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Molecular sieves of MCM-41 impregnated with MgO, MoO3, and Mo2C synthesized using a wet impregnation method. The compounds developed herein, were characterized by means of XRD, SEM-EDS and FT-IR techniques. The production capacity of acetaldehyde from ethanol was analyzed by catalytic processes employing as-synthesized MCM-41. The obtained results show that the changes in crystallinity of MCM-41 were due to the impregnation of oxides, which generate damage to the structure of MCM-41. However, the characteristics of the compounds are favorable, allowing them to be used as heterogeneous catalyst material due to the similarity of the characteristics between MCM-41 with and without of Mg and Mo oxides. The catalysis tests show the influence between the type of catalyst used and the temperature applied to the production process of acetaldehyde from ethanol, obtaining the best results in the samples impregnated with Mo2C at 250 °C, with a production percentage of acetaldehyde of 80.7% and 77.9% for the catalysts impregnated with 0.5% and 2.0% Mg and 3.0% Mo carbide, respectively.

In this work, a horseradish peroxidase (HRP) was immobilized onto diatomites by covalent bonding. Results indicated that the enzyme loading increased when diatomites were modified with (3-Aminopropyl)triethoxysilane (APTES) and glutaraldehyde. The immobilization was confirmed by SEM/EDX, XRD, DRIFT, and TGA analysis. Higher HRP concentrations of the immobilization solution and immobilization time had also a positive effect on the enzyme loading. Orange II (OII) adsorption onto diatomites and oxidative catalytic activity was evaluated. Results demonstrated that diatomites had a low OII adsorption capacity. However, under the presence of hydrogen peroxide, the dye removal was highly increased due to the catalytic activity of the immobilized HRP. A mathematical model that adequately describes the simultaneous adsorption and enzymatic oxidation of OII in batch tests was developed. Finally, immobilized diatomites were tested in the decolourization reaction of Orange II in a fixed-bed column reactor. Column results demonstrated that the immobilized HRP remained active for at least 12 h during three sequential OII removal tests.

In this research, the adsorption capacity of diatomaceous earth in the removal of the crystal violet dye (CV) in aqueous solution was evaluated. The experimental methodology began with the determination of the texture properties by adsorption-desorption isotherms with N2 a 77 K, the identification of functional groups by Fourier transform infrared spectrophotometry (FTIR), morphology by scanning electron microscopy (SEM) and, the pH of the isoelectric point by the point of zero charge (PZC). A categorical multifactorial design was developed with factors such as the initial concentration of the dye (300-1000 mg/dm3), the temperature (30, 35 and 40ºC) and the initial pH of the solution (5, 6, 7 and 8). The maximum adsorption capacity was of 96.1 mg/g up 30ºC and pH 8, satisfactorily fitting the experimental data to the Langmuir isotherm model with R2 = 0.99, suggesting a monolayer adsorption mechanism on a homogeneous surface. In conclusion, diatomaceous earth can be considered as an efficient adsorbent in the removal of CV in aqueous solution.

Lubricating oils are used in moving parts to minimize friction. The use of Nanofluids can enhance the properties of lubricants. The aim of this work is an enhancement of the rheology properties of base oil. The surface modification was conducted for inorganic nanoparticles (NPs) TiO₂-Al₂O₃ with oleic acid (OA) in this research. The OA renders the TiO₂-Al₂O₃ surface hydro-phobic. The OA-treated NPs show very stable dispersion in lubricating oil even after more than one month, compared with the unmodified NPs. The surface modification of NPs was characterized by Fourier transform infrared spectroscopy (FTIR). In this research, treated TiO2-Al₂O₃ are added to raw Iraqi lubricating oil 40 stock as a hybrid Nanofluid to study the rheology properties such as kinematic viscosity, viscosity index, and the stability of nano-fluid. Four different concentrations were prepared with a range of 0.2% to 1% by weight. Pour points and flashpoint was also identified. The TiO₂-Al₂O₃ nano lubricant shows a significant improvement in the viscosity index by 5.3–9.1 %, while the flashpoint increased by 5.8 – 14.36 % comparing with base oil.

Functional materials that have a prominent place in high technology are not classified according to their origin (metals, ceramics, polymers, glass, etc.) or processing technology, but by the functions they can perform. Zeolites are an interesting and important raw material for preparing a variety of target substances. From a traditional point of view, they are aluminosilicate oxide with a nanoporous structure. However, zeolites are adsorbents, ion exchangers, catalysts, and can be used in medicine, agriculture, construction and many other applications. The availability of natural minerals, as well as methods of synthesis of their analogues, and modification of both natural and synthetic materials, developed in sufficient detail, allows to obtain a variety of substances with predetermined properties, which are tailored for specific purposes. This paper will discuss a wide range of existing and also possible applications of these materials, such as catalysis, photocatalysis and sensor design. The specified modifications are achieved through controlled changes in the chemical composition of the zeolite surface.

In organizations, one of the biggest challenges lies in strategic decision-making. In the case of Project Portfolio Management, companies have been incorporating classic tools to reduce uncertainty in this process. This document proposes a new model based on system dynamics, with a different approach to classic strategic project management models. Initially a conceptual model constructed using the root definition of the "Soft System Methodology", proposed by the causal relationships proposed by Peter Chekland and Peter Senge, is proposed. Subsequently, the flow model built on the basis of what is proposed by Jhon Sterman is presented. The model translates the interactions of strategic thinking, project portfolio management, project management and production management into a mathematical model that explains the forms of growth and decline in the real system. It was applied in the Colombian poultry sector in order to validate it. The model is a virtual learning environment that reduces uncertainty in strategic decision-making associated with project portfolios.

Data is a fundamental part of information and management systems; thus, controlling its complexity is an essential step nowadays. NoSQL databases adopt new approaches to data management differing from relational structures. For this study, two database systems are considered, MongoDB as a NoSQL data storage model and PostgreSQL as a relational data model, they are compared and evaluated on a job search system. For this purpose, a dataset was defined, and its representation was constructed in databases based on each technology. This process allowed the data modeling in terms of the best practices, then, the development of a test plan prepared the environment for the determination of the comparison metrics of both databases under the methodology specified by the International Software Testing Qualifications Board (ISTQB) and the types of database testing. This study determined that the SQL schema provides greater functionality, that ensures the support of transactions and data integrity, the opposite happened with the NoSQL schemas, resulting in more efficient but lacking functionalities that are characteristic and required for data representation of a consistent system.

This work presents a new energy model based on the blocks’ functions for low power sensor nodes analysis. The procedure described generalizes the majority of low power sensor nodes applications by splitting their composing blocks into only five blocks plus the power unit. For each unit, a review of the most recent low power techniques is analyzed, as well as some of the most accepted low power principles to apply in sensor nodes. The current modeling offers a generalization since most of the existing models are application-dependent and are constrained to some fields of interest, such as wireless sensor nodes. An analytical formulation is offered, and a case study is shown as a usage example.

This work proposes a simulation approach to characterize the behavior of DP600 steel subjected to micro indentation tests. Axisymmetric and three-dimensional artificial RVEs were generated with statistical data from metallographic images. FEM simulations of the micro indentation tests used the Gurson model and Rodriguez-Gutierrez equation. There is proposed a new parameter called spherical radius to quantify the closeness of the hard particles to the indenter tip. The simulated 3D curves were compared with respect to a reference curve of DP600, obtaining relative errors below 20 percent. Results show that the hard particles near the indenter affect the stress, strain, and micro void fields by changing the indentation response behavior. The spherical radius allows capturing the effect of the hard particles closeness because when its value is low, or the hard particles are closer to the indenter tip, the relative error of the average curve decreases due to the hard particles are constraining the deformation caused by the indenter in the material.

Polymer composites are the result of incorporation of nanoparticles into the polymers and can lead to improvements even with a very small amount of reinforcement which can be tuned according to the applications. In order to understand the behaviour of these polymer composites we need to perform a number of characterizations and analyses which in turn requires investment of money and time.  Thus, to reduce the number of characterizations and analyses for developing polymer composites, computational techniques can prove helpful. By means of a computational technique known as artificial neural network (ANN), prediction of the thermo-mechanical properties was made possible. Here dynamic mechanical analysis (DMA) data set was used for characterization of polycarbonate / calcium carbonate-SiO2 core shell composites (polycarbonate composites). Using the dataset, the selected ANN model consisted of a network of [3-10-1]. The prediction accuracy achieved using ANN method, was around 90%. Applicability and performance of ANN to the existing system was also confirmed by mean squared error (MSE), which is favourably small for this case, in the range of 10-5. The output predicted by ANN had a coefficient of correlation of 0.999. Furthermore, sensitivity analysis confirmed the importance of various input variables in relation with output. An optimization of the variables facilitated to maximize the conditions thus predicting glass transition temperature.