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Review about the Applications of Nanoparticles in Batteries
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Nanoparticles are defined as an organic or non-organic structure of matter in at least one of its dimensions less than 100 nm. Nanoparticles proved their effectiveness in different fields because of their unique physicochemical properties. Using nanoparticles in the power field contributes to cleaning and decreasing environmental pollution, which means it is an environmentally friendly material. It could be used in many different parts of batteries, including an anode, cathode, and electrolyte. This study reviews different types of nanoparticles used in Lithium-ion batteries by collecting the advanced techniques for applying nanotechnology in batteries. In addition, this review presents an idea about the advantages and disadvantages of using nanoparticles in batteries to harness energy without harming the environment. This review showed that applying nanotechnology and using nanoparticles in the production technique of batteries open the field for developing energy storage in Nano sized batteries. This, in turn, is important in the new era of technology in the industries of electronic devices and precision electrical appliances such as mobile phones, digital cameras, etc.

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Publication Date
Fri Feb 06 2026
Journal Name
Optik
Mitigation optical losses via plasmonic core/shell (Co/Gr) nanoparticles introduced in ultra-thin film silicon solar cell
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Optical losses represent one of the primary obstacles to increasing the efficiency of silicon solar cells. The recommended solution to minimize optical losses is the use of plasmonic metal nanoparticles; however, they act as recombination centers within the solar cell construction, leading to a decrease in performance. The goal of this article is to introduce cobalt/graphene nanoparticles into the solar cell to minimize the optical losses. An ultra-thin film silicon PIN solar cell of dimensions (400 ×400 ×900) nm3 with ring metal contact shape was designed and numerically investigated using COMSOL Multiphysics software version 6.2 by the finite element method (FEM). Core/shell cobalt-graphene (Co/Gr) nanoparticles are periodically int

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Publication Date
Sat Jan 24 2026
Journal Name
Arab World English Journal
AI in Literary and Non-Literary Discourse: A Systematic Review of Human and Machine Authorship, and Style and Perception
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Artificial intelligence has quickly invaded the realms of both creative and information-based writing, raising new questions about human originality, authorship and style. Despite its ability to produce writings that are coherent and stylistically varied, there are still concerns over the uniqueness and cultural neutrality of AI programs such as ChatGPT. This review covers significant recent advancements with artificial intelligence applications in both the literary and non-literary fields. It analyzes 35 recent studies contrasting authorship and creativity, or stylistic considerations and impressions, between human and AI texts. These studies range from poetic and fictional writing through essay, news article and academic publicati

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Publication Date
Tue Jun 01 2021
Journal Name
Swarm And Evolutionary Computation
A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions
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Sensibly highlighting the hidden structures of many real-world networks has attracted growing interest and triggered a vast array of techniques on what is called nowadays community detection (CD) problem. Non-deterministic metaheuristics are proved to competitively transcending the limits of the counterpart deterministic heuristics in solving community detection problem. Despite the increasing interest, most of the existing metaheuristic based community detection (MCD) algorithms reflect one traditional language. Generally, they tend to explicitly project some features of real communities into different definitions of single or multi-objective optimization functions. The design of other operators, however, remains canonical lacking any inte

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Publication Date
Tue Jun 01 2021
Journal Name
Swarm And Evolutionary Computation
A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions
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Publication Date
Tue Jun 01 2021
Journal Name
Swarm And Evolutionary Computation
A review of heuristics and metaheuristics for community detection in complex networks: Current usage, emerging development and future directions
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Publication Date
Thu Nov 11 2021
Journal Name
Nano Express
Near-infrared PbS quantum dots functionalized with affibodies and ZnPP for targeted imaging and therapeutic applications
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Abstract<p>We report a new theranostic device based on lead sulfide quantum dots (PbS QDs) with optical emission in the near infrared wavelength range decorated with affibodies (small 6.5 kDa protein-based antibody replacements) specific to the cancer biomarker human epidermal growth factor receptor 2 (HER2), and zinc(II) protoporphyrin IX (ZnPP) to combine imaging, targeting and therapy within one nanostructure. Colloidal PbS QDs were synthesized in aqueous solution with a nanocrystal diameter of ∼5 nm and photoluminescence emission in the near infrared wavelength range. The Z<sub>HER2:432</sub> affibody, mutated through the introduction of two cysteine residues at the C-terminus (</p> ... Show More
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Publication Date
Mon Mar 30 2026
Journal Name
Iraqi Journal Of Science
A modified time series model using conditional and unconditional estimations with applications to a real dataset
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Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan

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Publication Date
Thu May 01 2025
Journal Name
Membrane And Water Treatment
Thin film nanocomposite (TFN) membranes filled with a novel metal organic framework for reverse osmosis applications
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This paper reports the synthesis and use of a novel metal-organic framework (MOF), named Zr-BADS, within the thin-film nanocomposite (TFN) membranes for reverse osmosis (RO) applications. Two types of zirconium-based MOFs, Zr-BADS-1 and Zr-BADS-2, were synthesized via a solvothermal method using bicinchoninic acid disodium salt as a linker and either dimethylformamide or ethanol as solvent, respectively. TFN membranes were prepared by embedding these MOFs within the polyamide thin film supported by a polysulfone support sheet. The specific surface area of Zr-BADS-1 and Zr-BADS-2 was determined to be 396.1 and 278.6 m2/g, respectively, indicating significant surface area conducive to water permeation. Scanning electron microscopic analysis r

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Publication Date
Fri Jan 31 2025
Journal Name
Passer Journal Of Basic And Applied Sciences
Enhanced Security Taxonomy for Fog-Enabled VANETs: A Comprehensive Survey on Attacks, Challenges, Applications and Architectures
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Vehicular Ad Hoc Networks (VANETs) are integral to Intelligent Transportation Systems (ITS), enabling real-time communication between vehicles and infrastructure to enhance traffic flow, road safety, and passenger experience. However, the open and dynamic nature of VANETs presents significant privacy and security challenges, including data eavesdropping, message manipulation, and unauthorized access. This study addresses these concerns by leveraging advancements in Fog Computing (FC), which offers lowlatency, distributed data processing near-end devices to enhance the resilience and security of VANET communications. The paper comprehensively analyzes the security frameworks for fog-enabled VANETs, introducing a novel taxonomy that c

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Publication Date
Fri Apr 14 2023
Journal Name
Journal Of Big Data
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for</p> ... Show More
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