Developing a thermoelectric generator(TEG) with shape conformable geometry for sustaining low-thermal impedance and large temperature gradient (∆ ) is fundamental for wearable and multi-scale energy harvesting applications. Here we demonstrate a flexible architectural design, with efficient thin film thermoelectric generator as a solution for this problem. This approach not only decreases the thermal impedance but also multiplies the temperature gradient, thereby increasing the power conversion efficiency (PCE) as comparable to bulk TEG. Intact thin films of Tin telluride (p-type) and Lead Telluride (n-type) are deposited on flexible substrate through physical vapor deposition and a thermoelectric module possessing a maximum output power density of 8.4mW/cm 2 is fabricated. We have demonstrated the performance of p-SnTe/n-PbTe based TEG as a flexible wearable power source for electronic gadgets, as a thermal touch sensor for real-time switching and temperature monitoring for exoskeleton applications.

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merit is used to characterize the material performance which is expressed as zT ( ) 2 2

Air pollution has emerged as an imminent issue in modern society. Prediction of pollutant levels is an important research topic in atmospheric environment today. For fulfilling such prediction, the use of neural network (NN), and in particular the multi-layer perceptrons, has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks, such as: 1) very slow convergence, and 2) easily getting stuck in a local minimum. In this paper, a newly developed method, particle swarm optimization (PSO) model, is adopted to train perceptrons, to predict pollutant levels, and as a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective by predicting some real air-quality problems.