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ANALYSIS OF ADSORBENTS POROSITY - METHODS AND MODELS

ANALYSIS OF ADSORBENTS POROSITY - METHODS AND MODELS . Magda Ziółkowska Janina Milewska-Duda Jan T. Duda. Faculty of Energy and Fuels Department of Fuels Technology Azerbaijan, Baku; 23rd may 2013. Methods of analysis microporous materials. Nonadsorptive methods:

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ANALYSIS OF ADSORBENTS POROSITY - METHODS AND MODELS

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  1. ANALYSIS OF ADSORBENTS POROSITY - METHODS AND MODELS Magda Ziółkowska Janina Milewska-Duda Jan T. Duda Faculty of Energy and FuelsDepartment of Fuels TechnologyAzerbaijan, Baku; 23rd may 2013

  2. Methods of analysis microporous materials • Nonadsorptive methods: • Mercury porosimetry • X-ray diffraction • Microscopic methods • Adsorptive methods: • Adsorption isotherms models. • Widely used adsorption models: • BET • LBET • DFT (NL, QS)

  3. uniBET model - introduction • Adsorption system is viewed as: • an aggregation of adsorbate molecules (clu-sterization) limited by pore geometry • consisting of a number of a subsystems (clu-sters), each satisfying equilibrium condition: • R - gas constant •  - relative fugacity of adsorbate (P, T) • H, S - total enthalpy and entropy change respectively • mpa- amount of adsorbate in a-th subsystem [mol/g] [1]

  4. uniBET model - assumptions • first layer (n=1) adsorption is localized at only one site enabling molecules to be held by adhesive forces • further layers (n>1, n=2,..,k) are formed due to the adhesive and cohesive forces between molecules dendrite-like cluster stuck-like cluster Mechanism of molecule clusterization in micropores • clusters enlarging doesn’t affect creation of other ones (configurational independence) • clusters can be grouped into classes  with the same adsorption energy profile and number of layers k [1]

  5. LBET model - assumptions • primary adsorption sites are the same energy QA • adsorption energy of further layers is the same QC > QA, QC 0 • adsorption energy at the k-th and (k-1)-th layers results the same coverage ratio • simple stack like clusters are dominant • number of primary sites mAk capable to start stack-like cluster is exponentially distributed: • mA - total number of primary sites •  - geometrical restrictions for multilayer adsorption parameter [1]

  6. LBET model - formula Uniformly heterogeneous surfaces adsorption capacity LBET formula is expressed:  - parameter of porous structure,  - relative adsorbate fugacity (P, T) R - gas constant, T - temperature BA, Bf- adsorption energy parameter for 1st (QA) and further (QsC) layers Amax, C - effective adsorbate-pore contact surface ratio on primary sites on pores at 1st and further layers  - average coverage ratio of the 2nd and higher layers  - cluster branching ratio [1], [3]

  7. Non-local DFT model • Adsorption system is viewed as: • corresponding to the grand canonical ensemble (, V, T) • grand potential (f) is a functional of the densityf(r)of adsorbed fluid • solid-fluid interactions are neglected, and modeled instead with an effective spatially distributed external potential Vext(r) • r - position vector inside the pore • F- Helmholtz free energy of the fluid • F - chemical potential of the fluid [2]

  8. Non-local DFT assumptions • Helmholtz free energy is the sum of the kinetic energy of the ideal gas Fid[f(r)], hard spheres repulsion forces Fex[f(r)] and attra-ctive term • uff - attractive fluid-fluid potential • solid-fluid interactions corresponds to the L-J potential for a given geometry (ignoring the real molecular structure) • FF, F interactions are modeled with the APPs [2]

  9. Complementary study • Complementary study of microporous carbon samples, CS1 (carbon molecular sieve, Supleco) and ACF10 (an activated carbon fiber, Nippon Kynol, Japan): • two incomparable models • NLDFT and LBET calculations fit relatively well the experimental adsorption data [2]

  10. Conclusions • insight into the modeling of adsorption process • fitting experimental data and predicting adsorption isotherms • incomparable models describes relatively well experimental data • crucial problem is the predicting material adsorptivity • further LBET researches yielded a model, which can be employed as a support in analysis and prediction of cheap adsorbents porous structures properties. [1], [2], [3]

  11. Bibliography • [1] Milewska-Duda, J.; Duda, J. T.; Appl. Surf. Sci.; 2002; 196; 115-125. • [2] Duda, J. T.; Jagiełło, L.; Jagiełło,J.; Milewska-Duda, J.; Appl. Surf. Sci.; 2007; 253, 5616-5621. • [3] Duda, J. T.; Milewska-Duda, J.; Kwiatkowski, M.; Ziółkowska, M.; Adsorption; 2013; 19, 545-555.

  12. ANALYSIS OF ADSORBENTS POROSITY - METHODS AND MODELS Magda Ziółkowska Janina Milewska-Duda Jan T. Duda Faculty of Energy and FuelsDepartment of Fuels TechnologyAzerbaijan, Baku; 23rd may 2013

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