This interactive educational workshop on polymer synthesis is suited for postgraduates or researchers from all countries to update their knowledge by interactive oral lectures. All the 3 lectures shall touch on the understanding of the basic science, terms, and concepts that are critical to chain-growth polymerization with a particular emphasis on polymerization-induced self-assembly (PISA). Thought-provoking insights into the experimental design, modeling/simulation, and synthesis using artificial intelligence; coupled with the results and discussion of research will be presented. This will provide a basis also for understanding research reports during the following days of the conference. Before the workshop, the instructors may share their PowerPoint slides on the website of the IUPAC sub-committee on Polymer Education accessible to the general public.
Postgraduates, higher-level undergraduates, and other researchers in polymer chemistry.
The educational workshop is a set of lectures by experts in the field describing different aspects of interest to the participants of MACRO2022. The following topics will be presented.
|1.25 pm – 1.30 pm||Welcoming speech by the Co-chair of IUPAC Project under Polymer Education Subcommittee|
|1.30 pm – 2.15 pm||Polymerization-induced self-assembly (PISA): Experimental approaches to preparing polymer nano-objects using PISA||Prof. Dr. Michael Cunningham
Queen’s University, Canada
|2.15 pm – 3.00 pm||Distributions, dispersity, and self-assembly||Prof. Dr. Simon Harrisson
|3.00 pm – 3.15 pm||Short break|
|3.15 pm – 4.00 pm||Advances in Polymerization and Self-Assembly Assisted via Machine Learning and Data Science||Prof. Dr. Su-Mi Hur
Chonnam National University, Korea
|4.00 pm – 4.10 pm||Closing of Workshop by Chairman of IUPAC Polymer Education Sub Committee|
Organization Team - MACRO2022 Educational Workshop
When you make inquiries, please use the subject line “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.
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.
Michael Cunningham is a Professor in Chemical Engineering at Queen’s University with a cross-appointment to the Department of Chemistry. His research program focuses on the synthesis of novel polymer functional nanoparticles using RDRP, CO2-switchable polymers, and hybrids of synthetic and renewable polymers. He is Chair of the International Polymer Colloids Group and recipient of several research awards including the NSERC Brockhouse Canada Prize for Interdisciplinary Research, Canadian Green Chemistry and Engineering Award, Syncrude Canada Innovation Award, and Professional Engineer’s Ontario Research Award. He is a Fellow of the Chemical Institute of Canada, Canadian Academy of Engineering and Engineering Institute of Canada.
Simon Harrisson is a researcher at the Centre National de Recherche Scientifique (CNRS), based at the Laboratoire de Chimie des Polymères Organiques (LCPO) in Bordeaux, France. He obtained his PhD at the University of New South Wales, Australia, under the supervision of Thomas P. Davis (UNSW) and Ezio Rizzardo, and Richard Evans of the Commonwealth Scientific and Industrial Research Organisation (CSIRO). After postdoctoral research with David Haddleton (University of Warwick, UK) and Karen Wooley (Washington University in St Louis, USA), he spent several years at CSIRO before moving to France in 2010. After initially working with Julien Nicolas and Patrick Couvreur at the Institut Galenique Paris-Sud, he joined the CNRS in 2014, first at the IMRCP laboratory in Toulouse, and since 2019 in Bordeaux. His research focuses on polymerization kinetics, the statistics of polymer structure and composition, and the synthesis and self-assembly of block, gradient and asymmetric (gradient-like) copolymers.
Su-Mi Hur studied chemical engineering at Seoul National University (SNU, Korea) and obtained her Ph.D at the University of California, Santa Barbara (UCSB, USA). She continued her research career as a postdoctoral associate in Pritzker School of Molecular Engineering at the University of Chicago and Argonne National Laboratory. In 2015, she joined the School of Polymer Science and Engineering at Chonnam National University (CNU, Korea) as an assistant professor and was promoted to an associated professor in 2019. Her research is concerned with applying statistical mechanical theory and field-/particle-based coarse-grained simulations to investigate structural, thermodynamic, and dynamic phenomena in polymer-based soft materials. Her interests also lie in designing macromolecular systems and processes assisted with computational techniques including machine learning.
About MACRO 2022
MACRO2022 will be an invaluable forum for polymer research communities in Canada to connect and share knowledge and professional experiences with the worldwide polymer research community.
Keep up to date with Conference News & Alerts.