SBA‐15 modified by incorporation of alkali metals: Effect of Its structural changes on the catalytic properties of Fe/SBA‐15 system in the Fischer–tropsch synthesis

A mesoporous SBA‐15 solid is doped with Li, K, or Cs. These systems are used as support of iron nanoparticles, and each composite is utilized as a catalyst in the Fischer-Tropsch synthesis. After the activation treatment, the same Fe species are detected in all solids: Fe3O4, α‐Fe, and Fe2+ inside SBA‐15 walls. However, the species percentages and their distribution are different according to the dopant present. In all "working" catalysts, a mixture of carbides ε´‐Fe2.2C and χ‐Fe2C5, Fe3O4, and ions Fe+2 inside the SBA‐15 walls are found. Alkali cations produce different amounts and strengths of basic sites. At 1 atm and T  = 703 K, the catalytic activity order is: Li > K > no doped > Cs, and the alkali metals favor the production of olefins. At 20 atm, the activity is considerably higher, even at a temperature as low as 543 K, which is attributed to structural properties of the support and to diffusional effects. All catalysts show a promising hydrocarbon production in the gasoline range. The influence of alkali metals is discussed in terms of electrostatic effects, and the limited hydrocarbon chain growth is attributed to the control over size of active iron species.Fil: Cano, Leonardo Andres. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones en Ciencia y Tecnología de Materiales. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones en Ciencia y Tecnología de Materiales; ArgentinaFil: Cagnoli, María V.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; ArgentinaFil: Bengoa, José. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; ArgentinaFil: Marchetti, Sergio Gustavo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Centro de Investigación y Desarrollo en Ciencias Aplicadas "Dr. Jorge J. Ronco". Universidad Nacional de la Plata. Facultad de Ciencias Exactas. Centro de Investigación y Desarrollo en Ciencias Aplicadas; Argentin

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1,395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; and 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared with information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known nonpathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification

Large scale multifactorial likelihood quantitative analysis of BRCA1 and BRCA2 variants: An ENIGMA resource to support clinical variant classification

Abstract The multifactorial likelihood analysis method has demonstrated utility for quantitative assessment of variant pathogenicity for multiple cancer syndrome genes. Independent data types currently incorporated in the model for assessing BRCA1 and BRCA2 variants include clinically calibrated prior probability of pathogenicity based on variant location and bioinformatic prediction of variant effect, co-segregation, family cancer history profile, co-occurrence with a pathogenic variant in the same gene, breast tumor pathology, and case-control information. Research and clinical data for multifactorial likelihood analysis were collated for 1395 BRCA1/2 predominantly intronic and missense variants, enabling classification based on posterior probability of pathogenicity for 734 variants: 447 variants were classified as (likely) benign, and 94 as (likely) pathogenic; 248 classifications were new or considerably altered relative to ClinVar submissions. Classifications were compared to information not yet included in the likelihood model, and evidence strengths aligned to those recommended for ACMG/AMP classification codes. Altered mRNA splicing or function relative to known non-pathogenic variant controls were moderately to strongly predictive of variant pathogenicity. Variant absence in population datasets provided supporting evidence for variant pathogenicity. These findings have direct relevance for BRCA1 and BRCA2 variant evaluation, and justify the need for gene-specific calibration of evidence types used for variant classification. This article is protected by copyright. All rights reserved.Peer reviewe