Fernando Allende Álvarez

Triblock copolymers functionalized with quaternary ammonium salts as dehydrating agents for heavy and extra-heavy crude oils

ABSTRACT Block copolymers PEO-PPO-PEO α,ω-functionalized with tertiary aliphatic or aromatic amines were synthesized to obtain a new type of ammonium salt. The compounds were evaluated as demulsifying agents in extra-heavy and heavy Mexican crude oils, 7.5° and 17.8° API. The grafting at the copolymer ends with trioctylamine and quinoline demonstrated to be the best functionalization for increasing the demulsifying efficiency in both crude oils. It was evidenced, for copolymers functionalized with aliphatic amines, that the length of the alkyl chain of the amine plays an important role in the potential to remove emulsified water. Furthermore, it was observed that higher positive value of log P parameter has a strong correlation with the performance of block copolymers functionalized with aliphatic amines. In the case of aromatic amines, the atomic charge of nitrogen proved to be an important factor to provoke the coalescence of the water drops. Finally, the molecular mass of block copolymer was also evaluated, showing that copolymers with higher are more efficient to remove the water from crude oils with huge amounts of asphaltenes. GRAPHICAL ABSTRACT

Novel petroleum demulsifiers based on acrylic random copolymers

Abstract Although normally the copolymers used to remove water from crude oil have a block structure, two series of random acrylic copolymers were evaluated as petroleum demulsifiers. These acrylic copolymers, with two different butyl acrylate (BuA) and 2-ethylhexyl acrylate (2-EHA) contents, were synthesized by semi-continuous emulsion polymerization. Their weight-average molar mass () was varied by modifying the amount of chain transfer agent relative to the monomers from 0 to 8 wt%. FTIR and 13C-NMR spectroscopies confirmed that the conversion of the monomers was complete. The synthesized acrylic copolymers were subsequently evaluated as demulsifier agents in a Mexican heavy crude oil (10.24°API). Copolymers with 70 wt% butyl acrylate monomer and a weight-average molar mass above of 11 000 g mol−1 presented the greatest ability to remove emulsified water from the system. The demulsifying capability of the copolymer was evidenced to be strongly dependent on their molecular mass. Highlights Synthesis of random acrylic copolymers (BuA-co-2-EHA) of different average molar mass (). Evaluation of random acrylic copolymers as demulsifiers of heavy crude oil. Weight-average molar mass () and monomer composition play an important role on the performance of random acrylic copolymers as demulsifiers. GRAPHICAL ABSTRACT

Assessment of Novel Water-Borne Coatings Obtained from Composite Latex Particles and Reinforced with TiO2 Nanotubes

Novel, environmentally friendly waterborne coatings were obtained from the filmification of nanostructured latex particles reinforced with inorganic nanotubes. The latex used to form the coatings consists of core-shell particles with a shell functionalized with different amounts of acrylic acid (AA). This external polymer layer was doped, in some cases, with TiO2 nanotubes at three different concentrations: 100, 500 and 1000 ppm. The composite particles were synthesized in two steps by semi-continuous emulsion polymerization at 75°C. A series of films was prepared by employing core-shell particles with different sizes, core cross-linking and shell functionalization. The coatings obtained were characterized by infrared spectroscopy (FTIR), tapping mode atomic force microscopy (TM-AFM), scanning electron microscopy (SEM), differential scanning calorimetry (DSC) and thermogravimetrical analysis (TGA). Drying rates and tests were also performed to further evaluate these films. It was observed that the addition of small amounts of TiO2 nanotubes contributes to improve the application properties, mainly adhesion to metallic substrates and water impermeability. The resistance to thermal degradation was also strongly increased, as showed by the DSC and TGA analyses. GRAPHICAL ABSTRACT

Neural networks for fitting PES data distributions of asphaltene interaction

Neural networks methodology is a tool to get potential energy surface (PES) in cases where there is too much dispersion of data; hence, a binding energy fitting can be found with this methodology on asphaltene-asphaltene molecular interaction. A data distribution of intermolecular pair potential (UAA) interaction in a vacuum between two molecular asphaltenes systems using compass classical force field has been previously reported (Energy Fuels 2006, 20, 195). In the latter, all possible interactions between the species were taken into account. Focusing in their data distribution, we have applied neural networks on the following molecule-molecule geometry orientations with the purpose of obtaining energy vs. contact distance, which is the minimum distance where the interacting species is not equal to zero: i) face-to-face distribution of asphaltene-asphaltene interactions; ii) all geometry asphaltene-asphaltene discrete distributions, and iii) the random distribution of asphaltene-asphaltene interactions. Neural networks fit provide a potential energy surface through a function approximation, for a data distribution of high dispersion; hence a binding energy is found with this methodology.