Extraction equilibrium conditions of beryllium and aluminium from a beryl ore for optimal industrial beryllium compound production

This study examines the extraction of beryllium and aluminium from a Nigerian beryl ore using Cyanex®272 in kerosene from an aqueous sulphate pregnant solution. Parameters such as extractant concentration and equilibrium pH that dictates the extraction yield were studied. Under the following conditions: temperature 27?±?2°C, phase ratio 1?1, about 45.50 and 46.76% of beryllium and aluminium were extracted by 0.15?mol?L^?1 Cyanex®272 concentration within 30?min. However, the extraction yield of beryllium and aluminium was increased to 91.68% and 97.89% at equilibrium pH of 3 and 4, respectively, for beryllium and aluminium at 27?±?2°C. A 0.05?mol?L^?1 H₂SO₄ solution was found to be adequate for the stripping of about 99.00% Be and 95.40% Al from the loaded organic phase. The pure solutions containing metal ions were accordingly beneficiated to obtain beryllium and aluminium compounds of industrial values.     

Graded evolution of anisotropic microstructure during sintering from crystal-oriented powder compact

Anisotropic sintering, including shrinkage and grain growth, was examined for c-axis-oriented (Sr,Ca)₂NaNb₅O₁₅ (SCNN) ceramics, which were prepared by colloidal processing under a magnetic field. In the c-axis-oriented SCNN powder compact, shrinkage and grain growth along the c-axis were higher than those along the a-axis. The anisotropic microstructural development was clearly associated with anisotropic sintering shrinkage. X-ray diffraction, scanning electron microscopy, and energy back scattering diffraction showed that the grain growth of oriented particles by including random grains contribute to the development of the oriented microstructure. Finally, the highly crystal-oriented SCNN ceramics with a densified microstructure were obtained through anisotropic sintering. These results clearly showed the potential to develop a well-defined anisotropic microstructure during sintering by designing and controlling the particle packing structure in a powder compact.     

Technological and sensorial properties of liquid nitrogen ice cream enriched with protein from brewing waste (trub)

Debittered trub (brewing waste) is an important source of protein source (70.26%). Trub and whey protein were used for 5% protein enrichment of ice cream frozen by liquid nitrogen. Three formulations were elaborated: ice cream standard (ICS), ice cream with whey protein (ICW) and ice cream with trub (ICT). Chemical composition, rheological properties, texture, overrun, melting rate, scanning electron microscopy and a sensorial test were performed. Results showed that ICT presented a higher viscosity, obtained on the upward curve up to 6.76 Pa s⁻¹, consistency index (22.96 (Pa s⁻¹)ⁿ), hysteresis area (140.40 mPa s⁻¹) and hardness (31113.33 g) but a lower melting rate (0.38 g min⁻¹), overrun (13.92%) and sensorial acceptability than the other formulations. The addition of trub debittered for protein enrichment improved ice cream properties and demonstrated that it could be used as a food ingredient.     

Alginate@Layered Silicate Composite Beads: Dye Elimination,Box–Behnken Design Optimization and Antibacterial Property

Journal of Inorganic and Organometallic Polymers and Materials - The discharge of industrial waste comprising organic pollutants into aquatic environment induces numerous health risks. Crosslinked...     

Application results of a prototype ultrasonic liquid film sensor to a 7 MPa steam–water two-phase flow experiment

A prototype ultrasonic liquid film sensor was applied to a high-temperature steam–water two-phase flow experiment. The liquid film sensor was vertically installed in a loop which was connected to HUSTLE, a multi-purpose steam source test facility. The hydraulic diameter of the measurement section was 9.4 mm. The output waveforms of the sensor were acquired with a digital oscilloscope. The fluid temperature and system pressure were kept at 288 °C and 7.2 MPa, respectively, during the experiment. The pulse-echo method was used to calculate the liquid film thickness. The cross-correlation calculation was utilized to determine the time difference between the pulse reflected at the sensor surface and the pulse reflected at the liquid film surface. The time-averaged liquid film thicknesses were less than 0.055 mm in the annular flow condition. The increase of the time-averaged thickness was small with the change of the gas momentum flux. The film thicknesses measured with the sensor were compared with the past experimental results; the former were smaller than one-fourth of the thickness estimated as the mean film thickness. The comparison results suggested that the continuous liquid sublayer thickness was measured with the liquid film sensor.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Phase Equilibria in the Tl<Subscript>2</Subscript>Te-Tl<Subscript>5</Subscript>Te<Subscript>3</Subscript>-Tl<Subscript>9</Subscript>TmTe<Subscript>6</Subscript> Section of the Tl-Tm-Te System

Phase equilibria in the Tl₂Te-Tl₅Te₃-Tl₉TmTe₆ section of the Tl-Tm-Te ternary system were experimentally studied by using the powder x-ray diffraction technique, differential thermal analysis, as well as microhardness measurements applied to equilibrated alloys. Several isopleth sections and isothermal section at 680 K, as well as projections of the liquidus and solidus surfaces, were constructed. The Tl₅Te₃-Tl₉TmTe₆ section is characterized by the formation of continuous series of solid solutions (δ-phase) with Tl₅Te₃ tetragonal structure, which penetrate deep into the concentration triangle and occupy more than 90% of its area. A narrow area of solid solutions (α-phase) based on Tl₂Te was detected.