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Bi2Te3-based compounds are important near room temperature thermoelectric materials with commercial applications in thermoelectric modules. However, new routes leading to improved thermoelectric performance are highly desirable. Incorporation of superparamagnetic nanoparticles was recently proposed as a means to promote the thermoelectric properties of materials, but its feasibility has rarely been examined in mainstream thermoelectric materials. In this study, high quality single-crystalline Bi2Te2.7Se0.3 nanoplates and Ni nanoparticles were successfully synthesized by solvothermal and thermal decomposition methods, respectively. Bulk nanocomposites consisting of Bi2Te2.7Se0.3 nanoplates and superparamagnetic Ni nanoparticles were prepared by spark plasma sintering. It was found that incorporation of Ni nanoparticles simultaneously increased the carrier concentration and provided additional scattering centers, which resulted in enlarged electric conductivities and Seebeck coefficients. The greatly improved ZT was achieved due to the increase in power factor. Spark plasma sintered bulk nanocomposites of Bi2Te2.7Se0.3 nanoplates incorporated by 0.4 mol %Ni nanoparticles (in molar ratio) showed a figure-of-merit ZT of 0.66 at 425 K, equivalent to 43% increase when compared to pure Bi2Te2.7Se0.3 nanoplates. The results revealed that incorporation of magnetic nanoparticles could be an effective approach for promoting the thermoelectric performance of conventional semiconductors.

Thermal-assistant is considered potentially as an effective approach to improve machinability of hard and brittle materials. Understanding the material removal and friction behaviour influenced by the purposely introduced heat is crucial to obtain high quality machined surface. This paper aims to reveal material removal and friction behaviours of RB-SiC ceramics scratched by a Vickers indenter at elevated temperatures. Material removal mode, scratching hardness, critical depth of ductile-brittle transition, scratching force and friction were discussed under different penetration depths. Size effect of scratching hardness was used to assess the plastic deformation at elevated temperature. A modified model was established to predict the critical depth at elevated temperatures by taking into account of the changes of mechanical properties. The results revealed that the material deformation and adhesive behaviour enhanced the material removal in ductile regime and the coefficient of friction at elevated temperatures.

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With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models.

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Pb-based group-IV chalcogenides including PbTe and PbSe have been extensively studied as high performance thermoelectric materials during the past few decades. However, the toxicity of Pb inhibits their applications in vast fields due to the serious harm to the environment. Recently the Pb-free group-IV chalcogenides have become an extensive research subject as promising thermoelectric materials because of their unique thermal and electronic transport properties as well as the enviromentally friendly advantage. This paper briefly summarizes the recent research advances in Sn-, Ge-, and Sichalcogenides thermoelectrics, showing the unexceptionally high thermoelectric performance in SnSe single crystal, and the significant improvement in thermoelectric performance for those polycrystalline materials by successfully modulating the electronic and thermal transport through using some well-developed strategies including band engineering, nanostructuring and defect engineering. In addition, some important issues for future device applications, including N-type doping and mechanical and chemical stabilities of the new thermoelectrics, are also discussed.