Lithium niobate (LiNbO₃) fibers, obtained from the heat treatment of composite fibers (polymer/inorganic precursors), were successfully prepared by using the blow-spinning technique. A chemical solution containing Li and Nb ions, added to poly(vinyl pyrrolidone) solution, was used as precursor solution. The best condition for producing composite fibers was determined. The morphology of green and crystallized fibers was characterized by scanning electron microscopy (SEM) and revealed fibrous structure with an average diameter around 800 nm. X-ray diffraction (XRD) measurement revealed a pure LiNbO₃ (LN) phase formation. Detailed studies of dielectric response at various frequencies and temperatures exhibited a dielectric anomaly at 364 ℃. The electrical properties (impedance, modulus, and conductivity) of the fibers were studied using impedance spectroscopy technique. The contributions of grain and grain boundary effects were observed in the LN fibers. The activation energy of the composite fibers was found to be 1.5 eV in the high temperature region (325–400 ℃).

A novel approach to distinguish soybean seed vigor based on Fourier transform infrared spectroscopy (FTIR) associated with chemometric methods is presented.

The molecular structure of wood is mainly based on cellulose, lignin, and hemicellulose. However, low concentrations of lipids, phenolic compounds, terpenoids, fatty acids, resin acids, and waxes can also be found. In general, their color, smell, texture, quantity, and distribution of pores are used in human sensory analysis to identify native wood species, which may lead to erroneous classification, impairing quality control and inspection of commercialized wood. This study developed a fast and accurate method to discriminate Brazilian native commercial wood species using Fourier Transform Infrared Spectroscopy (FTIR) and machine learning algorithms. It not only solves the limitations of traditional methods but also goes beyond as it allows fast analyses to be obtained at low cost and high accuracy. In this work, we provide the identification of five Brazilian native wood species: Angelim-pedra (Hymenolobium petraeum Ducke), Cambara (Gochnatia polymorpha), Cedrinho (Erisma uncinatum), Champagne (Dipteryx odorata), and Peroba do Norte (Goupia glabra Aubl). The results showed the great potential of FTIR and multivariate analysis for wood sample classification; here, the Linear SVM differentiated the five wood species with an accuracy of 98%. The developed method allows industries, laboratories, companies, and control bodies to identify the nature of the wood product after being extracted and semi-manufactured.