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Catalysts based on Ni and Fe nanoparticles deposited selectively on carbon nanotubes were investigated before and after the dry methane reforming. Three catalysts were synthesized and evaluated, varying the concentration of Ni inside and Fe outside the carbon tubes. BET analysis revealed that the acid treatment opened the ends of the nanotubes and resulted in a higher surface area. Transmission electron microscopy (TEM) showed 24 layers with inner diameter ranging from 4 to 6 nm and outer diameter ranging from 16 to 22 nm. Raman spectroscopy confirmed that after calcination at high temperature the structure of the nanotubes was maintained. X-ray diffraction (XRD) analysis of the catalysts confirmed the presence of NiO (2.6-3.2 nm) and Fe2O3 (4.3 nm) crystallites. The catalytic tests presented high activity in dry methane reform (DRM). The catalysts 10Ni@CNT and 10Ni@CNT/5Fe presented conversions of CH4 (63 and 67%) and CO2 (72 and 88%), respectively, at 800 °C, under atmospheric pressure. Analysis after the reaction showed an increasing ratio of ID/IG, which indicates the formation of defects. The Raman analysis showed that even after calcination at high temperatures the structures of the nanotubes were mostly preserved, and TEM images showed that during the reaction, there were formation of nanotubes occurring randomly.
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Nanopartículas , Nanotubos de Carbono , Metano/químicaRESUMO
Shear thickening fluids (STFs) refer to non-Newtonian fluids of the dilatant variety, wherein their viscosity experiences a significant surge with an escalation in the shear rate. In this investigative work, the friction behavior between yarns (pull-out) and absorption of static and kinetic energy during the phenomenon of friction between yarns in STFs are performed by monophase (MP-STF) adding nano SiO2 and dual-phase (MP-STF) adding carbon nanotubes. The ρ-Aramid fabrics were reinforced via the "foulard process", and carried out on MP-STF, and DP-STF/ρ-Aramid-impregnated fabrics to evaluate and compare with the enhancement in interfacial friction properties between yarns. The results showed that DP-STF has more significant than MP-STF and MP-STF in ultimate load, kinetic shear stress, static shear stress, and friction energy level effects. The DP-STF exhibits various friction enhancement mechanisms at the yarn interface, leading to higher absorption of static and kinetic energy related to interfacial friction, as indicated by the results obtained. Furthermore, the DP-STF/ρ-Aramid impregnated fabrics exhibited ultimate load (22.23 ± 0.522 N), kinetic shear stress (35.73 ± 0.850 MPa*100), static shear stress (36.28 ± 0.900 MPa*100), and friction energy level (610.33 ± 0.250). Increased ultimate load (581.7% and 180.7%), kinetic shear stress (621.4% and 174.6%), static shear stress (550.5% and 159.1%), and friction energy level (680.2 and 186.7%) compared to WT-STF and MP-STF, respectively. The current discoveries hold immense potential for various applications in the fields of engineering and smart material technologies. These applications span a multiplicity of industries, including sports products, medical advancements, space technology, as well as protective and shielding products.
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The present work exhibits the dynamic viscosity profile data of three distinct nanofluids, at a constant shear stress, and within a range of temperatures that include below-ambient conditions (from -10 to 20 °C). The nanofluids were as follows. Nanofluid I: 30% ethylene glycol and 70% distilled water (v/v), with graphene (0.32% in mass); Nanofluid II: 30% engine coolant NBR 13705; ASTM D-3306; ASTM D-4985) and 70% distilled water (v/v), with graphene (0.2% in mass); and Nanofluid III: 30% engine coolant and 70% distilled water (v/v), with Multi-Walled Carbon Nanotubes (MWCNT) (0.2% in mass). The present work was motivated by the scarcity of experimental data on the temperature dependence of viscosity for graphene, MWCNT, and their hybrid nanofluids, at below-ambient temperatures.
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Abstract The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.
Resumen El objetivo de este artículo es presentar el desarrollo de un sistema de medición en tiempo real de glucosa en medios acuosos. El sistema que se implementa incorpora dos lineas de investigación: i) diseño, síntesis e implementación de un sensor electroquímico no enzimático de Nanotubos de Carbono de Pared Múltiple con nanopartículas de Cobre (NTCPM-Cu) y ii) diseño e implementación de un algoritmo de aprendizaje automático basado en una Red Neuronal Perceptrón Multicapa (RN-PM), embebido en un ESP32 SoC (Sistema en Chip). Un dato de corriente que se extrae en tiempo real durante el proceso de oxidación-reducción a la que se somete un medio acuoso, alimenta el algoritmo embebido en el ESP32 para estimar el valor de glucosa. De los resultados experimentales se demuestra que el sensor nanoestructurado mejora la resolución en la respuesta amperométrica al identificar un lugar ideal para la toma de datos. Por su parte, la incorporación del algoritmo basado en una RN embebido en SoC otorga un nivel de 97.8 % de exactitud en la mediciones. Se concluye que incorporar algoritmos de aprendizaje automático embebidos en SoC de bajo costo en procesos experimentales complejos, mejora la manipulación de datos, incrementa la confiabilidad en resultados y adiciona portabilidad.
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The membranes' role is of supreme importance in the separation of compounds under different phases of matter. The topic addressed here is based on the use of membranes on the gases separation, specifically the advantages of mixed-matrix membranes (MMMs) when using carbon nanotubes as fillers to separate carbon dioxide (CO2) from other carrier gas. MMMs consist of a polymer support with additive fillers to improve their efficiency by increasing both selectivity and permeability. The most promising fillers in the MMM development are nanostructured molecules. Due to the good prospects of carbon nanotubes (CNTs) as MMM fillers, this article aims to concentrate the advances and developments of CNT-MMM to separate gases, such as CO2. The influence of functionalized CNT or mixtures of CNT with additional materials such as zeolites, hydrogel and, graphene sheets on membranes performance is highlighted in the present work.
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The demographic dividend has aroused interest among demographers and economists because it is seen as a window of oportunity for the economic development of countries that have experienced a demographic transition. There are reasons to question the sole virtuosity of the pure demographic dividend in economic growth. Crespo-Cuaresma et al. (2014) found that educational expansion has an important role in economic gains during the demographic dividend. To verify these results for the Brazilian case, we performed a decomposition exercise of economic support ratio (ESR), an alternative to demographic dependency ratio, to analyze the first demographic dividend. A simulation, applied for the period from 1970 to 2100 considering three scenarios of educational expansion, shows that educational expansion was and will be responsible for a big share of the economic gains of the Brazilian demographic dividend period, outperforming the change in age structure effect. In addition, an increase in a work-age population with post-secondary education appears to potentialize these results.
O dividendo demográfico tem despertado interesse entre demógrafos e economistas por ser visto como uma janela de oportunidade para o desenvolvimento econômico dos países que vivenciam a transição demográfica. É possível questionar a virtuosidade do dividendo puramente demográfico no crescimento econômico. Crespo-Cuaresma et al. (2014) observaram que a expansão educacional tem papel importante nos ganhos econômicos durante o dividendo demográfico. Para verificar esse resultado para o caso brasileiro, realizamos um exercício de decomposição da razão de suporte econômico (ESR), uma alternativa à taxa de dependência demográfica na análise do primeiro dividendo demográfico. A simulação realizada para o período entre 1970 e 2100, considerando três cenários de expansão educacional, mostrou que a ampliação do nível de escolaridade foi e será responsável pela maior parte dos ganhos econômicos do período do dividendo demográfico, superando o efeito da mudança da composição etária. Além disso, o aumento da população em idade ativa com ensino superior tende a potencializar esse resultado.
El dividendo demográfico ha despertado interés entre los demógrafos y economistas, ya que se considera una ventana de oportunidad para el desarrollo económico de los países que experimentan la transición demográfica. Es posible cuestionar el virtuosismo del dividendo puramente demográfico en el crecimiento económico. Crespo-Cuaresma y cols. (2014) vieron que la expansión educativa juega un papel importante en las ganancias económicas durante el dividendo demográfico. Para verificar este resultado para el caso brasileño, se hizo un ejercicio de descomposición del índice de apoyo económico (ESR), una alternativa al índice de dependencia demográfica en el primer análisis demográfico de dividendos. La simulación realizada para el período 1970-2100, considerando tres escenarios de expansión educativa, mostró que la expansión del logro educativo fue y será responsable de la mayoría de las ganancias económicas del período de dividendos demográficos, superando el efecto del cambio en la composición etaria. Además, el aumento de la población en edad laboral con educación superior tiende a potenciar este resultad
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Humanos , Desenvolvimento Econômico , Demografia , Crescimento , Universidades , Brasil , Educação , Escolaridade , Grupos EtáriosRESUMO
Carbon nanotubes (CNT) can be chemically modified by doping or functionalization to change the chemical and surface properties. These characteristic makes to CNT candidates for multiple applications including medical field in cardiovascular area. A novel method to CNT functionalization by formation of two compounds: α-bromoacid and the organic compound 2-(methacryloyloxy) ethyl phosphorylcholine (MPC), will be discussed in this article. According to results, CNT are suggested like candidates to repel oxidized low-density lipoproteins (ox-LDL) to prevent restenosis. The electronegative character on surface of functionalized CNT (F-CNT) is shown by wettability analysis observing a repellent behaviour in contact with ox-LDL after functionalization route. Here we analyse the toxicity of CNT and F-CNT on HepG2 cell line and find no damage to the cell membrane of HepG2 cells in concentration at doses below 1mg/ml.
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Nanotubos de Carbono , Lipoproteínas LDL , Stents , Propriedades de Superfície , MolhabilidadeRESUMO
Hierarchical assembly of hard/soft nanoparticles holds great potential as reinforcements for polymer nanocomposites with tailored properties. Here, we present a facile strategy to integrate polystyrene-grafted carbon nanotubes (PSgCNT) (0.05-0.3 wt %) and poly(styrene-b-[isoprene-ran-epoxyisoprene]-b-styrene) block copolymer (10 wt %) into epoxy coatings using an ultrasound-assisted noncovalent functionalization process. The method leads to cured nanocomposites with core-shell block copolymer (BCP) nanodomains which are associated with carbon nanotubes (CNT) giving rise to CNT-BCP hybrid structures. Nanocomposite energy dissipation and reduced Young's Modulus (E*) is determined from force-distance curves by atomic force microscopy operating in the PeakForce QNM imaging mode and compared to thermosets modified with BCP and purified carbon nanotubes (pCNT). Remarkably, nanocomposites bearing PSgCNT-BCP conjugates display an increase in energy dissipation of up to 7.1-fold with respect to neat epoxy and 53% more than materials prepared with pCNT and BCP at the same CNT load (0.3 wt %), while reduced Young's Modulus shows no significant change with CNT type and increases up to 25% compared to neat epoxy E* at a CNT load of 0.3 wt %. The energy dissipation performance of nanocomposites is also reflected by the lower wear coefficients of materials with PSgCNT and BCP compared to those with pCNT and BCP, as determined by abrasion tests. Furthermore, scanning electron microscopy (SEM) images taken on wear surfaces show that materials incorporating PSgCNT and BCP exhibit much more surface deformation under shear forces in agreement with their higher ability to dissipate more energy before particle release. We propose that the synergistic effect observed in energy dissipation arises from hierarchical assembly of PSgCNT and BCP within the epoxy matrix and provides clues that the CNT-BCP interface has a significant role in the mechanisms of energy dissipation of epoxy coating modified by CNT-BCP conjugates. These findings provide a means to design epoxy-based coatings with high-energy dissipation performance.
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Here we report the electrochemical performance of a interesting three-dimensional (3D) structures comprised of zero-dimensional (0D) cobalt oxide nanobeads, one-dimensional (1D) carbon nanotubes and two-dimensional (2D) graphene, stacked hierarchically. We have synthesized 3D self-assembled hierarchical nanostructure comprised of cobalt oxide nanobeads (Co-nb), carbon nanotubes (CNTs), and graphene nanosheets (GNSs) for high-performance supercapacitor electrode application. This 3D self-assembled hierarchical nanostructure Co3O4 nanobeads-CNTs-GNSs (3D:Co-nb@CG) is grown at a large scale (gram) through simple, facile, and ultrafast microwave irradiation (MWI). In 3D:Co-nb@CG nanostructure, Co3O4 nanobeads are attached to the CNT surfaces grown on GNSs. Our ultrafast, one-step approach not only renders simultaneous growth of cobalt oxide and CNTs on graphene nanosheets but also institutes the intrinsic dispersion of carbon nanotubes and cobalt oxide within a highly conductive scaffold. The 3D:Co-nb@CG electrode shows better electrochemical performance with a maximum specific capacitance of 600 F/g at the charge/discharge current density of 0.7A/g in KOH electrolyte, which is 1.56 times higher than that of Co3O4-decorated graphene (Co-np@G) nanostructure. This electrode also shows a long cyclic life, excellent rate capability, and high specific capacitance. It also shows high stability after few cycles (550 cycles) and exhibits high capacitance retention behavior. It was observed that the supercapacitor retained 94.5% of its initial capacitance even after 5000 cycles, indicating its excellent cyclic stability. The synergistic effect of the 3D:Co-nb@CG appears to contribute to the enhanced electrochemical performances.
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We study the depth sensitivity and spatial resolution of subsurface imaging of polymer nanocomposites using second harmonic mapping in Kelvin Probe Force Microscopy (KPFM). This method allows the visualization of the clustering and percolation of buried Single Walled Carbon Nanotubes (SWCNTs) via capacitance gradient (∂C/∂z) maps. We develop a multilayered sample where thin layers of neat Polyimide (PI) (â¼80 nm per layer) are sequentially spin-coated on well-dispersed SWCNT/Polyimide (PI) nanocomposite films. The multilayer nanocomposite system allows the acquisition of ∂C/∂z images of three-dimensional percolating networks of SWCNTs at different depths in the same region of the sample. We detect CNTs at a depth of â¼430 nm, and notice that the spatial resolution progressively deteriorates with increasing depth of the buried CNTs. Computational trends of ∂C/∂z vs CNT depth correlate the sensitivity and depth resolution with field penetration and spreading, and enable a possible approach to three-dimensional subsurface structure reconstruction. The results open the door to nondestructive, three-dimensional tomography and nanometrology techniques for nanocomposite applications.