I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

5.1. Nanoalloys of Group 11 (Cu, Ag, Au) 865 5.1.1. Cu−Ag 866 5.1.2. Cu−Au 867 5.1.3. Ag−Au 870 5.1.4. Cu−Ag−Au 872 5.2. Nanoalloys of Group 10 (Ni, Pd, Pt) 872 5.2.1. Ni−Pd 872 * To whom correspondence should be addressed. Phone: +39010 3536214. Fax:+39010 311066. E-mail: ferrando@fisica.unige.it. † Universita di Genova. ‡ Argonne National Laboratory. § University of Birmingham. | As of October 1, 2007, Chemical Sciences and Engineering Division. Volume 108, Number 3

Nanoalloys: From Theory to Applications of Alloy Clusters and Nanoparticles

Carbon nanotube–polymer composites: Chemistry, processing, mechanical and electrical properties

Thermal conductivity of carbon nanotubes and their polymer nanocomposites: A review

Heat transfer characteristics of nanofluids: a review

Homogenization of Reconstructed RVE

Although the material microstructure attributes play an important role in the mechanical properties of the additively manufactured (AM) part, their impact is largely ignored in the mechanics modeling of AM processes. A physics-based mechanical threshold stress (MTS) model is proposed to consider the material internal attributes, such as grain size and dislocation to dislocation barrier during simulation of the material flow stress and deformation. This model is based on the dislocation dynamics and thermal activation theory with a combination of three main components of athermal stress, thermal stress, and threshold stress. The MTS flow stress model is embedded into a fully coupled thermo-mechanical incremental plasticity model to predict the residual stress during AM processes from the prediction of temperature field and associated thermal stresses. The experimental measurements of residual stress are conducted on additively manufactured Ti-6Al-4 V samples for the model validation. The proposed model provides insight into the impact of microstructure on residual stress prediction in the additive manufacturing process modeling.

Microstructure affected residual stress prediction based on mechanical threshold stress in direct metal deposition of Ti-6Al-4 V

Thin films of La0.67M0.33MnO3 (M=Sr), LSMO were deposited on three different substrates of LAO, Sapphire, and Y-ZrO2 (YSZ) using metal–organic chemical vapor deposition methods. The effect of texture and orientation on the resistance (0 and 6T) and magnetoresistance (MR) of (LSMO) thin films on various substrates has been investigated. X-ray pole figures were measured using Philips X’Pert X-ray diffractometer equipped with the PopLa analysis package. A direct correlation was observed between the lattice mismatch strain and the structure of the thin film growth. LSMO/LAO seems to be the most perfect system for epitaxial growth due to the negligible lattice-mismatch (∼2%). The dominant orientation changes for the films deposited on LAO [100] and LAO [110] substrates while the transition temperature from ferromagnetic to paramagnetic state of the film on LAO [100] is 50 K higher than that of the film on LAO [100]. The MR data and TMI temperature were measured using standard 4-point resistivity devices and SQUID.

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Texture Analysis in LD-MOCVD Processed Thin Film Giant Magnetoresistant (LaM)MnO3 Materials

To improve the wear behavior of UHMWPE, the correlation between microstructure and wear resistance was studied. In this study it was found that the relationship between wear volume and sliding distance fits a power law. The exponent was used to represent the wear property, and crystal orientation distribution (texture) was used to represent microstructure. Texture analysis showed that four kinds of annealed UHMWPE sheets were all somehow isotropic. This correlates with the wear behavior of these four UHMWPE sheets.

Wear resistance and microstructure in annealed ultra high molecular weight polyethylenes

The evolution of micro-texture below the machined surface is computationally modeled and experimentally verified. The orientation distribution functions of the grains below the surface were represented in spectral form. The microstructure descriptor coefficients were derived, and their change with respect to the change in the cutting feed rate was computationally calculated and monitored. Micro-texture experimental observations conducted by electron back-scatter diffraction technique verify the modeling outputs. Continuation of changing the process parameter was done by finite element method, and the evolution in texture was investigated by computational modeling. The process path function which correlates micro-texture evolution and cutting feed rate, was obtained by applying the principle of orientation conservation in the Euler space. As a result of the major finding of this work, i.e., derivation of process path functions, the evolution of texture as a function of the material feed rate is numerically determined without any need to texture modeling or finite element analyses.

Hamid Garmestani

Papers

Homogenization of Reconstructed RVE

Microstructure affected residual stress prediction based on mechanical threshold stress in direct metal deposition of Ti-6Al-4 V

Texture Analysis in LD-MOCVD Processed Thin Film Giant Magnetoresistant (LaM)MnO3 Materials

Wear resistance and microstructure in annealed ultra high molecular weight polyethylenes

Derivation of Process Path Functions in Machining of Al Alloy 7075