Nomenclature r radius, mm angle of a single thermoelectric leg, degree half of the angle between two legs, degreeHigh performance and thermal stress analysis of a segmented annular thermoelectric generator

Effective thermal management of photovoltaic cells is essential for improving its conversion efficiency and increasing its life span. Solar cell temperature and efficiency have an inverse relationship therefore, cooling of solar cells is a critical research objective which numerous researchers have paid attention to. Among the widely adopted thermal management techniques is the use of thermoelectric generators to enhance the performance of photovoltaics. Photovoltaic cells can convert the ultra-violent and visible regions of the solar spectrum into electrical energy directly while thermoelectric modules utilize the infrared region to generate electrical energy. Consequently, the combination of photovoltaic and thermoelectric generators would enable the utilization of a wider solar spectrum. In addition, the combination of both systems has the potential to provide enhanced performance due to the compensating effects of both systems. The waste heat produced from the photovoltaic can be used by the thermoelectric generator to produce additional energy thereby increasing the overall power output and efficiency of the hybrid system. However, the integration of both systems is complex because of their opposing characteristics thus, effective coupling of both systems is essential. This review presents the concepts of photovoltaics and thermoelectric energy conversion, research focus areas in the hybrid systems, applications of such systems, discussion of the most recent research accomplishments and recommendations for future research. All the essential elements and research areas in hybrid photovoltaic/thermoelectric generator are discussed in detailed therefore, this review would serve as a valuable reference literature.

Preventing a world energy crisis is one of the most important tasks of the 21st century due to the significant rate at which the world's energy demand is growing because of population growth and industrialization [1]. In addition, the excessive use of the depleting fossil fuels has caused global warming and environmental pollution thus, a viable solution is to promote the use of renewable and clean energy [2,3]. Renewable energy sources offer several advantages such as; sustainability, low pollution and economic benefits. Therefore, an increased amount of research on the development and application of renewable energy sources is being carried out as a solution to reduce the over reliance on fossil fuels [4,5]. Thermoelectric (TE) device is a viable clean energy solution that can convert thermal energy into electrical energy when a temperature gradient is present via the Seebeck effect. While a reverse phenomenon called Peltier effect enables the TE device to generate thermal energy from electrical energy [6]. A thermoelectric generator (TEG) can be used to convert waste heat into electricity thereby, reducing the use of fossil fuel. Compared with other waste heat recovery technologies, thermoelectric generators offer several advantages including gas free emissions, solid-state operation, no noise, maintenance free operation, no moving parts, vast scalability, long periods of reliable operation and zero environmental pollution [7,8]. These unique advantages have increased the application of TEG for power generation in space and military applications [9], wearable sensors [10][11][12][13], waste heat recovery [14-18], wireless sensor network [19,20], micro-cogeneration [21] and marine engine application [22].

Grasp classification using data gloves can enable therapists to monitor patients efficiently by providing concise information about the activities performed by these patients. Although, classical machine learning algorithms have been applied in grasp classification, they require manual feature extraction to achieve high accuracy. In contrast, convolutional neural networks (CNNs) have outperformed popular machine learning algorithms in several classification scenarios because of their ability to extract features automatically from raw data. However, they have not been implemented on grasp classification using a data glove. In this study, we apply a CNN in grasp classification using a piezoresistive textile data glove knitted from conductive yarn and an elastomeric yarn. The data glove was used to collect data from five participants who grasped thirty objects each following Schlesinger's taxonomy. We investigate a CNN's performance in two scenarios where the validation objects are known and unknown. Our results show that a simple CNN architecture outperformed k-nn, Gaussian SVM, and Decision Tree algorithms in both scenarios in terms of the classification accuracy.

Weft knitted conductive fabrics can act as excellent textile strain sensors for human motion capture. The loop architecture dictates the overall electrical properties of weft knit strain sensors. Therefore, research into loop architecture is relevant for comprehensively investigating the design space of e-textile sensors. There are three main types of knit stitches, Knitted loop stitch, Miss stitch, and Tuck stitch. Nevertheless, most of the research into weft knit strain sensors has largely focused on fabrics with only knitted loop stitches. Miss and tuck stitches will affect the contact points in the sensor and, consequently, its piezoresistivity. Therefore, this paper investigates the impact of incorporating miss and tuck stitches on the piezoresistivity of a weft knit sensor. Particularly, the electromechanical models of a miss stitch and a tuck stitch in a weft knit sensor are proposed. These models were used in order to develop loop configurations of sensors that consist of various percentages of miss or tuck stitches. Subsequently, the developed loop configurations were simulated while using LTspice and MATLAB software; and, verified experimentally through a tensile test. The experimental results closely agree with the simulated results. Furthermore, the results reveal that increases in the percentage of tuck or miss stitches in weft knit sensor decrease the initial and average resistance of the sensor. In addition, it was observed that, although the piezoresistivity of a sensor with tuck or miss stitches is best characterised as a quadratic polynomial, increases in the percentage of tuck stitches in the sensor increase the linearity of the sensor’s piezoresistivity.