We present a constrained reverse Monte Carlo method for structural modeling of porous carbons. As in the original reverse Monte Carlo method, the procedure is to stochastically change the atomic positions of a system of carbon atoms to minimize the differences between the simulated and the experimental pair correlation functions. However, applying the original reverse Monte Carlo method without further constraints yields nonunique structures for carbons, due to the presence of strong three-body forces. In this respect, the uniqueness theorem of statistical mechanics provides a helpful guide to the design of reverse Monte Carlo methods that give reliable structures. In our method, we constrain the bond angle distribution and the average carbon coordination number to describe the three-body correlations. Using this procedure, we have constructed structural models of two highly disordered porous carbons prepared by pyrolysis of saccharose at two different temperatures. The resulting pair correlation functions are in excellent agreement with those obtained by diffraction experiments. Simulated transmission electron microscopy (TEM) images of the resulting models are compared to experimental images. Many of the features observed in the experimental images are also observed in the simulations. The model carbons are characterized by determination of the porosity, pore size distribution, adsorbent−adsorbate potential energy distribution, and adsorption properties at zero coverage, using a model of nitrogen as the adsorbate. Grand canonical Monte Carlo simulations of nitrogen adsorption in the model materials are presented, and it is found that the results can be explained in terms of the adsorbent−adsorbate potential energy distribution but not in terms of the pore size distribution. For both models, the isosteric heat of adsorption is a decreasing function of coverage, in agreement with typical experimental results in other porous carbons.

We apply a simulation protocol based on the reverse Monte Carlo (RMC) method, which incorporates an energy constraint, to model porous carbons. This method is called hybrid reverse Monte Carlo (HRMC), since it combines the features of the Monte Carlo and reverse Monte Carlo methods. The use of the energy constraint term helps alleviate the problem of the presence of unrealistic features (such as three- and four-membered carbon rings), reported in previous RMC studies of carbons, and also correctly describes the local environment of carbon atoms. The HRMC protocol is used to develop molecular models of saccharose-based porous carbons in which hydrogen atoms are taken into account explicitly in addition to the carbon atoms. We find that the model reproduces the experimental pair correlation function with good accuracy. The local structure differs from that obtained with a previous model (Pikunic, J.; Clinard, C.; Cohaut, N.; Gubbins, K. E.; Guet, J. M.; Pellenq, R. J.-M.; Rannou, I.; Rouzaud, J. N. Langmuir 2003, 19 (20), 8565). We study the local structure by calculating the nearest neighbor distribution, bond angle distribution, and ring statistics.

We present a study on the effects of activation on a saccharose-based carbon using molecular simulation. A constrained Reverse Monte Carlo method is used to build molecular models that match the experimental structure factors of both activated and unactivated carbon, using appropriate constraints for bond angle and coordination number to describe the three body correlation. The semi-coke sample, that is named CS1000, is obtained by pyrolyzing pure saccharose at 1000 • C under nitrogen flow. An activated form of this carbon, CS1000a, was obtained by heating CS1000 in an atmosphere of CO 2 for 20 hours. We built molecular models for CS1000 and CS1000a and also simulated the TEM images of the model. We perform GCMC simulation of a Lennard-Jones model of Argon on the resulting models to obtain the adsorption isotherms. We then study the difference in the morphology of CS1000 and CS1000a that lead to different adsorption properties in carbon upon activation.