University of Akron

About: University of Akron is a education organization based out in Akron, Ohio, United States. It is known for research contribution in the topics: Polymer & Polymerization. The organization has 17401 authors who have published 29127 publications receiving 702386 citations. The organization is also known as: The University of Akron.

Recent progress and applications for metallodendrimers

Abstract: Progress in metallodendritic architectures is discussed as it relates to catalysis, luminescence, sensors, and molecular switches. It is intended that the reader will gain an appreciation for the different roles that metals can play in dendritic connectivity as well as their vast potential for application-oriented nanoscale device construction.

Three-dimensional coordinates of individual atoms in materials revealed by electron tomography

Abstract: Crystallography, the primary method for determining the 3D atomic positions in crystals, has been fundamental to the development of many fields of science. However, the atomic positions obtained from crystallography represent a global average of many unit cells in a crystal. Here, we report, for the first time, the determination of the 3D coordinates of thousands of individual atoms and a point defect in a material by electron tomography with a precision of ∼19 pm, where the crystallinity of the material is not assumed. From the coordinates of these individual atoms, we measure the atomic displacement field and the full strain tensor with a 3D resolution of ∼1 nm(3) and a precision of ∼10(-3), which are further verified by density functional theory calculations and molecular dynamics simulations. The ability to precisely localize the 3D coordinates of individual atoms in materials without assuming crystallinity is expected to find important applications in materials science, nanoscience, physics, chemistry and biology.

Neutral products formed during backbone fragmentations of protonated peptides in tandem mass spectrometry

Abstract: Collisionally activated dissociation (CAD) of the protonated polyalanines Ala-Ala,Ala-Ala-Ala, and Ala-Ala-Ala-Ala causes breakup of the peptide bonds leading to sequence-indicative fragment ions. The neutral molecules eliminated during these reactions are identified here using neutralization-reionization mass spectrometry (NRMS). N-terminal acylium ions (bn) arise after the C-terminus is lost as an intact amino acid or peptide; further loss of CO leads to immonium ions (an). Upon generation of C-terminal sequence ions (yn), a hydrogen atom attached to a nitrogen rearranges from the N-terminal to the C-terminal side yielding a protonated amino acid (y1) or peptide (y > or = 2) as the ionic fragment; the complementary neutral fragment is an aziridinone if the N-terminal amino acid is cleaved and a diketopiperazine if two N-terminal amino acid units are eliminated. Detection of neutral dissociation products can reveal valuable structure information, as demonstrated with the tetrapeptides Val-Gly-Ser-Glu and Val-Gly-Asp-Glu.

Using path analysis to measure natural selection.

Abstract: We expand current methods for calculating selection coefficients using path analysis and demonstrate how to analyse nonlinear selection. While this incorporation is a straightforward extension of current procedures, the rules for combining these traits to calculate selection coefficients can be complex. We demonstrate our method with an analysis of selection in an experimental population of Arabidopsis thaliana consisting of 289 individuals. Multiple regression analyses found positive directional selection and positive nonlinear selection only for inflorescence height. In contrast, the path analyses also revealed positive directional selection for number of rosette leaves and positive nonlinear selection for leaf number and time of inflorescence initiation. These changes in conclusions came about because indirect selection was converted into direct selection with the change in causal structure. Path analysis has great promise for improving our understanding of natural selection but must be used with caution since coefficient estimates depend on the assumed causal structure.