Shock engineering the additive manufactured graphene-metal nanocomposite with high density nanotwins and dislocations for ultra-stable mechanical properties

The electronic and geometrical structures of the Fe(n), Fe(n)(–), and Fe(n)(+) series (n = 7–20) are studied using all-electron density functional theory with the generalized gradient approximation. Equilibria of the geometrical configurations of the lowest total energy states in all three series are found to be similar except for Fe(9)(–), Fe(9)(+), Fe(10)(–), Fe(10)(+), Fe(15)(–), and Fe(19)(+). Our computed ionization energies of the neutrals, vertical electron detachment energies, and energies of Fe atom abstraction are in good agreement with experiment. It is found that the one-electron model corresponding to the change in the total magnetic moment of ±1.0μ(B) due to either attachment or detachment of an electron is valid in most cases. The exceptions are Fe(4)(+), Fe(10)(–), Fe(10)(+), Fe(12)(–), Fe(13)(+), and Fe(14)(+), where the change in the total magnetic moment is +3μ(B) (Fe(10)(–) and Fe(12)(–)), −3μ(B) (Fe(4)(+), Fe(11)(+), and Fe(14)(+)), and −9μ(B) (Fe(13)(+)). The reason for an anomalously large quenching of the total spin magnetic moment in Fe(13)(+) is explained. Our computed total spin magnetic moments per atom match the recent experimental values within the experimental uncertainty bars.

Structure and Properties of Fen, Fen–, and Fen+ Clusters, n = 7–20

Artificial neural networks combined with experimental design: A “soft” approach for chemical kinetics

An ab initio investigation on CO2 homoclusters is done at MPWB1K∕6-31++G(2d) level of theory. Electrostatic guidelines are found to be useful for generating initial structures of (CO2)n clusters. The ab initio minimum energy geometries of (CO2)n with n=2–8 are T shaped, cyclic, trigonal pyramidal, tetragonal pyramidal, tetragonal bipyramidal, pentagonal bipyramidal, and pentagonal bipyramid with one CO2 molecule attached to it. A test calculation on (CO2)20 cluster is also reported. The geometric parameters of the energetically most favored (CO2)n clusters match quite well their experimental counterparts (wherever available) as well as those derived from molecular dynamics studies. The effect of clustering is quantified through the asymmetric C–O stretching frequency shift relative to the single CO2 molecule. (CO2)n clusters show an increasing blueshift from 1.8to9.6cm−1 on increasing number of CO2 molecules from n=2 to 8. The energetics and geometries of CO2(Ar)m clusters have also been explored at the s...

An ab initio investigation on (CO2)n and CO2(Ar)m clusters: geometries and IR spectra.

Background

Artificial tactile sensing is a method in which the existence of tumours in biological tissues can be detected and computerized inverse analyses used to produce ‘forward results’.



Methods

Three feed-forward neural networks (FFNN) have been developed for the estimation of tumour characteristics. Each network provides one of the three parameters of the tumour, i.e. diameter, depth and tumour:tissue stiffness ratio. A resilient back-propagation (RP) algorithm with a leave-one-out (LOO) cross-validation approach is used for training purposes.



Results

The proposed inverse approach based on neural networks is a reliable and efficient tool for diagnostic tests in order to accurately estimate the basic parameters of the tumour in the tissue.



Conclusion

There is a non-linear correlation between the tumour characteristics and their effects on the extracted features. In general, reliable estimation of tumour stiffness is obtained when the depth of tumour is small. Copyright © 2007 John Wiley & Sons, Ltd.

Application of artificial neural networks for the estimation of tumour characteristics in biological tissues

Size evolution of structures and energetics of iron clusters (Fen, n ≤ 36): Molecular dynamics studies using a Lennard–Jones type potential

Rate constants and absorption coefficients from experimental data: An inversion procedure based on recursive neural networks

A general algorithm to solve linear and nonlinear inverse problems, based on recursive neural networks, is discussed in this work. The procedure will be applied to physical chemical problems modeled by integral, differential and eigenvalue equations. Representative applications discussed are in positron lifetime spectroscopy, chemical kinetics and vibrational spectroscopy. The method is robust with respect to errors in the initial condition or in the experimental data. The present approach is simple, numerically stable and has a broad range of applicability.

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A General Algorithm to Solve Linear and Nonlinear Inverse Problems

Energetics and possible stable structures of CO2–Arn (n = 1–21) clusters are investigated by performing molecular-dynamics simulations. The pairwise-additive approximation is tested to construct th...

E. Borges

Papers

Size evolution of structures and energetics of iron clusters (Fen, n ≤ 36): Molecular dynamics studies using a Lennard–Jones type potential

Rate constants and absorption coefficients from experimental data: An inversion procedure based on recursive neural networks

A General Algorithm to Solve Linear and Nonlinear Inverse Problems

Structures and energetics of CO2–Arn clusters (n = 1–21) based on a non-rigid potential model

Force field inverse problems using recurrent neural networks