Study of Adsorption/Desorption Effect of 2-Mercaptobenzothiazole as Sweet Corrosion Inhibitor on API-5L X60 Steel

Protection of Metals and Physical Chemistry of Surfaces - In this study, the inhibitive performance of 2-mercaptobenzothiazole (2MBT) and 2-aminobenzothiazole (2ABT) were investigated on API-5L X60...     

Groundwater Level Simulation Using Soft Computing Methods with Emphasis on Major Meteorological Components

Water Resources Management - Precise estimation of groundwater level (GWL) might be of great importance for attaining sustainable development goals and integrated water resources management....     

Studies on influence of hydrogen and carbon monoxide concentration on reduction progression behavior of molybdenum oxide catalyst

Temperature programmed reduction (TPR) analysis was applied to investigate the chemical reduction progression behavior of molybdenum oxide (MoO₃) catalyst. The composition and morphology of the reduced phases were characterized by X-ray diffraction spectroscopy (XRD), X-ray photoelectron spectroscopy (XPS), and field emission scanning electron microscopy (FE-SEM). The reduction progression of MoO₃ catalyst was attained with different reductant types and concentration (10% H₂/N₂, 10% and 20% CO/N₂ (%, v/v)). Two different modes of reduction process were applied. The first approach of reduction involved non-isothermal mode reduction up to 700 °C, while the second approach of reduction involved the isothermal mode reduction for 60 min at 700 °C. Hydrogen temperature programmed reduction (H₂-TPR) results showed the reduction progression of three-stage reduction of MoO₃ (Mo⁶⁺ → Mo⁵⁺ → Mo⁴⁺ → Mo⁰) with Mo⁵⁺ and Mo⁴⁺. XRD analysis confirmed the formation of Mo₄O₁₁ phase as an intermediate phase followed by MoO₂ phase. After 60 min of isothermal reduction, peaks of metallic molybdenum (Mo) appeared. Whereas, FESEM analysis showed porous crater-like structure on the surface cracks of MoO₂ layer which led to the growth of Mo phase. Meanwhile, the reduction of MoO₃ catalyst in 10% carbon monoxide (CO) showed the formation of unstable intermediate phase of Mo₉O₂₆ at the early stage of reduction. Furthermore, by increasing 20% CO led to the carburization of MoO₂ phase, resulted in the formation of Mo₂C rather than the formation of metallic Mo, as confirmed by XPS analysis. Therefore, the presented study shows that hydrogen gave better reducibility due to smaller molecular size, which contributed to high diffusion rate and achieved deeper penetration into the MoO₃ catalyst compared to carbon monoxide reductant. Hence, the reduction of MoO₃ in carbon monoxide atmosphere promoted the formation of Mo₂C which was in agreement with the thermodynamic assessment.     

Dynamic Analysis of Truss‐Beam System

As the manned exploration of space continues, many complex structural components are being developed to construct the orbital platforms that will be used to house communication hardware, personnel, and manufacturing complexes. These components are extremely flexible and complex in their behavior. There is a need for a simple method for determining the dynamic characteristics of these space structures with a minimum of effort. A mathematical model of one of these structural elements, an articulating truss beam, has been developed to predict its dynamic response. Assumptions of the force interaction between the beam elements and the joints have been made for using this model. Algorithms are provided to determine the flexibility matrix of the truss beam for use in the equation of motion. The natural frequencies obtained from using this method are compared with those obtained by the finite element method. An experimental procedure is planned to validate the results from the theoretical method.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.