Designing sensing materials with novel morphologies and compositions is eminently challenging to achieve high-performance gas sensor devices. Herein, an in situ oxidative polymerization approach is developed to construct three-dimensional (3D) hollow quasi-graphite capsules/polyaniline (GCs/PANI) hierarchical hybrids by decorating protonated PANI on the surface of GCs; as a result, an immensely active and sensitive material was developed for sensing ammonia gas at room temperature. Moreover, the GCs possessed a capsule-like hollow/open structure with partially graphitized walls, and PANI nanospheres were uniformly decorated on the GC surfaces. Furthermore, the inflexible and rigid 3D ordered chemistry of these materials provides the resulting hybrids with a large interfacial surface area, which not only allows for rapid adsorption and charge transfer but also provides the necessary structural stability. The 3D hollow GCs/PANI hybrids exhibit excellent performance; the GCs/PANI-3 hybrid is highly sensitive (with a response value of 1.30) toward 10 ppm NH 3 gas and has short response and recovery times of 34 and 42 s, respectively. The GCs/PANI-3 hybrid also demonstrates a good selectivity, repeatability, and long-term stability, which are attributed to the substantial synergistic effect of the GCs and PANI. The design of such a unique 3D ordered framework provides a promising pathway to achieve room-temperature gas sensors for commercial applications.

No abstract

The Matlab code is available upon request and the SOS data can be downloaded from http://www.weizmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his website, http://www.che.udel.edu/systems/people/zak.

We have established Kirchhoff's first law in genetic networks and introduced a concept of gene flows using matrix decomposition method. The Kirchhoff's first law provides the theoretical foundations for mathematical framework for development defining network-based regulatory pathways, and applying convex analysis in decomposing the genetic networks into regulatory extreme pathways. We presented a new approach to characterize the extreme pathway and developed a new algorithm for identifying a set of extreme pathways. Convex analysis and extreme pathway structure provide a unified framework for functional and structural analysis of metabolic and genetic networks, which will increase our ability to analyze, interpret and predict the function of metabolic and genetic networks. The proposed models for network-based regulatory pathway analysis have been applied to apoptosis regulatory network.

No abstract