No abstract

Abstract:We describe the preparation of nanoporous carbon nanofibers (CNFs) decorated with platinum nanoparticles (PtNPs) in this work by electrospining polyacrylonitrile (PAN) nanofibers and subsequent carbonization and binding of PtNPs. The fabricated nanoporous CNF-PtNP hybrids were further utilized to modify glass carbon electrodes and used for the non-enzymatic amperometric biosensor for the highly sensitive detection of hydrogen peroxide (H2O2). The morphologies of the fabricated nanoporous CNF-PtNP hybrids were observed by scanning electron microscopy, transmission electron microscopy, and their structure was further investigated with Brunauer-Emmett-Teller (BET) surface area analysis, X-ray photoelectron spectroscopy, X-ray diffraction, and Raman spectrum. The cyclic voltammetry experiments indicate that CNF-PtNP modified electrodes have high electrocatalytic activity toward H2O2 and the chronoamperometry measurements illustrate that the fabricated biosensor has a high sensitivity for detecting H2O2. We anticipate that the strategies utilized in this work will not only guide the further design and fabrication of functional nanofiber-based biomaterials and nanodevices, but also extend the potential applications in energy storage, cytology, and tissue engineering. OPEN ACCESSNanomaterials 2015, 5 1892

No abstract

Incorporating prior knowledge into recurrent neural network (RNN) is of great importance for many natural language processing tasks. However, most of the prior knowledge is in the form of structured knowledge and is difficult to be exploited in the existing RNN framework. By extracting the logic rules from the structured knowledge and embedding the extracted logic rule into the RNN, this paper proposes an effective framework to incorporate the prior information in the RNN models. First, we demonstrate that commonly used prior knowledge could be decomposed into a set of logic rules, including the knowledge graph, social graph, and syntactic dependence. Second, we present a technique to embed a set of logic rules into the RNN by the way of feedback masks. Finally, we apply the proposed approach to the sentiment classification and named entity recognition task. The extensive experimental results verify the effectiveness of the embedding approach. The encouraging results suggest that the proposed approach has the potential for applications in other NLP tasks.

No abstract