Electrochemical metal 3D printing

Additive manufacturing (AM) is the process of creating 3D objects from digital models through the layer-by-layer deposition of materials. Electrochemical additive manufacturing (ECAM) is a relatively new technique which can create metallic components-based on depositing layers of metal onto the surface of the conductive substrate through the reduction of metal ions. It is advantageous compared to other metal AM processes due to the absence of high temperature processes enabling a lower-cost and safer fabrication process, however, to date, all of the presented ECAM methods (Localized Electrochemical Deposition (LED) and Meniscus Confined Electrochemical Deposition (MCED) have been designed to achieve micro or nanoscale structures with limited deposition rates, and only focused on single material fabrication. Furthermore, all the printed structures are limited in the complexity of geometries, with the majority being wire-based architectures of porous and rough morphologies, with limited characterisation of the properties of the printed structures. Additionally, there is no available system able to create temperature-reactive multi-metallic functional 4D structures and no research has been presented on the potential application of ECAM in the field of electrochemical energy storage devices. To bridge the gaps, this thesis investigates the development of a low-cost ECAM system capable of producing single and multi-metal structures by using multi-meniscus confined extrusion heads with volumetric deposition rates 3 times higher than what has previously been reported (~ 2×104 μm3.s-1), enabling large-scale fabrication of complex structures in multiple metallic materials. Scanning electron microscopy, X-ray computed tomography and energy dispersive X-ray spectroscopy measurements confirm that multi-metallic structures can be successfully created, with a tightly bound interface. Analysis of the thermo-mechanical properties of the printed strips shows that mechanical deformations can be generated in Cu-Ni strips at temperatures up to 300 °C, which is due to the thermal expansion coefficient mismatch generating internal stresses in the printed structures. Electrical conductivity measurements show that the bimetallic structures have a conductivity between those of nanocrystalline copper and nickel. Vicker’s hardness tests, show that there is a clear correlation between the applied potential and the hardness of the printed product, with higher potentials resulting in a harder deposition. This increased hardness was found to be due to the smaller grain sizes produced during higher potential deposition which restricted dislocation movement through the material. Finally, this thesis presents the first reported combination of electrochemical 3D printing and electrospinning for building a high mass loading and high performance copper-fibre based supercapacitor which enables the potential to create more integrated electrodes and eventually to enhance the performance of supercapacitors. The results highlight the influence of the substrate conditioning and the resulting effects on the wetting characteristics of the meniscus and the subsequent distribution of the deposition which impacts the electronic conductivity of the overall electrode. In this the fibre-based supercapacitor was constructed, the carbon was doped with manganese oxides to enhance the capacitance through introducing pseudo-capacitance at the cost of electronic conductivity. With the printing of current collectors, a highly bound electrode-current collector interface was formed, reducing the interfacial resistance and enhancing the accessible capacitance at high scan rates. In summary, this thesis presents work towards creating lower cost metal additive manufacturing through the development of an electrochemical metal 3D printer. A meniscus confined approach was taken to localise the deposition, with subsequent microstructural, mechanical and spectroscopic analysis of the printed product. Novel contributions to the field were further presented through developing understanding around multi-metal ECAM, with investigations around their coupled thermo-mechanical properties. Finally, the applicability of this approach was investigated in the field of electrochemical devices, where the influence of a porous substrate was investigated, whereby tightly bound and highly conductive current collectors were printed onto fibre based supercapacitors, enhancing their accessible capacitance. This work, therefore, demonstrates the potential for the ECAM approach in a diversity of applications.Open Acces

Data collection and transmission for leisure time boats : based on Arduino WSNs and LTE

There has been an astonishing research development in the field of wireless sensor networks (WSNs) in the last decade. A large number of low power capacity devices have been implemented in different vehicles, where sensor nodes act as a team to monitor the environment and forecast the potential defects. In this thesis, we aim to design a data collection system using a WSN on a leisure boat in order to monitor and maintain the boat after sale. The designed system aims to collect data from different sensors on board using WSNs and transmits the collected data to a remote server through cellular network. For the WSNs part, we select a low-power driven Adruino Lilypad as a controller and a XBee interface as transceiver for each sensor node in order to provide a reliable data collection mechanism with a low amount of power consumption. Furthermore, to upload the collected data to a remote server, we adopt a 3G/LTE cellular network for the long range wireless communication. We utilize a PandaBoard as a gateway to connect the WSN and the 3G/LTE network. The designed network is implemented and tested in a lab scenario at university and on a Marex boat along the coast

Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.Comment: 6 page

An Automatic Ship Detection Method Based on Local Gray-Level Gathering Characteristics in SAR Imagery

This paper proposes an automatic ship detection method based on gray-level gathering characteristics of synthetic aperture radar (SAR) imagery. The method does not require any prior knowledge about ships and background observation. It uses a novel local gray-level gathering degree (LGGD) to characterize the spatial intensity distribution of SAR image, and then an adaptive-like LGGD thresholding and filtering scheme to detect ship targets. Experiments on real SAR images with varying sea clutter backgrounds and multiple targets situation have been conducted. The performance analysis confirms that the proposed method works well in various circumstances with high detection rate, fast detection speed and perfect shape preservation

CRISPR/Cas9-mediated gene manipulation to create single-amino-acid-substituted and floxed mice with a cloning-free method.

Clustered regulatory interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology is a powerful tool to manipulate the genome with extraordinary simplicity and speed. To generate genetically modified animals, CRISPR/Cas9-mediated genome editing is typically accomplished by microinjection of a mixture of Cas9 DNA/mRNA and single-guide RNA (sgRNA) into zygotes. However, sgRNAs used for this approach require manipulation via molecular cloning as well as in vitro transcription. Beyond these complexities, most mutants obtained with this traditional approach are genetically mosaic, yielding several types of cells with different genetic mutations. Recently, a growing body of studies has utilized commercially available Cas9 protein together with sgRNA and a targeting construct to introduce desired mutations. Here, we report a cloning-free method to target the mouse genome by pronuclear injection of a commercial Cas9 protein:crRNA:tracrRNA:single-strand oligodeoxynucleotide (ssODN) complex into mouse zygotes. As illustration of this method, we report the successful generation of global gene-knockout, single-amino-acid-substituted, as well as floxed mice that can be used for conditional gene-targeting. These models were produced with high efficiency to generate non-mosaic mutant mice with a high germline transmission rate