Copper-ion solid-state battery systems involving sulphonium iodide electrolytes
THE past 10 years has seen a considerable interest in solid-state battery systems involving electrolytes based on silver iodide1–3. Studies of electrolytes involving organic sulphonium iodides in conjunction with silver iodide have been reported3,5–9, but recently the possibility of using cuprous ions to replace silver ions as the mobile species has attracted attention, because of the potential advantage of lower cost and higher voltage. We report here the results of our investigations of sulphonium iodide–cuprous iodide electrolytes.
Transport studies of the copper solid superionic conductor CuPb2Br7
Thermoelectric power (S) and electrical conductivity (σ) of pressed pellets of CuPb2Br7 are studied in the temperature range 330 to 560 K. Copper is found to be the better electrode material for such studies. The density (dp) and σ depend upon pelletizing pressure (P) but constancy in their values is observed for pellets made at P ≧ 6 × 106 p cm−2 [5.88 × 108 Pa]. A sharp increase in the magnitude of σ followed by a change in the slope of log σ vs. T−1 plot is observed around 435 K, which corresponds to the phase transition temperature of the material from the normal ionic solid (NIS) to the superionic conducting (SIC) phase. The S vs. T−1 plot shows only a change in slope around the phase transition temperature of 435 K. The plots of log σ vs. T−1 are linear excepting around the phase transition temperature. From thermoelectric power data the heats of transport are found to be 0.42 and 0.33 eV as against the activation energies of 0.44 and 0.34 eV determined from the electrical conductivity data in NIS and SIC phases, respectively. Comparison of the results in the SIC phase to existing theoretical models shows that the data on this material support the extended lattice gas model. 
 
 
 
Thermospannung (S) und elektrische Leitfahigkeit (σ) von gepresten CuPb2Br7-Pellets werden im Temperaturbereich zwischen 330 und 560 K untersucht. Es wird gefunden, das Kupfer das bessere Elektrodenmaterial fur derartige Untersuchungen ist. Die Dichte (dp) und σ hangen von dem Pelletierungsdruck (P) ab, jedoch werden konstante Werte fur Pellets gefunden, die bei P ≧ 6 × 106 p cm−2 [5.88 × 108 Pa] hergestellt wurden. Ein steiler Anstieg der Grose von σ gefolgt von einer Anstiegsanderung der log σ vs. T−1. Kurve wird bei etwa 435 K beobachtet, was mit der Phasenubergangstemperatur des Materials von der normalen Ionenfestkorper(NlS)- zur Superionenleiter (SIC)-Phase ubereinstimmt. Die σ-T−1-Kurve zeigt nur eine Anstiegsanderung bei der Phasenubergangstemperatur 435 K. Die Darstellungen log σ uber T−1 sind linear mit Ausnahme des Bereichs der Phasenubergangstemperatur. Aus den Werten der Thermospannung wird die Transportwarme zu 042 und 0,33 eV bestimmt, gegenuber den Aktivierungsenergien von 0,44 und 0,34 eV, bestimmt aus elektrischen Leitfahigkeitsdaten in der NIS-bzw. SIC-Phase. Ein Vergleich der Ergebnisse der SIC-Phase mit existierenden theoretischen Modellen zeigt, das die Daten uber dieses Material das Modell des ausgedehnten Gittergases bestatigen.
A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization
This paper proposes an approach for distribution system load forecasting, which aims to provide highly accurate short-term load forecasting with high resolution utilizing a support vector regression (SVR) based forecaster and a two-step hybrid parameters optimization method. Specifically, because the load profiles in distribution systems contain abrupt deviations, a data normalization is designed as the pretreatment for the collected historical load data. Then an SVR model is trained by the load data to forecast the future load. For better performance of SVR, a two-step hybrid optimization algorithm is proposed to determine the best parameters. In the first step of the hybrid optimization algorithm, a designed grid traverse algorithm (GTA) is used to narrow the parameters searching area from a global to local space. In the second step, based on the result of the GTA, particle swarm optimization is used to determine the best parameters in the local parameter space. After the best parameters are determined, the SVR model is used to forecast the short-term load deviation in the distribution system. The performance of the proposed approach is compared to some classic methods in later sections of this paper.