Nanogrooved carbon microtubes for wet three‐dimensional printing of conductive composite structures

Recent advances in three‐dimensional (3D) printing have enabled the fabrication of interesting structures which are not achievable using traditional fabrication approaches. The 3D printing of carbon microtube composite inks allows fabrication of conductive structures for practical applications in soft robotics and tissue engineering. However, it is challenging to achieve 3D printed structures from solution‐based composite inks, which requires an additional process to solidify the ink. Here, we introduce a wet 3D printing technique which uses a coagulation bath to fabricate carbon microtube composite structures. We show that through a facile nanogrooving approach which introduces cavitation and channels on carbon microtubes, enhanced interfacial interactions with a chitosan polymer matrix are achieved. Consequently, the mechanical properties of the 3D printed composites improve when nanogrooved carbon microtubes are used, compared to untreated microtubes. We show that by carefully controlling the coagulation bath, extrusion pressure, printing distance and printed line distance, we can 3D print composite lattices which are composed of well‐defined and separated printed lines. The conductive composite 3D structures with highly customised design presented in this work provide a suitable platform for applications ranging from soft robotics to smart tissue engineering scaffolds. © 2019 Society of Chemical Industry     

Fire safe,sustainable loose furnishing

The aim of this exploratory study has been to investigate the fire properties and environmental aspects of different upholstery material combinations, mainly for domestic applications. An analysis of the sustainability and circularity of selected textiles, along with lifecycle assessment, is used to qualitatively evaluate materials from an environmental perspective. The cone calorimeter was the primary tool used to screen 20 different material combinations from a fire performance perspective. It was found that textile covers of conventional fibres such as wool, cotton and polyester, can be improved by blending them with fire resistant speciality fibres. A new three‐dimensional web structure has been examined as an alternative padding material, showing preliminary promising fire properties with regard to ignition time, heat release rates and smoke production.     

CuxNi1-xO nanostructures and their nanocomposites with reduced graphene oxide: Synthesis,characterization, and photocatalytic applications

NiO nanostructure was synthesized using a simple co-precipitation method and was embedded on reduced graphene oxide surface via ultrasonication. Structural investigations were made through X-ray diffraction (XRD) and functional groups were confirmed by Fourier transform infrared spectroscopy (FTIR). XRD analysis revealed the grain size reduction with doping. Fourier transform infrared spectroscopy confirmed the presence of metal-oxygen bond in pristine and doped NiO nanostructure as well as the presence of carbon containing groups. Scanning electron microscopy (SEM) indicated that the particle size decreased when NiO nanostructure was doped with copper. BET surface area was found to increase almost up to 43 m²/g for Cu doped NiO nanostructure/rGO composite. Current-voltage measurements were performed using two probe method. UV–Visible spectroscopic profiles showed the blue and red shift for Cu doped NiO nanostructure and Cu doped NiO Nanostructure/rGO composite respectively. Rate constant for Cu doped NiO nanostructure/rGO composite found to increase 4.4 times than pristine NiO nanostructure.     

Quality enhancement of the abundant under‐valued crustacean,lobster krill (Munida spp.), during its chilled storage

Lobster krill (Munida genus) represents an under‐valued crustacean frequently caught on European fishing banks. In this work, its sensory, microbiological and biochemical qualities were evaluated during chilled storage. Additionally, the effects of a prestorage antimelanosic treatment consisting of soaking in sodium metabisulphite (SMB) solutions at two different concentrations (0.25% and 0.75%) were also studied. SMB prestorage treatment provided lobster specimens that still exhibited acceptable sensory quality after 10 days of storage, while control specimens were unacceptable at that time. SMB treatment also resulted in a significant (P < 0.05) inhibition of microbial growth, mainly of Enterobacteriaceae, psychrotrophes and proteolytic bacteria. Low lipid oxidation levels were observed for all batches; however, a significantly higher (P < 0.05) retention of polyunsaturated fatty acids was found in SMB‐treated lobster, especially in the 0.75% SMB batch. The results presented here open the way to the potential commercialisation of currently under‐utilised lobster krill as a chilled product.     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.