A comparative study of the effect of ions of different atomic masses on the magnetoresistive properties of Co90Fe10/Cu superlattices

The effect of inert gas ions with different atomic masses (Ar+, Xe+) on the magnetoresistance of Co90Fe10/Cu superlattices deposited on a silicon substrate has been investigated by comparison. The Ar+ ion irradiation has been found to decrease the magnetoresistance more significantly than Xe+ ion irradiation, which seems to be due to a larger average projective range for Ar+ (Rp = 5–6 nm) than that for Xe+ (Rp = 3.3–4.3 nm) and, accordingly, a greater depth of the atom mixing zone (∽(2–3)×Rp) when ions move from the top layers of the superlattice toward the substrate.

Atomic Structure and Isotopes

Atomic Structure and Isotopes covers the fundamentals of the atomic theory of matter. Dalton’s atomic theory is summarized along with the laws derived from it. The basic structure of the atom is reviewed with emphasis on calculations involving atomic number and mass number. The concepts of isotopes and relative atomic masses are presented from a quantitative viewpoint.

Criticism of Inaccuracies and Ordering of the Periodic Table of Elements

The atomic nucleus model was developed to clarify the revised table of elements. Between lutetium and hafnium, the difference in atomic masses does not reach four units, while new elements with atomic numbers 72-75 are located there. How can nucleons be packed in a nucleus so that it is droplet and shell and with the required number of neutrons? Such a nucleus is obtained if alpha particles are placed in the surface layer, and only neutrons are inside the nucleus. In this case, for new chemical elements with numbers 72-75 inside the nucleus, the neutron can be replaced by a proton, and therefore the atomic mass of the elements between lutetium and hafnium will change insignificantly. The model was obtained by considering the structures of atomic nuclei from heavy to light.

Isolating structural errors in reaction networks in systems biology

Motivation The growing complexity of reaction-based models necessitates early detection and resolution of model errors. Considerable work has been done on the detection of mass balance errors, especially atomic mass analysis (AMA) (which compares the counts of atoms in the reactants and products) and Linear Programming analysis (which detects stoichiometric inconsistencies). This article extends model error checking to include: (i) certain structural errors in reaction networks and (ii) error isolation. First, we consider the balance of chemical structures (moieties) between reactants and products. This balance is expected in many biochemical reactions, but the imbalance of chemical structures cannot be detected if the analysis is done in units of atomic masses. Second, we improve on error isolation for stoichiometric inconsistencies by identifying a small number of reactions and/or species that cause the error. Doing so simplifies error remediation. Results We propose two algorithms that address isolating structural errors in reaction networks. Moiety analysis finds imbalances of moieties using the same algorithm as AMA, but moiety analysis works in units of moieties instead of atomic masses. We argue for the value of checking moiety balance, and discuss two approaches to decomposing chemical species into moieties. Graphical Analysis of Mass Equivalence Sets (GAMES) provides isolation for stoichiometric inconsistencies by constructing explanations that relate errors in the structure of the reaction network to elements of the reaction network. We study the effectiveness of moiety analysis and GAMES on curated models in the BioModels repository. We have created open source codes for moiety analysis and GAMES. Availability and implementation Our project is hosted at https://github.com/ModelEngineering/SBMLLint, which contains examples, documentation, source code files and build scripts used to create SBMLLint. Our source code is licensed under the MIT open source license. Supplementary information Supplementary data are available at Bioinformatics online.