Extrinsic Ion Distribution Induced Field Effect in CsPbIBr2 Perovskite Solar Cells

Excellent power conversion efficiency (PCE) and stability are the primary forces that propel the all‐inorganic cesium‐based halide perovskite solar cells (PSCs) toward commercialization. However, the intrinsic high density of trap state and internal nonradiative recombination of CsPbIBr₂ perovskite film are the barriers that limit its development. In the present study, a facile additive strategy is introduced to fabricate highly efficient CsPbIBr₂ PSCs by incorporating sulfamic acid sodium salt (SAS) into the perovskite layer. The additive can control the crystallization behaviors and optimize morphology, as well as effectively passivate defects in the bulk perovskite film, thereby resulting in a high‐quality perovskite. In addition, SAS in perovskite has possibly introduced an additional internal electric field effect that favors electron transport and injection due to inhomogeneous ion distribution. A champion PCE of 10.57% (steady‐output efficiency is 9.99%) is achieved under 1 Sun illumination, which surpasses that of the contrast sample by 16.84%. The modified perovskite film also exhibits improved moisture stability. The unencapsulated device maintains over 80% initial PCE after aging for 198 h in air. The results provide a suitable additive for inorganic perovskite and introduce a new conjecture to explain the function of additives in PSCs more rationally.     

Recent Development in Separators for High‐Temperature Lithium‐Ion Batteries

Lithium‐ion batteries (LIBs) are promising energy storage devices for integrating renewable resources and high power applications, owing to their high energy density, light weight, high flexibility, slow self‐discharge rate, high rate charging capability, and long battery life. LIBs work efficiently at ambient temperatures, however, at high‐temperatures, they cause serious issues due to the thermal fluctuation inside batteries during operation. The separator is a key component of batteries and is crucial for the sustainability of LIBs at high‐temperatures. The high thermal stability with minimum thermal shrinkage and robust mechanical strength are the prime requirements along with high porosity, ionic conductivity, and electrolyte uptake for highly efficient high‐temperature LIBs. This Review deals with the recent studies and developments in separator technologies for high‐temperature LIBs with respect to their structural layered formation. The recent progress in monolayer and multilayer separators along with the developed preparation methodologies is discussed in detail. Future challenges and directions toward the advancement in separator technology are also discussed for achieving remarkable performance of separators in a high‐temperature environment.     

Recent progress in the modification of carbon materials and their application in composites for electromagnetic interference shielding

With the development in the modern technologies such as telecommunication instruments and scientific electronic devices, large amount of the electromagnetic radiations are produced, which lead to harmful effect on the highly sensitive electronic devices as well as on the health of human beings. To minimize the effect of electromagnetic radiations produced by different technologies, more efficient shielding materials are required which must be cost-effective, lightweight and good corrosion resistive. In this review, we focused on the shielding materials based on composites of carbon nanotubes and graphene. The typical surface modification of carbon nanotubes and graphene to optimize their interactions with polymers matrix has also summarized. It was found that the composites based on these carbon fillers were more efficient for electromagnetic interference shielding due to their unique properties (i.e., superior electrical, mechanical and thermal) together with lightweight, easy processing. Hence, the carbon nanotubes and graphene-based composites are excellent shielding materials against the electromagnetic radiations.     

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.     

Power Allocation Strategy for Secret Key Generation Method in Wireless Communications

Secret key generation (SKG) is an emerging technology to secure wireless communication from attackers. Therefore, the SKG at the physical layer is an alternate solution over traditional cryptographic methods due to wireless channels’ uncertainty. However, the physical layer secret key generation (PHY-SKG) depends on two fundamental parameters, i.e., coherence time and power allocation. The coherence time for PHY-SKG is not applicable to secure wireless channels. This is because coherence time is for a certain period of time. Thus, legitimate users generate the secret keys (SKs) with a shorter key length in size. Hence, an attacker can quickly get information about the SKs. Consequently, the attacker can easily get valuable information from authentic users. Therefore, we considered the scheme of power allocation to enhance the secret key generation rate (SKGR) between legitimate users. Hence, we propose an alternative method, i.e., a power allocation, to improve the SKGR. Our results show 72% higher SKGR in bits/sec by increasing power transmission. In addition, the power transmission is based on two important parameters, i.e., epsilon and power loss factor, as given in power transmission equations. We found out that a higher value of epsilon impacts power transmission and subsequently impacts the SKGR. The SKGR is approximately 40.7% greater at 250 from 50 mW at epsilon = 1. The value of SKGR is reduced to 18.5% at 250 mW when epsilonis 0.5. Furthermore, the transmission power is also measured against the different power loss factor values, i.e., 3.5, 3, and 2.5, respectively, at epsilon = 0.5. Hence, it is concluded that the value of epsilon and power loss factor impacts power transmission and, consequently, impacts the SKGR.     

DDoS Detection in SDN using Machine Learning Techniques

Software-defined network (SDN) becomes a new revolutionary paradigm in networks because it provides more control and network operation over a network infrastructure. The SDN controller is considered as the operating system of the SDN based network infrastructure, and it is responsible for executing the different network applications and maintaining the network services and functionalities. Despite all its tremendous capabilities, the SDN face many security issues due to the complexity of the SDN architecture. Distributed denial of services (DDoS) is a common attack on SDN due to its centralized architecture, especially at the control layer of the SDN that has a network-wide impact. Machine learning is now widely used for fast detection of these attacks. In this paper, some important feature selection methods for machine learning on DDoS detection are evaluated. The selection of optimal features reflects the classification accuracy of the machine learning techniques and the performance of the SDN controller. A comparative analysis of feature selection and machine learning classifiers is also derived to detect SDN attacks. The experimental results show that the Random forest (RF) classifier trains the more accurate model with 99.97% accuracy using features subset by the Recursive feature elimination (RFE) method.     

Multilayer heuristics based clustering framework (MHCF) for author name disambiguation

Scientometrics - Author name ambiguity is a nontrivial problem currently faced by digital libraries and scholarly data search engines affecting their findings related to the authorship data...     

Introduction,production, characterization and applications of defects in graphene

Journal of Materials Science: Materials in Electronics - The successful preparation of graphene in research laboratory has brought a revolutionary progress in almost all the research fields...     

Completing features for author name disambiguation (AND): an empirical analysis

Scientometrics - This study presents a feature enriched AND dataset to develop diverse and better performance achieving AND techniques, by utilizing AND features which have better discriminating...     

Interference Aware Route Discovery in Wireless Mobile Ad Hoc Networks

In this paper, we propose an interference aware expanding region search algorithm to locate a destination in mobile ad hoc networks. In the proposed approach, signal to interference plus noise ration (SINR) is used in place of TTL field of a route request packet. The source node initializes the search query with a threshold value of SINR. Each relay node forwards the packet if its SINR satisfies the threshold criteria provided by the source node in RREQ packet. As a result, the low SINR nodes are removed in route discovery phase prior to the establishment of routes. The simulation results show that proposed algorithm provides significant improvement in performance of reactive routing protocol in terms of reduced routing overhead, reduced energy consumption, and increased network throughput.