Mexican Institute of Petroleum

About: Mexican Institute of Petroleum is a government organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Catalysis & Asphaltene. The organization has 3273 authors who have published 4170 publications receiving 87269 citations.

New insights into the defective structure and catalytic activity of Pd/ceria

Abstract: A surfactant-assisted synthesis method was applied to prepare nanosized ceria particles used as the three-way catalyst supports. The Rietveld refinements of crystalline structures of ceria calcined at various temperatures, together with XRD, FTIR, and TGA techniques, confirm that the surfactant cations are incorporated into the structural network of hydrous cerium oxide during the preparation stage, thus reducing the surface tension and diminishing the shrinkage of the mesoporous channels in the dried materials. The surface area is therefore enhanced but it provokes a significant distortion effect on the structure. It is evidenced by XRD analyses that a well-ordered cubic structure of ceria was formed at a temperature as low as 80 °C. The average crystallite size of ceria is determined by XRD within nanoscale range (i.e., 9−31 nm) when the calcination temperature increases from 80 to 800 °C, which is in good agreement with the data determined by TEM and electron diffraction methods. In addition, cationic ...

MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle.

Abstract: The increasing number of metagenomic and genomic sequences has dramatically improved our understanding of microbial diversity, yet our ability to infer metabolic capabilities in such datasets remains challenging. We describe the Multigenomic Entropy Based Score pipeline (MEBS), a software platform designed to evaluate, compare, and infer complex metabolic pathways in large "omic" datasets, including entire biogeochemical cycles. MEBS is open source and available through https://github.com/eead-csic-compbio/metagenome_Pfam_score. To demonstrate its use, we modeled the sulfur cycle by exhaustively curating the molecular and ecological elements involved (compounds, genes, metabolic pathways, and microbial taxa). This information was reduced to a collection of 112 characteristic Pfam protein domains and a list of complete-sequenced sulfur genomes. Using the mathematical framework of relative entropy (H΄), we quantitatively measured the enrichment of these domains among sulfur genomes. The entropy of each domain was used both to build up a final score that indicates whether a (meta)genomic sample contains the metabolic machinery of interest and to propose marker domains in metagenomic sequences such as DsrC (PF04358). MEBS was benchmarked with a dataset of 2107 non-redundant microbial genomes from RefSeq and 935 metagenomes from MG-RAST. Its performance, reproducibility, and robustness were evaluated using several approaches, including random sampling, linear regression models, receiver operator characteristic plots, and the area under the curve metric (AUC). Our results support the broad applicability of this algorithm to accurately classify (AUC = 0.985) hard-to-culture genomes (e.g., Candidatus Desulforudis audaxviator), previously characterized ones, and metagenomic environments such as hydrothermal vents, or deep-sea sediment. Our benchmark indicates that an entropy-based score can capture the metabolic machinery of interest and can be used to efficiently classify large genomic and metagenomic datasets, including uncultivated/unexplored taxa.