The Power of Automation in Polymer Chemistry: Precision Synthesis of Multiblock Copolymers with Block Sequence Control

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Abstract

Machines can revolutionize the field of chemistry and material science, driving the development of new chemistries, increasing productivity, and facilitating reaction scale up. The incorporation of automated systems in the field of polymer chemistry has however proven challenging owing to the demanding reaction conditions, rendering the automation setup complex and costly. There is an imminent need for an automation platform which uses fast and simple polymerization protocols, while providing a high level of control on the structure of macromolecules via precision synthesis. This work combines an oxygen tolerant, room temperature polymerization method with a simple liquid handling robot to automatically prepare precise and high order multiblock copolymers with unprecedented livingness even after many chain extensions. The highest number of blocks synthesized in such a system is reported, demonstrating the capabilities of this automated platform for the rapid synthesis and complex polymer structure formation.

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Self-assembly of block copolymers.

Yiyong Mai, Adi Eisenberg (2012)

Block copolymer (BCP) self-assembly has attracted considerable attention for many decades because it can yield ordered structures in a wide range of morphologies, including spheres, cylinders, bicontinuous structures, lamellae, vesicles, and many other complex or hierarchical assemblies. These aggregates provide potential or practical applications in many fields. The present tutorial review introduces the primary principles of BCP self-assembly in bulk and in solution, by describing experiments, theories, accessible morphologies and morphological transitions, factors affecting the morphology, thermodynamics and kinetics, among others. As one specific example at a more advanced level, BCP vesicles (polymersomes) and their potential applications are discussed in some detail.

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A mobile robotic chemist

Benjamin Burger, Phillip M. Maffettone, Vladimir V. Gusev … (2020)

Technologies such as batteries, biomaterials and heterogeneous catalysts have functions that are defined by mixtures of molecular and mesoscale components. As yet, this multi-length-scale complexity cannot be fully captured by atomistic simulations, and the design of such materials from first principles is still rare1-5. Likewise, experimental complexity scales exponentially with the number of variables, restricting most searches to narrow areas of materials space. Robots can assist in experimental searches6-14 but their widespread adoption in materials research is challenging because of the diversity of sample types, operations, instruments and measurements required. Here we use a mobile robot to search for improved photocatalysts for hydrogen production from water15. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm16-18. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones. Our strategy uses a dexterous19,20 free-roaming robot21-24, automating the researcher rather than the instruments. This modular approach could be deployed in conventional laboratories for a range of research problems beyond photocatalysis.