Influence of temperature, ripening time and calcination on the morphology and crystallinity of hydroxyapatite nanoparticles

Nano-sized hydroxyapatite (HA) particles were prepared by chemical precipitation through aqueous solutions of calcium chloride and ammonium hydrogenphosphate. The influence of temperature, ripening time and calcination on the crystallinity and morphology of the HA nanoparticles were investigated. It was found that the crystallinity and crystallite size increased with the increase of synthetic temperature and ripening time. XRD and TEM results showed that the morphology change of HA nanoparticles was related to their crystallinity. High crystallinity of HA led to regular shape and smooth surface of the nanoparticles. The crystallinity of HA powders increased greatly after calcination at 650 C for 6 h but the change of the crystallite size after calcination was dependent on the crystallinity and crystallite size of ‘‘as prepared’’ HA nanoparticles

Fabrication of Insulation Coatings on Additively Manufactured CuCrZr Electrical Windings

To lower the ac losses in electrical machines, additive manufacturing (AM) has been adopted to exploit the geometrical freedom in winding design. However, AM brings about new challenges such as surface roughness and porosity which can create difficulties for post processing of the windings such as applying insulation coatings. The article investigates the influence of surface roughness (profile) of AM-processed CuCrZr as a potential candidate for electrical windings in terms of geometry, surface roughness, porosity, and oxidation on their insulation. The feasibility and characteristics of insulations applied via three processing techniques namely powder, spray, and dip coating are compared. The entire process is quantified via techniques such as computed tomography, surface profilometry, optical microscopy, X-ray photon spectroscopy, and breakdown voltage (BV) at different stages of the coating process. The study also includes coating on a commercial rectangular copper wire as a reference. The initial assessment of coatings concludes that surface roughness and the coating process are both vital determinants for the success of insulating AM components. Basic surface smoothening is needed to get rid of burs and the spray coating technique was the best among others for its capability to produce conformal coating

Microwave-enhanced densification of sol-gel alumina films

Alumina films prepared by the sol-gel method were sintered at 1160 °C and 1200 °C using a 2.45 GHz microwave / conventional hybrid furnace in order to study the influence of microwave power on the sintering process and resultant samples. Experiments were designed to ensure that each series of samples underwent an identical thermal history in terms of temperature / time profiles. Sintering was carried out using three different heating approaches: pure conventional heating and hybrid heating with 600 W and 1000 W of microwave radiation, respectively. The results obtained showed that, compared with pure conventional heating, the presence of the microwave field led to higher sintered densities and crystallinity in the samples, indicating that the microwave field enhanced the sintering of the sol-gel alumina films and supporting the existence of the microwave effect

Generalising Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses matching specific photo instance using free-hand sketch as a query modality. Existing models aim to learn an embedding space in which sketch and photo can be directly compared. While successful, they require instance-level pairing within each coarse-grained category as annotated training data. Since the learned embedding space is domain-specific, these models do not generalise well across categories. This limits the practical applicability of FGSBIR. In this paper, we identify cross-category generalisation for FG-SBIR as a domain generalisation problem, and propose the first solution. Our key contribution is a novel unsupervised learning approach to model a universal manifold of prototypical visual sketch traits. This manifold can then be used to paramaterise the learning of a sketch/photo representation. Model adaptation to novel categories then becomes automatic via embedding the novel sketch in the manifold and updating the representation and retrieval function accordingly. Experiments on the two largest FG-SBIR datasets, Sketchy and QMUL-Shoe-V2, demonstrate the efficacy of our approach in enabling crosscategory generalisation of FG-SBIR