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Summary Dispersity (Ɖ) can significantly affect polymer properties and is a key parameter in materials design. Here, we report a straightforward and versatile batch method based on reversible addition-fragmentation chain transfer (RAFT) polymerization to tailor the molecular weight distributions for a wide range of monomer classes, including acrylates, acrylamides, methacrylates, and styrene. In addition, our methodology is compatible with more challenging monomers, such as methacrylic acid, methyl vinyl ketone, and vinyl acetate. Control over Ɖ is achieved by mixing two RAFT agents with sufficiently different chain-transfer activities in various ratios, affording polymers with monomodal molecular weight distributions over a broad dispersity range (Ɖ ∼ 1.09–2.10). Our experimental findings were supported by simulations through the use of deterministic kinetic modeling, and our method was further demonstrated for the preparation of well-defined block co-polymers, regardless of the initial homopolymer dispersity.

Molecularly imprinted polymers (MIPs) can selectively bind target molecules and can therefore be advantageously used as a low-cost and robust alternative to replace fragile and expensive natural receptors. Yet, one major challenge in using MIPs for sensor development is the lack of simple and cost-effective techniques that allow firm fixation as well as controllable and consistent receptor material distribution on the sensor substrate. In this work, a convenient method is presented wherein microfluidic systems in conjunction with in situ photo-polymerization on functionalized diamond substrates are used. This novel strategy is simple, efficient, low-cost and less time consuming. Moreover, the approach ensures a tunable and consistent MIP material amount and distribution between different sensor substrates and thus a controllable active sensing surface. The obtained patterned MIP structures are successfully tested as a selective sensor platform to detect physiological concentrations of the hormone disruptor testosterone in buffer, urine and saliva using electrochemical impedance spectroscopy. The highest added testosterone concentration (500 nM) in buffer resulted in an impedance signal of 10.03 ± 0.19% and the lowest concentration (0.5 nM) led to a measurable signal of 1.8 ± 0.15% for the MIPs. With a detection limit of 0.5 nM, the MIP signals exhibited good linearity between a 0.5 nM and 20 nM concentration range. Apart from the excellent and selective recognition offered by these MIP structures, they are also stable during and after the dynamic sensor measurements. Additionally, the MIPs can be easily regenerated by a simple washing procedure and are successfully tested for their reusability.

A variety of polymerizations has long been performed in continuous flow reactors on an industrial scale; comparatively, on smaller scales, continuous polymerization methods have only gained significant attention in recent years. Yet, within the last decade, the field has moved from the rare occurrence of flow reactors to their abundant use today. A wide variety of polymer reactions have been performed in a continuous fashion on small and intermediate scales. The advantages of applying flow chemistry principles for polymer reactions include increased reproducibility and synthetic precision, significant increases in reaction performances for photochemical reactions, the ability to couple reactors to create complex materials in a single reactor pass, as well as the unique combination of online monitoring and machine learning. In this review we give a comprehensive overview of polymer reactions being carried out in continuous flow reactors to date. The development of the field is discussed, concluding with the most recent examples on automated polymer synthesis, reactor telescoping and nanoparticle synthesis. Finally, the design of flow reactors is discussed to help newcomers contribute to the current and future developments in the field.

The preparation of well-dispersed graphene/polymer nanocomposites is challenging due to the poor miscibility of graphene sheets in a polymer matrix. To enhance the interaction between both phases, graphene sheets can be decorated with polymer chains. Herein, different strategies to graft poly(methyl methacrylate) (PMMA) and poly(di(ethylene glycol) ethyl ether acrylate) (PDEGA) chains at various positions on graphene oxide and reduced graphene oxide (GO/rGO) sheets are compared. Chain attachment was achieved by “grafting-to” and “grafting-from” methods. Grafting-to was performed by classical copper (I)-catalyzed alkyne azide cycloaddition. Using a grafting-from approach, PMMA and PDEGA brushes were grown from GO and rGO sheets via surface-initiated photo-induced copper-mediated polymerization (SI-photoCMP). SI-photoCMP is a robust and efficient method that allows polymerizations to be carried out under mild conditions and with reduced catalyst concentration. Moreover, the successful implementation of SI-photoCMP in a continuous-flow set-up enables easy upscaling of the system and is, therefore, a more efficient and environmentally friendly process for GO/rGO surface modification. By using the grafting-to approach, the grafting density of PMMA (Mn = 2,600 g/mol) was one chain per 990 carbons of graphene. In contrast, longer PMMA chains (Mn = 40,300 g/mol) and higher grafting density were obtained via the grafting-from method (one PMMA chain per 140 carbons of graphene).

While methods to control dispersity of a molecular weight distribution are slowly emerging, the question remains how any arbitrary distribution, and more precisely distribution shape, can be created in a polymer sample. Ultimately, if the aim is to create any shape form, then this can only be achieved by blending individual polymers. In here, we describe an approach to first generate molecular weight distributions of any shape with selectable average molecular weight and dispersity. With such software tool at hand, we discuss the influences of shape and dispersity on the residual structure. Molecular weight distributions are often less intuitive than anticipated, and effects can be complex. In the next step we show how these generated distributions are automatically fitted to interpolated RAFT polymer distributions, providing a detailed instruction which polymers need to be blended in which quantity in order to obtain the desired additive distribution. The software tools required for this task are demonstrated and discussed, and are made available to the reader for download.