New approach for simulating chain conformations in dense polymers using fully populated lattice models

A two-dimensional implementation of a new computational approach for the simulation of the microscopic chain conformations in dense linear polymers is presented. The macromolecular chains are represented as self-avoiding and mutually avoiding random walks on a fully populated lattice corresponding to the amorphous regions of a lamellar semicrystalline morphology. In this approach, information is generated based on a transfer matrix approach in terms of the permutations of the vertical and the horizontal bonds in the lattice rows. The data are then subsequently corrected to eliminate contributions from unwanted microscopic states containing closed loop (ring) chain structures. It is shown that the linear chain conformational entropy can be estimated from first principles by an efficient accounting of all the feasible microstates. In addition, statistical information on the chain conformations can also be obtained. The chain statistics presented here are compared with the predictions of ideal or nearly ideal random walks (Gambler’s ruin models) from the literature where little or no excluded volume effects are taken into account. It is shown that the chain connectivity influences the chain statistics significantly. © 1998 American Institute of Physics.