Sandwich nanoarchitecture of LiV3O8/graphene multilayer nanomembranes via layer-by-layer self-assembly for long-cycle-life lithium-ion battery cathodes

A lack of suitable high-performance cathode materials has become the major barrier to their applications in future advanced communication equipment and electric vehicle power systems. In this paper, we have developed a layer-by-layer self-assembly approach for fabricating a novel sandwich nanoarchitecture of multilayered LiV₃O₈ nanoparticle/graphene nanosheet (M-nLVO/GN) hybrid electrodes for potential use in high performance lithium ion batteries by using a porous Ni foam as a substrate. The prepared sandwich nanoarchitecture of M-nLVO/GN hybrid electrodes exhibited high performance as a cathode material for lithium-ion batteries, such as high reversible specific capacity (235 mA h g^-1 at a current density of 0.3 A g^-1), high coulombic efficiency (over 98%), fast rate capability (up to a current density of 10 A g^-1), and superior capacity retention during cycling (90% capacity retention with a current density of 0.3 A g^-1 after 300 cycles). Very significantly, this novel insight into the design and synthesis of sandwich nanoarchitecture would extend their application to various electrochemical energy storage devices, such as fuel cells and supercapacitors.

Structural reinforcement of microvascular networks using electrostatic layer-by-layer assembly with halloysite nanotubes

We demonstrate a method for tailoring local mechanical properties near channel surfaces of vascular structural polymers in order to achieve high structural performance in microvascular systems. While synthetic vascularized materials have been created by a variety of manufacturing techniques, unreinforced microchannels act as stress concentrators and lead to the initiation of premature failure. Taking inspiration from biological tissues such as dentin and bone, these mechanical deficiencies can be mitigated by complex hierarchical structural features near to channel surfaces. By employing electrostatic layer-by-layer assembly (ELbL) to deposit films containing halloysite nanotubes onto scaffold surfaces followed by matrix infiltration and scaffold removal, we are able to controllably deposit nanoscale reinforcement onto 200 micron diameter channel surface interiors in microvascular networks. High resolution strain measurements on reinforced networks under load verify that the halloysite reduces strain concentrations and improves mechanical performance.

Surface modification of LiV3O8 nanosheets via layer-by-layer self-assembly for high-performance rechargeable lithium batteries

In this work, an economical route based on hydrothermal and layer-by-layer (LBL) self-assembly processes has been developed to synthesize unique Al ₂O₃-modified LiV₃O₈ nanosheets, comprising a core of LiV₃O₈ nanosheets and a thin Al ₂O₃ nanolayer. The thickness of the Al₂O ₃ nanolayer can be tuned by altering the LBL cycles. When evaluated for their lithium-storage properties, the 1 LBL Al₂O ₃-modified LiV₃O₈ nanosheets exhibit a high discharge capacity of 191 mA h g^-1 at 300 mA g^-1 (1C) over 200 cycles and excellent rate capability, demonstrating that enhanced physical and/or chemical properties can be achieved through proper surface modification. © 2014 Elsevier B.V. All rights reserved.

Porous Materials with Tunable Structure and Mechanical Properties via Templated Layer-by-Layer Assembly

The deposition of stiff and strong coatings onto porous templates offers a novel strategy for fabricating macroscale materials with controlled architectures at the micro- and nanoscale. Here, layer-by-layer assembly is utilized to fabricate nanocomposite-coated foams with highly customizable properties by depositing polymer–nanoclay coatings onto open-cell foam templates. The compressive mechanical behavior of these materials evolves in a predictable manner that is qualitatively captured by scaling laws for the mechanical properties of cellular materials. The observed and predicted properties span a remarkable range of density-stiffness space, extending from regions of very soft elastomer foams to very stiff, lightweight honeycomb and lattice materials.

Encapsulation of phase change materials using rice-husk-char

This paper explored a new approach to prepare phase change microcapsules using carbon-based particles via Pickering emulsions for energy storage applications. Rice-husk-char, a by-product in biofuel production, containing 53.58 wt% of carbon was used as a model carbon-based material to encapsulate hexadecane. As a model phase change material, hexadecane was emulsified in aqueous suspensions of rice-husk-char nanoparticles. Water soluble polymers poly(diallyldimethyl-ammonium chloride) and poly(sodium styrene sulfonate) were used to fix the rice-husk-char nanoparticles on the emulsion droplets through layer-by-layer assembly to enhance the structural stability of the microcapsules. The microcapsules formed are composed of a thin shell encompassing a large core consisting of hexadecane. Thermal gravimetrical and differential scanning calorimeter analyses showed the phase change enthalpy of 80.9 kJ kg−1 or 120.0 MJ m−3. Design criteria of phase change microcapsules and preparation considerations were discussed in terms of desired applications. This work demonstrated possible utilisations of biomass-originated carbon-based material for thermal energy recovery and storage applications, which can be a new route of carbon capture and utilisation.

A combined experimental and theoretical study of the generation of palladium clusters on Au(111) with a scanning tunnelling microscope

Palladium clusters have been deposited on the surface of a Au(111) electrode with the tip of a scanning tunnelling microscope. The distance over which the tip was moved towards the surface has a decisive influence on the properties of the clusters: the larger this distance, the larger the generated clusters, and the more stable they are. These findings are supported by computer simulations, which further suggest that the larger clusters contain a sizable amount of gold, which enhances their stability. Dissolution of the clusters occurs from the edges rather than layer by layer.

Generation of palladium clusters on Au(111) electrodes: Experiments and simulations

The properties of palladium clusters, generated with the electrochemical scanning tunneling microscope, have been investigated both by experiments and by computer simulations. The clusters are found to be larger and more stable if the tip is moved further towards the electrode surface in the generation process. The simulations suggest that the larger clusters consist of a palladium - gold mixture, which is more stable than pure palladium. Dissolution of the clusters occurs from the edges rather than layer by layer

A k-round Elimination Contention Scheme for WLANs

currently in press. This is the first published attempt to engineer QoS into a contention-based MAC layer protocol. The work was based on a cross-layer approach to providing programmability into wireless LANs. The work arose from an EPSRC grant in the "programmable networks" call, with Philips / STM research in Italy (Dr Melpignano). Subsequent follow-on includes the formation of a spin-out company (TOM) based on the idea.

Computationally efficient sequential learning algorithms for direct link resource-allocating networks

Computionally efficient sequential learning algorithms are developed for direct-link resource-allocating networks (DRANs). These are achieved by decomposing existing recursive training algorithms on a layer by layer and neuron by neuron basis. This allows network weights to be updated in an efficient parallel manner and facilitates the implementation of minimal update extensions that yield a significant reduction in computation load per iteration compared to existing sequential learning methods employed in resource-allocation network (RAN) and minimal RAN (MRAN) approaches. The new algorithms, which also incorporate a pruning strategy to control network growth, are evaluated on three different system identification benchmark problems and shown to outperform existing methods both in terms of training error convergence and computational efficiency. (c) 2005 Elsevier B.V. All rights reserved.