UAM Azcapotzalco

About: UAM Azcapotzalco is a based out in . It is known for research contribution in the topics: Catalysis & Cognitive radio. The organization has 323 authors who have published 353 publications receiving 2806 citations. The organization is also known as: UAMA & UAM-A.

Highly efficient photocatalytic elimination of phenol and chlorinated phenols by CeO2/MgAl layered double hydroxides

Abstract: Mg/Al layered double hydroxides (LDHs) were synthesized by a simple, environment friendly method; then, cerium oxide was incorporated following two different procedures. Thus obtained bifunctional basic/semiconducting catalysts were characterized by several techniques, such as XRD, SEM/EDS, chemical analysis and N 2 physisorption, to investigate their crystalline structure, morphology, CeO 2 content and textural properties. The band gap energy was determined to be ∼3.2 eV. The photocatalytic properties of the Ce–Mg–Al system were tested for the first time for the degradation, under UV irradiation, of three major pollutants: phenol, 4-chlorophenol and 2,4,6-trichlorophenol. The results obtained with phenol, which is the most recalcitrant of the studied molecules, were by far superior to those obtained with benchmark Degussa P25 TiO 2 photocatalyst tested under the same conditions. Furthermore, a charge-transfer mechanism is proposed as the initiator of the photocatalytic degradation of phenol and 4-chlorophenol.

Evolutionary multiobjective optimization: open research areas and some challenges lying ahead

Abstract: Evolutionary multiobjective optimization has been a research area since the mid-1980s, and has experienced a very significant activity in the last 20 years. However, and in spite of the maturity of this field, there are still several important challenges lying ahead. This paper provides a short description of some of them, with a particular focus on open research areas, rather than on specific research topics or problems. The main aim of this paper is to motivate researchers and students to develop research in these areas, as this will contribute to maintaining this discipline active during the next few years.

Indicator-based Multi-objective Evolutionary Algorithms: A Comprehensive Survey

Abstract: For over 25 years, most multi-objective evolutionary algorithms (MOEAs) have adopted selection criteria based on Pareto dominance. However, the performance of Pareto-based MOEAs quickly degrades when solving multi-objective optimization problems (MOPs) having four or more objective functions (the so-called many-objective optimization problems), mainly because of the loss of selection pressure. Consequently, in recent years, MOEAs have been coupled with indicator-based selection mechanisms in furtherance of increasing the selection pressure so that they can properly solve many-objective optimization problems. Several research efforts have been conducted since 2003 regarding the design of the so-called indicator-based (IB) MOEAs. In this article, we present a comprehensive survey of IB-MOEAs for continuous search spaces since their origins up to the current state-of-the-art approaches. We propose a taxonomy that classifies IB-mechanisms into two main categories: (1) IB-Selection (which is divided into IB-Environmental Selection, IB-Density Estimation, and IB-Archiving) and (2) IB-Mating Selection. Each of these classes is discussed in detail in this article, emphasizing the advantages and drawbacks of the selection mechanisms. In the final part, we provide some possible paths for future research.