Understanding the role of restructuring in flocculation: The application of a population balance model

The effect of shear on floc properties was observed through population balance to comprehend the mechanisms of flocculation, in particular the role of restructuring. Little fundamental attention has been given before to the shear influence responsible for creating compact aggregates, while the floc characteristics might differ in other conditions. It is crucial to understand how aggregates evolve to steady state, if their properties are to be ‘tailored’ to suit subsequent solid-liquid separation. From a previous experimental study (Langmuir 18(6) (2002) 1974), restructuring was observed to occur extensively in the flocculation of latex particles in couette-flow, and was proposed to be responsible for the decrease in floc size on their transition to equilibrium. On the other hand, flocs of larger primary particles were more susceptible to breakage, with densification occurring as a result of fragmentation and re-aggregation. Denser flocs were found when structural deformation dominated, particularly in the initial stage of the process, while comparatively tenuous ones were observed when formation and breakage kinetics were the governing mechanisms. The distinct manners in which aggregates of different primary particle sizes evolved with time, were replicated with a population balance that incorporated the floc structural variation; verifying that restructuring indeed played a crucial role under certain flocculation conditions.     

From Images to Shape Models for Object Detection

We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes).     

The Geometry and Matching of Lines and Curves Over Multiple Views

This paper describes the geometry of imaged curves in two and three views. Multi-view relationships are developed for lines, conics and non-algebraic curves. The new relationships focus on determining the plane of the curve in a projective reconstruction, and in particular using the homography induced by this plane for transfer from one image to another. It is shown that given the fundamental matrix between two views, and images of the curve in each view, then the plane of a conic may be determined up to a two fold ambiguity, but local curvature of a curve uniquely determines the plane. It is then shown that given the trifocal tensor between three views, this plane defines a homography map which may be used to transfer a conic or the curvature from two views to a third. Simple expressions are developed for the plane and homography in each case.A set of algorithms are then described for automatically matching individual line segments and curves between images. The algorithms use both photometric information and the multiple view geometric relationships. For image pairs the homography facilitates the computation of a neighbourhood cross-correlation based matching score for putative line/curve correspondences. For image triplets cross-correlation matching scores are used in conjunction with line/curve transfer based on the trifocal geometry to disambiguate matches. Algorithms are developed for both short and wide baselines. The algorithms are robust to deficiencies in the segment extraction and partial occlusion.Experimental results are given for image pairs and triplets, for varying motions between views, and for different scene types. The methods are applicable to line/curve matching in stereo and trinocular rigs, and as a starting point for line/curve matching through monocular image sequences.     

Minimising non-selective defects in ultrathin reduced graphene oxide membranes with graphene quantum dots for enhanced water and NaCl separation

Reduced graphene oxide (rGO) membranes have been intensively evaluated for desalination and ionic sieving applications,benefiting from their stable and well-confined interlayer channels.However,rGO membranes generally suffer from low permeability due to the high transport resistance resulting from the narrowed two-dimensional (2D) channels.Although high permeability can be realized by reducing membrane thickness,membrane selectivity normally declines because of the formation of non-selective defects,in particular pinholes.In this study,we demonstrate that the non-selective defects in ultrathin rGO membranes can be effectively minimised by a facile posttreatment via surface-deposition of graphene quantum dots (GQDs).The resultant GQDs/rGO membranes obtained a good trade-off between water permeance (14 L·m-2·h-1·MPa-1) and NaCl rejection (91％).This work provides new insights into the design of high quality ultrathin 2D laminar membranes for desalination,molecular/ionic sieving and other separation applications.