arXiv subjects

Qiang Lu

Publications and source records attributed to Qiang Lu.

25 records · Page 2Linked to original sources

arXiv2015

Effect of randomly occurring Stone-Wales defects on mechanical properties of carbon nanotubes using atomistic simulation

While CNTs are found to have ultra high stiffness and strength, an enormous scatter is also observed in available laboratory results. This paper studies the effects of randomly distributed Stone Wales (SW or 5 7 7 5) defects on the mechanical properties of single walled nanotubes (SWNTs) using the technique of atomistic simulation (AS). A Matern hard core random field applied on a finite cylindrical surface is used to describe the spatial distribution of the Stone Wales defects. We simulate a set of displacement controlled tensile loading up to fracture of SWNTs with (6,6) armchair and (10, 0) zigzag configurations and aspect ratio around 6. A modified Morse potential is adopted to model the interatomic forces. We found that fracture invariably initiates from a defect if one is present; for a defect-free tube the crack initiates at quite random locations. The force-displacement curve typically behaves almost linearly up to about half way, although there is no obvious yield point. Three mechanical properties - stiffness, ultimate strength and ultimate strain - are calculated from the simulated force and displacement time histories. The randomness in mechanical behavior resulting only from initial velocity distribution was found to be insignificant at room temperature. The mean values of stiffness, ultimate strength and ultimate strain of the tube decrease as the average number of defects increases although the coefficients of variation do not show such monotonic trend. The introduction of an additional defect has the most pronounced effect on the randomness in mechanical properties when the tube is originally defect free. We also found that, for a given mean number of defects in the tube, the zigzag configuration has less strength and less ultimate strain on the average, but more uncertainty in its stiffness and ultimate strain, compared with the armchair tube.

cond-mat.mtrl-sci↗

arXiv2013

Qualitative detection of oil adulteration with machine learning approaches

The study focused on the machine learning analysis approaches to identify the adulteration of 9 kinds of edible oil qualitatively and answered the following three questions: Is the oil sample adulterant? How does it constitute? What is the main ingredient of the adulteration oil? After extracting the high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil samples, we applied the adaptive boosting with multi-class Hamming loss (AdaBoost.MH) to distinguish the oil adulteration in contrast with the support vector machine (SVM). Further, we regarded the adulterant oil and the pure oil samples as ones with multiple labels and with only one label, respectively. Then multi-label AdaBoost.MH and multi-label learning vector quantization (ML-LVQ) model were built to determine the ingredients and their relative ratio in the adulteration oil. The experimental results on six measures show that ML-LVQ achieves better performance than multi-label AdaBoost.MH.

cs.CE↗

arXiv2011

Theory and Algorithms for Partial Order Based Reduction in Planning

Search is a major technique for planning. It amounts to exploring a state space of planning domains typically modeled as a directed graph. However, prohibitively large sizes of the search space make search expensive. Developing better heuristic functions has been the main technique for improving search efficiency. Nevertheless, recent studies have shown that improving heuristics alone has certain fundamental limits on improving search efficiency. Recently, a new direction of research called partial order based reduction (POR) has been proposed as an alternative to improving heuristics. POR has shown promise in speeding up searches. POR has been extensively studied in model checking research and is a key enabling technique for scalability of model checking systems. Although the POR theory has been extensively studied in model checking, it has never been developed systematically for planning before. In addition, the conditions for POR in the model checking theory are abstract and not directly applicable in planning. Previous works on POR algorithms for planning did not establish the connection between these algorithms and existing theory in model checking. In this paper, we develop a theory for POR in planning. The new theory we develop connects the stubborn set theory in model checking and POR methods in planning. We show that previous POR algorithms in planning can be explained by the new theory. Based on the new theory, we propose a new, stronger POR algorithm. Experimental results on various planning domains show further search cost reduction using the new algorithm.

cs.AI↗

arXiv2010

Effect of edge structures on elastic modulus and fracture of graphene nanoribbons under uniaxial tension

Based on atomistic simulations, the nonlinear elastic properties of monolayer graphene nanoribbons under quasistatic uniaxial tension are predicted, emphasizing the effect of edge structures (armchair and zigzag, without and with hydrogen passivation). The results of atomistic simulations are interpreted within a theoretical framework of thermodynamics, which enables determination of the nonlinear functions for the strain-dependent edge energy and the hydrogen adsorption energy, for both zigzag and armchair edges. Due to the edge effects, the initial Young's modulus of graphene nanoribbons under infinitesimal strain varies with the edge chirality and the ribbon width. Furthermore, it is found that the nominal strain to fracture is considerably lower for armchair graphene nanoribbons than for zigzag ribbons. Two distinct fracture mechanisms are identified, with homogeneous nucleation for zigzag ribbons and edge-controlled heterogeneous nucleation for armchair ribbons. Hydrogen passivation of the edges is found to have negligible effect on the mechanical properties of zigzag graphene nanoribbons, but its effect is more significant for armchair ribbons.

cond-mat.mes-hall↗

arXiv2009

Excess energy and deformation along free edges of graphene nanoribbons

Change of the bonding environment at the free edges of graphene monolayer leads to excess edge energy and edge force, depending on the edge morphology (zigzag or armchair). By using a reactive empirical bond-order potential and atomistic simulations, we show that the excess edge energy in free-standing graphene nanoribbons can be partially relaxed by both in-plane and out-of-plane deformation. The excess edge energy and edge force are calculated for graphene nanoribbons with parallel zigzag or armchair edges. Depending on the longitudinal constraint, the compressive edge force leads to either in-plane elongation of the ribbon or out-of-plane buckling deformation. In the former case, the longitudinal strain is inversely proportional to the ribbon width. In the latter case, energy minimization predicts an intrinsic wavelength for edge buckling to be 6.2 nm along the zigzag edge and 8.0 nm along the armchair edge. For graphene nanoribbons of width less than the intrinsic wavelength, interaction between the two free edges becomes significant, leading to anti-phase correlation of the buckling waves.

cond-mat.mes-hall↗

arXiv2009

Elastic Bending Modulus of Monolayer Graphene

A new formula for elastic bending modulus of monolayer graphene is derived analytically from an empirical potential for solid-state carbon-carbon bonds. Two physical origins are identified for the non-vanishing bending modulus of the atomically thin graphene sheet, one due to the bond angle effect and the other resulting from the bond order term associated with dihedral angles. The analytical prediction compares closely with ab initio energy calculations. Pure bending of graphene monolayers are simulated by a molecular mechanics approach, showing slight nonlinearity and anisotropy in the tangent bending modulus as the bending curvature increases. An intrinsic coupling between bending and in-plane strain is noted for graphene monolayers rolled into carbon nanotubes.

cond-mat.mtrl-sci↗

arXiv2006

Household-Specific Regressions Using Clickstream Data

This paper makes three contributions: (1) the paper provides a better understanding of online behavior by showing the main drivers of Internet portal choice, (2) the rich data allow for a deeper understanding of brand substitution patterns than previously possible and (3) the paper introduces a wider statistics community to a new data opportunity and a recently developed method.

math.ST↗