Characterisation of locust bean gum with asymmetric flow field-flow fractionation (AF4) and light scattering.

We present a detailed characterisation of locust bean gum (LBG), an industrially significant galactomannan, utilising asymmetric flow field-flow fractionation (AF4) and light scattering. Molecular weight and size determination of galactomannans is complicated by their tendency to aggregate, even in dilute solutions; AF4 allows us to confirm the presence of aggregates, separate these from well-dispersed polymer, and characterise both fractions. For the dispersed polymer, we find Mw=9.2×105 g mol-1 and Rg,z=82.1 nm; the distribution follows Flory scaling (Rg∼Mν) with ν∼ 0.63, indicating good solvent conditions. The aggregate fraction exhibited radii of up to 1000 nm and masses of up to 3×1010 g mol-1. Furthermore, we demonstrate how both fractions are influenced by changes to filtration procedure and solvent conditions. Notably, a 200 nm nylon membrane effectively removes the aggregated fraction; we present a concentration-dependent investigation of solutions following this protocol, using static and dynamic light scattering, which reveals additional weak aggregation in these unfractionated samples. Overall, we demonstrate that AF4 is highly suited to LBG characterisation, providing structural information for both well-dispersed and aggregated fractions, and expect the methods employed to apply similarly to other galactomannans and associating polymer systems.

Multi-Task Deep Learning for Image Segmentation Using Recursive Approximation Tasks.

Fully supervised deep neural networks for segmentation usually require a massive amount of pixel-level labels which are manually expensive to create. In this work, we develop a multi-task learning method to relax this constraint. We regard the segmentation problem as a sequence of approximation subproblems that are recursively defined and in increasing levels of approximation accuracy. The subproblems are handled by a framework that consists of 1) a segmentation task that learns from pixel-level ground truth segmentation masks of a small fraction of the images, 2) a recursive approximation task that conducts partial object regions learning and data-driven mask evolution starting from partial masks of each object instance, and 3) other problem oriented auxiliary tasks that are trained with sparse annotations and promote the learning of dedicated features. Most training images are only labeled by (rough) partial masks, which do not contain exact object boundaries, rather than by their full segmentation masks. During the training phase, the approximation task learns the statistics of these partial masks, and the partial regions are recursively increased towards object boundaries aided by the learned information from the segmentation task in a fully data-driven fashion. The network is trained on an extremely small amount of precisely segmented images and a large set of coarse labels. Annotations can thus be obtained in a cheap way. We demonstrate the efficiency of our approach in three applications with microscopy images and ultrasound images.

Time-Resolved Tomographic Quantification of the Microstructural Evolution of Ice Cream.

Ice cream is a complex multi-phase colloidal soft-solid and its three-dimensional microstructure plays a critical role in determining the oral sensory experience or mouthfeel. Using in-line phase contrast synchrotron X-ray tomography, we capture the rapid evolution of the ice cream microstructure during heat shock conditions in situ and operando, on a time scale of minutes. The further evolution of the ice cream microstructure during storage and abuse was captured using ex situ tomography on a time scale of days. The morphology of the ice crystals and unfrozen matrix during these thermal cycles was quantified as an indicator for the texture and oral sensory perception. Our results reveal that the coarsening is due to both Ostwald ripening and physical agglomeration, enhancing our understanding of the microstructural evolution of ice cream during both manufacturing and storage. The microstructural evolution of this complex material was quantified, providing new insights into the behavior of soft-solids and semi-solids, including many foodstuffs, and invaluable data to both inform and validate models of their processing.

A microfluidic-based method for the transfer of biopolymer particles from an oil phase to an aqueous phase.

Biopolymer microgels produced in microfluidic devices via the formation of a water-in-oil emulsion are usually collected at the outlet of the device and thoroughly washed from the oil phase in an additional, lengthy processing step. This paper reports a microfluidic-based method which allows for continuous on-chip manufacture of aqueous-based biopolymer microparticles in an oily continuous phase and thereafter the transfer of these particles from the oily carrier phase to a second aqueous continuous phase. This was achieved by surface patterning the PDMS channel walls using UV polymerization of poly(acrylic acid) (PAA) in order to obtain a hybrid device with distinct hydrophilic and hydrophobic sections. The surface patterning was stable for at least 4 months. This selective surface patterning of the channel was shown to initiate and assist the transfer of the biopolymer particles from the oil phase into the aqueous phase. The flow conditions required for a stable biphasic flow in the transfer section of the device were evaluated based on the theoretical shear stress at the interface of the two fluids. Experimental outcomes were found to be in good agreement with the prediction. After the particles cross the liquid-liquid interface and are transferred into the aqueous phase, they are collected and characterized. The resulting suspension was found to be stable for several weeks and no aggregation was observed.

The effect of PEO block lengths on the size and stability of complex coacervate core micelles.

We report on a series of polyion complexes from mixtures of poly(ethylene oxide)-block-poly(N,N-diethylaminoethylmethacrylate) (PEO-PDEAMA) and poly(ethylene oxide)-block-poly(aspartic acid) (PEO-PAsp). As expected, the micelle size, polydispersity and stability are dependant on the relative and absolute lengths of the polyelectrolyte chains. However, we also demonstrate that whilst the length of the charged polyelectrolyte blocks is important, the length of the PEO chains is an equally relevant variable in determining both the size and stability of the final micelles as well as the degree of charge neutralisation at which micellisation occurs. We also show that the kinetics of formation can result in very different stability of the final micelles.