We present here the spontaneous formation of nanoporous sheets in aqueous solution without a template to guide them, which is based on the self-assembly of dumbbell-shaped rod amphiphiles driven by a fine balance of anisotropic hydrophobic interactions, and steric constraints endowed with bulky dendritic wedges. These planar nets transform into closed 2D sheets when the rod segment increases in length. Such a unique arrangement of rigid rod building blocks might provide a new strategy for the design of nanoporous materials simultaneously with biological and electro-optical functions.

Donuts anyone? Molecules based on a meta‐linked aromatic segment self‐assemble into hexameric macrocycles that, in turn, stack on top of each other to from elongated tubular helical suprastructures. The formed tubules dissociate into discrete stacks of toroids in response to the addition of silver salt (see picture).

No abstract

As a result of advances in skin imaging technology and the development of suitable image processing techniques during the last decade, there has been a significant increase of interest in the computer-aided diagnosis of melanoma. Automated border detection is one of the most important steps in this procedure, since the accuracy of the subsequent steps crucially depends on it. In this paper, a fast and unsupervised approach to border detection in dermoscopy images of pigmented skin lesions based on the Statistical Region Merging algorithm is presented. The method is tested on a set of 90 dermoscopy images. The border detection error is quantified by a metric in which a set of dermatologist-determined borders is used as the ground-truth. The proposed method is compared to six state-of-the-art automated methods (optimized histogram thresholding, orientation-sensitive fuzzy c-means, gradient vector flow snakes, dermatologist-like tumor extraction algorithm, meanshift clustering, and the modified JSEG method) and borders determined by a second dermatologist. The results demonstrate that the presented method achieves both fast and accurate border detection in dermoscopy images.