Nanoalloying in real time: a high resolution STEM and computer simulation study

Bimetallic nanoparticles constitute a promising type of catalysts, mainly because their physical and chemical properties may be tuned by varying their chemical composition, atomic ordering, and size. Today, the design of novel nanocatalysts is possible through a combination of virtual lab simulations on massive parallel computing and modern electron microscopy with picometre resolution on one hand, and the capability of chemical analysis at the atomic scale on the other. In this work we show how the combination of theoretical calculations and characterization can solve some of the paradoxes reported about nanocatalysts: Au-Pd bimetallic nanoparticles. In particular, we demonstrate the key role played by adsorbates, such as carbon monoxide (CO), on the structure of nanoalloys. Our results imply that surface condition of nanoparticles during synthesis is a parameter of paramount importance.Fil: Mariscal, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Mayoral, Alba. Universidad de Zaragoza. Instituto de Nanociencia de Aragón; EspañaFil: Olmos Asar, Jimena Anahí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Físico-química de Córdoba. Universidad Nacional de Córdoba. Facultad de Ciencias Químicas. Instituto de Investigaciones en Físico-química de Córdoba; ArgentinaFil: Magen, César. Universidad de Zaragoza. Instituto de Nanociencia de Aragón; EspañaFil: Mejia Rosales, Sergio Javier. Universidad Autónoma de Nuevo León; MéxicoFil: Pérez Tijerina, Eduardo. Universidad Autónoma de Nuevo León; MéxicoFil: José Yacamán, Miguel. University of Texas; Estados Unido

Genome-based trait prediction in multi- environment breeding trials in groundnut

Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut

From Mendel’s discovery on pea to today’s plant genetics and breeding

In 2015, we celebrated the 150th anniversary of the presentation of the seminal work of Gregor Johann Mendel. While Darwin’s theory of evolution was based on differential survival and differential reproductive success, Mendel’s theory of heredity relies on equality and stability throughout all stages of the life cycle. Darwin’s concepts were continuous variation and “soft” heredity; Mendel espoused discontinuous variation and “hard” heredity. Thus, the combination of Mendelian genetics with Darwin’s theory of natural selection was the process that resulted in the modern synthesis of evolutionary biology. Although biology, genetics, and genomics have been revolutionized in recent years, modern genetics will forever rely on simple principles founded on pea breeding using seven single gene characters. Purposeful use of mutants to study gene function is one of the essential tools of modern genetics. Today, over 100 plant species genomes have been sequenced. Mapping populations and their use in segregation of molecular markers and marker–trait association to map and isolate genes, were developed on the basis of Mendel's work. Genome-wide or genomic selection is a recent approach for the development of improved breeding lines. The analysis of complex traits has been enhanced by high-throughput phenotyping and developments in statistical and modeling methods for the analysis of phenotypic data. Introgression of novel alleles from landraces and wild relatives widens genetic diversity and improves traits; transgenic methodologies allow for the introduction of novel genes from diverse sources, and gene editing approaches offer possibilities to manipulate gene in a precise manner

Coalescence and sintering of Pt nanoparticles: in situ observation by aberration-corrected HAADF STEM

Abstract An aberration-corrected JEOL 2200FS scanning-transmission electron microscope (STEM), equipped with a high-angle annular dark-field detector (HAADF), is used to monitor the coalescence and sintering of Pt nanoparticles with an average diameter of 2.8 nm. This in situ STEM capability is combined with an analysis methodology that together allows direct measurements of mass transport phenomena that are important in understanding how particle size influences coalescence and sintering at the nanoscale. To demonstrate the feasibility of this methodology, the surface diffusivity is determined from measurements obtained from STEM images acquired during the initial stages of sintering. The measured surface diffusivities are in reasonable agreement with measurements made on the surface of nanoparticles, using other techniques. In addition, the grain boundary mobility is determined from measurements made during the latter stages of sintering