Isotopic effects on the thermal conductivity of graphene nanoribbons: localization mechanism

Thermal conductivity of graphene nanoribbons (GNR) with length 106~{\AA} and width 4.92~{\AA} after isotopic doping is investigated by molecular dynamics with quantum correction. Two interesting phenomena are found: (1) isotopic doping reduces thermal conductivity effectively in low doping region, and the reduction slows down in high doping region; (2) thermal conductivity increases with increasing temperature in both pure and doped GNR; but the increasing behavior is much more slowly in the doped GNR than that in pure ones. Further studies reveal that the physics of these two phenomena is related to the localized phonon modes, whose number increases quickly (slowly) with increasing isotopic doping in low (high) isotopic doping region.Comment: 6 fig

Spectral Analysis Network for Deep Representation Learning and Image Clustering

Deep representation learning is a crucial procedure in multimedia analysis and attracts increasing attention. Most of the popular techniques rely on convolutional neural network and require a large amount of labeled data in the training procedure. However, it is time consuming or even impossible to obtain the label information in some tasks due to cost limitation. Thus, it is necessary to develop unsupervised deep representation learning techniques. This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis, which is a popular technique with solid theory foundations. Compared with the existing spectral analysis methods, the proposed network structure has at least three advantages. Firstly, it can identify the local similarities among images in patch level and thus more robust against occlusion. Secondly, through multiple consecutive spectral analysis procedures, the proposed network can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples. Thirdly, it can elegantly integrate different spectral analysis procedures, so that each spectral analysis procedure can have their individual strengths in dealing with different data sample distributions. Extensive experimental results show the effectiveness of the proposed methods on various image clustering tasks

Nuclear transport models can reproduce charged-particle-inclusive measurements but are not strongly constrained by them

Nuclear transport models are important tools for interpretation of many heavy-ion experiments and are essential in efforts to probe the nuclear equation of state. In order to fulfill these roles, the model predictions should at least agree with observed single-particle-inclusive momentum spectra; however, this agreement has recently been questioned. The present work compares the Vlasov-Uehling-Uhlenbeck model to data for mass-symmetric systems ranging from 12C+12C to 139La+139La, and we find good agreement within experimental uncertainties at 0.4A and 0.8A GeV. For currently available data, these uncertainties are too large to permit effective nucleon-nucleon scattering cross sections in the nuclear medium to be extracted at a useful level of precision

Mpemba Effect in Crystallization of Polybutene-1

The Mpemba effect and its inverse can be understood as a result of nonequilibrium thermodynamics. In polymers, changes of state are generally non-equilibrium processes. However, the Mpemba effect has been rarely reported in the crystallization of polymers. In the melt, polybutene-1 (PB-1) has the lowest critical cooling rate in polyolefins and tends to maintain its original structure and properties with thermal history. A nascent PB-1 sample was prepared by using metallocene catalysis at low temperature, and the crystallization behavior and crystalline structure of the PB-1 were characterized by DSC and WAXS. Experimentally, a clear Mpemba effect is observed not only in the crystallization of the nascent PB-1 melt in form II but also in form I obtained from the nascent PB-1 at low melting temperature. It is proposed that this is due to the differences in the chain conformational entropy in the lattice which influence conformational relaxation times. The entropy and the relaxation time can be predicted using the Adam-Gibbs equations, whereas non-equilibrium thermodynamics is required to describe the crystallization with the Mpemba effect