Graphene has been ranked among one of the most remarkable nanostructures in the carbon world. Graphene modification and nanocomposite formation have been used to expand the practical potential of graphene nanostructure. The overview is an effort to highlight the indispensable synthesis strategies towards the formation of graphene nanocomposites. Consequently, graphene has been combined with useful matrices (thermoplastic, conducting, or others) to attain the desired end material. Common fabrication approaches like the in-situ method, solution processing, and melt extrusion have been widely involved to form the graphene nanocomposites. Moreover, advanced, sophisticated methods such as three- or four-dimensional printing, electrospinning, and others have been used to synthesize the graphene nanocomposites. The focus of all synthesis strategies has remained on the standardized graphene dispersion, physical properties, and applications. However, continuous future efforts are required to resolve the challenges in synthesis strategies and optimization of the parameters behind each technique. As the graphene nanocomposite design and properties directly depend upon the fabrication techniques used, there is an obvious need for the development of advanced methods having better control over process parameters. Here, the main challenging factors may involve the precise parameter control of the advanced techniques used for graphene nanocomposite manufacturing. Hence, there is not only a need for current and future research to resolve the field challenges related to material fabrication, but also reporting compiled review articles can be useful for interested field researchers towards challenge solving and future developments in graphene manufacturing.

To study the environment of the Kipushi mining locality (LMK), the evolution of its landscape was observed using Landsat images from 2000 to 2020. The evolution of the landscape was generally modified by the unplanned expansion of human settlements, agricultural areas, associated with the increase in firewood collection, carbonization, and exploitation of quarry materials. The problem is that this area has never benefited from change detection studies and the LMK area is very heterogeneous. The objective of the study is to evaluate the performance of classification algorithms and apply change detection to highlight the degradation of the LMK. The first approach concerned the classifications based on the stacking of the analyzed Landsat image bands of 2000 and 2020. And the second method performed the classifications on neo-images derived from concatenations of the spectral indices: Normalized Difference Vegetation Index (NDVI), Normalized Difference Building Index (NDBI) and Normalized Difference Water Index (NDWI). In both cases, the study comparatively examined the performance of five variants of classification algorithms, namely, Maximum Likelihood (ML), Minimum Distance (MD), Neural Network (NN), Parallelepiped (Para) and Spectral Angle Mapper (SAM). The results of the controlled classifications on the stacking of Landsat image bands from 2000 and 2020 were less consistent than those obtained with the index concatenation approach. The Para and DM classification algorithms were less efficient. With their respective Kappa scores ranging from 0.27 (2000 image) to 0.43 (2020 image) for Para and from 0.64 (2000 image) to 0.84 (2020 image) for DM. The results of the SAM classifier were satisfactory for the Kappa score of 0.83 (2000) and 0.88 (2020). The ML and NN were more suitable for the study area. Their respective Kappa scores ranged between 0.91 (image 2000) and 0.99 (image 2020) for the LM algorithm and between 0.95 (image 2000) and 0.96 (image 2020) for the NN algorithm.

This study examined the impact of aluminium doping on the structural, electrical, and magnetic properties of Li_(0.5)Co_(0.75)Al_xFe_(2−x)O₄ spinel ferrites (x =0.15 to 0.60). The samples were synthesised using the sol-gel auto-combustion technique, and they were examined using X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), dielectric measurements, and vibrating sample magnetometry (VSM). All samples possessed a single-phase cubic spinel structure with Fd-3m space group, according to XRD analyses. SEM images showed the creation of homogeneous particles with an average size of about 21 nm. All samples had spinel ferrite phases, confirmed from FTIR spectra. DC electrical conductivity studies showed that the conductivity increased with increasing aluminium content up to x = 0.45 before dropping at x = 0.60. The maximum saturation magnetization value was found at x = 0.45, according to VSM measurements, which demonstrated that the magnetic characteristics were strongly correlated with the amount of aluminium.

Cobalt-based sulfides have emerged as promising candidates for next-generation high-performance anode materials for lithium-ion batteries (LIBs) due to their high theoretical specific capacity and reversible conversion reaction mechanisms. However, their practical application is hindered by volume expansion effects and relatively low rate performance. Guided by theoretical principles, this study synthesizes nanoscale Bi/CoS-C and Bi/Co4S3-C (denoted as Bi/CS-C) composite materials using Co and Bi2S3 as precursors via a solid-state ball milling method. The electrochemical properties of these materials were systematically investigated. When employed as anodes for LIBs, Bi/CoS-C and Bi/CS-C exhibit excellent rate capabilities. At current densities of 0.1, 0.5, 1, 4, and 10 A/g, the reversible capacities of Bi/CoS-C were 939.2, 730.7, 655.6, 508.1, and 319 mAh/g, respectively. In contrast, Bi/CS-C exhibited reversible capacities of 760.4, 637.6, 591.9, 484.3, and 295.4 mAh/g, respectively. Moreover, Co4S3, as an active component, enables superior long-cycle performance compared to CoS. After 300 cycles at 0.2 A/g, the Bi/CoS-C and Bi/CS-C electrodes retained capacities of 193.1 and 788.8 mAh/g, respectively. This study demonstrates that nanostructure design and carbon-based composite materials can effectively mitigate the volume expansion issue of cobalt-based sulfides, thereby enhancing their rate performance and cycling stability. This strategy provides new insights for the development of high-performance anode materials for lithium-ion batteries and is expected to accelerate their practical application in next-generation energy storage devices.

The rapid expansion of smart cities has led to the widespread deployment of Internet of Things (IoT) devices for real-time data collection and urban optimization. However, these interconnected systems face critical cybersecurity risks, including data tampering, unauthorized access, and privacy breaches. This paper proposes a blockchain-based framework designed to enhance the security, integrity, and resilience of IoT data in smart city environments. Leveraging a private blockchain, the system ensures decentralized, tamper-proof data storage, and transaction verification through digital signatures and a lightweight Proof of Work consensus mechanism. Smart contracts are employed to automate access control and respond to anomalies in real time. A Python-based simulation demonstrates the framework’s effectiveness in securing IoT communications. The system supports rapid transaction validation with minimal latency and enables timely detection of anomalous patterns through integrated machine learning. Evaluations show that the framework maintains consistent performance across diverse smart city components such as transportation, healthcare, and building security. These results highlight the potential of the proposed solution to enable secure, scalable, and real-time IoT ecosystems for modern urban infrastructures.