Equilibration of linear polyethylene melts with pre-defined molecular weight distributions employing united atom Monte Carlo simulations

Possessing control over the molecular size (molecular weight/chain length/degree of polymerization) distribution of a polymeric material is extremely important in applications. This is manifested de facto by the extensive contemporary scientific literature on processes for controlling this distribution experimentally. Yet, the literature on computational techniques for achieving prescribed molecular size distributions in simulations and exploring their impact on properties is much less abundant than its experimental/technical counterpart. Here, we develop-on the basis of united atom melt simulations employing connectivity-altering Monte Carlo moves-a new Metropolis selection criterion that drives the multichain system to a prescribed but otherwise arbitrary distribution of molecular sizes. The new formulation is a generalization of that originally proposed [P. V. K. Pant and D. N. Theodorou, Macromolecules 28, 7224 (1995)], but simpler and more computationally efficient. It requires knowledge solely of the target distribution, which need not be normalized. We have implemented the new formulation on long-chain linear polyethylene melts, obtaining excellent results. The target molecular size distribution can be provided in tabulated form, allowing absolute freedom as to the types of chain size profiles that can be simulated. Distributions for which equilibration has been achieved here for linear polyethylene include a truncated most probable, a truncated Schulz-Zimm, an arbitrary one defined in tabulated form, a broad truncated Gaussian, and a bimodal Gaussian. The last two are comparable to those encountered in industrial applications. The impact of the molecular size distribution on the properties of the simulated melts, such as density, chain dimensions, and mixing thermodynamics, is explored.

The challenges of controlling polymer synthesis at the molecular and macromolecular level

In this Perspective, we outline advances and challenges in controlling the structure of polymers at various size regimes in the context of structural features such as molecular weight distribution, end groups, architecture, composition and sequence.

Tuning Ligand Concentration in Cu(0)-RDRP: A Simple Approach to Control Polymer Dispersity

Cu(0)-reversible deactivation radical polymerization (RDRP) is a versatile polymerization tool, providing rapid access to well-defined polymers while utilizing mild reaction conditions and low catalyst loadings. However, thus far, this method has not been applied to tailor dispersity, a key parameter that determines the physical properties and applications of polymeric materials. Here, we report a simple to perform method, whereby Cu(0)-RDRP can systematically control polymer dispersity (Đ = 1.07-1.72), while maintaining monomodal molecular weight distributions. By varying the ligand concentration, we could effectively regulate the rates of initiation and deactivation, resulting in polymers of various dispersities. Importantly, both low and high dispersity PMA possess high end-group fidelity, as evidenced by MALDI-ToF-MS, allowing for a range of block copolymers to be prepared with different dispersity configurations. The scope of our method can also be extended to include inexpensive ligands (i.e., PMDETA), which also facilitated the polymerization of lower propagation rate constant monomers (i.e., styrene) and the in situ synthesis of block copolymers. This work significantly expands the toolbox of RDRP methods for tailoring dispersity in polymeric materials.

Precise Control of Both Dispersity and Molecular Weight Distribution Shape by Polymer Blending

The breadth and the shape of molecular weight distributions can significantly influence fundamental polymer properties that are critical for various applications. However, current approaches require the extensive synthesis of multiple polymers, are limited in dispersity precision and are typically incapable of simultaneously controlling both the dispersity and the shape of molecular weight distributions. Here we report a simplified approach, whereby on mixing two polymers (one of high Đ and one of low Đ), any intermediate dispersity value can be obtained (e.g. from 1.08 to 1.84). Unrivalled precision is achieved, with dispersity values obtained to even the nearest 0.01 (e.g. 1.37→1.38→1.39→1.40→1.41→1.42→1.43→1.44→1.45), while maintaining fairly monomodal molecular weight distributions. This approach was also employed to control the shape of molecular weight distributions and to obtain diblock copolymers with high dispersity accuracy. The straightforward nature of our methodology alongside its compatibility with a wide range of polymerisation protocols (e.g. ATRP, RAFT), significantly expands the toolbox of tailored polymeric materials and makes them accessible to all researchers.

Controlling Polymer Molecular Weight Distribution through a Latent Mediator Strategy with Temporal Programming

Polymer molecular weight distribution (MWD) is a key parameter of polymers. Here we present a robust method for controlling polymer MWD in controlled cationic polymerizations. A latent mediator strategy was designed and combined with temporal programming to regenerate mediators at different times during polymerization. Both the breadths and shapes of MWD curves were tuned easily by adjusting an external light source. Bimodal, trimodal, and tetramodal distributions were obtained, and the breadths could be varied from 1.06 to 2.09. Polymers with different MWDs prepared by this method had good chain end fidelity, which was demonstrated with successful chain-extension experiments. In addition, the introduction of temporal programming with a computer-controlled single chip for the light source opened an avenue for the use of artificial intelligence in polymer synthesis.

Tailoring polymer dispersity by mixing ATRP initiators

Herein we present a simple batch method to control polymer dispersity using a mixture of two ATRP initiators with different reactivities.