Behaviour of hydrogen impurity in aluminium alloys during anodizing

The present study examines the behaviour of hydrogen impurity in an Al-6.5 at.% W alloy during anodizing, using elastic recoil detection and nuclear reaction analyses. Increased concentrations of hydrogen are found near the alloy/anodic film interface, amounting to ∼2×10¹⁵ H atoms cm⁻² for the particular alloy, containing 0.1-0.3 at.% hydrogen in the bulk regions, and conditions of anodizing. The enrichment arises from hydrogen in the alloy (i) diffusing to the interface, which acts as a trap, or (ii) accumulating at the interface, due to the growth of the anodic film, or a combination of both processes. Diffusion is consistent with the known mobility of hydrogen in aluminium near ambient temperature. Further, accumulation, and subsequent oxidation, of hydrogen are expected based on the general behaviour of alloying elements in anodized aluminium. The anodic films contained ∼0.1-0.3 at.% hydrogen, originating from either the electrolyte or the alloy.     

Effect of water vapor and hydrocarbons in removing NOxby using nonthermal plasma and catalyst

Removal of NO_x in flue gas was investigated by using nonthermal plasma with catalysts. In this experiment, flue gas contained 5%-15% water vapor and hydrocarbons, as well as nitrogen, oxygen, and carbon dioxide. Catalysts tested in this paper were copper- and sodium-coated zeolite (CuZSM-5, NaZSM-5) and a conventional three-way catalyst (Pt-Rh, alumina cordierite). The simulated flue gases had from 0% to 15% water vapor, 70% NO removal was achieved with NaZSM-5 catalyst at 200°C-500°C, with 10% moisture and the power to the reactor turned off. High-temperature removal of NO_x was the result of plasma chemical reactions and adsorption in the catalyst. However, nonthermal plasma degrades the NO_x removal with CuZSM-5 catalyst, when the gas temperature is 300°C or above. When the gas temperature was 100°C, the nonthermal plasma process was enhanced by the combination of nonthermal plasma with any type of catalyst. The catalysts investigated in this paper do not work at lower temperatures by themselves. Adsorption characteristics were also investigated and only NaZSM-5 catalyst showed significant adsorption     

Electrical characterization of poly(amide-imide) for application in organic field effect devices

The admittance spectra and current–voltage (I–V) characteristics are reported of metal–insulator–metal (MIM) and metal–insulator–semiconductor (MIS) capacitors employing cross-linked poly(amide–imide) (c-PAI) as the insulator and poly(3-hexylthiophene) (P3HT) as the active semiconductor. The capacitance of the MIM devices are constant in the frequency range from 10 Hz to 100 kHz, with tan δ values as low as 7 × 10⁻³ over most of the range. Except at the lowest voltages, the I–V characteristics are well-described by the Schottky equation for thermal emission of electrons from the electrodes into the insulator. The admittance spectra of the MIS devices displayed a classic Maxwell–Wagner frequency response from which the transverse bulk hole mobility was estimated to be ∼2 × 10⁻⁵ cm² V⁻¹s⁻¹ or ∼5 × 10⁻⁸ cm² V⁻¹s⁻¹ depending on whether or not the surface of the insulator had been treated with hexamethyldisilazane (HMDS) prior to deposition of the P3HT. From the maximum loss observed in admittance-voltage plots, the interface trap density was estimated to be ∼5 × 10¹⁰ cm⁻² eV⁻¹ or ∼9 × 10¹⁰ cm⁻² eV⁻¹ again depending whether or not the insulator was treated with HMDS. We conclude, therefore, that HMDS plays a useful role in promoting order in the P3HT film as well as reducing the density of interface trap states. Although interposing the P3HT layer between the insulator and the gold electrode degrades the insulating properties of the c-PAI, nevertheless, they remain sufficiently good for use in organic electronic devices.     

Avalanche breakdown voltage of GaAs hyperabrupt junctions

The avalanche breakdown voltage of a GaAs hyperabrupt junction diode is calculated by using unequal ionization rates for electrons and holes, and shown graphically as a function of the parameters which characterize the impurity profile of the diode. The breakdown voltage decreases abruptly at the critical point of the characteristic length L_c which varies in accordance with the impurity concentration N₀ at X = 0. For example, the critical length L_c is 7.7 × 10⁻⁶ cm and 3.3 × 10⁻⁵ cm for N₀ = 1 × 10¹⁸ cm⁻³ and 1 × 10¹⁷ cm⁻³, respectively. The breakdown voltage of a diode with extremely short or long characteristic length can be estimated from the results for corresponding abrupt junctions. The experimental results agree well with the calculated ones.     

Comparison of isolation methods using commercially available kits for obtaining extracellular vesicles from cow milk

Extracellular vesicles (EV) are important for delivering biologically active substances to facilitate cell-to-cell communication. Milk-derived EV are widely known because of their potential for immune enhancement. However, procedures for isolating milk-derived EV have not been fully established. To obtain pure milk-derived EV and accurately reveal their function, such procedures must be established. The aim of the present study was to compare methods using commercially available kits for isolating milk-derived EV. Initially, we investigated procedures to remove casein, which is the major obstacle in determining milk-derived EV purity. We separated whey using centrifugation only, acetic acid precipitation, and EDTA precipitation. Then, we isolated milk-derived EV by ultracentrifugation, membrane affinity column, size exclusion chromatography (SEC), polymer-based isolation, or phosphatidylserine-affinity isolation. Using EV count per milligram of protein, which is a good indicator of purity, we determined that acetic acid precipitation was the best method for removing casein. Using nanoparticle tracking analysis, protein quantity analysis, and RNA quantity analysis, we comprehensively compared each isolation method for its purity and yield. We found that SEC-based qEV column (Izon Science) could collect purer milk-derived EV at higher quantities. Thus, a combination of acetic acid precipitation and qEV can effectively isolate high amounts of pure extracellular vesicles from bovine milk.     

Determination of γ-glutamyl-valyl-glycine in raw scallop and processed scallop products using high pressure liquid chromatography–tandem mass spectrometry

The determination of the kokumi peptide, γ-glutamyl-valyl-glycine (γ-Glu-Val-Gly) in raw scallop and processed scallop products was carried out using high pressure liquid chromatography–tandem mass spectrometry (LC/MS/MS). The detection of γ-Glu-Val-Gly was achieved using a multiple reaction monitoring (MRM) method. The optimised condition enabled the precise determination of γ-Glu-Val-Gly. Raw scallop contained 0.08 μg/g γ-Glu-Val-Gly, and the γ-Glu-Val-Gly levels in processed scallop products, such as dried-scallop and scallop extract, were measured to be 0.64 and 0.77 μg/g, respectively. This is the first report to confirm the existence of γ-Glu-Val-Gly in foodstuff.     

Numerical computation of two-loop box diagrams with masses

A new approach is presented to evaluate multi-loop integrals, which appear in the calculation of cross-sections in high-energy physics. It relies on a fully numerical method and is applicable to a wide class of integrals with various mass configurations. As an example, the computation of two-loop planar and non-planar box diagrams is shown. The results are confirmed by comparisons with other techniques, including the reduction method, and by a consistency check using the dispersion relation.     

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.