Nanotechnology is the exploitation of the unique properties of materials at the nanoscale. Nanotechnology has gained popularity in several industries, as it offers better built and smarter products. The application of nanotechnology in medicine and healthcare is referred to as nanomedicine, and it has been used to combat some of the most common diseases, including cardiovascular diseases and cancer. The present review provides an overview of the recent advances of nanotechnology in the aspects of imaging and drug delivery.

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Nanotechnology and its use in imaging and drug delivery (Review)

A nanoscale, solid-state physically evolving network is experimentally demonstrated, based on the self-organization of Ag nanoclusters under an electric field. The adaptive nature of the network is determined by the collective inputs from multiple terminals and allows the emulation of heterosynaptic plasticity, an important learning rule in biological systems. These effects are universally observed in devices based on different switching materials.

Memristive Physically Evolving Networks Enabling the Emulation of Heterosynaptic Plasticity.

Microfabrication and nanotechnology in manufacturing system – An overview

Nanomedicine: Photo-activated nanostructured titanium dioxide, as a promising anticancer agent.

The structural and electrical evolution of chemical vapor deposition grown graphene by electron beam irradiation induced disorder

The efficiency of conventional techniques used to harvest energy in nuclear reactors lies around 35%. This limit exists, because the nuclear energy is converted to electrical energy via heat engines. We study an alternative approach where the kinetic energy of nuclear reaction products is directly converted into electric energy in a stack of charged capacitors with a gap size of 500 nm and graphene electrodes. Graphene is expected to be chemically and mechanically stable in high-radiation environments, because its tensile strength of 130 GPa is very large, about 100 times larger than most metals. The dielectric strength of such nanocapacitors exceeds 1 GV/m, because avalanching is suppressed at small gap sizes. In a 1 GV/m electric field charged nuclear reaction products, such as 5.6 MeV alpha particles, come to rest in of a stack with 5000 nanocapacitors. We show that during the deceleration process more than 90% of kinetic energy of charged nuclear reaction products is converted to electric energy and stored as electric energy in the stack. Each stack is 2.5-mm thick and produces a high-voltage DC current. A device with a 1-Ci241Am source is expected to generate 22 mW of electric power.

Nuclear energy conversion with stacks of graphene nanocapacitors

Interacting electrical conductors self-assemble to form tree like networks in the presence of applied voltages or currents. Experiments have shown that the degree distribution of the steady state networks are identical over a wide range of network sizes. In this work we develop a new model of the self-assembly process starting from the underlying physical interaction between conductors. In agreement with experimental results we find that for steady state networks, our model predicts that the fraction of endpoints is a constant of 0.252, and the fraction of branch points is 0.237. We find that our model predicts that these scaling properties also hold for the network during the approach to the steady state as well. In addition, we also reproduce the experimental distribution of nodes with a given Strahler number for all steady state networks studied.

/pdf/topological-properties-of-a-self-assembled-electrical-2um9fpxpax.pdf

Topological properties of a self-assembled electrical network via ab initio calculation.

Physically Evolving Networks (PENs) were first proposed by Alan Turing in his 1948 paper “Intelligent Machines” [1]. PENs capture some important features of the information processing of biological neural systems, such as mimicking animal and human error patterns, and implement massively parallel, non-algorithmic processes, where the sequence and concurrency of operations is determined at run time. PENs simulations on conventional digital computers have been successfully used for speech recognition, image analysis, and adaptive control. But PEN implementations on computers with von Neumann architecture are slow compared to other algorithms which are optimized for the sequential processing of explicit instructions. In the following we discuss hardware implementations of PENs. Would it be possible to fabricate a PEN of the size of a human brain with a billion times more neurons? And if so, how could such a PEN be programmed or trained?

Fabrication and programming of large physically evolving networks

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Nuclear energy conversion with stacks of graphene nanocapacitors

Topological properties of a self-assembled electrical network via ab initio calculation.

Fabrication and programming of large physically evolving networks