Viscoelasticity and high buckling stress of dense carbon nanotube brushes

We report on the mechanical behavior of a dense brush of small-diameter (1–3 nm) non-catalytic multiwall (2–4 walls) carbon nanotubes (CNTs), with ~10 times higher density than CNT brushes produced by other methods. Under compression with spherical indenters of different radii, these highly dense CNT brushes exhibit a higher modulus (~17–20 GPa) and orders of magnitude higher resistance to buckling than vapor phase deposited CNT brushes or carbon walls. We also demonstrate the viscoelastic behavior, caused by the increased influence of the van der Waals’ forces in these highly dense CNT brushes, showing their promise for energy-absorbing coatings

Kinking nonlinear elastic solids, nanoindentations, and geology

Physical Review Letters, 92(25): pp. 2555081-4.The physical mechanism responsible for nonlinear elastic, hysteretic, and discrete memory response of nonlinear mesoscopic elastic solids has to date not been identified.We show, by nanoindenting mica single crystals, that this response is most likely due to the formation of dissipative and fully reversible, dislocation-based kink bands. We further claim that solids with high c=a ratios, which per force are plastically anisotropic, should deform by kinking, provided they do not twin. These kinking nonlinear elastic solids include layered ternary carbides, nitrides, oxides, and semiconductors, graphite, and the layered phases, such as mica, present in nonlinear mesoscopic elastic solids

Spherical nanoindentations and kink bands in Ti3SiC2

Journal of Materials Research, 19(4): pp. 1139-1148. Retrieved September 19, 2006 from http://www.mse.drexel.edu/max/pdf%20references/drexel_pdfs/papers/MRS_Anand.pdf.We report for the first time on load versus depth-of-indentation response of Ti3SiC2 surfaces loaded with a 13.5 m spherical tipped diamond indenter up to loads of 500 mN. Using orientation imaging microscopy, two groups of crystals were identified; one in which the basal planes were parallel to, and the other normal to, the surface. When the load-penetration depth curves were converted to stress-strain curves the following was apparent: when the surfaces were loaded normal to the c axis, the response at the lowest loads was linear elastic—well described by a modulus of 320 GPa—followed by a clear yield point at approximately 4.5 GPa. And while the first cycle was slightly open, the next 4 on the same location were significantly harder, almost indistinguishable, and fully reversible. At the highest loads (500 mN) pop-ins due to delaminations between basal planes were observed. When pop-ins were not observed the indentations, for the most part, left no trace. When the load was applied parallel to the c axis, the initial response was again linear elastic (modulus of 320 GPa) followed by a yield point of approximately 4 GPa. Here again significant hardening was observed between the first and subsequent cycles. Each cycle resulted in some strain, but no concomitant increase in yield points. This orientation was even more damage tolerant than the orthogonal direction. This response was attributed to the formation of incipient kink bands that lead to the formation of regular kink bands. Remarkably, these dislocation-based mechanisms allow repeated loading of Ti3SiC2 without damage, while dissipating significant amounts of energy per unit volume, Wd, during each cycle. The values of Wd measured herein were in excellent agreement with corresponding measurements in simple compression tests reported earlier, confirming that the same mechanisms continue to operate even at the high (≈9 GPa) stress levels typical of the indentation experiments

Microscale modeling of kinking nonlinear elastic solids

Physical Review B: Condensed Matter and Materials Physics, 71(13): pp. 134101-1—134101-8. Retrieved September 19, 2006 from http://www.mse.drexel.edu/max/pdf%20references/drexel_pdfs/papers/PRBTheory_71_134101.pdf. DOI: http://dx.doi.org/10.1103/PhysRevB.71.134101Recently we identified and classified a class of solids as kinking nonlinear elastic sKNEd because they deform by the formation of kink bands. KNE solids represent a large family that include, among others, layered ternary carbides and nitrides, layered oxides and semiconductors, zinc, cadmium, graphite, ice, and the layered silicates, such as mica, present in nonlinear mesoscopic elastic solids. Herein we present a microscale model that accounts for the mechanical response of KNE solids to compressive stresses and apply it to two very different solids: Ti3SiC2 and graphite. Building on the Frank and Stroh model put forth in the 1950’s for the formation of kink bands, we developed a comprehensive theory that accounts for the contributions of incipient kink bands sIKBsd and dislocations pile-ups produced by normal glide processes to the nonlinear strains and stored strain energies. The theory provides estimates for the densities of IKBs, the dislocation densities, both from the IKBs and dislocation pileups, as well as the energy dissipated by the motion of the dislocations

Dynamic elastic hysteretic solids and dislocations

Physical Review Letters, 94(8): pp. 085501-1—085501-4. Retrieved September 19, 2006 from http://www.mse.drexel.edu/max/pdf%20references/drexel_pdfs/papers/PhysRevLett_94_085501.pdf. DOI: http://dx.doi.org/10.1103/PhysRevLett.94.085501Recently we showed that the quasistatic response of nonlinear mesoscopic elastic solids to stress can be explained by invoking the formation of dislocation-based incipient kink bands. In this Letter, using resonant ultrasound spectroscopy, we confirm that the dynamical behavior of these nonlinear elastic systems is due to the interaction of dislocations with the ultrasound waves, thus resolving a long-standing mystery

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment- and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out