화학공학소재연구정보센터
Langmuir, Vol.16, No.5, 2311-2320, 2000
Unified approach to pore size characterization of microporous carbonaceous materials from N-2, Ar, and CO2 adsorption isotherms
We present a unified approach to pore size characterization of microporous carbonaceous materials such as activated carbon and carbon fibers by nitrogen, argon, and carbon dioxide adsorption at standard temperatures, 77 K for N-2 and Ar and 273 K for CO2. Reference isotherms of N-2, Ar, and CO2 in a series of model slit-shaped carbon pores in the range from 0.3 to 36 nm have been calculated from the nonlocal density functional theory (NLDFT) using validated parameters of intermolecular interactions. Carbon dioxide isotherms have also been generated by the grand canonical Monte Carlo (GCMC) method based on the 3-center model of Harris and Yung. The validation of model parameters includes three steps: (1) prediction of vapor-liquid equilibrium data in the bulk system, (2) prediction of adsorption isotherm on graphite surface, (3) comparison of the NLDFT adsorption isotherms in pores to those of GCMC simulations, performed with the parameters of fluid-fluid interactions, which accurately reproduce vapor-liquid equilibrium data of the bulk fluid. Pore size distributions are calculated by an adaptable procedure of deconvolution of the integral adsorption equation using regularization methods. The deconvolution procedure implies the same grid of pore sizes and relative pressures for all adsorbates and the intelligent choice of regularization parameters. We demonstrate the consistency of our approach on examples of pore structure characterization of activated carbons from adsorption isotherms of different gases and from different models (NLDFT and GCMC). Since the CO2 isotherms measured up to 1 atm are not sensitive to pores wider then 1 nm, the NLDFT method for CO2 has been extended to high-pressure CO2 adsorption up to 34 atm. The methods developed are suggested as a practical alternative to traditional phenomenological approaches such as DR, HK, and BJH methods.