In silico screening of carbon-capture materials

Journal name:
Nature Materials
Volume:
11,
Pages:
633–641
Year published:
DOI:
doi:10.1038/nmat3336
Received
Accepted
Published online

Abstract

One of the main bottlenecks to deploying large-scale carbon dioxide capture and storage (CCS) in power plants is the energy required to separate the CO2 from flue gas. For example, near-term CCS technology applied to coal-fired power plants is projected to reduce the net output of the plant by some 30% and to increase the cost of electricity by 60–80%. Developing capture materials and processes that reduce the parasitic energy imposed by CCS is therefore an important area of research. We have developed a computational approach to rank adsorbents for their performance in CCS. Using this analysis, we have screened hundreds of thousands of zeolite and zeolitic imidazolate framework structures and identified many different structures that have the potential to reduce the parasitic energy of CCS by 30–40% compared with near-term technologies.

At a glance

Figures

  1. Hybrid pressure and temperature swing adsorption.
    Figure 1: Hybrid pressure and temperature swing adsorption.

    In the adsorption step (1) the flue gas is brought into contact with the solid adsorbent. The material selectively adsorbs CO2 and (nearly) pure N2 leaves the adsorber. When the adsorber is saturated, it is regenerated (2) by heating the system and/or applying a vacuum. The purge (3) and cooling or repressurization step (4) brings the system back to its original state (1). The amount of CO2 that is removed from the flue gas in a single cycle defines the working capacity of a material. The regenerated CO2 is subsequently pressurized to 150 bar for geological storage.

  2. (Mixture) adsorption isotherms.
    Figure 2: (Mixture) adsorption isotherms.

    af, Probability distribution of the energies of a particle inserted in the pores (a,d) pure component isotherms for CO2 and N2 and pure CO2 isotherms at different temperatures (b,e) and mixture isotherms (c,f) for two materials: the zeolite SIV (ac) and the predicted zeolite PCOD8286959 (df). The symbols are the results from the GCMC simulations and the lines are the results of our methodology using the GPU calculations.

  3. Parasitic energy as a function of the Henry coefficient of CO2 for all silica zeolite structures.
    Figure 3: Parasitic energy as a function of the Henry coefficient of CO2 for all silica zeolite structures.

    a,b, The Henry coefficient can be obtained from the adsorption isotherm; at sufficiently low pressure the Henry coefficient multiplied by the pressure gives the number of adsorbed molecules. In a we compare the IZA zeolite structures (red squares) with the predicted structures (blue circles). The open blue circles are computationally predicted structures near the low-density feasibility line, which are most likely to be synthesizable. The green line gives the parasitic energy of the current monoethanolamine (MEA) technology, and the black line is the minimal parasitic energy observed for a given value of the Henry coefficient in the all-silica structures. In the Supplementary Information we show the sensitivity of the parasitic energy to uncertainties in our parameters. In this graph, we plotted a representative fraction of all structures. More data can be found at www.carboncapturematerials.org. On the website, every data point can also be linked to a structure. b gives some examples of the optimal all-silica structures; out of the fifty top performing materials we selected the six most diverse. The figures show the atoms of materials as ball and stick (O, red; Si, tan). The surface gives the local free energies in the pores of the material, where warmer colours indicate the dominant CO2 adsorption sites.

  4. Adsorption isotherms.
    Figure 4: Adsorption isotherms.

    The loading in the zeolite is plotted as a function of the partial pressure of CO2 (green or purple) or N2 (orange). Adsorption is set by the flue gas conditions (40 °C, 1 atm and 14% CO2 and 86% N2) and the desorption is at a temperature Tfinal. The working capacity follows from the difference in the amount of adsorbed CO2 at adsorption and desorption conditions. In most of these materials the N2 adsorption is so small that it does not contribute much to the parasitic energy, and only for materials where the adsorption of CO2 is equally small do we consider the contribution of N2. At sufficiently low pressure, these adsorption isotherms are linearly related to the pressure, with the proportionality constant defined as the Henry coefficient. a, A material for which the Henry coefficient is sufficiently low such that both the adsorption and desorption are in the Henry regime. A low Henry coefficient (green) gives a relatively small working capacity and purity of the product stream. Increasing the Henry coefficient (purple) gives a significant increase of the working capacity. b, If the Henry coefficient becomes much larger, the number of adsorbed CO2 molecules is so large that CO2CO2 interactions in the materials are important at the partial pressure of CO2 corresponding to flue gas conditions. Hence, the adsorption cannot be characterized with a Henry coefficient only. c, For those materials with a very high Henry coefficient, a further increase of the Henry coefficient will have little effect on the uptake value at adsorption, as this is now dominated by the pore volume. For desorption, however, increasing the Henry coefficient will further decrease the working capacity. For b and c, as desorption occurs at higher temperatures, the desorption pressure is still in the Henry regime.

  5. Optimal materials.
    Figure 5: Optimal materials.

    The parasitic energy as a function of the binding energy for a CO2 molecule. The binding energy is defined as the lowest energy that can be observed in a given structure. If this binding is sufficiently strong, dual-site adsorption behaviour will arise. The fraction of each material’s volume which is occupied by low-energy strong adsorption sites is displayed as coloured solid circles. The colour bar gives the volume fraction of these low-energy regions. Structures without these specific features (that is, single site adsorption behaviour) are displayed as open blue circles.

  6. Parasitic energy for zeolites with cations.
    Figure 6: Parasitic energy for zeolites with cations.

    The parasitic energy as a function of the CO2 Henry coefficient for known zeolite structures with different Al/Si ratios. The all-silica IZA structures are shown as red squares and the corresponding structures with different cation concentrations are labelled as indicated by open symbols.

  7. Parasitic energy for ZIFs.
    Figure 7: Parasitic energy for ZIFs.

    a, The parasitic energy as a function of the CO2 Henry coefficient for ZIFs is shown. The green lines give the parasitic energy of the current MEA technology, and the black line is the minimal parasitic energy calculated for a given value of the Henry coefficient in the all-silica structures. In this graph, we plotted a representative fraction of all structures. More data can be found at www.carboncapturematerials.org. On the website, every data point can also be linked to a structure. b, Out of the fifty top performing ZIFs, we selected the six most diverse. The figures show the atoms of materials as ball and stick models (Zn, blue–grey; N, blue; H, white; C, grey). The surface gives the local free energies in the pores of the material.

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Author information

  1. These authors contributed equally to this work

    • Li-Chiang Lin,
    • Adam H. Berger,
    • Richard L. Martin &
    • Jihan Kim

Affiliations

  1. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720-1462, USA

    • Li-Chiang Lin,
    • Joseph A. Swisher &
    • Berend Smit
  2. Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

    • Li-Chiang Lin,
    • Jihan Kim,
    • Joseph A. Swisher,
    • Kuldeep Jariwala &
    • Berend Smit
  3. Electric Power Research Institute (EPRI), 3420 Hillview Avenue, Palo Alto, California 94304, USA

    • Adam H. Berger &
    • Abhoyjit S. Bhown
  4. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720-8139, USA

    • Richard L. Martin,
    • Chris H. Rycroft &
    • Maciej Haranczyk
  5. Department of Mathematics, University of California, Berkeley, California 94720, USA

    • Chris H. Rycroft
  6. Departments of Bioengineering and Physics and Astronomy, Rice University, Houston, Texas 77005, USA

    • Michael W. Deem
  7. Department of Chemistry, University of California, Berkeley, California 94720-1462, USA

    • Berend Smit

Contributions

All authors contributed significantly to the work presented in this paper.

Competing financial interests

The authors declare no competing financial interests.

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