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The use of natural fibres for polymer composite applications has been widely researched due to the biodegradable and lightweight nature of natural fibres. To achieve good adhesion and compatibility between the matrix and the fibre filler, prior modification of the fibre surface via the use of various methods has been found to be effective. The natural fibres have been modified using chemical, physical, radiation, grafting and biological methods. The current study aims to evaluate the effect of sodium hydroxide-treated waste pineapple leaf fibres (PALF) content on the chemical, thermal, and morphological properties of polybutylene succinate (PBS) composites. PBS-PALF composites with fibre content ranging from 0 to 20 wt% were prepared using an internal mixer and their properties were studied using Fourier transform infrared (FTIR), X-ray diffraction (XRD), Differential scanning calorimetry (DSC), Thermogravimetric analysis (TGA) and Scanning electron microscope (SEM). The FTIR results showed no noticeable functionality differences among the composites, however, carbonyl groups from PBS polymer at ∼1700 cm-1 and hydroxyl groups from PALF at ∼3000 cm-1 were observed in the composites. The water absorption uptake of the composites increased with fibre content due to the hydrophilic nature of the PALF fibres and the highest water absorption percentage achieved was ∼30 %. The incorporation of the fibres into the PBS matrix decreased the crystallinity of the composites as shown by the XRD peaks at 2Ï´ = 22 and 30°. SEM images of the composites with 20 wt% exhibited morphologies where the fibres protruded out from the polymer matrix, and this was ascribed to the agglomerated fibres which were poorly mixed with the matrix at the higher fibre content. Overall, the incorporation of high PALF content in the composites disrupted the crystallinity and thermal stability of the PBS matrix. The composites have potential in industrial agricultural mulching film applications due to their sustainability characteristics.

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An erbium ion (Er3+ )-activated gadolinium aluminate (GdAlO3 ) nanophosphor was synthesized by utilizing urea assisted gel-combustion method. The crystal structure along with all other crystal parameters was determined by X-ray diffraction (XRD) patterns. The selected samples are of orthorhombic phase with Pnma space group. The agglomerated particles within nanorange have been confirmed by transmission electron microscopy (TEM) micrographs. Elemental investigation was performed by energy dispersive X-ray spectroscopy (EDX). Photoluminescence excitation (PLE) spectrum reveals a strong excitation band corresponding to the gadolinium ion (Gd3+ ) (276 nm) and a band near ultraviolet (UV) absorption for Er3+ (377 nm). Strong excitation band of Gd3+ was evident for the energy transfer between Gd3+ and Er3+ ions. All the doped sampled are excited at 377 nm wavelength. The photoluminescence (PL) spectrum exhibits an intense band at 546 nm (4 S3/2 → 4 I15/2 ) which is responsible for the green emission in the processed samples. The color coordinate values define their color in the green region and correlated color temperature (CCT) values affirm their utility as a cold light source.

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Beaches with monazitic sands show high natural radiation, and the knowledge of this radiation is fundamental to simulate the effects of natural terrestrial radiation on biological systems. Monazite-rich sand from a beach in the southeastern Brazil were collected and analyzed by X-ray fluorescence, X-ray diffraction, and magnetic susceptibility. The natural terrestrial radiation of the beach sand showed a positive correlation with the Th and Y elements, which are closely associated with Ce, Nd, Ca, and P, suggesting that this grouping is mainly associated with local natural radiation. Based on the sand characterization, a physical simulator of natural gamma radiation was built with parameters similar to those of the monazite beach sand, considering areas with high natural radiation levels. The simulation revealed that the natural radiation of the monazite sands has a significant effect on reducing the growth of the bacteria strains of E. coli and S. aureus present in the beach sand, with a reduction of 23.8% and 18.4%, respectively.

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The direct use of ethanol in fuel cells presents unprecedented economic, technical, and environmental opportunities in energy conversion. However, complex challenges need to be resolved. For instance, ethanol oxidation reaction (EOR) requires breaking the rigid C-C bond and results in the generation of poisoning carbonaceous species. Therefore, new designs of the catalyst electrode are necessary. In this work, two trimetallic PdxAgyNiz/C samples are prepared using a facile borohydride reduction route. The catalysts are characterized by X-ray diffraction (XRD), Energy-Dispersive X-ray spectroscopy (EDX), X-ray photoelectron Spectroscopy (XPS), and Transmission Electron Microscopy (TEM) and evaluated for EOR through cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS). The XRD patterns have shown a weak alloying potential between Pd, and Ag prepared through co-reduction technique. The catalysts prepared have generally shown enhanced performance compared to previously reported ones, suggesting that the applied synthesis may be suitable for catalyst mass production. Moreover, the addition of Ag and Ni has improved the Pd physiochemical properties and electrocatalytic performance towards EOR in addition to reducing cell fabrication costs. In addition to containing less Pd, The PdAgNi/C is the higher performing of the two trimetallic samples presenting a 2.7 A/mgPd oxidation current peak. The Pd4Ag2Ni1/C is higher performing in terms of its steady-state current density and electrochemical active surface area.

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Materials design and discovery through the high-throughput exploration of materials space has been recognized as a new paradigm in materials science. However, typical high-throughput exploration methods deliver high-dimensional and very diverse data sets that pose the challenge of extracting the key features and patterns that could guide the discovery process. Unraveling patterns is a nontrivial task as quite often the underlying physical phenomena are uncertain and latent variables governing the performance are mainly unknown. In this paper, we discuss challenges related to designing a data analytics tool for clustering high-throughput measurements performed on the compositional library of materials. The critical aspects of our methodology are (i) learning the similarity measures, as opposed to using fixed similarity measures (e.g., Euclidean distance, dynamic time warping), while (ii) imposing the similarity in the composition space. Our methodology is based on the multitask learning approach that is formulated to account for the composition neighborhoods that are specific to the compositional libraries. We demonstrate the advantages of our methodology for the library of cyclic voltammetry curves generated for model multimetal catalysts, as well as X-ray diffraction patterns from experimental studies. We also compare our approach with the current state-of-the-art methods used in similar problems. This work has important implications for designing high-throughput exploration including catalysts for electrochemical systems, such as fuel cells and metal-air batteries.

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