Influence of the Ethylene Glycol, Water Treatment and Microwave Irradiation on the Characteristics and Performance of VPO Catalysts for n-Butane Oxidation to Maleic Anhydride

In this paper, the preparation of vanadium phosphate catalysts was shown to be improved by (1) using V₂O₅ and ethylene glycol as starting and reducing agent material, respectively for VOPO₄ · 2H₂O, (2) subsequent water treatment and (3) microwave irradiation. In particular, the preparation route, based on the reduction of VOPO₄ · 2H₂O with various alcohols, is described in detail and contrasted with other three established methods performed by using ethylene glycol and isobutyl alcohol as reductant and solvent for V₂O₅ or distilled water as a solvent material. The preparation of catalyst precursor is carried out by two different methods, namely conventional heating and microwave irradiation. With this new technique, catalysts derived from the reduction of VOPO₄ · 2H₂O by ethylene glycol exhibit substantially higher surface area (typically >40 m² g^?1) and activity. In fact, the surface area of the catalyst is significantly enhanced when the precursor is refluxed by distilled water and dried by microwave heating. The characterization of catalysts was carried out using X-ray diffraction (XRD), Brunauer–Emmer–Teller (BET) surface area measurement, temperature programmed reduction (H₂-TPR), temperature-programmed reaction (TPRn) and scanning electron microscopy (SEM). This study shows that employing ethylene glycol as reducing agent, followed by adding the water treatment step to catalyst synthesis procedure, and using microwave irradiation would give rise to enhanced surface area, activity and selectivity of the catalyst. Moreover, it introduces a more energy efficient procedure for preparation of vanadium phosphate catalyst used in selective oxidation of n-butane process.     

Isogeometric analysis of coupled thermo-elastodynamic problems under cyclic thermal shock

The isogeometric analysis (IGA) method was extended for the solution of the coupled thermo-elastodynamic equations. The dimensionless formulation was accepted in discretization of the uncoupled and coupled thermoelasticity equations and the Generalized Newmark method was used in the time integration procedure. First, the performance of the proposed method was verified against a two-dimensional benchmark example subjected to constant thermal shock with available exact analytical solutions. Then a two-dimensional half-space benchmark example under thermal shock was solved. Finally, cyclic thermal shock (CTS) loading was applied on the half-space problem. The results dedicated that IGA can be used as a suitable approach in the analysis of the general thermomechanical problems.     

On the practicability of a new bio sorbent: Lasani sawdust and coconut coir for cleanup of oil spilled on water

In this article, two bio-sorbents have been selected: lasani sawdust (LS) (a new bio-based material) and coconut coir (CC) for the removal of used motor oil from the aqueous phase. The physical nature of the materials was characterized using Fourier Transform Infrared Spectroscopy and Constitutional Analysis of lignin and cellulose. The adsorption process was evaluated using various kinetic and adsorption models. The evaluated sorption capacities for coconut coir and lasani sawdust were 12.82?g g^?1 and 0.36?g g^?1, respectively. Maximum sorption of oil from the aqueous solution conveniently took place in 20?minutes. To ascribe statistically which model describes the adsorption phenomenon best, Root Mean Square Error (RMSE) and Average Relative Error (ARE) were used. The kinetics of the adsorption was best described by Pseudo-second order. Similarly, Langmuir isotherm model had the least value for the two error functions and a higher q_max value for coir than for lasani. It was concluded that the increased absorptive ability of coir over lasani was due to the difference in the composition of lignin and cellulose of the two materials.     

Effect of key process variables on mechanical properties of blended vortex spun yarns

A study of significant factors and their interaction during vortex yarn spinning has been carried out to achieve desired mechanical properties of the polyethylene terephthalate/cotton blended yarns which can offer process and performance advantages. The key significant factors, i.e. feed ratio, air pressure and spindle size were varied in a mixed-level factorial design. The mechanical properties (count lea strength product, tenacity and elongation at break) were studied and feed ratio was found to significantly affect the tenacity and elongation at break of spun yarns. A significant effect of these parameters was observed on the diameter and hairiness of the Muratec vortex spun yarn.     

Vibration control of laminated truncated conical shell via magnetostrictive layers

Abstract

In this study feedback control is applied to control the free vibration response of an isotropic truncated conical shell embedded with magnetostrictive layers. Classical shell theory is applied to derive the shell vibration equations. The results are derived based on the Galerkin method and the results are compared with published results and the results of finite element software in order to determine the accuracy of using method. The influence of several parameters such as the thickness of magnetostrictive layers, control gain, length and radius of the large edge of the shell on the vibration suppression of fundamental frequency is determined.     

Zinc-doped hydroxyapatite and poly(propylene fumarate) nanocomposite scaffold for bone tissue engineering

Hydroxyapatite (HA) is a bioceramic material that shares similar crystal and chemical structures with inorganic components of the bone. However, HA lacks osteoinductive activity and has a brittle nature, making it challenging to apply for direct load-bearing bone applications. In this study, we used a wet chemical method to synthesize zinc-doped hydroxyapatite powders with different Zn/(Zn+Ca) molar ratios of 0, 0.025, 0.05, and 0.1. The corresponding Zn-HA was designated as HA, Zn2.5-HA, Zn5-HA, and Zn10-HA. The Zn-HA powders at 30 wt% were used to fabricate poly(propylene fumarate) (PPF)-based nanocomposite scaffolds (HA/PPF, Zn2.5-HA/PPF, Zn5-HA/PPF, and Zn10-HA/PPF). The physical properties of obtained scaffolds were examined by scanning electron microscopy, energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), and atomic force microscopy (AFM). Live/dead cell viability assay showed that these scaffolds were biocompatible and supported excellent adhesion of MC3T3-E1 preosteoblast cells. Additionally, the proliferation of cells was detected at 1, 4, and 7 days on these scaffolds. Alkaline phosphatase (ALP) activity measurement and alizarin red staining showed good osteogenic differentiation and matrix mineralization for MC3T3-E1 cells growing on these scaffolds. Taken together, the results here indicate that Zn5-HA/PPF nanocomposite scaffolds are promising scaffold material for bone tissue engineering.

Graphical abstract

     

Investigation of functional core-rim composite part production by inserted powder injection moulding

In this study, the use of Cu and Ni interlayers have been investigated for functional core-rim composite part production with WC-Co 9?wt-% feedstock/steel. For this purpose, different experiments have been performed and joining condition, shear strength and microstructure of the intermediate region have been examined. It has been found that AISI 4340 insert/WC-Co have been joined and 85.8?MPa shear strength achieved, but high speed steel insert has not joined. Moreover, it has been determined that better results are obtained with Ni interlayer. Under the same conditions, when the 40?µm Ni interlayer has been used between AISI 4340 core and WC-Co rim, shear strength has been increased approximately twice and has been 162.7?MPa.     

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.