Information security is a growing concern in our modern world, where almost everything can be done online. To protect security, classical encryption protocols, such as RSA, are used. These encryption protocols are almost always based on mathematical problems that are computational difficult. Therefore, the security is only valid under limited computational resources, and therefore do not provide provable security. An exception is the one-time pad protocol, which is provably secure but requires an existing shared key that is as long as the information it must encrypt. The exchange of such a key can be challenging, often requiring the two partied to physically meet to exchange the key or the use a trusted courier to physically carry the key on a hard drive. Quantum key distribution (QKD) offers a solution by utilizing quantum mechanics to grow a secure cryptographic key shared between two distant parties. The quantum nature of the particles used in the exchange ensure that any eavesdropper would leave signs of their presence, allowing the users to precisely quantify the security of the key that is produced. This allows secure generation of a random key which can be used for the one-time pad encryption protocol which, unlike most encryption protocols, does not rely on computational assumptions, and is secure regardless of available computational power. Current implementations of QKD are limited to a single link distance of ∼200 km, preventing implementation of QKD on a global scale, or even between distant cities within a single country, without some additional techniques. One promising solution is the use of orbiting low Earth orbit satellite platforms as trusted nodes in a quantum communications network. The main purpose of this work has been to show the viability of this solution, trough theoretical performance modeling and experimental demonstrations. Thorough numerical simulations have been developed to evaluate the performance and challenges in implementing QKD using a low Earth orbit satellite platform and determine an optimal approach to its implementation. The simulations include a realistic satellite orbit analysis, all expected loss mechanisms, estimates of background contributions and realistic simulation of quantum optical processes. This work addresses the questions of optimal wavelength and beam waist, the effect of the telescope design and pointing error, and the impact of detector degradation due to exposure to radiation in the space

A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience.

In this paper, we present a novel approach to extract complex and overlapping temporally correlated features directly from spike-based dynamic vision sensors. A spiking neural network capable of performing multilayer unsupervised learning through Spike-Timing-Dependent Plasticity is introduced. It shows exceptional performances at detecting cars passing on a freeway recorded with a dynamic vision sensor, after only 10 minutes of fully unsupervised learning. Our methodology is thoroughly explained and first applied to a simpler example of ball trajectory learning. Two unsupervised learning strategies are investigated for advanced features learning. Robustness of our network to synaptic and neuron variability is assessed and virtual immunity to noise and jitter is demonstrated.

Silence, please! Single-wall carbon nanotubes (NTs) are produced as a mixture of metals and semiconductors. Metallic NTs can be selectively derivatized by diazonium, but the selectivity does not allow preservation of semiconductor NTs; therefore, diazonium was replaced by diazoesters. The diazoester-coupled NTs could be used without any purification for the fabrication of NT-field-effect transistors (see graphic). Detailed facts of importance to specialist readers are published as ”Supporting Information”. Such documents are peer-reviewed, but not copy-edited or typeset. They are made available as submitted by the authors. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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Carbon nanotubes (CNTs) have exceptional physical properties that make them one of the most promising building blocks for future nanotechnologies. They may in particular play an important role in the development of innovative electronic devices in the fields of flexible electronics, ultra-high sensitivity sensors, high frequency electronics, opto-electronics, energy sources and nano-electromechanical systems (NEMS). Proofs of concept of several high performance devices already exist, usually at the single device level, but there remain many serious scientific issues to be solved before the viability of such routes can be evaluated. In particular, the main concern regards the controlled synthesis and positioning of nanotubes. In our opinion, truly innovative use of these nano-objects will come from: i) the combination of some of their complementary physical properties, such as combining their electrical and mechanical properties, ii) the combination of their properties with additional benefits coming from other molecules grafted on the nanotubes, and iii) the use of chemically- or bio-directed self-assembly processes to allow the efficient combination of several devices into functional arrays or circuits. In this article, we outline the main issues concerning the development of carbon nanotubes based electronics applications and review our recent results in the field.

Surface modification at the nanometer scale is a challenge for the future of molecular electronics. In particular, the precise anchoring and electrical addressing of biological scaffolds such as complex DNA nanonetworks is of importance for generating bio-directed assemblies of nano-objects for nanocircuit purposes. Herein, we consider the individual modification of nanoelectrodes with different oligonucleotide sequences by an electrochemically driven co-polymerization process of pyrrole and modified oligonucleotide sequences bearing pyrrole monomers. We demonstrate that this one-step technique presents the advantages of simplicity, localization of surface modification, mechanical, biological and chemical stability of the coatings, and high lateral resolution.

Carbon nanotubes are intrinsically high impedance objects. The high frequency (HF) characterization of these nano-objects is crucial for applications such as interconnects in future integrated circuits, but still represents a daunting challenge. This letter presents HF characterization of an individual metallic single walled carbon nanotube up to 7 GHz. The equivalent circuit values are directly extracted from these HF measurements without numerical procedure, thus proving that the intrinsic transport parameters of a single carbon nanotube can be determined up to gigahertz frequencies.

Thermal conduction across thin films is measured optically by using the surface thermal lensing effect. Pump-probe laser measurements combined with numerical modeling are used to study thermal conduction in a variety of materials as thin as 10 nm. The method is relatively simple, robust, rapid, and offers an alternative to current techniques. Thermal conductivity in gold films is found to drop from 300 to 100 W/Km when the film thickness is reduced from 2000 to 100 nm. Results for silver, tin and aluminum films are also presented and compared with results from other studies.

Resistance programming of carbon-nanotube-based optoelectronic memory devices at sub-microsecond speed is demonstrated. The use of silicon nanowires as programming electrodes allows study of the scaling rules of the programming speed and the charge-retention mechanism. Such fast and nonvolatile memory elements have potential for the development of adaptive circuits.

Using the high spatial resolution of focused laser beams, we probe the thermal properties of micrometersized single droplets and grains. Volumes as small as 0.5 nl are analyzed and possible improvements to picoliter are discussed. (C) 2010 Optical Society of America

We report here the first realization of an artificial branched DNA template where a single wall carbon nanotube is positioned with the necessary geometry of an individually gated field effect transistor.

A POM to remember: Hexanuclear Fe(III) polyoxometalate (POM) single-molecule magnets (see structure) can be noncovalently assembled on the surface of single-wall carbon nanotubes. Complementary characterization techniques (see TEM image and magnetic hysteresis loops) demonstrate the integrity and bistability of the individual molecules, which could be used to construct single-molecule memory devices.

While the purification of carbon nanotubes may be considered as an already sorted matter, their use in some highly demanding fields such as electrochemistry, biological studies or magnetism may be precluded by the remaining catalyst and carbonaceous impurities. For these purposes, the widely-used purification methods need to be improved. In this paper, a comprehensive study of the well-known gas-phase purification procedure of single wall nanotubes (SWNTs) is performed, which aims at depicting the nature and the amount of the remaining impurities, of both the catalyst and carbon types, and at getting insights into its mechanism. This has been achieved by cross-referencing data emerging from transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), electron diffraction, magnetization measurements, X-ray diffraction (XRD) and Raman spectroscopy. We demonstrate that this method can lead to the elimination of the largest part of the impurities while maintaining fairly good yields. According to the mechanistic picture we have drawn, we suggest some possible improvement to the procedure that should lead to fully purified SWNTs with limited subsequent losses.

This paper presents the high frequency performance of single-walled carbon nanotube (SWNT) field-effect transistors, with channel consisting of dense networks of high purity semiconducting SWNTs. Using SWNT samples containing 99% pure semiconducting SWNTs, we achieved operating frequencies above 80 GHz. This record frequency does not require aligned SWNTs, thus demonstrating the remarkable potential of random networks of sorted SWNTs for high frequency electronics.

On the tube: The coupling of diazonium ions onto single-walled carbon nanotubes is shown to proceed through a radical chain reaction by kinetic analysis of the absorption peak drop (see picture). Radical species are also revealed by ESR. Metallic (m) nanotubes play a special catalytic role in the functionalization of semiconducting (sc) nanotubes.Due to its simplicity and versatility, diazonium coupling is the most widely used method for carbon nanotube (CNT) functionalization to increase CNT processability and add new functionalities. Yet, its mechanism is so far mostly unknown. Herein, we use kinetic analysis to shed light on this complex mechanism. A free-radical chain reaction is revealed by absorption spectroscopy and ESR. Metallic CNTs are shown to play an unexpected catalytic role. The step determining the selectivity towards metallic CNTs is identified by a Hammett correlation. A mechanistic model is proposed that predicts reactivity and selectivity as a function of diazonium electrophilicity and metallic-to-semiconducting CNT ratio, thus opening perspectives of controlled high-yield functionalization and purification.

Hybrid Nano (e.g. Nanotube and Nanowire) /CMOS circuits combine both the advantages of Nano-devices and CMOS technologies; they have thus become the most promising candidates to relax the intrinsic drawbacks of CMOS circuits beyond Moore's law. A functional simulation model for an hybrid Nano/CMOS design is presented in this paper. It is based on Optically Gated Carbon NanoTube Field Effect Transistors (OG-CNTFET), which can be used as 2-terminal programmable resistors. Their resistance can be adjusted precisely, reproducibly and in a non-volatile way, over three orders of magnitude. These interesting behaviors of OG-CNTFET promise great potential for developing the non-volatile memory and neuromorphic adaptive computing circuits. The model is developed in Verilog-A language and implemented on Cadence Virtuoso platform with Spectre 5.1.41 simulator. Many experimental parameters are included in this model to improve the simulation accuracy.