Stability issues of dye solar cells

The thesis discusses dye solar cells (DSCs) which are emerging as a potential candidate for many applications. The goal of the work was to find more stable and higher performing materials for flexible DSCs, improve understanding of the effects on the DSC stability, and to develop experimental methods that give improved resolution of the degradation mechanisms. First an intensive critical literature review was done to highlight the important degradation mechanisms in DSCs. It was concluded that techniques giving chemical information are needed to understand the degradation reactions and their effect on electrical performance. It would be advantageous to have methods that enable monitoring chemical changes in operating DSCs, or periodically over their lifetime during accelerated ageing tests. Here the focus was on new and advanced in-situ methods that allow continuous study of the aging of the cells. In this regard, optical techniques such as Raman spectroscopy, newly introduced image processing method and recently introduced segmented cell method were employed to bridge the link between the chemical changes in the DSCs and the standard PV measurement methods. Here for instance the image processing was demonstrated to study the bleaching of electrolyte under ultraviolet and visible light at 85°C. The results obtained with the image processing method and the standard electrical measurements were in agreement and showed that the bleaching of electrolyte was initiated by TiO2 and slowed down by the presence of the dye. For the roll-to-roll production of DSCs cheap, flexible and stable substrates are required. In this work, a series of metals i.e. StS 304, StS 321, StS 316, StS 316L and Ti were successfully stabilized at the CE of a DSC by using a sputtered Pt catalyst layer that doubled also as a corrosion blocking layer. This work was an important step forward towards stable flexible DSCs. Finally, the degradation due to the manufacturing step related to the electrolyte filling in the DSC was studied. With the help of recently introduced segmented cell method, it was found the nanoporous film of TiO2 was acting as filter for some of the commonly used electrolyte additives i.e. tBP and NMBI. This resulted in spatial performance variation in the DSC which lead to significant losses in the overall performance (here up to 35 % losses in the up-scaling) and thus it has important implications for large area DSCs

Technologies and solutions for location-based services in smart cities: past, present, and future

Location-based services (LBS) in smart cities have drastically altered the way cities operate, giving a new dimension to the life of citizens. LBS rely on location of a device, where proximity estimation remains at its core. The applications of LBS range from social networking and marketing to vehicle-toeverything communications. In many of these applications, there is an increasing need and trend to learn the physical distance between nearby devices. This paper elaborates upon the current needs of proximity estimation in LBS and compares them against the available Localization and Proximity (LP) finding technologies (LP technologies in short). These technologies are compared for their accuracies and performance based on various different parameters, including latency, energy consumption, security, complexity, and throughput. Hereafter, a classification of these technologies, based on various different smart city applications, is presented. Finally, we discuss some emerging LP technologies that enable proximity estimation in LBS and present some future research areas

Cell degradation detection based on an inter-cell approach

Fault management is a crucial part of cellular network management systems. The status of the base stations is usually monitored by well-defined key performance indicators (KPIs). The approaches for cell degradation detection are based on either intra-cell or inter-cell analysis of the KPIs. In intra-cell analysis, KPI profiles are built based on their local history data whereas in inter-cell analysis, KPIs of one cell are compared with the corresponding KPIs of the other cells. In this work, we argue in favor of the inter-cell approach and apply a degradation detection method that is able to detect a sleeping cell that could be difficult to observe using traditional intra-cell methods. We demonstrate its use for detecting emulated degradations among performance data recorded from a live LTE network. The method can be integrated in current systems because it can operate using existing KPIs without any major modification to the network infrastructure

A Marketplace for Efficient and Secure Caching for IoT Applications in 5G Networks

As the communication industry is progressing towards fifth generation (5G) of cellular networks, the traffic it carries is also shifting from high data rate traffic from cellular users to a mixture of high data rate and low data rate traffic from Internet of Things (IoT) applications. Moreover, the need to efficiently access Internet data is also increasing across 5G networks. Caching contents at the network edge is considered as a promising approach to reduce the delivery time. In this paper, we propose a marketplace for providing a number of caching options for a broad range of applications. In addition, we propose a security scheme to secure the caching contents with a simultaneous potential of reducing the duplicate contents from the caching server by dividing a file into smaller chunks. We model different caching scenarios in NS-3 and present the performance evaluation of our proposal in terms of latency and throughput gains for various chunk sizes

Anticoagulant potential and total phenolic content of six species of the genus Ficus from Azad Kashmir, Pakistan

Purpose: To investigate the total phenolic and flavonoid contents of Ficus benghalensis, Ficus elasticaa, Ficus palmata, Ficus religiosa, Ficus semicordata and Ficus auriculata, and to determine their anticoagulant potential. Methods: Crude methanol extracts were prepared from the plant leaves, and fractionated using liquidliquid partition with n-hexane, chloroform and ethyl acetate. The total flavonoid and total phenolic contents of the extracts and their fractions were determined. The anticoagulant potential of the six Ficus species were evaluated in healthy human plasma, using activated partial thromboplastin time (APTT) and prothrombin time (PT) methods. Results: Phytochemical analysis showed the presence of considerable amounts of flavonoids ranging from 5.3 ± 0.7 to 11.8 ± 0.3 mg rutin equivalents (RE)/g, and phenolic compounds ranging from 8.0 ± 0.7 to 86.5 ± 1.5 mg gallic acid equivalents (GAE)/g in each fraction of the six species. Results from in vitro anticoagulant potential assays showed significant anticoagulant properties, with prothrombin time (PT) ranging from 17.7 ± 0.7 to 26.7 ± 2.2 s, and activated partial thromboplastin time (APTT) varying from 47.7 ± 3.3 to 72.3 ± 5.4 s. Conclusion: The results indicate that F. semicordata and F. Religiosa have higher anticoagulant potential than the other Ficus species studied

Applying deep neural networks for user intention identification

© 2020, Springer-Verlag GmbH Germany, part of Springer Nature. The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods

A business and legislative perspective of V2X and mobility applications in 5G networks

Vehicle-to-everything (V2X) communication is a powerful concept that not only ensures public safety (e.g., by avoiding road accidents) but also offers many economic benefits (e.g., by optimizing the macroscopic behavior of the traffic across an area). On the one hand, V2X communication brings new business opportunities for many stakeholders, such as vehicle manufacturers, retailers, Mobile Network Operators (MNOs), V2X service providers, and governments. On the other hand, the convergence of these stakeholders to a common platform possesses many technical and business challenges. In this article, we identify the issues and challenges faced by V2X communications, while focusing on the business models. We propose different solutions to potentially resolve the identified challenges in the framework of 5G networks and propose a high-level hierarchy of a potential business model for a 5G-based V2X ecosystem. Moreover, we provide a concise overview of the legislative status of V2X communications across different regions in the world

A novel CuFe2O4 ink for the fabrication of low-temperature ceramic fuel cell cathodes through inkjet printing

Inkjet printing is a mask-free, contactless, and precise thin film and coating fabrication technique, which can tailor the electrode microstructure of solid oxide fuel cells to provide a larger surface area with more reaction sites. For the first time, printable and functional CuFe2O4 inks were developed by analyzing particle size, viscosity, surface tension, density, and thermal properties. Two inks, named Ink (1) and Ink (2), were formulated with different compositions. Ink (2), containing 20 wt% 1,5-pentandiol, exhibited smaller particle sizes (0.87 μm) and a lower activation loss compared to Ink (1). For further optimization, NLK-GDC porous electrolyte substrates were inkjet printed with 30, 40, 50, 100 and 200 layers of Ink (2), with estimated thicknesses of 4.2, 5.6, 7, 14, and 28 μm. The best performance was achieved with a 100-layer inkjet-printed symmetric cell, exhibiting an ASR of 9.91 Ω cm2. To enhance the rheological properties of Ink (2), cyclopentanone was added, resulting in Ink (2) - Samba, which had improved characteristics. Ink (2) - Samba possessed an average particle size (D50) of 0.68 μm and a Z number of 3.89. Finally, EIS analysis compared a 100-layer inkjet-printed symmetric cell with Ink (2) - Samba to a drop-cast cell with the same ink to evaluate how the fabrication technique influences cell performance. Inkjet printing demonstrated a hierarchical porous microstructure, increased reaction sites, and reduced ASR from 19.59 Ω cm2 to 5.99 Ω cm2. Additionally, SEM images confirmed that inkjet printing reduced the particle size distribution during deposition. These findings highlight the significant impact of manufacturing techniques on electrode quality and fuel cell electrochemical performance.Peer reviewe