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Controlling Polymer Properties Through the Shape of the Molecular-​Weight Distribution

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Controlling Polymer Properties Through the Shape of the Molecular-​Weight Distribution REVIEWS Controlling polymer properties through the shape of the molecular- weight distribution Dillon T. Gentekos , Renee J. Sifri and Brett P. Fors * Abstract | The manipulation of a polymer’s properties without altering its chemical composition is a major challenge in polymer chemistry, materials science and engineering. Although variables such as chemical structure, branching, molecular weight and dispersity are routinely used to control the architecture and physical properties of polymers, little attention is given to the often profound effect of the breadth and shape of the molecular- weight distribution (MWD) on the properties of polymers. Synthetic strategies now make it possible to explore the importance of parameters such as skew and the higher moments of the MWD function beyond the average and standard deviation. In this Review, we describe early accounts of the effect of MWD shape on polymer properties; discuss synthetic strategies for controlling MWD shape; describe current endeavours to understand the influence of MWD shape on rheological and mechanical properties and phase behaviour; and provide insight into the future of using MWDs in the design of polymeric materials. The molecular-weight distributions (MWDs) of poly- homopolymers and block copolymers with precisely mers have a striking impact over their properties, from defined MWD shapes and compositional gradients. processability and mechanical strength to nearly all We also highlight the impact of polymer MWD shape aspects of morphological phase behaviour1–10. The most on polymer properties, from tensile strength and rheo- common molecular parameter used to describe poly- logical behaviour (for example, viscosity) to a variety of mer MWDs is the dispersity (Ð) — the normalized morphological characteristics for block copolymer self- standard deviation of chain sizes in a polymer sample, assembly, such as domain spacing, and the position of which is defined as the ratio of the weight- average molar morphological phase boundaries. mass (Mw) to the number- average molar mass (Mn). A more detailed description of these terms is provided Synthetic control of MWD shape in BOXES 1,2. The properties of a polymer depend on the breadth and The parameter Ð has been used to manipulate the shape of the MWD in the final polymeric material5,20 properties of polymeric materials11–19. However, Ð is an and, as a result, a number of approaches have been incomplete representation of a polymer’s true distribu- developed to control polymer Ð. Owing to the synthetic tion because it describes only the relative breadth of a difficulty in controlling both the molar mass averages MWD and contains no information regarding the pre- and the MWD breadth in a one- pot setup, early stud- cise composition of polymer chain lengths13,14 (BOX 1). ies used post- polymerization blending strategies to For this reason, interest has arisen in using the entire tailor the MWD composition21–26. However, this pro- distribution of masses of a polymer sample to dictate cess requires the synthesis of multiple polymers under its function. In theory, this would allow for limitless highly controlled conditions and results in multimodal variation of the distribution function. However, until MWDs. Although these studies provided insight into recently, this avenue of controlling polymer structure the influence of MWD on polymer properties, such and function remained largely unexplored, owing to the heterogeneous molar mass compositions are unsuitable lack of methods for controlling the absolute composition for some applications, owing to the potential for unde- Department of Chemistry and 21,22 Chemical Biology, Cornell of chain lengths in a polymer sample. sirable macrophase separation . Therefore, systems University, Ithaca, NY, USA. In this Review, we explore advances in the synthetic that produce polymers with continuous monomodal *e- mail: [email protected] control of polymer MWD shape, from temporally MWDs in a one- reactor setup are more desirable. https://doi.org/10.1038/ controlled polymer initiation and termination to reac- Uncontrolled polymerization is one such synthetic s41578-019-0138-8 tant feeds in continuous- flow setups that produce strategy for producing monomodal MWDs with large NATURE REVIEWS | MATERIALS REVIEWS breadths27–29. Although this approach has proven useful production of a vast repertoire of MWDs in atom- in generating polymers with Ð ≈ 2, it grants little control transfer radical polymerization (ATRP)37. Reinforcement over Mn or the shape of the resultant MWD. This issue learning is a machine- learning technique that allows a has been circumvented by leveraging polymer chain system to react with its environment and make decisions growth kinetics in radical, ring- opening and anionic based on reward points. Whenever the system takes an polymerizations to prepare polymers with controllable action that moves it closer to the desired goal, it receives dispersities30–33. For example, controlled atom- transfer a reward, thus providing a guide to reach the final goal. radical coupling (ATRC) has allowed for changes in In this case for ATRP, the reinforcement learning model the MWD breadth, as well as the ability to achieve allows the system to take actions, such as changing the both monomodal and multimodal MWDs34. However, amount of monomer, initiator or solvent, to achieve a ATRC is limited in its monomer scope and lacks the desired MWD. These numerical simulations yielded precise control needed to obtain exact MWD shapes. reactor- control policies that should afford targeted Furthermore, mathematical models of catalyst- transfer MWD shapes, from monomodal distributions with polycondensation polymerizations have been studied deviating Ð values to multimodal MWDs. as a means of deviating from ideal Poisson behaviour35. Although the aforementioned approaches can be Although these models may provide the foundation for used to modify Ð with high fidelity, they are not a means gaining control over Ð and the distribution function, of governing the precise composition or shape of the this model is specific to only a few mechanisms based poly mer MWD. In contrast with strategies for modifying on polymer kinetics. Alternatively, to tune the poly- Ð, few have been developed for customizing the MWD mer Ð, researchers have developed an organocatalytic shape. In early work, control over the MWD shape was process that takes advantage of monomer mixtures reported in anionic polymerization of styrene through to manipulate the dispersity of any block of a block oscillating monomer and alkyllithium initiator feed rates copolymer36. in continuous- flow reactors38,39. This method monitors Recent work has made strides towards MWD con- the MWD in real time and uses mathematical modelling trol by deep reinforcement learning to simulate the to alter the flow rates of the reacting species to achieve the desired MWD. Although the polymers had the same general MWD shape as the desired material, there was Box 1 | MWDs and the limits of dispersity significant deviation between the anticipated and mea- Polymer molecular-weight distributions (MWDs) are defined most typically using a sured MWDs. It was proposed that these deviations were a result of termination of the living polymer chain ends combination of the molar mass averages, Mn and Mw, which are determined by size- exclusion chromatography and are defined as: and incomplete conversion of monomer, owing to exper- imental unfeasibility of excluding all impurities from the ∑ MNii Mn = reaction setup. ∑ Ni In a separate semibatch process, monomer purity was taken into account to improve the fits40. In this work, the MN2 automatic control process accounts for the concentra- M ∑ ii w = tion of impurities in the reactor feeds. This model leads ∑ MNii to better agreement between the predicted and meas- Using these two molar mass averages, Ð is defined as: ured MWDs39 compared with the same system that did not take purity into account; however, there remained Mw substantial deviations between these MWDs and the Ð = Mn a priori desired distribution function (Fig. 1a). Although the presence of impurities was considered in the control which is related to the standard deviation (σ) of the distribution function by the model, termination of the active polymer chains can relationship: only be approximated. The discrepancies between the 2 desired, predicted and measured MWDs are likely a con- σ Ð =+1 M2 sequence of the high sensitivity of the polymerization n kinetics to the impurity concentration, which makes it These equations reveal that Ð is the relative breadth of the MWD function or, more difficult to accurately determine the initial concentration specifically, it describes the standard deviation of chain lengths normalized to the of alkyllithium initiating species. More recently, a fully number- average molar mass. Thus, Ð provides information about MWD breadth only automated control system was developed for continuous when the average molar mass of the polymers being compared is constant; that is, stirred-tank reactors using an inverse process model to polymers can have the same Ð but contain vastly different breadths of their respective gain better control of MWD shape in homopolymers41. 13,46,47,124 MWDs . This model assumes that each living polymerization In addition to being a limited description of the relative span of molecular weights in leads to a uniform MWD (that is, each polymer in the a polymer sample, Ð offers no information on the shape of a distribution
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