This paper reviews MAX phases (bulk) and their 2D derivative, MXenes, focusing on synthesis methods, properties, and applications. Traditional and advanced synthesis techniques, including solid-state synthesis and spark plasma sintering, are examined, emphasizing structural diversity. Key characteristics, such as thermal stability, electrical conductivity, and mechanical resilience, are explored alongside their mechanisms. The review also highlights advancements in energy harvesting applications, such as H 2 production, solar cells, energy storage, catalysis, spintronics, electronic devices, and environmental remediation. Additionally, future research directions are outlined to address existing gaps and enhance their role in next-generation technologies and environmental remediation.

In order to optimize the manufacturing of polypropylene-derived few-layer graphene, an innovative utilization of nonsupported iron oxide nanoparticles generated under various fuel environment conditions was studied. Three distinct fuel combustion environment circumstances (fusion, fuel shortage, and fuel excess) produced a variety of Fe2O3 nanoparticles for cost-effective and green graphene deposition. XRD, H2- TPR, Raman, and TGA measurements were used to characterize both new and spent catalysts. Remarkably, the microstructure of the generated Fe2O3 nanoparticles could be controlled by the citric acid/iron nitrate ratio, ranging from spheroids ( Fe2O3(0)) to sheets ( Fe2O3(0.5-0.75)) and a hybrid microstructure that consists of sheets, spheroids, and interconnected strips ( Fe2O3(1-2)). According to fuel situation (citric acid/iron nitrate ratio, Fe2O3( 0-2)), various graphitization level and yields of graphene derivatives including sheets, ribbons, and onions have been developed. With the ideal fuel/oxidant ratio (ɸ = 1), the Fe2O3( 0.75) catalyst demonstrated the best catalytic activity to deposit the largest yield of highly graphitized few graphene layers (280%). Lean and rich fuel conditions (1 > ɸ > 1) have detrimental effects on the amount and quality of graphene deposition. It is interesting to note that in addition to graphene sheets, an excess of citric acid caused the production of metallic cores, hollow, and merged carbon nano-onions, and graphene nano-ribbons. It was suggested that carbon nano-onions be converted into graphene nano-ribbons and semi-onion shell-like graphene layers.

The accurate detection of vital biomarkers such as Ascorbic Acid (AA), Uric Acid (UA) and Nitrite ( NO2 −) is crucial for human health surveillance. However, existing methods often struggle with concurrent detection and quantification of multiple species, highlighting the need for a more effective solution. To address this challenge, this study aimed to develop a multifunctional electrochemical sensor capable of parallel detection of AA, UA and NO2 − using a synergistic combination of Graphene Oxide (GO) and Cadmium Sulfide (CdS) materials. Notably, the fabricated CdS@GO/Glassy Carbon Electrode (GCE) exhibited exceptional electrochemical activity, as evidenced by Differential Pulse Voltammetry (DPV) analysis. The sensor demonstrated remarkable sensitivity (8.13, 10.12, and 9.05 μA·μM−1·cm−2) and ultra-low detection limits (0.034, 0.062, and 0.084 μM) for AA, UA and NO2 −, respectively. Furthermore, it successfully identified single molecules of each analyte in aqueous and biologic fluid samples, with recovery values comparable to those obtained using High-Performance Liquid Chromatography (HPLC) standard addition methods. The significance of this study lies in developing a novel CdS@ GO/GCE sensor that enables concurrent detection and quantification of multiple vital biomarkers, offering a promising tool for human health monitoring and diagnosis.

We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm finds networks most correctly and defines galaxy networks in a way that most closely resembles human vision.