On the Electronic Transport Mechanism in Conducting Polymer Nanofibers

Here, we present theoretical analysis of electron transport in polyaniline based (PANi) nanofibers assuming the metalic state of the material. To build up this theory we treat conducting polymers as a special kind of granular metals, and we apply the quantum theory of conduction in mesoscopic systems to describe the transport between metallic-like granules. Our results show that the concept of resonance electron tunneling as the predominating mechanism providing charge transport between the grains is supported with recent experiments on the electrical characterization of single PANi nanofibers. By contacting the proposed theory with the experimental data we estimate some important parameters characterizing the electron transport in these materials. Also, we discuss the origin of rectifying features observed in current-voltage characteristics of fibers with varying cross-sectional areas.Comment: 5 pages, 1 figure, accepted for publication in Phys. Rev. B, Vol.72, xxxx (2005

Predictive-State Decoders: Encoding the Future into Recurrent Networks

Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement training loss. RNNs are used to model dynamic processes that are characterized by underlying latent states whose form is often unknown, precluding its analytic representation inside an RNN. In the Predictive-State Representation (PSR) literature, latent state processes are modeled by an internal state representation that directly models the distribution of future observations, and most recent work in this area has relied on explicitly representing and targeting sufficient statistics of this probability distribution. We seek to combine the advantages of RNNs and PSRs by augmenting existing state-of-the-art recurrent neural networks with Predictive-State Decoders (PSDs), which add supervision to the network's internal state representation to target predicting future observations. Predictive-State Decoders are simple to implement and easily incorporated into existing training pipelines via additional loss regularization. We demonstrate the effectiveness of PSDs with experimental results in three different domains: probabilistic filtering, Imitation Learning, and Reinforcement Learning. In each, our method improves statistical performance of state-of-the-art recurrent baselines and does so with fewer iterations and less data.Comment: NIPS 201

Temperature dependent charge transport mechanisms in carbon sphere/polymer composites

Carbon spheres (CS) with diameters in the range $2 - 10 \mu m$ were prepared via hydrolysis of a sucrose solution at $200^o C,$ and later annealed in $N_2$ at $800^o C.$ The spheres were highly conducting but difficult to process into thin films or pressed pellets. In our previous work, composite samples of CS and the insulating polymer polyethylene oxide (PEO) were prepared and their charge transport was analyzed in the temperature range $80 K < T < 300 K.$ Here, we analyze charge transport in CS coated with a thin polyaniline (PANi) film doped with hydrochloric acid (HCl), in the same temperature range. The goal is to study charge transport in the CS using a conducting polymer (PANi) as a binder and compare with that occurring at CS/PEO. A conductivity maxima was observed in the CS/PEO composite but was absent in CS/PANi. Our data analysis shows that variable range hopping of electrons between polymeric chains in PANi-filled gaps between CS takes on a predominant part in transport through CS/PANi composites, whereas in CS/PEO composites, electrons travel through gaps between CS solely by means of direct tunneling. This difference in transport mechanisms results in different temperature dependences of the conductivity.Comment: 7 pages, 6 figure

Controlled Doping of Graphene Using Ultraviolet Irradiation

The electronic properties of graphene are tunable via doping, making it attractive in low dimensional organic electronics. Common methods of doping graphene, however, adversely affect charge mobility and degrade device performance. We demonstrate a facile shadow mask technique of defining electrodes on graphene grown by chemical vapor deposition (CVD) thereby eliminating the use of detrimental chemicals needed in the corresponding lithographic process. Further, we report on the controlled, effective, and reversible doping of graphene via ultraviolet (UV) irradiation with minimal impact on charge mobility. The change in charge concentration saturates at ~2 x 1012cm-2 and the quantum yield is 10-5 e/photon upon initial UV exposure. This simple and controlled strategy opens the possibility of doping wafer-size CVD graphene for diverse applications

Electrospun Hybrid Organic/Inorganic Semiconductor Schottky Nanodiode

We report on a simple method to fabricate, under ambient conditions and within seconds, Schottky nanodiodes using electrospun polyaniline nanofibers and an inorganic n-doped semiconductor. In addition to being a rectifier, the advantage of our design is the complete exposure of the rectifying nanojunction to the surrounding environment, making them attractive candidates in the potential fabrication of low power, supersensitive, and rapid response sensors as well. The diode parameters were calculated assuming the standard thermionic emission model of a Schottky junction, and the use of this junction as a gas sensor was examined

Field Effect Transistor Behavior in Electrospun Polyaniline/Polyethylene Oxide Nanofibers

Novel transistors and logic devices based on nanotechnology concepts are under intense development. The potential for ultra-low-power circuitry makes nanotechnology attractive for applications such as digital electronics and sensors. For NASA applications, nanotechnology offers tremendous opportunities for increased onboard data processing, and thus autonomous decision-making ability, and novel sensors that detect and respond to environmental stimuli with little oversight requirements. Polyaniline (PANi) is an intriguing material because its electrical conductivity can be changed from insulating to metallic by varying the doping levels and conformations of the polymer chain, and when combined with polyethylene oxide (PEO), can be formed into nanofibers with diameters ranging from approximately 50 to 500 nm (depending on the deposition conditions). The initial goal of this work was to demonstrate transistor behavior in these nanofibers, thus creating a foundation for future logic devices

Graphene Based Reversible Nano-Switch/Sensor Schottky Diode (NANOSSSD) Device

A nanostructure device is provided and performs dual functions as a nano-switching/sensing device. The nanostructure device includes a doped semiconducting substrate, an insulating layer disposed on the doped semiconducting substrate, an electrode formed on the insulating layer, and at least one layer of graphene formed on the electrode. The at least one layer of graphene provides an electrical connection between the electrode and the substrate and is the electroactive element in the device