Boosted hydrogen evolution activity from Sr doped ZnO/CNTs nanocomposite as visible light driven photocatalyst

CNTs were decorated onto Sr doped ZnO nanoparticles to construct an efficient photocatalyst via a facile sol-gel method. The as-fabricated Sr doped ZnO/CNTs with recyclability exhibits Sr and CNTs content dependent hydrogen evolution activit under visible light illumination. The Sr doped ZnO/CNTs photocatalyst shows the highest hydrogen evolution rate of 2732.2 μmolh^?1g^?1, which is 33.7 and 2.83 times higher than pure ZnO and Sr doped ZnO photocatalysts, respectively. The improved hydrogen evolution activity of Sr doped ZnO/CNTs is primarily assigned to high surface area, Sr doping and construction of heterojunction, which can extend the light absorption, decrease the optical band gap and improve the charge separation. Moreover, the underlying photocatalytic mechanism is proposed on the basis of Mott-Schottky study and explains the interfacial charge transfer process from ZnO to CNTs and Sr. This work open new strategies to synthesize CNTs based nanocomposite for hydrogen evolution.     

Identification of Proteins Modulated in the Date Palm Stem Infested with Red Palm Weevil (Rhynchophorus ferrugineus Oliv.) Using Two Dimensional Differential Gel Electrophoresis and Mass Spectrometry

A state of the art proteomic methodology using Matrix Assisted Laser Desorption/Ionization-Time of Flight (MALDI TOF) has been employed to characterize peptides modulated in the date palm stem subsequent to infestation with red palm weevil (RPW). Our analyses revealed 32 differentially expressed peptides associated with RPW infestation in date palm stem. To identify RPW infestation associated peptides (I), artificially wounded plants (W) were used as additional control beside uninfested plants, a conventional control (C). A constant unique pattern of differential expression in infested (I), wounded (W) stem samples compared to control (C) was observed. The upregulated proteins showed relative fold intensity in order of I > W and downregulated spots trend as W > I, a quite interesting pattern. This study also reveals that artificially wounding of date palm stem affects almost the same proteins as infestation; however, relative intensity is quite lower than in infested samples both in up and downregulated spots. All 32 differentially expressed spots were subjected to MALDI-TOF analysis for their identification and we were able to match 21 proteins in the already existing databases. Relatively significant modulated expression pattern of a number of peptides in infested plants predicts the possibility of developing a quick and reliable molecular methodology for detecting plants infested with date palm.     

Investigation of charge transport mechanism in semiconducting La<Subscript>0.5</Subscript>Ca<Subscript>0.5</Subscript>Mn<Subscript>0.5</Subscript>Fe<Subscript>0.5</Subscript>O<Subscript>3</Subscript> manganite prepared by sol–gel method

Here in, we report the charge transport mechanism in semiconducting La_0.5Ca_0.5Mn_0.5Fe_0.5O₃ (LCMFO) polycrystalline material synthesized via sol–gel auto combustion route. X-ray diffraction (XRD) analysis confirmed the orthorhombic phase of the prepared material. Temperature dependent resistivity and impedance spectroscopy measurements have been carried out to probe the dielectric and electrical conduction mechanism which revealed a change of Mott variable range to the small polaronic hopping conduction mechanism around 303 K. The complex impedance and modulus spectra undoubtedly showed the contribution of both grain and grain boundary effect on the conduction properties of LCMFO. An equivalent circuit [(R_gbQ_gb) (R_gQ_g)] model has been used to address the electrical parameters associated with the different phases (grains and grain boundaries) having different relaxation times. The values of resistances of two phases obtained after fitting the equivalent circuit in the nyquist plot have been analyzed which confirmed the change of conduction mechanism around 303 K. The resultant change in conduction mechanism is also supported by the conductivity plots.     

Exhaust emission profiling of fatty acid methyl esters and NOx control studies using selective synthetic and natural additives

Clean Technologies and Environmental Policy - The present study was focused on the optimized biodiesel production using Moringa oleifera (M. oleifera) and rice bran oils, characterization, and...     

Composite mixed matrix membranes incorporating microporous carbon molecular sieve as filler in polyethersulfone for CO2/CH4 separation

Membrane technology has been considered a key factor for sustainable growth in high-efficiency gas separation. Current mixed matrix membranes (MMMs) technology is rising, but these membranes in the dense structure are having difficulties in operating at high pressures and scale up for commercialization. The purpose of this research is to synthesize composite MMMs (CMMMs) consisting of polyethersulfone (PES), carbon molecular sieve (CMS 1–5 wt %), and Novatex 2471 nonwoven fabric (support layer). The membranes' physical, chemical, and thermal properties were evaluated by different analytical equipment. The morphology of both PES and PES-CMS composite membranes had a porous and asymmetric structure, in which CMS was uniformly distributed in the polymer matrix. The thermal properties showed that the membranes were stable up to 350 °C with a single glass transition temperature. The functional groups in the membrane were confirmed by spectral analysis. The gas performance results showed that carbon dioxide permeance increased with increased CMS concentration and methane permeance decreased due to the hindering effect of CMS under similar operating conditions. The highest selectivity achieved was 12.774 using CMMM of 5 wt % of CMS at 10 bar, which on average was 137.80%, improved selectivity compared to pure PES membrane. The support layer was able to withstand high operating pressures and showed the ability to scale up. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48476.     

Root Cause Failure Analysis of a Domestic Pressure Cooker Through Metallurgical Characterization and Computational Simulation

Pressure cooker is a closed domestic pressure cooking vessel for use with external heat source and capable of maintaining nominal cooking pressure up to 1.0 kgf/cm² (100 kN/m² approximately) gauge nominal. In pressure cookers, despite the development of electronic controllers, the basic edition of such vessels are still equipped with fewer advanced safety functions due to economic constraints. Although the provision of pressure relief valves (PRV) is considered as one of the major protective features, however, pressure cooker failure accidents have been reported frequently. This paper describes the analysis of pressure cooker failure that failed prematurely after 1 year of service considering the design life of > 5 years. It was hypothesized that the root cause of pipe failure was either material degradation from exposure to an aggressive environment or an inherent defect in the pressure cooker. To test this hypothesis, a thorough visual examination of the exhumed failed section and the fracture surface was undertaken, followed by liquid penetrant testing, material identification, hardness testing, and metallographic analysis. Computational models of static and transient loading were also used to determine the stress distribution along the actual geometry of the failed cooker and to understand the main causes of recurrent failures. Visual and macroscale examination revealed significant body deformation at the lower dish-ended shell showing distorted locking grooves. It was also noticed that dirt and food particle, from the earlier cooking, were stuck in the pressure valves orifice. In addition, no evidence of metallurgical defect was observed. The inspection indicated that the cause of failure is primarily due to the choking of pressure relief value (PRV) and overpressure safety valves. Consequently, the pressure release occurred from the sealing side of the top lid, which resulted in its ejection.     

Recent Developments and Applications of Ionic Liquids in Gas Separation Membranes

Flue gas emissions and the harmful effects of these gases urge to separate and capture these unwanted gases. Ionic liquids due to negligible vapor pressure, thermal stability, and wide electrochemical stability have expanded its application in gas separations. A comprehensive overview of the recent developments and applications of ionic liquid membranes (ILMs) for gas separation is given. The three general classifications of ILMs, such as supported ionic liquid membranes (SILMs), ionic liquid polymeric membranes (ILPMs), and ionic liquid mixed‐matrix membranes (ILMMMs) along with their applications, for the separation of various mixed gases systems is discussed in detail. Furthermore, issues, challenges, computational study, and future perspectives for ILMs are also considered.     

Intelligent tutoring system model based on fuzzy logic and constraint-based student model

Neural Computing and Applications - A model for an intelligent tutoring system (ITS) that uses fuzzy logic and a constraint-based student model (CBM) is proposed. The goal of the ITS is to teach...     

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.