The role of impregnants in determining the behaviour of graphite anodes

The lifetime of graphite anodes in sulphuric acid media can be increased by a factor of 3.2 by multiple impregnation with coal tar pitch. For single impregnation, linseed oil yielded the largest improvement. The results were interpreted on the premise that properties such as viscosity, melting point and coking value determine the effectiveness of the impregnant. The proper selection of the impregnant and procedure of impregnation is dependent on the electrolysis temperature.     

A Bayesian assessment of the PCB temporal trends in Lake Erie fish communities

The temporal trends of polychlorinated biphenyls (PCBs) in Lake Erie fish were evaluated using 30 years of fish contaminant data (1977–2007). The first step of our statistical analysis was based on simple exponential decay models parameterized with Bayesian inference techniques to assess the declining rates in four intensively sampled fish species, i.e., walleye (Stizostedion vitreum), coho salmon (Oncorhynchus kisutch), rainbow trout (Oncorhynchus mykiss) and white bass (Morone chrysops). Because the exponential model postulates monotonic decrease of the PCB levels, we included first- or second-order random error terms in our statistical formulations to accommodate non-monotonic patterns in the dataset studied. Generally, our results suggest that the PCBs have been decreasing over the last 30 years with relatively weak rates that vary among the different fish species examined. Yet, our analysis with the exponential decay model also identified an increasing trend in the PCB concentrations of walleye skinless–boneless filet data, which is manifested after the mid-90s. In the second step, we used dynamic linear modeling (DLM) analysis to account for the fact that the fish length covaries with the PCB concentrations and that different sized fish may have been sampled over time. Our DLM analysis suggests that the previously reported trend of the walleye filet data is actually an artifact associated with the bias of the fish sampling practices followed. The coho salmon and rainbow trout PCB concentrations have been decreasing steadily during the study period but the associated rates were relatively weak. Finally, the PCB trends in white bass appear to have been stabilized over that last decade, although the robustness of this result remains to be confirmed due to the temporal inconsistencies of the information used. We conclude by emphasizing the importance of explicitly accounting for the different covariates (e.g., length, age, lipid content) that can potentially hamper the detection of the actual temporal trends of fish contaminants.     

Non-linear inelastic analysis of steel arches at elevated temperatures

An innovative non-discretisation mechanical-based method is developed in this paper to analyse a steel arch at elevated temperatures so that its behaviour can be quantified. The steel arch has a generic but singly-symmetric cross-section with elastic and plastic parts, and it is subjected to an arbitrary thermal profile which varies along the length of the arch as well as through the depth of the cross-section. The effects of geometric and material non-linearity as well as potential catenary action which can occur at high temperatures are taken into account in the formulation. The efficiency and accuracy of the generic model developed is demonstrated by a comparison with a finite element model undertaken using ABAQUS. The proposed method is then utilised to elucidate some significant factors, such as the magnitude of the temperature at bottom fibre of the cross-section as well as the ratio of the temperature at the top fibre to that at the bottom fibre, on the response of a steel arch member during fire loading. The proposed model provides a computationally superior formulation to that of commercial finite element packages, and forms a platform which can be used for structural steel arch design and evaluation in the development of codified approaches to fire design on a performance basis.     

Design of an Energy Harvesting Capable Pixel in 90 nm CMOS Technology Optimized for Implantable Retinal Prosthesis

An energy harvesting capable pixel is designed in 90 nm standard complementary metal oxide semiconductor (CMOS) technology with the spectral response optimization considerations. The pixel can perform 2.22 nW power harvesting in 60 klx of illumination per pixel, while the pixel itself consumes 56.26 pW power. Moreover, the pixel could achieve 1.70 μV?e^? conversion gain and 60.72 dB of dynamic range. The high energy harvesting capability in spite of 90 nm CMOS technology power production limitations is achieved due to triple and dual junctions applications in photosensitive area and floating diffusion regions respectively. In addition, the pixel is engineered to utilize all available pn junctions in energy harvesting mode. It should be noted that, using a triple junction in photosensitive area has enabled spectral response engineering capability, which results in an optimized spectral response of pixel for the spectrums that the human eye exhibits most relative sensitivity (within a spectral range of about 550 nm).     

Ultrasensitive plasmonic sensor based on MDM waveguide containing Persian Orsi window cavities for refractive index and blood plasma sensing

We developed a plasmonic refractive index (RI) with a metal-dielectric-metal (MDM) structure that utilizes two Persian Orsi windows-like separated cavities with a high figure of merit (FoM) and ultrasensitivity. The simulated and optimized Ag-air-Ag MDM sensor for surface plasmon resonance (SPR) offers high RI sensitivity (SRI) and the ability to detect blood plasma concentration (BPC). The results verified that structural parameters have an effect on SRI, full width at half maximum (FWHM), FoM, sensitivity of blood plasma (Sp) for right and left peaks, whose values are 1 345.45 nm.RIU−1, 32 nm, 42.04 RIU−1, 0.26 and 0.19 nm.L.g-1, respectively. The proposed design opens a new horizon in sensor development.     

Detection of the spatiotemporal trends of mercury in Lake Erie fish communities: a Bayesian approach

The temporal trends of total mercury (THg) in four fish species in Lake Erie were evaluated based on 35 years of fish contaminant data. Our Bayesian statistical approach consists of three steps aiming to address different questions. First, we used the exponential and mixed-order decay models to assess the declining rates in four intensively sampled fish species, i.e., walleye (Stizostedion vitreum), yellow perch (Perca flavescens), smallmouth bass (Micropterus dolomieui), and white bass (Morone chrysops). Because the two models postulate monotonic decrease of the THg levels, we included first- and second-order random walk terms in our statistical formulations to accommodate nonmonotonic patterns in the data time series. Our analysis identified a recent increase in the THg concentrations, particularly after the mid-1990s. In the second step, we used double exponential models to quantify the relative magnitude of the THg trends depending on the type of data used (skinless-boneless fillet versus whole fish data) and the fish species examined. The observed THg concentrations were significantly higher in skinless boneless fillet than in whole fish portions, while the whole fish portions of walleye exhibited faster decline rates and slower rates of increase relative to the skinless boneless fillet data. Our analysis also shows lower decline rates and higher rates of increase in walleye relative to the other three fish species examined. The food web structural shifts induced by the invasive species (dreissenid mussels and round goby) may be associated with the recent THg trends in Lake Erie fish.     

Air pollution forecasting application based on deep learning model and optimization algorithm

Air pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO₂ and SO₂ by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO₂, and SO₂ for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO₂ and SO₂); type (2) includes just lagged values of the output variables (NO₂ and SO₂). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables.

Graphic abstract

     

Silver recovery from radiographic film processing effluents by hydrogen peroxide: Modeling and optimization using response surface methodology

The recovery of silver from X-ray film processing effluents by precipitation using hydrogen peroxide as the precipitating agent was studied. Response surface methodology (RSM) and central composite design (CCD) were applied to achieve optimum conditions. Linear, square and interactions effects between parameters to study of a second order polynomial equation were obtained. Optimum condition included the volume of H₂O₂ 0.8 ml/min, pH=5.5, ethylene glycol 9 ml in the experimental condition. In these conditions silver recovery percentage was predicted as 92.8%. The experiment was conducted in triplicate under optimized conditions. Silver recovery percentage and average of precipitate were obtained as 91.5% and 423.19mg, respectively, which were close to the predicted amount achieved by the model.     

Mechanism of the corrosion inhibition of Zn Anode in NaOH by gelatine and some inorganic anions

The quantity of electricity required for passivation of Zn anode in NaOH solutions has been determined in absence and presence of different concentrations of gelatine, phthalic acid, sodium sulphide, sodium silicate or potassium chromate. The results obtained for gelatine in dilute alkali conform with a simple Langmuir model, whereas deviations are observed for the rest of the additives. The concentrations needed to establish a certain inhibiting efficiency increase wth increase in alkali concentration. The results are interpreted in the light of dissolution-precipitation mechanism.     

The effect of changes in the microstructure of steel on the inhibitive properties of some anodic and cathodic inhibitors

The electrode behaviour of spectroscopically pure Fe, steel samples containing 0·15–1·3%C and grey cast Fe in aerated 0·1M solutions of NaNO₂, Na benzoate and ZnSO₄, in 0·01M quinoline and in saturated CaCO₃ has been studied. The anodic (and cathodic) behaviour of these samples under potentiostatic or galvanostatic conditions has been followed in de-aerated NaNO₂ and ZnSO₄ solutions. It has been shown that anodic inhibitors are more sensitive to changes in the C-content of the samples than cathodic inhibitors, ZnSO₄ being almost indifferent to these changes.The protective nature of the cubic oxide formed in NaNO₂ or Na benzoate solutions is less efficient in presence of separate phases of ferrite, carbide or graphite than in its absence. Quinoline is more preferentially adsorbed on these separate phases. The effectiveness (in situ) of precipitated Ca(OH)₂ in blocking cathodic sites is counteracted by the presence of separate phases.