Dual growth of graphene nanoplatelets and carbon nanotubes hybrid structure via chemical vapor deposition of methane over Fe–MgO catalysts

Abstract

In this study, Fe–MgO catalyst substrates with various Fe and MgO combinations were evaluated for the growth of different types of carbon nanostructure materials (CNMs), particularly graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) via chemical vapor deposition using methane as a carbon source. The hydrogen yield was also determined as a valuable by-product in this process. Therefore, a set of Fe–MgO catalysts with different iron loadings (30, 80, 85, 90 and 100?wt %) were prepared by the combustion method to realize this target. The physicochemical properties of freshly calcined Fe–MgO catalysts were investigated by XRD, TPR and BET, while the as-grown CNMs were studied by HR-TEM, XRD and Raman spectroscopy. The results verified that the morphology of as-grown CNMs as well as the H₂ yield was directly correlated to the iron content in the catalyst composition. The XRD and TPR results showed that various FeMgO_x species with deferent levels of interactions were produced with the gradual incorporation of MgO content. TEM images indicated that GNPs were individually grown on the surface of high loaded iron-containing catalysts (90–100?wt %) due to the presence of highly aggregated iron particles. While multi-walled carbon nanotubes (MWCNTs) with uniform diameters were grown on the low iron-loaded catalyst (30%Fe/MgO) due to the formation of highly dispersed FeMgO_x particles. On the other hand, GNPs/MWCNTs hybrid materials were grown on the surface of 80%Fe and 85%Fe/MgO catalysts. This behavior can be interpreted by the co-existence of highly aggregated and highly dispersed Fe₂O₃ particles in the catalyst matrix. The results demonstrated that the catalyst composition has a notable effect on the nature of CNMs products and H₂ yield.     

Pricing and location decisions in multi-objective facility location problem with M/M/m/k queuing systems

This article presents a new multi-objective model for a facility location problem with congestion and pricing policies. This model considers situations in which immobile service facilities are congested by a stochastic demand following M/M/m/k queues. The presented model belongs to the class of mixed-integer nonlinear programming models and NP-hard problems. To solve such a hard model, a new multi-objective optimization algorithm based on a vibration theory, namely multi-objective vibration damping optimization (MOVDO), is developed. In order to tune the algorithms parameters, the Taguchi approach using a response metric is implemented. The computational results are compared with those of the non-dominated ranking genetic algorithm and non-dominated sorting genetic algorithm. The outputs demonstrate the robustness of the proposed MOVDO in large-sized problems.     

ZnFe2O4 and ZnO-Zn1−xMxFe2O4+δ (M = Sm3+, Eu3+ and Ho3+): Synthesis,physical properties and high performance visible light induced photocatalytic degradation of malachite green

The present work reports the synthesis of ZnFe₂O₄ and ZnO-Zn_1-xM_xFe₂O_4+δ (Ln?=?Sm, Eu and Ho) nanomaterials by conventional solid state reactions between Ln₂O₃, Zn(NO₃)₂, Fe(NO₃)₃·9H₂O and/or FeCl₃·6H₂O raw materials at 800?°C for 10?h and 15?h. Stoichiometric and nonstoichiometric reactions were explored for the synthesis of ZnFe₂O₄. The two Fe sources (Fe(NO₃)₃ and FeCl₃) were used to study the proper raw material type for the synthesis of the ZnFe₂O₄. The synthesized nanomaterials were characterized by powder X-ray diffraction (PXRD) technique. Rietveld analysis showed that the obtained materials were crystallized well in cubic crystal system with the space group Fd3m and lattice parameters a?=?b?=?c?=?8.4?Å. The rietveld data showed that the purity of ZnFe₂O₄ was increased from 14% to 88% when the Fe source was changed from FeCl₃ to Fe(NO₃)₃ meanwhile the reaction time was changed from 10 to 15?h. However, the purity was increased to 96% when the stoichiometry of Zn:Fe was changed from 1:2 to 0.8:2 at 800?°C for 15?h. The PXRD data revealed that dopant ion type had a considerable influence on the crystal phase purity of the obtained materials. It was found that Yb₂O₃ decreased more the purity of the obtained target compared to the other dopant ions. Ultraviolet-visible spectra showed that the synthesized nanomaterials had strong light absorption in the visible light region. Photocatalytic performance of the as-synthesized ZnFe₂O₄ was investigated for the degradation of pollutant Malachite Green (MG) in aqueous solution under direct visible light irradiation. The degradation yield at the optimized condition (0.09?mL H₂O₂, 30?mg catalyst and 60?min) was 98%.     

Application of a Coordination Model for a Large Number of Stakeholders with a New Game Theory Model

Water Resources Management - Water allocation is an important issue for systems with multiple stakeholders. Individual and collective decisions are very important for such systems. Thus, a new...     

Role of parietal and principal gastric mucosa cells in the phenomenon of concentration of aluminum and indium

The subcellular behavior of aluminum and indium, used in medical and industrial fields, was studied in the gastric mucosa and the liver after their intragastric administration to rats, using, two of the most sensitive methods of observation and microanalysis, the transmission electron microscopy, and the secondary ion mass spectrometry. The ultrastructural study showed the presence of electron dense deposits, in the lysosomes of parietal and principal gastric mucosa cells but no loaded lysosomes were observed in the different studied hepatic territories. The microanalytical study allowed the identification of the chemical species present in those deposits as aluminum or indium isotopes and the cartography of their distribution. No modification was observed in control rats tissues. In comparison to previous studies describing the mechanism of aluminum concentration in the gastric mucosa and showing that this element was concentrated in the lysosomes of fundic and antral human gastric mucosa, our study provided additional informations about the types of cells involved in the phenomenon of concentration of aluminum and indium, which are the parietal and the principal cells of the gastric mucosa. Our study demonstrated that these cells have the ability to concentrate selectively aluminum and indium in their lysosomes, as a defensive reaction against intoxication by foreign elements.     

Development of nano-channel single crystals and verification of their structures by small angle X-ray scattering

Nano-channel single crystals were developed via consecutive growth of various polymer single-crystal channels comprising homo and block copolymers by self-seeding method. Poly(ethylene glycol)-b-polystyrene (PEG-b-PS) and poly(ethylene glycol)-b-poly(methyl methacrylate) (PEG-b-PMMA) block copolymers were synthesized by atom transfer radical polymerization. Self-seeding temperature, concentration, and crystallization time affected the width of the channels. This might provide a new way to investigate directional absorption, diffusion, and immobilization of biomacromolecules on the surface. The crystalline blocks of PEG-b-PS and PEG-b-PMMA diblock copolymers were similar, therefore, the continuity of channel-wire growth was guaranteed. Development of complete square channels next to the channels covered with high molecular weight brushes was infeasible. It was ascribed to a higher hindrance of primarily existing tethered chains on the single-crystal channel. Finally, the consecutive channel-wire single crystals were compared with single-step-grown pyramidal and conic structures. These multilayer crystals grew spirally and formed non-flat channels. The structure and morphology of different crystalline channels were detected by atomic force microscopy (AFM) and small angle X-ray scattering (SAXS). In this work, for the first time, the SAXS data of channel-wire single crystals were reported and they were compared by non-flat channel-like crystals. A profound investigation of PEG-b-PS, PEG-b-PMMA copolymers and PEG homopolymer channel-wire single crystals by SAXS and their comparison with AFM data was a novel work in the field of single-crystal engineering.     

Causes and consequences of desertification in Kuwait: a case study of land degradation

Desertification in Kuwait is a process of environmental degradation under fragile ecological conditions and intensive human activities and the consequences of Gulf War. In Kuwait, very severe desertification prevails, due to increasing formation of new active sandy bodies, deterioration of many areas of natural vegetation cover to less than 10%, and limited water resources for large-scale forage production. Average annual desertified land in Kuwait is estimated to be 285 km². In Kuwait, three indicators of land degradation are encountered. These are vegetation, soil, and surface hydrological changes. Based on field measurements of soil compaction and vegetation changes, in the west Jahra area in the northern part of the country, degradation levels were assessed. Results of these measurements show that the average infiltration rate in compacted soil decreased by 53.8% in comparison with non-compacted soil, while the average soil penetration resistance in compacted soil increased by 154.1% in comparison with non-compacted soil. The bulk density in open sites was 23.4% higher than that in protected sites. The percentage of litter in open sites decreased by 77.3% in comparison with protected sites, while the percentage of total vegetation in open sites decreased by 6.1% in comparison with protected sites. Electronic Publication     

A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases

Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficient knowledge, many patients are not diagnosed. Nowadays, the advances in high-throughput sequencing technologies such as whole genome sequencing, single-cell and others, have boosted the understanding of RDs. To extract biological meaning using the data generated by these methods, different analysis techniques have been proposed, including machine learning algorithms. These methods have recently proven to be valuable in the medical field. Among such approaches, unsupervised learning methods via neural networks including autoencoders (AEs) or variational autoencoders (VAEs) have shown promising performances with applications on various type of data and in different contexts, from cancer to healthy patient tissues. In this review, we discuss how AEs and VAEs have been used in biomedical settings. Specifically, we discuss their current applications and the improvements achieved in diagnostic and survival of patients. We focus on the applications in the field of RDs, and we discuss how the employment of AEs and VAEs would enhance RD understanding and diagnosis.