The conductive polymer complex poly (3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS) is the most explored conductive polymer for conductive textiles applications. Since PEDOT:PSS is readily available in water dispersion form, it is convenient for roll-to-roll processing which is compatible with the current textile processing applications. In this work, we have made a comprehensive review on the PEDOT:PSS-based conductive textiles, methods of application onto textiles and their applications. The conductivity of PEDOT:PSS can be enhanced by several orders of magnitude using processing agents. However, neat PEDOT:PSS lacks flexibility and strechability for wearable electronics applications. One way to improve the mechanical flexibility of conductive polymers is making a composite with commodity polymers such as polyurethane which have high flexibility and stretchability. The conductive polymer composites also increase attachment of the conductive polymer to the textile, thereby increasing durability to washing and mechanical actions. Pure PEDOT:PSS conductive fibers have been produced by solution spinning or electrospinning methods. Application of PEDOT:PSS can be carried out by polymerization of the monomer on the fabric, coating/dyeing and printing methods. PEDOT:PSS-based conductive textiles have been used for the development of sensors, actuators, antenna, interconnections, energy harvesting, and storage devices. In this review, the application methods of PEDOT:SS-based conductive polymers in/on to a textile substrate structure and their application thereof are discussed.

Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced.

Background Manual microscopic examination of Leishman/Giemsa stained thin and thick blood smear is still the “gold standard” for malaria diagnosis. One of the drawbacks of this method is that its accuracy, consistency, and diagnosis speed depend on microscopists’ diagnostic and technical skills. It is difficult to get highly skilled microscopists in remote areas of developing countries. To alleviate this problem, in this paper, we propose to investigate state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from microscopic image of thick blood slides. Results YOLOV3 and YOLOV4 models, which are state-of-the-art object detectors in accuracy and speed, are not optimized for detecting small objects such as malaria parasites in microscopic images. We modify these models by increasing feature scale and adding more detection layers to enhance their capability of detecting small objects without notably decreasing detection speed. We propose one modified YOLOV4 model, called YOLOV4-MOD and two modified models of YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. Besides, new anchor box sizes are generated using K-means clustering algorithm to exploit the potential of these models in small object detection. The performance of the modified YOLOV3 and YOLOV4 models were evaluated on a publicly available malaria dataset. These models have achieved state-of-the-art accuracy by exceeding performance of their original versions, Faster R-CNN, and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. YOLOV4-MOD has achieved the best detection accuracy among all the other models with a mAP of 96.32%. YOLOV3-MOD2 and YOLOV3-MOD1 have achieved mAP of 96.14% and 95.46%, respectively. Conclusions The experimental results of this study demonstrate that performance of modified YOLOV3 and YOLOV4 models are highly promising for detecting malaria parasites from images captured by a smartphone camera over the microscope eyepiece. The proposed system is suitable for deployment in low-resource setting areas.

In the last three decades, the development of new kinds of textiles, so-called smart and interactive textiles, has continued unabated. Smart textile materials and their applications are set to drastically boom as the demand for these textiles has been increasing by the emergence of new fibers, new fabrics, and innovative processing technologies. Moreover, people are eagerly demanding washable, flexible, lightweight, and robust e-textiles. These features depend on the properties of the starting material, the post-treatment, and the integration techniques. In this work, a comprehensive review has been conducted on the integration techniques of conductive materials in and onto a textile structure. The review showed that an e-textile can be developed by applying a conductive component on the surface of a textile substrate via plating, printing, coating, and other surface techniques, or by producing a textile substrate from metals and inherently conductive polymers via the creation of fibers and construction of yarns and fabrics with these. In addition, conductive filament fibers or yarns can be also integrated into conventional textile substrates during the fabrication like braiding, weaving, and knitting or as a post-fabrication of the textile fabric via embroidering. Additionally, layer-by-layer 3D printing of the entire smart textile components is possible, and the concept of 4D could play a significant role in advancing the status of smart textiles to a new level.

In this work, we have successfully produced a conductive and stretchable knitted cotton fabric by screen printing of poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) and poly(dimethylsiloxane-b-ethylene oxide)(PDMS-b-PEO) conductive polymer composite. It was observed that the mechanical and electrical properties highly depend on the proportion of the polymers, which opens a new window to produce PEDOT:PSS-based conductive fabric with distinctive properties for different application areas. The bending length analysis proved that the flexural rigidity was lower with higher PDMS-b-PEO to PEDOT:PSS ratio while tensile strength was increased. The SEM test showed that the smoothness of the fabric was better when PDMS-b-PEO is added compared to PEDOT:PSS alone. Fabrics with electrical resistance from 24.8 to 90.8 kΩ/sq have been obtained by varying the PDMS-b-PEO to PEDOT:PSS ratio. Moreover, the resistance increased with extension and washing. However, the change in surface resistance drops linearly at higher PDMS-b-PEO to PEDOT:PSS ratio. The conductive fabrics were used to construct textile-based strain, moisture and biopotential sensors depending upon their respective surface resistance.