Potentiometric determination of the gibbs energies of formation of lead aluminates

The Gibbs energies of formation of three compounds in the PbOAl2O3 system—2PbO·Al2O3, PbOAl2O3 , and PbO· 6A1203—have been determined from potentiometric measurements on reversible solid‐state galvanic cells Pt, Ir I Pb,αAl2O3, PbO ·6A1203 I ZrO2CaO I NiO, Ni I Pt Pt I NiO, Ni I ZrO2CaO I Pb, PbO·6A1203, PbO· A1203 I It, Pt and Pt I NiO, Ni I ZrO2CaO I Pb, PbO·A12O3, 2PbO·Al2O3 It, Pt in the temperature range 850–1375 K. The results are discussed in the light of reported phase diagrams for the PbOA1203 system. The partial pressures of different lead oxide species, PbnOn, n = 1−6, in the gas phase in equilibrium with the aluminates are calculated by combining the results of this study with the mass‐spectrometric data of Drowart et al.(1) for polymerization equilibria in the gas phase. The concentration of oxygen in lead in equilibrium with the aluminates are also derived from the results and the literature data on the Gibbs energy of solution of oxygen in liquid lead

Single Gate P-N Junctions in Graphene-Ferroelectric Devices

Graphene's linear dispersion relation and the attendant implications for bipolar electronics applications have motivated a range of experimental efforts aimed at producing p-n junctions in graphene. Here we report electrical transport measurements of graphene p-n junctions formed via simple modifications to a PbZr$_{0.2}$Ti$_{0.8}$O$_3$ substrate, combined with a self-assembled layer of ambient environmental dopants. We show that the substrate configuration controls the local doping region, and that the p-n junction behavior can be controlled with a single gate. Finally, we show that the ferroelectric substrate induces a hysteresis in the environmental doping which can be utilized to activate and deactivate the doping, yielding an `on-demand' p-n junction in graphene controlled by a single, universal backgate

Chaotic exploration and learning of locomotion behaviours

We present a general and fully dynamic neural system, which exploits intrinsic chaotic dynamics, for the real-time goal-directed exploration and learning of the possible locomotion patterns of an articulated robot of an arbitrary morphology in an unknown environment. The controller is modeled as a network of neural oscillators that are initially coupled only through physical embodiment, and goal-directed exploration of coordinated motor patterns is achieved by chaotic search using adaptive bifurcation. The phase space of the indirectly coupled neural-body-environment system contains multiple transient or permanent self-organized dynamics, each of which is a candidate for a locomotion behavior. The adaptive bifurcation enables the system orbit to wander through various phase-coordinated states, using its intrinsic chaotic dynamics as a driving force, and stabilizes on to one of the states matching the given goal criteria. In order to improve the sustainability of useful transient patterns, sensory homeostasis has been introduced, which results in an increased diversity of motor outputs, thus achieving multiscale exploration. A rhythmic pattern discovered by this process is memorized and sustained by changing the wiring between initially disconnected oscillators using an adaptive synchronization method. Our results show that the novel neurorobotic system is able to create and learn multiple locomotion behaviors for a wide range of body configurations and physical environments and can readapt in realtime after sustaining damage