Characterization of diblock copolymer order-order transitions in semidilute aqueous solution using fluorescence correlation spectroscopy

The temperature and pH-dependent diffusion of poly(glycerol monomethacrylate)-block-poly(2-hydroxypropyl methacrylate) nanoparticles prepared via polymerization-induced self-assembly in water is characterized using fluorescence correlation spectroscopy (FCS). Lowering the solution temperature or raising the solution pH induces a worm-to-sphere transition and hence an increase in diffusion coefficient by a factor of between four and eight. FCS enables morphological transitions to be monitored at relatively high copolymer concentrations (10% w/w) compared to those required for dynamic light scattering (0.1% w/w). This is important because such transitions are reversible at the former concentration, whereas they are irreversible at the latter. Furthermore, the FCS data suggest that the thermal transition takes place over a very narrow temperature range (less than 2 °C). These results demonstrate the application of FCS to characterize order-order transitions, as opposed to order-disorder transitions. The temperature and pH-dependent diffusion of poly(glycerol monomethacrylate)-block-poly(2-hydroxypropylmethacrylate)- nanoparticles in water is characterized using fluorescence correlation spectroscopy. Lowering either the solution temperature or pH induces a worm-to-sphere transition and hence an increase in diffusion coefficient by a factor of between four and eight

Highly Tunable Nanostructures in a Doubly pH-Responsive Pentablock Terpolymer in Solution and in Thin Films

Multiblock copolymers with charged blocks are complex systems that show great potential for enhancing the structural control of block copolymers. A pentablock terpolymer PMMA-b-PDMAEMA-b-P2VP-b-PDMAEMA-b-PMMA is investigated. It contains two types of midblocks, which are weak cationic polyelectrolytes, namely poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA) and poly(2-vinylpyridine) (P2VP). Furthermore, these are end-capped with short hydrophobic poly(methyl methacrylate) (PMMA) blocks in dilute aqueous solution and thin films. The self-assembly behavior depends on the degrees of ionization α of the P2VP and PDMAEMA blocks, which are altered in a wide range by varying the pH value. High degrees of ionization of both blocks prevent structure formation, whereas microphase-separated nanostructures form for a partially charged and uncharged state. While in solutions, the nanostructure formation is governed by the dependence of the P2VP block solubility of the and the flexibility of the PDMAEMA blocks on α, in thin films, the dependence of the segregation strength on α is key. Furthermore, the solution state plays a crucial role in the film formation during spin-coating. Overall, both the mixing behavior of the 3 types of blocks and the block sequence, governing the bridging behavior, result in strong variations of the nanostructures and their repeat distances

Soft matter roadmap

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts