yang, W.; Li, S.; Liang, H.; Shi, Z.; Qiao, Z. Large-Scale Screening and Machine Learning to Predict the Computation-Ready, Experimental Metal-Organic Frameworks for CO2 Capture from Air. Appl. Sci. 2020, 10, 569. (81) Beerdsen, E.; Dubbeldam, D.; Smit, B. Loading Dependence of the Diffusion Coefficient of Methane in Nanoporous Materials. J. Phys. Chem. B 2006, 110, 22754-22772. (82) Chen, C.; Ozcan, A.; Yazaydin, A. O.; Ladewig, B. P. Gas permeation through single-crystal ZIF-8 membranes. J. Membr. Sci. 2019, 575, 209-216. (83) Chen, L.; Reiss, P. S.; Chong, S. Y.; Holden, D.; Jelfs, K. E.; Hasell, T.; Little, M. A.; Kewley, A.; Briggs, M. E.; Stephenson, A., et al. Separation of rare gases and chiral molecules by selective binding in porous organic cages. Nat. Mater. 2014, 13, 954-960. (84) Camp, J. S.; Sholl, D. S. Transition State Theory Methods To Measure Diffusion in Flexible Nanoporous Materials: Application to a Porous Organic Cage Crystal. J. Phys. Chem. C 2016, 120, 1110-1120. (85) Battisti, A.; Taioli, S.; Garberoglio, G. Zeolitic imidazolate frameworks for separation of binary mixtures of CO2, CH4, N2 and H2: A computer simulation investigation. Microporous Mesoporous Mater. 2011, 143, 46-53. (86) Wu, X.; Liu, C.; Caro, J.; Huang, A. Facile synthesis of molecular sieve membranes following “like grows like” principle. J. Membr. Sci. 2018, 559, 1-7. (87) Li, S.; Chung, Y. G.; Simon, C. M.; Snurr, R. Q. High-Throughput Computational Screening of Multivariate Metal–Organic Frameworks (MTV-MOFs) for CO2 Capture. J. Phys. Chem. Lett. 2017, 8, 6135-6141. (88) Watanabe, T.; Manz, T. A.; Sholl, D. S. Accurate Treatment of Electrostatics during Molecular Adsorption in Nanoporous Crystals without Assigning Point Charges to Framework Atoms. J. Phys. Chem. C 2011, 115, 4824-4836. (89) Haldoupis, E.; Nair, S.; Sholl, D. S. Finding MOFs for Highly Selective CO2/N2 Adsorption Using Materials Screening Based on Efficient Assignment of Atomic Point Charges. J. Am. Chem. Soc. 2012, 134, 4313-4323. (90) Erucar, I.; Manz, T. A.; Keskin, S. Effects of electrostatic interactions on gas adsorption and permeability of MOF membranes. Mol. Simul. 2014, 40, 557-570. (91) Comesaña-Gándara, B.; Chen, J.; Bezzu, C. G.; Carta, M.; Rose, I.; Ferrari, M.-C.; Esposito, E.; Fuoco, A.; Jansen, J. C.; McKeown, N. B. Redefining the Robeson upper bounds for CO2/CH4 and CO2/N2 separations using a series of ultrapermeable benzotriptycene-based polymers of intrinsic microporosity. Energy Environ. Sci. 2019, 12, 2733-2740. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80723 | - |
| dc.description.abstract | 金屬有機骨架為一種由金屬離子與有機配子所形成的配位孔洞材料，這類材料具有很多元的拓樸結構，其孔洞大小也多為超微孔尺度，因此極適合用來作為分子篩進行埃尺度的分離。而在分子篩領域中，孔洞限制直徑被定義為該結構中最大能自由移動球體之直徑，亦可理解為氣體分子於此材料中須跨過之拓樸瓶頸的尺寸，這個數值常被用來當成判斷分離程序的準則。然而此研究點出，孔洞限制直徑並無法很好的評斷非惰性氣體分子於金屬有機骨架中的輸送現象，其中庫倫位能是主要原因，以二氧化碳為例，其與結構間的庫倫作用力有機會使得二氧化碳於金屬有機骨架中的質傳瓶頸與結構的拓樸瓶頸有所偏移。不僅如此，我也觀察到，比起以孔洞大小篩選，藉由設計骨架的開放金屬點位的排列，讓二氧化碳於結構中獲得一個均勻的庫倫能量分佈，將能夠得到更高的碳捕捉能力的金屬有機骨架材料。此外，此論文更點出即使是惰性氣體分子的分離，現行計算孔洞限制直徑方法上，忽略了氣體分子本身所具有的動能，因此該數值在評斷分離程序亦有時失準，本論文提出並計算出與質傳較相關的孔洞限制直徑，並證明其能夠提高與選擇率的相關性至高二十個百分比。在論文的最後，我也初探機器學習於預測具有高二氧化碳吸附選擇性金屬有機骨架的能力，結果顯示，卷積神經網路具有很高的潛力。希望藉由此份研究，可以加速在金屬有機骨架薄膜材料的挑選階段，也能夠對於過去及未來的實驗結果能夠有更正確的理解以及更好的詮釋。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:14:08Z (GMT). No. of bitstreams: 1 U0001-1510202101060600.pdf: 9050291 bytes, checksum: 476dcc953135a9254f858c7fdf7793a4 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 致謝 i 摘要 ii Abstract iv Table of Contents vi List of Figures viii List of Tables xviii Chapter 1. Background and Motivation 1 1.1. MOFs and MOF Membranes 1 1.2. Size Exclusion and Solution-diffusion Model 2 1.3. MOF Membranes and Carbon Capturing 6 1.4. Molecular Simulations for MOF Membranes for Gas Separations 9 1.5. Machine Learnings for MOFs for Gas Separations 12 1.6. Motivation and Scope of This Work 13 Chapter 2. Computational Details 16 2.1. Collecting Structures from Databases 16 2.2. Topological Analysis of a MOF Structure 17 2.3. Force Fields and General Settings 18 2.4. Computations of Thermodynamics Properties of Gaseous Molecules in a MOF Structure 21 2.4.1. Adsorption Behavior of Gaseous Molecules in a MOF Structure 21 2.4.2. Energy Landscape of Gaseous Molecules in a MOF Structure 22 2.5. Computations of Self-diffusivity of Gaseous Molecules in a MOF Structure 24 2.5.1. Classical Molecular Dynamics Simulation 24 2.5.2. Transition State Theory 26 2.5.3. Kinetic Monte Carlo Simulation 27 2.6. Computations of Permeability of Gaseous Molecules in a MOF Structure 27 2.7. Generating Various Structural Configurations of a Flexible MOF Structure 28 2.7.1. Classical Molecular Dynamics Simulation 29 2.7.2. Ab initio Dynamics Simulation 29 2.8. Deriving Transport-relevant van der Waals Radii 31 2.9. Generating Element/Point-charge Matrix for a Porous Structure 32 2.10. The Architecture of Convolutional Neural Network 33 Chapter 3. Coulombic Effect on Permeation of CO2 in MOF Membranes 35 3.1. Coulombic Effect on Transport Bottleneck 35 3.2. Key to Identify Highly CO2 Permeable MOFs 45 3.3. Framework Flexibility versus Coulombic Effect 56 3.4. Summary 62 Chapter 4. Transport-relevant Pore Limiting Diameter for Molecular Separations in MOF Membranes 63 4.1. Transport-relevant van der Waals Radii and Derived PLD 63 4.2. Self-diffusivity and Permeability as a Function of Transport-relevant PLD 74 4.3. Consideration of Framework Flexibility of MOFs 82 4.4. Summary 85 Chapter 5. Machine Learning on Predicting Adsorption Selectivity of CO2 over CH4 for a MOF Structure 87 5.1. Result and Discussions 87 5.2. Summary 95 Chapter 6. Conclusions 96 Chapter 7. Outlook 97 References 98 Appendices 105 Personal Biography 122 | |
| dc.language.iso | en | |
| dc.subject | 薄膜氣體分離 | zh_TW |
| dc.subject | 輸送現象 | zh_TW |
| dc.subject | 分子篩 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 金屬有機骨架 | zh_TW |
| dc.subject | Molecular sieve | en |
| dc.subject | Transport phenomenon | en |
| dc.subject | Metal-Organic framework | en |
| dc.subject | Convolutional neural network | en |
| dc.subject | Membrane gas separation | en |
| dc.title | 以電腦計算探討金屬有機骨架內氣體的輸送現象及其在薄膜分離的應用 | zh_TW |
| dc.title | Computational Studies on Gas Transport in Metal-Organic Frameworks for Membrane Separations | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.coadvisor | 林立強(Li-Chiang Lin) | |
| dc.contributor.oralexamcommittee | 李奕霈(Hsin-Tsai Liu),游琇伃(Chih-Yang Tseng),張博凱,邱政超 | |
| dc.subject.keyword | 金屬有機骨架,分子篩,輸送現象,薄膜氣體分離,卷積神經網路, | zh_TW |
| dc.subject.keyword | Metal-Organic framework,Molecular sieve,Transport phenomenon,Membrane gas separation,Convolutional neural network, | en |
| dc.relation.page | 123 | |
| dc.identifier.doi | 10.6342/NTU202103741 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-28 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 化學工程學研究所 | zh_TW |
| 顯示於系所單位： | 化學工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-1510202101060600.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 8.84 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。