Explore the vast range of titles available.

Owing to electric-field screening, the modification of magnetic properties in ferromagnetic metals by applying small voltages is restricted to a few atomic layers at the surface of metals. Bulk metallic systems usually do not exhibit any magneto-electric effect. Here, we report that the magnetic properties of micron-scale ferromagnetic metals can be modulated substantially through electrochemically-controlled insertion and extraction of hydrogen atoms in metal structure. By applying voltages of only ~ 1 V, we show that the coercivity of micrometer-sized SmCo 5 , as a bulk model material, can be reversibly adjusted by ~ 1 T, two orders of magnitudes larger than previously reported. Moreover, voltage-assisted magnetization reversal is demonstrated at room temperature. Our study opens up a way to control the magnetic properties in ferromagnetic metals beyond the electric-field screening length, paving its way towards practical use in magneto-electric actuation and voltage-assisted magnetic storage. “Electric field control of ferromagnetism is typically limited by screening, and is restricted to the first few layers of metals. Here the authors overcome this limitation via the absorption of hydrogen into metal structure, demonstrating voltage control of magnetic properties of micron-scale SmCo5.”

We present an exact derivation of the isobaric partition function of lattice hard rods with arbitrary nearest-neighbor and long-range interactions. Free energy and all thermodynamics functions are derived accordingly, and they are written in a form that is suitable for numerical implementation. As an application, we have considered lattice rods with pure hard-core interactions, rods with long-range gravitational attraction, and finally a charged hard rods with charged boundaries (Bose gas), a model that is relevant for studying several phenomena such as charge regulation, ionic liquids near charged interfaces, and an array of charged smectic layers or lipid multilayers. In all cases, thermodynamic analyses have been done numerically using the Broyden algorithm.

This article describes the first syringe pump spray pyrolysis synthesis of orthorhombic lanthanum sesquisulfide (α-La 2 S 3 ) thin films. Two precursors lanthanum nitrate La(NO 3 ) 3 .6H 2 O and thiourea (SC(NH 2 ) 2 ) were used to develop these rare earth chalcogenides in two different “[S]:[La]” ratios of 5 and 11. The films were developed on glass and silicon substrates. The microstructure, chemical composition, and optical properties of the (α-La 2 S 3 ) films were thoroughly characterized using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), microanalysis, scanning electron microscopy (SEM), FTIR spectroscopy, and UV–Vis spectrometry. Microstructural analyses showed that both synthesized films had α-La 2 S 3 structure and were polycrystalline. The morphology, vibration intensities, and grain sizes of the films are affected by the amount of sulfide present. The XPS results give a qualitative description of La and S as well as an indication of their potential chemical state. Measurements of particle-induced X-ray emission (PIXE) provide information on the stoichiometric ratios of the constituent components in α-La 2 S 3 . The optical bandgap was calculated to be 3.39 eV and 3.45 eV, respectively, for ratios 5 and 11 of the disulfide content.

Aluminum doped zinc oxides show a high electrical conductivity owing to their high electron concentration in the conduction band, which significantly hinders the development of p-n junction due to the formation of degenerate states. To overcome this limitation, it is proposed to improve the electron mobility rather than the free electron concentration. For this specific aspect, vanadium appears to be one of the most suited alternatives as a doping element. In this work, we report on the preparation of ZnO and vanadium-doped ZnO thin films by spray pyrolysis process. Vanadium loads were varied from 0 to 4 at.% in the ZnO films and its effect on the structural, morphological, chemical, optical, and mechanical properties of the fabricated thin films was investigated through a bench of characterizations techniques, including X-ray diffraction (XRD), atomic force microscope (AFM), X-ray photoelectron spectroscope (XPS), time-of-flight secondary ion mass spectroscopy (TOF-SIMS), UV-Vis spectrophotometer, and digital Vickers microhardness tester. The obtained results demonstrate the successful formation of pristine ZnO films and V-doped ZnO, which were found to be polycrystalline with a hexagonal wurtzite crystal structure. According to the self-correlation function, AFM images reveal that the particle size increases with respect to the V-load. TOF-SIMS analyses confirm the constant distribution of Zn, O and V elements throughout the film thickness. Moreover, our films are found to be optically transparent in the 400–1200 nm range with associated band gaps energy ranging from 3.18 to 3.26 eV. Finally, mechanical measurements have been carried out using a conventional diamond-pyramidal-indenter Vickers test. The results confirmed that by increasing V concentration, the microhardness increases. Graphical abstract Highlights V-doped ZnO films successfully grown by the chemical spray pyrolysis technique. V-doped ZnO films are polycrystalline structure with a dominant (101) orientation. The surface roughness of the films is well controlled with respect to the V concentration and the average optical transmittance of all films was between 60% and 80%. TOF-SIMS analysis confirmed the presence of all the deposited elements over the surface and over the entire film thickness. The energy band gap varies from 3.18 and 3.26 eV w/r to V concentration.

Reduction of the operating temperature to an intermediate temperature range between 350 °C and 600 °C is a necessity for Solid Oxide Fuel/Electrolysis Cells (SOFC/SOECs). In this respect the application of proton-conducting oxides has become a broad area of research. Materials that can conduct protons and electrons at the same time, to be used as electrode catalysts on the air electrode, are especially rare. In this article we report on the proton conduction in expitaxially grown BaFeO2.5+δ (BFO) thin films deposited by pulsed laser deposition on Nb:SrTiO3 substrates. By using Electrochemical Impedance Spectroscopy (EIS) measurements under different wet and dry atmospheres, the bulk proton conductivity of BFO (between 200 °C and 300 °C) could be estimated for the first time (3.6 × 10−6 S cm−1 at 300 °C). The influence of oxidizing measurement atmosphere and hydration revealed a strong dependence of the conductivity, most notably at temperatures above 300 °C, which is in good agreement with the hydration behavior of BaFeO2.5 reported previously.

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM 10 ) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9 % has been reached.

The rocksalt structured (Co,Cu,Mg,Ni,Zn)O entropy-stabilized oxide (ESO) exhibits a reversible phase transformation that leads to the formation of Cu-rich tenorite and Co-rich spinel secondary phases. Using atom probe tomography, kinetic analysis, and thermodynamic modeling, we uncover the nucleation and growth mechanisms governing the formation of these two secondary phases. We find that these phases do not nucleate directly, but rather they first form Cu-rich and Co-rich precursor phases, which nucleate in regions rich in Cu and cation vacancies, respectively. These precursor phases then grow through cation diffusion and exhibit a rocksalt-like crystal structure. The Cu-rich precursor phase subsequently transforms into the Cu-rich tenorite phase through a structural distortion-based transformation, while the Co-rich precursor phase transforms into the Co-rich spinel phase through a defect-mediated transformation. Further growth of the secondary phases is controlled by cation diffusion within the primary rocksalt phase, whose diffusion behavior resembles other common rocksalt oxides. Graphical abstract

Reduction of the operating temperature to an intermediate temperature range between 350 °C and 600 °C is a necessity for Solid Oxide Fuel/Electrolysis Cells (SOFC/SOECs). In this respect the application of proton-conducting oxides has become a broad area of research. Materials that can conduct protons and electrons at the same time, to be used as electrode catalysts on the air electrode, are especially rare. In this article we report on the proton conduction in expitaxially grown BaFeO (BFO) thin films deposited by pulsed laser deposition on Nb:SrTiO₃ substrates. By using Electrochemical Impedance Spectroscopy (EIS) measurements under different wet and dry atmospheres, the bulk proton conductivity of BFO (between 200 °C and 300 °C) could be estimated for the first time (3.6 × 10 S cm at 300 °C). The influence of oxidizing measurement atmosphere and hydration revealed a strong dependence of the conductivity, most notably at temperatures above 300 °C, which is in good agreement with the hydration behavior of BaFeO reported previously.

Issue Title: Selected Papers from the 2nd Contaminated Land, Ecological Assessment and Remediation (CLEAR 2014) Conference: Environmental Pollution and Remediation Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 [mu]m (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9 % has been reached.

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 mu m (PM sub(10)) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9 % has been reached.