Mechanical, thermal and colorimetric properties of LLDPE coloured with a blue nanopigment and conventional blue pigments

Purpose – This research deals with a new kind of nanopigment, obtained from the combination of organic dyes and layered nanoclays, that the authors call nanoclay-colorant pigment (NCP). Whilst they have already been employed in inks and coatings, to date these nanopigments have not been used as pigments for polymers. The existing lack of knowledge surrounding them must be redressed in order to bridge the gap between current academic studies and commercial exploitation. Therefore, the main purpose of this paper is to examine the hitherto unknown aspects of the NCP, which relate specifically to their applicability as a new type of colorant for polymers. Design/methodology/approach – A blue NCP has been prepared at the laboratory according to the patented method of synthesis (patent WO0104216), using methylene blue and montmorillonite nanoclay. It has then been applied to a thermoplastic polymer (linear low-density polyethylene – LLDPE) to obtain a coloured sample. Furthermore, samples with the same polymer but using conventional blue colorants have been prepared under the same processing conditions. The mechanical, thermal and colorimetric properties of these materials have been compared. Findings – The thermal stability of the sample coloured with NCP is reduced to some extent, while the mechanical strength is slightly increased. Moreover, this sample has better colour performance than the conventionally pigmented samples. Originality/value – In this paper, a blue NCP has been synthesised and successfully employed with polyethylene and the obtained sample shows better colour performance than polyethylene with conventional pigments.

An improvement of a SLAM RGB-D method with movement prediction derived from a study of visual features

This paper presents a method for the fast calculation of a robot’s egomotion using visual features. The method is part of a complete system for automatic map building and Simultaneous Location and Mapping (SLAM). The method uses optical flow to determine whether the robot has undergone a movement. If so, some visual features that do not satisfy several criteria are deleted, and then egomotion is calculated. Thus, the proposed method improves the efficiency of the whole process because not all the data is processed. We use a state-of-the-art algorithm (TORO) to rectify the map and solve the SLAM problem. Additionally, a study of different visual detectors and descriptors has been conducted to identify which of them are more suitable for the SLAM problem. Finally, a navigation method is described using the map obtained from the SLAM solution.