Acidity Characterization of Amorphous Silica-Alumina

Surface characterization of amorphous silica-alumina (ASA) by COads IR, pyridine(ads) IR, alkylamine temperature-programmed desorption (TPD), Cs+ and Cu(EDA)(2)(2+) exchange, H-1 NMR, and m-xylene isomerization points to the presence of a broad range of Bronsted and Lewis acid sites. Careful interpretation of IR spectra of adsorbed CO or pyridine confirms the presence of a few very strong Bronsted acid sites (BAS), typically at concentrations lower than 10 mu mol/g. The general procedure for alkylamine TPD, which probes both Bronsted and Lewis acidity, is modified to increase the selectivity to strong Bronsted acid sites. Poisoning of the m-xylene isomerization reaction by a base is presented as a novel method to quantify strong BAS. The surface also contains a weaker form of BAS, in concentrations between 50 and 150 mu mol/g, which can be quantified by COads IR Cu(EDA)(2)(2+) exchange also probes these sites. The structure of these sites remains unclear, but they might arise from the interaction of silanol groups with strong Lewis acid Al3+ sites. The surface also contains nonacidic aluminol and silanol sites (200-400 mu mol/g) and two forms of Lewis acid sites: (i) a weaker form associated with segregated alumina domains containing five-coordinated Al, which make up the interface between these domains and the ASA phase and (ii) a stronger form, which are undercoordinated Al sites grafted onto the silica surface. The acid catalytic activity in bifunctional n-heptane hydroconversion correlates with the concentration of strong BAS. The influence of the support electronegativity on the neopentane hydrogenolysis activity of supported Pt catalysts is considerably larger than that of the support Bronsted acidity. It is argued that strong Lewis acid sites, which are present in ASA but not in gamma-alumina, are essential to transmit the Sanderson electronegativity of the oxide support to the active Pt phase.

Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.


Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829