Research ArticlePHYSICAL SCIENCE

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

See allHide authors and affiliations

Science Advances  14 Oct 2016:
Vol. 2, no. 10, e1600909
DOI: 10.1126/sciadv.1600909
  • Fig. 1 Simplified schematic of precombustion CO2 capture.

    Schematic was adapted from Wilcox’s study (1). Natural gas, which is mainly methane, is reformed to produce a mixture of CO and H2, which then goes through a WGSR to produce a mixture of CO2 and H2. The stream from the WGSR goes through a CO2 separation unit to produce high-purity hydrogen, which is combusted to generate electricity.

  • Fig. 2 Overview and validation of the GA.

    (A) An example chromosome and the corresponding hMOF structure. Colors help illustrate the correspondence between the genes and the hMOF structural features. (B) Workflow of GA. (C to E) Histograms for all hMOFs (gray) and for the initial population used in the GA runs (green). (C) Methane working capacity. (D) Gravimetric surface area. (E) Volumetric surface area. (F to H) Histograms collected from 100 GA runs show the fitness of the top-performing MOF at the end of each run. (F) Methane working capacity. (G) Gravimetric surface area. (H) Volumetric surface area. The vertical lines in (F) to (H) correspond to the fitness of the top performer from the initial population (black) and from the whole database (red).

  • Fig. 3 Performance of the GA.

    (A to C) Results for three independent GA runs dedicated to optimize (A) CO2 working capacity, (B) CO2/H2 selectivity, and (C) APS.

  • Fig. 4 Gene evolution during GA optimization of APS.

    (A to D) Genes corresponding to (A) inorganic building blocks, (B) primary organic linkers, (C) secondary organic linkers, and (D) functional groups.

  • Fig. 5 Aggregated data from the GA search (circles) for precombustion CO2 capture.

    Each point corresponds to an hMOF and is colored according to the value of the APS. The data point for the synthesis target identified from the GA search (NOTT-101/OEt) obtained from GCMC simulations is shown in gray. Data points for MOFs experimentally tested in the literature for the operating conditions studied here are shown in black-outlined yellow squares. The properties of Cu-BTTri and MOF-74 were computed on the basis of the mixture isotherms obtained from IAST reported by Herm et al. (14) and Herm et al. (16), respectively (see section S10). The properties of Ni-4PyC were approximated from simulated and experimental data reported by Nandi et al. (17). Exp., experimental; sim., simulated.

  • Fig. 6 MOF studied for precombustion CO2 capture.

    (A) Inorganic node and organic ligand used to synthesize NOTT-101/OEt. (B) Atomistic representation of NOTT-101/OEt. Copper, carbon, and oxygen atoms are shown in orange, black, and red, respectively. Hydrogen atoms are omitted for clarity. Purple spheres represent the cavities of NOTT-101/OEt. (C) Experimental and simulated absolute single-component CO2 and H2 isotherms for NOTT-101/OEt at 313 K. (D) Crystal structures of other MOFs listed in Table 2. Mg-MOF-74, Cu-BTTri, Ni-4PyC, and VEXTUO are based on Mg2, Cu4Cl, Ni2O, and Ni2O inorganic nodes, respectively, connected by the linkers illustrated below each MOF. MOF pore cages are illustrated with colored spheres. (The MOFs are not all drawn to the same scale.)

  • Table 1 Comparison of computational effort for brute force search versus GA.

    ΔN1 is the CO2 working capacity, Embedded Image is the CO2/H2 selectivity, and APS is the adsorbent performance score, as defined in Eqs. 1 to 3. The number of GCMC simulations for the GA search corresponds to the number of simulations carried out up to 10 generations.

    MethodFitness
    measure
    Number of GCMC
    simulations
    Relative computational
    time (%)
    Brute
    force
    51,163100
    GAΔN13400.66
    Embedded Image3220.63
    APS2680.52
  • Table 2 CO2 working capacity and CO2/H2 selectivity for several MOFs.
    MOFCO2 working capacity (mol/
    kg)
    CO2/H2
    selectivity
    Reference
    NOTT-101/
    OEt
    3.860This work
    Cu-BTTri3.720(15)
    Ni-4PyC*3.4279(17)
    VEXTUO3.148This work
    Mg-MOF-742.6365(14)

    *Results are approximate and are obtained on the basis of experimental and simulation data reported by Nandi et al. (17). Specifically, selectivity was obtained from mixture simulation data at 313 K/20 bar, and working capacity was obtained from mixture simulation data at 313 K/20 bar and pure-component experimental data at 303 K/1 bar.

    Measured at 303 K.

    Supplementary Materials

    • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/2/10/e1600909/DC1

      section S1. Computational methods.

      section S2. Genetic information for the WLLFHS hMOF database.

      section S3. Genetic algorithm.

      section S4. Discussion about a definition of CO2 working capacity.

      section S5. Identification of top-performing hMOFs for synthesis.

      section S6. Synthesis of NOTT-101/OEt and VEXTUO.

      section S7. Powder x-ray diffraction data.

      section S8. N2 sorption data.

      section S9. CO2 and H2 simulated and measured isotherms for VEXTUO.

      section S10. IAST calculations.

      fig. S1. Nitrogen model.

      fig. S2. Carbon dioxide model.

      fig. S3. Hydrogen model.

      fig. S4. Correspondence between genes and inorganic nodes.

      fig. S5. Correspondence between genes and organic linkers.

      fig. S6. Correspondence between genes and functional groups.

      fig. S7. Examples of duplicate MOFs.

      fig. S8. Duplicity of structures in the WLLFHS hMOF database.

      fig. S9. Performance similarity among duplicate hMOFs.

      fig. S10. Textural properties in the original and reduced WLLFHS hMOF database.

      fig. S11. Gene distribution in the reduced WLLFSH hMOF database.

      fig. S12. Suitable combinations of building blocks.

      fig. S13. A workflow for the genetic operations of crossover and mutation.

      fig. S14. Determination of the optimal length of GA runs.

      fig. S15. Schematic for a Skarstrom cycle of a CO2/H2 separation unit.

      fig. S16. Gas concentration profiles during the Skarstrom cycle.

      fig. S17. Impact of CO2/H2 selectivity on H2 purity.

      fig. S18. Rearrangement of functional groups in potential synthesis targets.

      fig. S19. MOF representative cluster for DFT calculations.

      fig. S20. Performance of investigated hMOFs and CoRE MOFs.

      fig. S21. Ligand for NOTT-101/OEt.

      fig. S22. Ligand for VEXTUO.

      fig. S23. Nitrogen adsorption for NOTT-101/OEt.

      fig. S24. Nitrogen adsorption for VEXTUO.

      fig. S25. Powder x-ray diffraction data patterns for NOTT-101/OEt.

      fig. S26. Powder x-ray diffraction data patterns for VEXTUO.

      fig. S27. CO2 and H2 single-component adsorption for VEXTUO.

      fig. S28. Dual-site Langmuir fit for CO2 and H2 isotherms of NOTT-101/OEt.

      fig. S29. Dual-site Langmuir fit for CO2 and H2 isotherms of VEXTUO.

      fig. S30. Dual-site Langmuir fit for CO2 and H2 isotherms of Mg-MOF-74.

      fig. S31. Dual-site Langmuir fit for CO2 and H2 isotherms of Cu-BTTri.

      fig. S32. IAST accuracy test on NOTT-101/OEt.

      fig. S33. CO2/H2 selectivity versus pressure for NOTT-101/OEt.

      fig. S34. CO2 working capacity as a function of pressure.

      table S1. Number of hMOFs in different subsets with the gene-based identification criteria.

      table S2. Data from GA testing.

      table S3. CO2/H2 adsorption properties for the Zn- and Cu-based nbo hMOFs.

      table S4. Textural properties of the top 1% of evaluated hMOFs for three performance measures.

      table S5. List of top 30 CoRE MOFs.

      table S6. IAST parameters for the 20:80 mixture of CO2/H2.

      References (4566)

    • Supplementary Materials

      This PDF file includes:

      • section S1. Computational methods.
      • section S2. Genetic information for the WLLFHS hMOF database.
      • section S3. Genetic algorithm.
      • section S4. Discussion about a definition of CO2 working capacity.
      • section S5. Identification of top-performing hMOFs for synthesis.
      • section S6. Synthesis of NOTT-101/OEt and VEXTUO.
      • section S7. Powder x-ray diffraction data.
      • section S8. N2 sorption data.
      • section S9. CO2 and H2 simulated and measured isotherms for VEXTUO.
      • section S10. IAST calculations.
      • fig. S1. Nitrogen model.
      • fig. S2. Carbon dioxide model.
      • fig. S3. Hydrogen model.
      • fig. S4. Correspondence between genes and inorganic nodes.
      • fig. S5. Correspondence between genes and organic linkers.
      • fig. S6. Correspondence between genes and functional groups.
      • fig. S7. Examples of duplicate MOFs.
      • fig. S8. Duplicity of structures in the WLLFHS hMOF database.
      • fig. S9. Performance similarity among duplicate hMOFs.
      • fig. S10. Textural properties in the original and reduced WLLFHS hMOF database.
      • fig. S11. Gene distribution in the reduced WLLFSH hMOF database.
      • fig. S12. Suitable combinations of building blocks.
      • fig. S13. A workflow for the genetic operations of crossover and mutation.
      • fig. S14. Determination of the optimal length of GA runs.
      • fig. S15. Schematic for a Skarstrom cycle of a CO2/H2 separation unit.
      • fig. S16. Gas concentration profiles during the Skarstrom cycle.
      • fig. S17. Impact of CO2/H2 selectivity on H2 purity.
      • fig. S18. Rearrangement of functional groups in potential synthesis targets.
      • fig. S19. MOF representative cluster for DFT calculations.
      • fig. S20. Performance of investigated hMOFs and CoRE MOFs.
      • fig. S21. Ligand for NOTT-101/OEt.
      • fig. S22. Ligand for VEXTUO.
      • fig. S23. Nitrogen adsorption for NOTT-101/OEt.
      • fig. S24. Nitrogen adsorption for VEXTUO.
      • fig. S25. Powder x-ray diffraction data patterns for NOTT-101/OEt.
      • fig. S26. Powder x-ray diffraction data patterns for VEXTUO.
      • fig. S27. CO2 and H2 single-component adsorption for VEXTUO.
      • fig. S28. Dual-site Langmuir fit for CO2 and H2 isotherms of NOTT-101/OEt.
      • fig. S29. Dual-site Langmuir fit for CO2 and H2 isotherms of VEXTUO.
      • fig. S30. Dual-site Langmuir fit for CO2 and H2 isotherms of Mg-MOF-74.
      • fig. S31. Dual-site Langmuir fit for CO2 and H2 isotherms of Cu-BTTri.
      • fig. S32. IAST accuracy test on NOTT-101/OEt.
      • fig. S33. CO2/H2 selectivity versus pressure for NOTT-101/OEt.
      • fig. S34. CO2 working capacity as a function of pressure.
      • table S1. Number of hMOFs in different subsets with the gene-based identification criteria.
      • table S2. Data from GA testing.
      • table S3. CO2/H2 adsorption properties for the Zn- and Cu-based nbo hMOFs.
      • table S4. Textural properties of the top 1% of evaluated hMOFs for three performance measures.
      • table S5. List of top 30 CoRE MOFs.
      • table S6. IAST parameters for the 20:80 mixture of CO2/H2.
      • References (4566)

      Download PDF

      Files in this Data Supplement: