Complex structural contribution of the morphotropic phase boundary in Na0.5Bi0.5TiO3 - CaTiO3 system

The correlation between structure and dielectric properties of lead-free (1-x)Na_0.5Bi_0.5TiO₃ - xCaTiO₃ ((1-x)NBT - xCT) polycrystalline ceramics was investigated systematically by X-ray diffraction, combined with impedance spectroscopy for dielectric characterizations. The system shows high miscibility in the entire composition range. A morphotropic phase boundary (MPB), at 0.09?≤?x?<?0.15 was identified where rhombohedral and orthorhombic symmetries coexist at room temperature. The fraction of orthorhombic phase increases gradually with x in the MPB region. Dielectric measurements reveal that the relative permittivity increase with addition of Ca²⁺. This behavior is unusual with this kind of doping. A thermal hysteresis occurred only in the MPB composition which varies in a non-monotonically manner with x, detected by dielectric properties. This phenomenon is related to the crystalline microstructure by a linear relationship between the fraction of each phase and dielectric properties, and, more precisely, to the strong interaction between rhombohedral and orthorhombic phases.     

Palladized dysprosium fluoride nanorods as a new performance catalyst in direct methanol fuel cell

Fuel cells are a new type of batteries that produce electricity from a continuous source of alcohols as long as fuel is inserted. In this study, decorated palladium nanoparticles (PdNPs) on dysprosium fluoride (DyF₃) nanorods (DyFNRs)‐multiwalled carbon nanotubes (MWCNTs) were used for electrooxidation of methanol. DyFNRs were synthesized by the hydrothermal method, and the proposed multifunctional catalyst (DyFNRs/MWCNT‐PdNPs) was identified by several methods such as X‐ray diffraction, elemental mapping images, field emission scanning electron microscopy, energy dispersive analysis of X‐rays, and transmission electron microscopy which demonstrated a uniform distribution and high dispersion of the PdNPs on the supports. The electrocatalytic activity toward methanol electrooxidation on glassy carbon electrode (GCE) with DyFNRs/MWCNT‐PdNPs (DyFNRs/MWCNT‐PdNPs/GCE) was investigated by cyclic voltammetry (CV) and chronoamperometry (CA). Experimental results showed a high improvement in oxidation potential and peak current of methanol electrooxidation by DyFNRs/MWCNT‐PdNPs in comparison to DyFNRs and PdNPs. The values of the catalytic rate constant (k) and physical dimension (D_s) for methanol oxidation on the DyFNRs/MWCNT‐PdNPs/GCE catalyst were calculated 0.008 s^?1 and 1.43, respectively. Moreover, the order of reaction was determined to be 0.43 and 0.13 for CH₃OH and NaOH, repectively. Finally, the synthesized catalyst was evaluated in direct methanol fuel cell (DMFC). The single DMFC with proposed anodic catalyst, DyFNRs/MWCNT‐PdNPs, indicated a power density of 4.4 mW·cm^?2 at a current density of 18 mA·cm^?2 in alcohol (1 M) and NaOH (1 M).     

Photo-electrochemistry of metallic titanium/mixed phase titanium oxide

In this study, the effect of potassium hydroxide concentration in anodization bath, anodization time, and calcination temperature on the photo-electrochemical behavior of metallic titanium/mixed phase titanium oxide is investigated. Further, the phase structure of a titanium oxide photocatalyst prepared on a titanium electrode through a high-voltage anodization method is examined. The study exploits photo-electrochemical, Fourier transform infrared spectroscopy attenuated total reflectance (FTIR–ATR), X-ray diffraction, and Raman spectroscopic methods to obtain better insights into the mechanism of mixed-phase titanium oxide formation. In this regard, the photo-electrochemical properties of the photocatalysts prepared in single excitation energy, violet light (410 nm), were investigated. The anodization time and the potassium hydroxide concentration in the anodization bath have significant effects on the photo-electrochemical properties of the photocatalysts. The experiments show that the effect of potassium hydroxide concentration is a function of the anodization potential applied, demonstrating different patterns as the anodization potential changes. Furthermore, FTIR-ATR, X-ray diffraction, and Raman spectroscopic studies reveal that the extended anodization times decrease the population of OH-containing groups, leading to lower photo-electrochemical performance. On the other hand, the formation of anatase phases becomes more favorable only in the extended anodization times before application of the calcination process. Additionally, the calcination temperature has a significant impact on the anatase to rutile ratio. Finally, increasing potassium hydroxide concentration leads to the formation of an amorphous titanium oxide layer. It can be concluded that the obtained information might have a significant impact on the preparation of titanium oxide and other metal oxide photocatalysts through the high voltage anodization process.     

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.     

BSig: evaluating the statistical significance of biclustering solutions

Statistical evaluation of biclustering solutions is essential to guarantee the absence of spurious relations and to validate the high number of scientific statements inferred from unsupervised data analysis without a proper statistical ground. Most biclustering methods rely on merit functions to discover biclusters with specific homogeneity criteria. However, strong homogeneity does not guarantee the statistical significance of biclustering solutions. Furthermore, although some biclustering methods test the statistical significance of specific types of biclusters, there are no methods to assess the significance of flexible biclustering models. This work proposes a method to evaluate the statistical significance of biclustering solutions. It integrates state-of-the-art statistical views on the significance of local patterns and extends them with new principles to assess the significance of biclusters with additive, multiplicative, symmetric, order-preserving and plaid coherencies. The proposed statistical tests provide the unprecedented possibility to minimize the number of false positive biclusters without incurring on false negatives, and to compare state-of-the-art biclustering algorithms according to the statistical significance of their outputs. Results on synthetic and real data support the soundness and relevance of the proposed contributions, and stress the need to combine significance and homogeneity criteria to guide the search for biclusters.     

Influence of Reaction Conditions on Diacid Formation During Au-Catalyzed Oxidation of Glycerol and Hydroxymethylfurfural

The selective oxidation of glycerol and 5-hydroxymethylfurfural (HMF) to diacids over supported gold catalysts (Au/C and Au/TiO₂) in liquid water at mild temperatures was a strong function of the added base such as NaOH. Use of hydrotalcite as a solid base in place of NaOH in the HMF reaction medium facilitated the production of diacid over Au/TiO₂, but extensive leaching of magnesium suggested that hydrotalcite was consumed stoichiometrically in the reaction. Production of diacids from glycerol oxidation over supported Au catalysts was promoted by operating in a continuous flow reactor and by increasing the catalyst loading in a semi-batch reactor. Trace inhibitors formed by conversion of the product monoacid are proposed to account for the generally low selectivity to diacids over gold catalysts.