Educational Workshop

Polymerization Induced Self-Assembly (PISA) has emerged as a powerful technique for preparing a broad range of novel nanoparticle morphologies not attainable using conventional routes for polymer chain self-assembly. Traditional approaches involve first synthesizing and isolating polymers with the desired chain structure (e.g. diblock or triblock copolymers) and then dissolving the polymers in a given solvent or solvent mixture in which at least one of the copolymer blocks is soluble. By changing the solvency of the mixture, for example by adding a non-solvent for one of the copolymer blocks, self-assembly of the chains can be induced. In contrast, with PISA, it is the polymerization of one of the blocks that changes the overall polymer solvency and induces the self-assembly process, rather than a change in the solvency of the medium. A rich variety of morphologies with tunable structure and dimensions have been made, including spheres, vesicles, aggregates, and worm-like nano-objects. This presentation will present an overview of the synthetic approaches used to prepare nano-objects using PISA, and the principles governing the self-assembly of growing polymer chains

A polymer is a collection of many molecules with a distribution of chain lengths. This is a key difference between polymer chemistry and other branches of synthetic chemistry, which typically aim to produce a single species of molecule. The presence of a chain length distribution affects every property of the polymer, and the breadth of this distribution is characterized by its dispersity or the ratio of the weight average to number average degrees of polymerization. While this seems a simple enough concept, it can be very difficult to visualize: polymer chain length distributions are typically much broader than the distributions we encounter in everyday life. More complex polymer structures such as statistical, gradient, and random copolymers also have distributions of composition, with the result that the idealized structures we draw can be very different from the polymers that we actually synthesize. For amphiphilic copolymers, these differences will affect their self-assembly behavior in the bulk and in solution. This presentation will focus on some basic concepts of polymer science, with an in-depth look at why distributions are central to polymer science, and how these affect polymer properties including the self-assembly of amphiphilic block and gradient copolymers.

Further reading:

1. The downside of dispersity: why the standard deviation is a better measure of dispersion in precision polymerization. S. Harrisson, Chem. 2018, 9, 1366.
2. Effect of hydrophilic monomer distribution on self-assembly of a pH-responsive copolymer: Spheres, worms and vesicles from a single copolymer composition. J. Zhang et al., Chem. Int. Ed. 2021, 60, 4925.

Theoretical and numerical studies have provided valuable insights into understanding the underlying physical principles in the self-assembly of polymeric systems and powerful tools and guidelines for designing experiments. However, complicated interactions, massive combinatorial parameter space, a wide range of length and time scales related to self-assembled structures in polymeric systems restrict the usages of existing numerical models to be applicable in limited cases. Recently, with the advances in acquiring more extensive datasets by either experimental or computational techniques, the contemporary artificial intelligence approach has been attracted as a viable alternative tool for designing polymers and processes. This lecture explains basic concepts and procedures of data-driven machine learning (ML) methods by introducing various ML techniques and relevant tools. Recent advances in the application of ML techniques in the discovery and rational design of polymeric materials will be discussed.