Thermophysical properties study of graphene

Graphene is a two-dimensional (2D) material that exhibits exceptional electric and optical properties. The high electron mobility and thermal conductivity of graphene are of great interest for interconnects, electronic devices and radio frequency devices. In spite of the extensive experimental and theoretical studies on single layer graphene (SLG), its thermal properties have not yet been fully addressed and vast work need to be done to reveal the phonon transport mechanism within this micro/nanoscale material. A transient molecular dynamics technique is developed to characterize the thermophysical properties of two-dimensional graphene nanoribbons (GNRs). By directly tracking the thermal relaxation history of GNR that is heated by a thermal impulse, we are able to determine its thermal diffusivity fast and accurate. In the right-angle bended GNR system, three peculiar features about the phonon energy transport have been observed for the first time. An energy inversion phenomenon has been observed during the transient thermal transport in GNR system. Phonon energy coupling among different phonon modes are investigated and it is found that both dynamic and static heat sources can evoke the energy inversion in GNR. The unique thermal properties of GNR enable it to support a bi-directional heat transfer in the system. And when the bi-directional heat conduction reaches steady state, a single thermal conductivity cannot be used to reflect the relation between the heat flux and the temperature gradient. The calculated thermal conductivities are dependent on the net heat fluxes and the app of graphene are calculated at positive, negative, zero and infinite values, depending on the proportions of each phonon mode energy added/subtracted to/from the heating/cooling areas. The dynamic response of graphene to a thermal impulse is investigated and the interfacial thermal resistance between graphene and Si is evaluated. A transient pump-probe method is designed for interfacial thermal resistance characterization

Dynamic and Thermodynamic Stability of Charged Perfect Fluid Stars

We perform a thorough analysis of the dynamic and thermodynamic stability for the charged perfect fluid star by applying the Wald formalism to the Lagrangian formulation of Einstein-Maxwell-charged fluid system. As a result, we find that neither the presence of the additional electromagnetic field nor the Lorentz force experienced by the charged fluid makes any obstruction to the key steps towards the previous results obtained for the neutral perfect fluid star. Therefore, the criterion for the dynamic stability of our charged star in dynamic equilibrium within the symplectic complement of the trivial perturbaions with the ADM $3$-momentum unchanged is given by the non-negativity of the canonical energy associated with the timelike Killing field, where it is further shown for both non-axisymmetric and axisymmetric perturbations that the dynamic stability against these restricted perturbations also implies the dynamic stability against more generic perturbations. On the other hand, the necessary condition for the thermodynamic stability of our charged star in thermodynamic equilibrium is given by the positivity of the canonical energy of all the linear on-shell perturbations with the ADM angular momentum unchanged in the comoving frame, which is equivalent to the positivity of the canonical energy associated with the timelike Killing field when restricted onto the axisymmetric perturbations. As a by-product, we further establish the equivalence of the dynamic and thermodynamic stability with respect to the spherically symmetric perturbations of the static, spherically symmetric isentropic charged star.Comment: 20 pages, 1 figur

Differential repression of Otx2 underlies the capacity of NANOG and ESRRB to induce germline entry

Primordial germ cells (PGCs) arise from cells of the post-implantation epiblast in response to cytokine signaling. PGC development can be recapitulated in vitro by differentiating epiblast-like cells (EpiLCs) into PGC-like cells (PGCLCs) through cytokine exposure. Interestingly, the cytokine requirement for PGCLC induction can be bypassed by enforced expression of the transcription factor (TF) NANOG. However, the underlying mechanisms are not fully elucidated. Here, we show that NANOG mediates Otx2 downregulation in the absence of cytokines and that this is essential for PGCLC induction by NANOG. Moreover, the direct NANOG target gene Esrrb, which can substitute for several NANOG functions, does not downregulate Otx2 when overexpressed in EpiLCs and cannot promote PGCLC specification. However, expression of ESRRB in Otx2(+/−) EpiLCs rescues emergence of PGCLCs. This study illuminates the interplay of TFs occurring at the earliest stages of PGC specification

Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

One of two-dimensional transition metal dichalcogenide materials, tungsten disulfide (WS2), has aroused much research interest, and its mechanical properties play an important role in a practical application. Here the mechanical properties of h-WS2 and t-WS2 monolayers in the armchair and zigzag directions are evaluated by utilizing the molecular dynamics (MD) simulations and machine learning (ML) technique. We mainly focus on the effects of chirality, system size, temperature, strain rate, and random vacancy defect on mechanical properties, including fracture strain, fracture strength, and Young’s modulus. We find that the mechanical properties of h-WS2 surpass those of t-WS2 due to the different coordination spheres of the transition metal atoms. It can also be observed that the fracture strain, fracture strength, and Young’s modulus decrease when temperature and vacancy defect ratio are enhanced. The random forest (RF) supervised ML algorithm is employed to model the correlations between different impact factors and target outputs. A total number of 3600 MD simulations are performed to generate the training and testing dataset for the ML model. The mechanical properties of WS2 (i.e., target outputs) can be predicted using the trained model with the knowledge of different input features, such as WS2 type, chirality, temperature, strain rate, and defect ratio. The mean square errors of ML predictions for the mechanical properties are orders of magnitude smaller than the actual values of each property, indicating good training results of the RF model

Joint Relay and Jammer Selection for Secure Two-Way Relay Networks

In this paper, we investigate joint relay and jammer selection in two-way cooperative networks, consisting of two sources, a number of intermediate nodes, and one eavesdropper, with the constraints of physical layer security. Specifically, the proposed algorithms select two or three intermediate nodes to enhance security against the malicious eavesdropper. The first selected node operates in the conventional relay mode and assists the sources to deliver their data to the corresponding destinations using an amplify-and-forward protocol. The second and third nodes are used in different communication phases as jammers in order to create intentional interference upon the eavesdropper node. Firstly, we find that in a topology where the intermediate nodes are randomly and sparsely distributed, the proposed schemes with cooperative jamming outperform the conventional non-jamming schemes within a certain transmitted power regime. We also find that, in the scenario in which the intermediate nodes gather as a close cluster, the jamming schemes may be less effective than their non-jamming counterparts. Therefore, we introduce a hybrid scheme to switch between jamming and non-jamming modes. Simulation results validate our theoretical analysis and show that the hybrid switching scheme further improves the secrecy rate.Comment: 25 pages, 7 figures; IEEE Transactions on Information Forensics and Security, 201