The role of the Tm3+ concentration on CaMoO4 properties processed by microwave hydrothermal under stirring condition

The compounds based on calcium molybdate (CaMoO₄) are the subject of extensive research due to their excellent optical properties and a broad range of potential technological applications. In this work, we report a systematic study of CaMoO₄:Tm³⁺ phosphors synthesized by coprecipitation and processed in a microwave-hydrothermal system at low temperature (100°C) and stirring. The effect of the Tm³⁺ doping content (0%–12%) is studied in full detail to understand their role in the CaMoO₄:Tm³⁺ morphological, structural, and luminescent properties. The X-ray diffraction, Raman, and Fourier Transform Infrared spectroscopic techniques revealed that all the prepared powders have a tetragonal crystal structure with a distinct density of cation vacancies and structural disorders. The band gap remains almost constant for doping levels lower than 8%, but it narrows strongly for powders doped with 12% Tm³⁺ ions. The designed phosphors have shown two emission bands in which intensity depends on the Tm³⁺ ions doping level. For doping levels lower than 2%, the photoluminescence profile displays a broad emission band peaking at 543 nm (green). For concentrations higher than 4%, the band centered at 543 nm decreases in intensity and the near-infrared emission band at around 800 nm, assigned to ³F₃, ³H₄ → ³H₆ transitions from Tm³⁺ ion, become more intense. The outcomes of this work reveal that appropriated Tm³⁺ ions doping levels can be applied to suppress the PL emission in the visible range and improve that in the near-infrared region in CaMoO₄-based materials.     

Fabrication of hybrid proton-exchange membranes using a brandnew high temperature ionic liquid as charge transporting and clay modifier

The search for more compatibility between ionic liquids (ILs) and polymer matrices in proton-exchange membrane fuel cells (PEMFCs) is one of the ways in which IL leaking from proton-exchange membranes could be minimized. In this work, it is presented the synthesis of an aromatic high temperature ionic liquid (HTIL), which, incorporated into an aromatic matrix such as sulfonated polyether ether ketone (sPEEK), is expected to diminish the IL leaking that normally affects PEMFC. Phenylethylammonium trifluoromethane sulfonate (PhetaTfO) was successfully synthesized and characterized. Its melting point of 88°C makes it to classify as a HTIL and it was employed as modifier of natural Montmorillonite, forming the phenylethylammonium intercalated montmorillonite (MmtPheta) and thus, ternary membranes containing PhetaTfO, MmtPheta, and sPEEK were prepared and characterized. Immersion tests demonstrated a higher compatibility of PhetaTfO with matrix when compared to the reference DemaTfO, which was reflected in up to 30% lower IL loss by the synthesized IL than the DemaTfO; X-rays diffraction (XRD) patterns demonstrated that the modified clay was properly dispersed inside the membranes, while dynamic mechanical analyses (DMA) results indicated a strong plasticizer effect along the increase of PhetaTfO content inside the membrane, while at the same time, the conductivity increased in an exponential manner, which permitted to identify an empiric exponential equation to evaluate the effect of concentration on ionic conductivity. The maximum conductivity obtained at IL concentrations of around 38 wt% was 0.2 mS/cm. It could expect high ionic conductivities of 10 mS/cm when the concentration of this IL is 60%; nevertheless, in order to achieve that, crosslinking treatments should be done to give the membranes enough mechanical resistance.     

Rheological,thermal and morphological properties of polyethylene terephthalate/polyamide 6/rice husk ash composites

This work deals with the rheological, morphological, and thermal properties of composites having poly(ethylene terephthalate) (PET), polyamide-6 (PA6), and their blends as matrices, and rice husk ash (RHA) as a filler. The study determines the effect of composition on the change in viscosity and rate of degradation during processing in a torque rheometer. Our data indicates that thermal stability and degradation during processing depend on matrix composition and filler concentration. SEM micrographs show both partial adhesion of the filler to the matrices and filler pullout. Optical microscopy shows particle agglomeration and that agglomerate size increased with filler content. FTIR investigates the shifting of absorption bands of PET/PA6 composite after the addition of RHA and attributes the selective dispersion of RHA to the formation of hydrogen bonds. Our data supports the idea that filler employed here is an option to develop polymer composites with improved properties.     

When a Tritrophic Interaction Goes Wrong to the Third Level: Xanthoxylin From Trees Causes the Honeybee Larval Mortality in Colonies Affected by the River Disease

The “River Disease” (RD), a disorder impacting honeybee colonies located close to waterways with abundant riparian vegetation (including Sebastiania schottiana, Euphorbiaceae), kills newly hatched larvae. Forager bees from RD-affected colonies collect honeydew excretions from Epormenis cestri (Hemiptera: Flatidae), a planthopper feeding on trees of S. schottiana. First-instar honeybee larvae fed with this honeydew died. Thus, we postulated that the nectars of RD-affected colonies had a natural toxin coming from either E. cestri or S. schottiana. An untargeted metabolomics characterization of fresh nectars extracts from colonies with and without RD allowed to pinpoint xanthoxylin as one of the chemicals present in higher amounts in nectar from RD-affected colonies than in nectars from healthy colonies. Besides, xanthoxylin was also found in the aerial parts of S. schottiana and the honeydew excreted by E. cestri feeding on this tree. A larva feeding assay where xanthoxylin-enriched diets were offered to 1^st instar larvae showed that larvae died in the same proportion as larvae did when offered enriched diets with nectars from RD-colonies. These findings demonstrate that a xenobiotic can mimic the RD syndrome in honeybee larvae and provide evidence of an interspecific flow of xanthoxylin among three trophic levels. Further, our results give information that can be considered when implementing measures to control this honeybee disease.

     

Alternative technique for hydroxyapatite coatings

Flame Assisted Chemical Vapor Deposition (FACVD), a novel technique that shows an enormous potential in porous oxides deposition, was employed for the first time aiming to obtain hydroxyapatite (HA) coatings on 316 L stainless steel metallic substrates. Calcium acetate and ammonium phosphate diluted in ethanol were employed as precursor salts. A Ca/P molar ratio of 1.66 was employed in precursor solution, which is equivalent to stoichiometric hydroxyapatite. A porous coating, formed by an open and interconnected network, was observed by scanning electronic microscopy (SEM) and associated with homogenous reactions. Thickness of hydroxyapatite coating was 412 ± 3 μm. X-ray diffraction (XRD) analysis indicated the presence of crystalline coatings, mainly constituted by hydroxyapatite phase and traces of tricalcium phosphate (β-TCP). Carbonate in the hydroxyapatite coatings was identified by Fourier transform-infrared (FTIR) spectroscopy.     

Pointwise and pairwise clothing annotation: combining features from social media

In this paper, we present effective algorithms to automatically annotate clothes from social media data, such as Facebook and Instagram. Clothing annotation can be informally stated as recognizing, as accurately as possible, the garment items appearing in the query photo. This task brings huge opportunities for recommender and e-commerce systems, such as capturing new fashion trends based on which clothes have been used more recently. It also poses interesting challenges for existing vision and recognition algorithms, such as distinguishing between similar but different types of clothes or identifying a pattern of a cloth even if it has different colors and shapes. We formulate the annotation task as a multi-label and multi-modal classification problem: (i) both image and textual content (i.e., tags about the image) are available for learning classifiers, (ii) the classifiers must recognize a set of labels (i.e., a set of garment items), and (iii) the decision on which labels to assign to the query photo comes from a set of instances that is used to build a function, which separates labels that should be assigned to the query photo, from those that should not be assigned. Using this configuration, we propose two approaches: (i) the pointwise one, called MMCA, which receives a single image as input, and (ii) a multi-instance classification, called M3CA, also known as pairwise approach, which uses pair of images to create the classifiers. We conducted a systematic evaluation of the proposed algorithms using everyday photos collected from two major fashion-related social media, namely pose.com and chictopia.com. Our results show that the proposed approaches provide improvements when compared to popular first choice multi-label, multi-modal, multi-instance algorithms that range from 20 % to 30 % in terms of accuracy.     

One-Way Functions Using Algorithmic and Classical Information Theories

We prove several results relating injective one-way functions, time-bounded conditional Kolmogorov complexity, and time-bounded conditional entropy. First we establish a connection between injective, strong and weak one-way functions and the expected value of the polynomial time-bounded Kolmogorov complexity, denoted here by?E(K ^t (x|f(x))). These results are in both directions. More precisely, conditions on?E(K ^t (x|f(x))) that imply that?f is a weak one-way function, and properties of?E(K ^t (x|f(x))) that are implied by the fact that?f is a strong one-way function. In particular, we prove a separation result: based on the concept of time-bounded Kolmogorov complexity, we find an interval in which every function?f is a necessarily weak but not a strong one-way function. Then we propose an individual approach to injective one-way functions based on Kolmogorov complexity, defining Kolmogorov one-way functions and prove some relationships between the new proposal and the classical definition of one-way functions, showing that a Kolmogorov one-way function is also a deterministic one-way function. A relationship between Kolmogorov one-way functions and the conjecture of polynomial time symmetry of information is also proved. Finally, we relate?E(K ^t (x|f(x))) and two forms of time-bounded entropy, the unpredictable entropy?H ^unp, in which ??one-wayness?? of a function can be easily expressed, and the Yao⁺ entropy, a measure based on compression/decompression schema in which only the decompressor is restricted to be time-bounded.     

A dynamic overproduce-and-choose strategy for the selection of classifier ensembles

The overproduce-and-choose strategy, which is divided into the overproduction and selection phases, has traditionally focused on finding the most accurate subset of classifiers at the selection phase, and using it to predict the class of all the samples in the test data set. It is therefore, a static classifier ensemble selection strategy. In this paper, we propose a dynamic overproduce-and-choose strategy which combines optimization and dynamic selection in a two-level selection phase to allow the selection of the most confident subset of classifiers to label each test sample individually. The optimization level is intended to generate a population of highly accurate candidate classifier ensembles, while the dynamic selection level applies measures of confidence to reveal the candidate ensemble with the highest degree of confidence in the current decision. Experimental results conducted to compare the proposed method to a static overproduce-and-choose strategy and a classical dynamic classifier selection approach demonstrate that our method outperforms both these selection-based methods, and is also more efficient in terms of performance than combining the decisions of all classifiers in the initial pool.     

LEGClust—A Clustering Algorithm Based on Layered Entropic Subgraphs

Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and also a clustering algorithm (LEGCIust) that builds layers of subgraphs based on this matrix and uses them and a hierarchical agglomerative clustering technique to form the clusters. Our approach capitalizes on both a graph structure and a hierarchical construction. Moreover, by using entropy as a proximity measure, we are able, with no assumption about the cluster shapes, to capture the local structure of the data, forcing the clustering method to reflect this structure. We present several experiments on artificial and real data sets that provide evidence on the superior performance of this new algorithm when compared with competing ones.