Hydroxyapatite growth on cotton fibers modified chemically

We have prepared carboxymethyl cellulose fibers (CMC) by chemically modifying cotton cellulose with monochloroacetic acid and calcium chloride solution. This modification favored the growth of hydroxyapatite (HAP) on the surface of the CMC fibers in contact with simulated body fluid solutions (SBF). After soaking in SBF for periods of 7, 14 and 21 days, formation of HAP was observed. Analysis by scanning electron microscopy and X-ray diffraction showed that crystallinity, crystallite size, and growth of HAP increased with the soaking time. The amount of HAP deposited on CMC fibers increased greatly after 21 days of immersion in the SBF, while the substrate surface was totally covered with hemispherical aggregates with the size of the order of 2 microns. Elemental analysis showed the presence of calcium and phosphate, with calcium/phosphate atomic ratio of 1.54. Fourier transform infrared spectroscopy bands confirmed the presence of HAP. The results suggest that cotton modified by calcium treatment has a nucleating ability and can accelerate the nucleation of HAP crystals.     

Single-ion-conducting nanocomposite polymer electrolytes based on PEG400 and anionic nanoparticles: Part 2. Electrical characterization

Molecular relaxation and polarization phenomena of twelve single-ion-conducting nanocomposite polymer electrolytes (nCPEs) are studied using Broadband Electrical Spectroscopy (BES). The electrolytes are obtained by combining PEG400 oligomers with increasing amounts of anionic nanofiller comprised of fluorinated-TiO₂ associated with Li⁺ cations (LiFT^®), resulting in [PEG400/(LiFT)_y] systems with 0 ≤ y ≤ 26.4. This new class of [PEG400/(LiFT)_y] electrolytes allows us to achieve a significant single-ion conductivity (1.1·10⁻⁵ S cm⁻¹ at 30 °C for n_Li/n_O = 0.113) without the addition of lithium salts. To the best of our knowledge, this is the highest conductivity value reported for this class of electrolytes. This study, in conjunction with the results reported in Part 1, leads us to hypothesize a conduction mechanism in terms of two types of long-range charge-transfer processes. The first charge-transfer occurs at the interface between the filler nanoparticles and filler-PEG domains, while the second occurs through the PEG400 matrix with the assistance of polymer segmental motion. The measured Li⁺ transference numbers confirm that the studied materials are single-ion conductors.     

Modification of appetite by bread consumption: A systematic review of randomized controlled trials

The inclusion of different ingredients or the use of different baking technologies may modify the satiety response to bread, and aid in the control of food intake. The aim of this study was to perform a systematic search of randomized clinical trials on the effect of bread consumption on appetite ratings in humans. The search equation was (“Bread”[MeSH]) AND (“Satiation”[MeSH] OR “Satiety response”[MeSH]), and the filter “clinical trials.” As a result of this procedure, 37 publications were selected. The satiety response was considered as the primary outcome. The studies were classified as follows: breads differing in their flour composition, breads differing in ingredients other than flours, breads with added organic acids, or breads made using different baking technologies. In addition, we have revised the data related to the influence of bread on glycemic index, insulinemic index and postprandial gastrointestinal hormones responses. The inclusion of appropriate ingredients such as fiber, proteins, legumes, seaweeds and acids into breads and the use of specific technologies may result in the development of healthier breads that increase satiety and satiation, which may aid in the control of weight gain and benefit postprandial glycemia. However, more well-designed randomized control trials are required to reach final conclusions.     

Viscosity virtual sensor to control combustion in fossil fuel power plants

Thermo-electrical power plants utilize fossil fuel oil to transform the calorific power of fuel into electric power. An optimal combustion in the boiler requires the fuel oil to be in its best conditions. One of fuel's most important properties to consider is viscosity. Viscosity has influence on the optimal combustion between fuel and air. Hardware viscosity meters for fuel oils are expensive and unreliable to operate in power plant conditions. Chemical laboratory measures viscosity accurately with special apparatus, but they cannot be used in a real time process. This paper describes the development of a virtual sensor that estimates fuel oil viscosity in the combustion process of a power plant. A virtual sensor or soft sensor is a computer program that estimates the value of a certain variable based on related measurements and a model of the process where the variable participates. In this project, a probabilistic model is constructed using automatic learning algorithms with historical data and experts' advice. The learning and validation experiments are described and discussed. The virtual sensor is installed in the Tuxpan Power Plant in Veracruz, Mexico.