Corrosion Behavior of 316LN and 316 Stainless Steels During Long-term Exposure to Aerated 0.5 M NaCl Using Electrochemical Noise Technique

In the present work 316LN as well as 316 stainless steel (SS) coupons each of dimensions (0.025 × 0.018 × 0.006 m³) in well-polished condition were used as two nominal electrodes which were immersed in the aerated solution of 0.5 M NaCl. Correlated current and potential electrochemical noise (EN) signals were collected at 1 Hz sampling frequency for 1 h daily over a period of 30 days. The detrended EN data were used to calculate the noise resistance (R _N) as well as the spectral noise resistance at zero frequency (R _SN^o) values and other statistical parameters. To study the nature of pits and distribution of their diameters as well as depths, extensive observations of the pitted and the blank specimens were carried out using Confocal Laser Scanning Microscopy (CLSM). The current and the potential records of the two alloys showed distinct differences in their corrosion behavior. It was observed that within less than 4 h of immersion, 316SS showed signals indicative of unstable pitting and onset of stable pitting was noticed after 48 h of exposure. However, until about 24 h, 316LN showed just the random signals and unstable pitting was observed after 28 h. The signals clearly indicated continuous growth of the stable pits in 316SS as against the repassivation of the unstable pits in 316LN after 7 days exposure. It was observed that R _N values showed a continuous decrease in the case of 316SS, but were quite stable in the case of 316LN over the exposure period. Concurrent to these observations it was observed that 316SS specimen was extensively pitted. The frequency distributions of pit diameters as well pit depths were observed to be highest at 10-20 μm and 5-10 μm, respectively. However, pits with as large as 70-80 μm diameter and as deep as 20-25 μm too were observed. No pits were observed in case of 316LN even after 30 days of exposure, an observation that corroborates well with the stable R _N values. Thus, in the present investigation, the long-term tests using EN technique coupled with CLSM studies conclusively prove that many unstable pits initiated in 316SS turn into stable ones resulting in insidious localized corrosion attack whereas the unstable pits initiated in 316LN get passivated in the simulated coastal environment.     

Studies on the structural and ionic transport properties at elevated temperatures of La1−xMnO3±δ synthesized by a wet chemical method

Structural transformation and ionic transport properties are investigated on wet-chemically synthesized La_1−xMnO₃ (x=0.0–0.18) compositions. Powders annealed in oxygen/air at 1000–1080 K exhibit cubic symmetry and transform to rhombohedral on annealing at 1173–1573 K in air/oxygen. Annealing above 1773 K in air or in argon/helium at 1473 K stabilized distorted rhombohedral or orthorhombic symmetry. Structural transformations are confirmed from XRD and TEM studies. The total conductivity of sintered disks, measured by four-probe technique, ranges from 5 S cm⁻¹ at 298 K to 105 S cm⁻¹ at 1273 K. The ionic conductivity measured by blocking electrode technique ranges from 1.0×10⁻⁶ S cm⁻¹ at 700 K to 2.0×10⁻³ S cm⁻¹ at 1273 K. The ionic transference number of these compositions ranges from 3.0×10⁻⁵ to 5.0×10⁻⁵ at 1273 K. The activation energy deduced from experimental data for ionic conduction and ionic migration is 1.03–1.10 and 0.80–1.00 eV, respectively. The activation energy of formation, association and migration of vacancies ranges from 1.07 to 1.44 eV.     

Differential evolution with Gaussian mutation for combined heat and power economic dispatch

This paper presents differential evolution with Gaussian mutation to solve the complex non-smooth non-convex combined heat and power economic dispatch (CHPED) problem. Valve-point loading and prohibited operating zones of conventional thermal generators are taken into account. Differential evolution (DE) is a simple yet powerful global optimization technique. It exploits the differences of randomly sampled pairs of objective vectors for its mutation process. This mutation process is not suitable for complex multimodal optimization. This paper proposes Gaussian mutation in DE which improves search efficiency and guarantees a high probability of obtaining the global optimum without significantly impairing the simplicity of the structure of DE. The effectiveness of the proposed method has been verified on five test problems and three test systems. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed differential evolution with Gaussian mutation-based approach is able to provide better solution.     

An improved differential evolution trained neural network scheme for nonlinear system identification

This paper presents an improved nonlinear system identification scheme using di？erential evolution （DE）, neural network （NN） and Levenberg Marquardt algorithm （LM）. With a view to achieve better convergence of NN weights optimization during the training, the DE and LM are used in a combined framework to train the NN. We present the convergence analysis of the DE and demonstrate the efficacy of the proposed improved system identification algorithm by exploiting the combined DE and LM training of the NN and suitably implementing it together with other system identification methods, namely NN and DE＋NN on a number of examples including a practical case study. The identification results obtained through a series of simulation studies of these methods on different nonlinear systems demonstrate that the proposed DE and LM trained NN approach to nonlinear system identification can yield better identification results in terms of time of convergence and less identification error.     

A differential evolution based neural network approach to nonlinear system identification

This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input–multi-output system (TRMS) to verify the identification performance.     

Differential Evolution and Levenberg Marquardt Trained Neural Network Scheme for Nonlinear System Identification

This paper proposes a new nonlinear system identification scheme using differential evolution (DE), neural network and Levenberg Marquardt algorithm (LM). Here, DE and LM in a combined framework are used to train a neural network for achieving better convergence of neural network weight optimization. A number of examples including a practical case-study have been considered for implementation of different system identification methods namely, only NN, DE+NN and DE+LM+NN. After, a series of simulation studies of these methods on the different nonlinear systems it has been confirmed that the proposed DE and LM trained NN approach to nonlinear system identification has yielded better identification results in terms of time of convergence and less identification error.     

Cholesterol is critical to the integrity of neuronal porosome/fusion pore

Secretion is one of the most fundamental cellular processes. Porosomes have been demonstrated as the universal secretory machinery in cells. Earlier studies determine the presence of a number of proteins in porosomes, among them the N- and P/Q-type calcium channels, actin, syntaxin-1, synaptotagmin-1, vimentin, the N-ethylmaleimide-sensitive factor (NSF), the chloride channel CLC-3, and the alpha subunit of the heterotrimeric GTP-binding protein G(o). Studies demonstrate that t-SNAREs localize at the base of porosomes, and directly interact with calcium channels. In the present study, we demonstrate that Syntaxin-1 co-localizes with cholesterol in solubilized synaptosomal membrane preparations. Depletion of cholesterol, results in the dissociation of both Syntaxin-1 and N-type calcium channel from neuronal porosomes. Thus, cholesterol participates as an integral component of the neuronal porosome complex, and is required for its stability.     

Novel wet-chemical synthesis and characterization of nanocrystalline CeO2 and Ce0.8Gd0.2O1.9 as solid electrolyte for intermediate temperature solid oxide fuel cell (IT-SOFC) applications

Novel wet-chemical methods of synthesis have been adopted to synthesize nano-crystalline CeO₂ and Gd-substituted compositions aiming to explore an efficient oxide ion conducting solid electrolyte for intermediate temperature solid oxide fuel cell (IT-SOFC) applications. Nano-crystalline CeO₂ powders were synthesized by combustion method using redox mixture of cerric ammonium nitrate or cerium nitrate and maleic acid or 1,3-dimethylurea and compared with high surface area CeO₂ powders prepared by hydrothermal technique with microwave precipitated precursor from aqueous solutions of (NH₄)₂Ce(NO₃)₆ and urea. The grain size achieved by the hydrothermal technique is ∼7 nm which is smaller than that of commercial nano CeO₂ powders. Conventional or microwave sintering was used to prepare dense Ce_0.8Gd_0.2O_1.9 pellets from the ceria powders made of redox mixture of cerium nitrate, 1,3-dimethylurea (DMU) and Gd₂O₃ as the starting ingredients. The samples were characterized by X-ray diffractometry (XRD), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), scanning electron microscopy (SEM), and ac impedance spectroscopy. The ionic conductivity measured for the pellet sintered at 1400 °C is 1 × 10⁻² and 2.4 × 10⁻² S/cm at 700 °C and 800 °C respectively.     

Novel organic–inorganic hybrid coatings prepared by the sol–gel process: corrosion and mechanical properties

(Hyperbranched polyurethane‐urea)/[(3‐aminopropyl)triethoxysilane]‐ZnO (HBPUU‐APTES‐ZnO) hybrid coatings were synthesized using an inexpensive mixing technique by varying the APTES‐modified ZnO concentration. The mechanical and surface properties of the hybrid coating films were studied and compared with unmodified and modified ZnO. The corrosion, solvent and abrasion resistance show significant enhancement in HBPUU‐APTES‐ZnO hybrids and their properties are increased with increasing APTES‐ZnO concentration. This hybrid coating has opened up an opportunity for automotive topcoat application. Copyright © 2012 Society of Chemical Industry