IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i22p5976-d445831.html
   My bibliography  Save this article

Modeling Adsorption in Silica Pores via Minkowski Functionals and Molecular Electrostatic Moments

Author

Listed:
  • Filip Simeski

    (Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA)

  • Arnout M. P. Boelens

    (Department of Energy Resources Engineering, Stanford University, Stanford, CA 94305, USA)

  • Matthias Ihme

    (Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA)

Abstract

Capillary condensation phenomena are important in various technological and environmental processes. Using molecular simulations, we study the confined phase behavior of fluids relevant to carbon sequestration and shale gas production. As a first step toward translating information from the molecular to the pore scale, we express the thermodynamic potential and excess adsorption of methane, nitrogen, carbon dioxide, and water in terms of the pore’s geometric properties via Minkowski functionals. This mathematical reconstruction agrees very well with molecular simulations data. Our results show that the fluid molecular electrostatic moments are positively correlated with the number of adsorption layers in the pore. Moreover, stronger electrostatic moments lead to adsorption at lower pressures. These findings can be applied to improve pore-scale thermodynamic and transport models.

Suggested Citation

  • Filip Simeski & Arnout M. P. Boelens & Matthias Ihme, 2020. "Modeling Adsorption in Silica Pores via Minkowski Functionals and Molecular Electrostatic Moments," Energies, MDPI, vol. 13(22), pages 1-17, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5976-:d:445831
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/22/5976/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/22/5976/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Spagnolo, B. & Valenti, D. & Guarcello, C. & Carollo, A. & Persano Adorno, D. & Spezia, S. & Pizzolato, N. & Di Paola, B., 2015. "Noise-induced effects in nonlinear relaxation of condensed matter systems," Chaos, Solitons & Fractals, Elsevier, vol. 81(PB), pages 412-424.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jin, Yanfei & Wang, Heqiang, 2020. "Noise-induced dynamics in a Josephson junction driven by trichotomous noises," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    2. Shi, Zhuozheng & Liao, Zhiqiang & Tabata, Hitoshi, 2022. "Boosting learning ability of overdamped bistable stochastic resonance system based physical reservoir computing model by time-delayed feedback," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    3. Fang, Yuwen & Luo, Yuhui & Ma, Zhiqing & Zeng, Chunhua, 2021. "Transport and diffusion in the Schweitzer–Ebeling–Tilch model driven by cross-correlated noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    4. Li, Jun-Feng & Jahanshahi, Hadi & Kacar, Sezgin & Chu, Yu-Ming & Gómez-Aguilar, J.F. & Alotaibi, Naif D. & Alharbi, Khalid H., 2021. "On the variable-order fractional memristor oscillator: Data security applications and synchronization using a type-2 fuzzy disturbance observer-based robust control," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    5. Chen, Ruyin & Xiong, Yue & Li, Zekun & He, Zhifen & Hou, Fang & Zhou, Jiawei, 2022. "Effects of correlated noises on binocular rivalry," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    6. Bashkirtseva, Irina A. & Ryashko, Lev B. & Pisarchik, Alexander N., 2020. "Ring of map-based neural oscillators: From order to chaos and back," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    7. Guarcello, C. & Bergeret, F.S., 2021. "Thermal noise effects on the magnetization switching of a ferromagnetic anomalous Josephson junction," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    8. Duan, Wei-Long & Lin, Ling, 2021. "Noise and delay enhanced stability in tumor-immune responses to chemotherapy system," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    9. Duan, Wei-Long, 2020. "The stability analysis of tumor-immune responses to chemotherapy system driven by Gaussian colored noises," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    10. Yang, Jinwoong & Ryu, Hojeong & Kim, Sungjun, 2021. "Resistive and synaptic properties modulation by electroforming polarity in CMOS-compatible Cu/HfO2/Si device," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    11. Matrozova, E.A. & Pankratov, A.L., 2023. "Noise and generation effects in parallel Josephson junction chains," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    12. Ladeynov, D.A. & Egorov, D.G. & Pankratov, A.L., 2023. "Stochastic versus dynamic resonant activation to enhance threshold detector sensitivity," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    13. Guseinov, D.V. & Matyushkin, I.V. & Chernyaev, N.V. & Mikhaylov, A.N. & Pershin, Y.V., 2021. "Capacitive effects can make memristors chaotic," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    14. Slepukhina, Evdokia & Bashkirtseva, Irina & Ryashko, Lev, 2020. "Stochastic spiking-bursting transitions in a neural birhythmic 3D model with the Lukyanov-Shilnikov bifurcation," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    15. Xu, Chaoqun, 2020. "Probabilistic mechanisms of the noise-induced oscillatory transitions in a Leslie type predator-prey model," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    16. Ryu, Ji-Ho & Kim, Sungjun, 2020. "Artificial synaptic characteristics of TiO2/HfO2 memristor with self-rectifying switching for brain-inspired computing," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    17. Chen, Ruyin & Xiong, Yue & Zhuge, Shengying & Li, Zekun & Chen, Qitie & He, Zhifen & Wu, Dingqiang & Hou, Fang & Zhou, Jiawei, 2023. "Regulation and prediction of multistable perception alternation," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    18. Scharf, Yael, 2017. "A chaotic outlook on biological systems," Chaos, Solitons & Fractals, Elsevier, vol. 95(C), pages 42-47.
    19. Zhang, Hongxia & Xu, Wei & Guo, Qin & Han, Ping & Qiao, Yan, 2020. "First escape probability and mean first exit time for a time-delayed ecosystem driven by non-Gaussian colored noise," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    20. Shchanikov, Sergey & Zuev, Anton & Bordanov, Ilya & Danilin, Sergey & Lukoyanov, Vitaly & Korolev, Dmitry & Belov, Alexey & Pigareva, Yana & Gladkov, Arseny & Pimashkin, Alexey & Mikhaylov, Alexey & K, 2021. "Designing a bidirectional, adaptive neural interface incorporating machine learning capabilities and memristor-enhanced hardware," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5976-:d:445831. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.