Nonlinear thermal transport and negative differential thermal conductance in graphene nanoribbons

We employ classical molecular dynamics to study the nonlinear thermal transport in graphene nanoribbons (GNRs). For GNRs under large temperature biases beyond linear response regime, we have observed the onset of negative differential thermal conductance (NDTC). NDTC is tunable by varying the manner of applying the temperature biases. NDTC is reduced and eventually disappears when the length of the GNR increases. We have also observed NDTC in triangular GNRs, where NDTC exists only when the heat current is from the narrower to the wider end. These effects may be useful in nanoscale thermal managements and thermal signal processing utilizing GNRs.Comment: 5 pages, 4 figure

Tunable thermal rectification in graphene nanoribbons through defect engineering: A molecular dynamics study

Using non-equilibrium molecular dynamics, we show that asymmetrically defected graphene nanoribbons (GNR) are promising thermal rectifiers. The optimum conditions for thermal rectification (TR) include low temperature, high temperature bias, similar to 1% concentration of single-vacancy or substitutional silicon defects, and a moderate partition of the pristine and defected regions. TR ratio of similar to 80% is found in a 14-nm long and 4-nm wide GNR at a temperature of 200 K and bias of 90 K, where heat conduction is in the ballistic regime since the bulk effective phonon mean-free-path is around 775 nm. As the GNR length increases towards the diffusive regime, the TR ratio decreases and eventually stabilizes at a length-independent value of about 3%-5%. This work extends defect engineering to 2D materials for achieving TR. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3703756

Fast Learnings of Coupled Nonnegative Tensor Decomposition Using Optimal Gradient and Low-rank Approximation

Tensor decomposition is a fundamental technique widely applied in signal processing, machine learning, and various other fields. However, traditional tensor decomposition methods encounter limitations when jointly analyzing multi-block tensors, as they often struggle to effectively explore shared information among tensors. In this study, we first introduce a novel coupled nonnegative CANDECOMP/PARAFAC decomposition algorithm optimized by the alternating proximal gradient method (CoNCPD-APG). This algorithm is specially designed to address the challenges of jointly decomposing different tensors that are partially or fully linked, while simultaneously extracting common components, individual components and, core tensors. Recognizing the computational challenges inherent in optimizing nonnegative constraints over high-dimensional tensor data, we further propose the lraCoNCPD-APG algorithm. By integrating low-rank approximation with the proposed CoNCPD-APG method, the proposed algorithm can significantly decrease the computational burden without compromising decomposition quality, particularly for multi-block large-scale tensors. Simulation experiments conducted on synthetic data, real-world face image data, and two kinds of electroencephalography (EEG) data demonstrate the practicality and superiority of the proposed algorithms for coupled nonnegative tensor decomposition problems. Our results underscore the efficacy of our methods in uncovering meaningful patterns and structures from complex multi-block tensor data, thereby offering valuable insights for future applications.Comment: 15 pages, 6 figure

Size Effect in Non-equilibrium Molecular Dynamics

Direct method is commonly used to compute the thermal conductivity of a nanoscale material after molecular dynamics simulation. Direct method simply applies Fourier\u27s Law to get the value of thermal conductivity, which requires heat flux, cross sectional area and temperature gradient. A typical structure includes one heat source, one heat sink and a device region between them. Although it is usually assumed that the temperature gradient is a constant through the entire device region, the temperature profile is not linear for a material in nanoscale because phonon mean free path is comparable to the size of the whole system. Furthermore, bath length and device length can have influence on temperature profile. In this project, two methods of temperature gradient computing and the size effect of each method are discussed. Method 1 uses the center region of the device to get temperature gradient and method 2 uses the temperature difference between hot bath and cold bath divided by the device length as temperature gradient. The thermal conductivity computed from Green-Kubo method is used as a standard to test the two calculation methods and the size effect. Argon with atomic weight 40 is used as the nanoscale material because of its moderate phonon mean free path. Result shows that both method 1 and method 2 can compute the bulk-limit thermal conductivity but the necessary size conditions are different. Method 1 requires a long device and method 2 requires a long bath region

PARAFAC2-based Coupled Matrix and Tensor Factorizations

Coupled matrix and tensor factorizations (CMTF) have emerged as an effective data fusion tool to jointly analyze data sets in the form of matrices and higher-order tensors. The PARAFAC2 model has shown to be a promising alternative to the CANDECOMP/PARAFAC (CP) tensor model due to its flexibility and capability to handle irregular/ragged tensors. While fusion models based on a PARAFAC2 model coupled with matrix/tensor decompositions have been recently studied, they are limited in terms of possible regularizations and/or types of coupling between data sets. In this paper, we propose an algorithmic framework for fitting PARAFAC2-based CMTF models with the possibility of imposing various constraints on all modes and linear couplings, using Alternating Optimization (AO) and the Alternating Direction Method of Multipliers (ADMM). Through numerical experiments, we demonstrate that the proposed algorithmic approach accurately recovers the underlying patterns using various constraints and linear couplings

Genome-Wide Profiling of Extracellular Vesicles Derived from B16 Melanoma Cells Reflects Dynamic Changes in Mutation Profiles of Melanoma Cells

Background: Extracellular vesicles (EVs) are carriers of DNA derived from parental cells, presenting a promising avenue for monitoring tumor progression. This aimed to investigate the relationship between EV DNA and the parental cell genome to establish a theoretical foundation for utilizing EVs to dynamically monitor tumor progression. Methods: Utilizing a classical model of cell tumor evolution, B16 melanoma cell lines (B16-F0, B16-F1, and B16-F10) with varying metastatic potentials, we demonstrated that EVs derived from these cells harbor stable double-stranded (dsDNA) fragments ranging from 15 to 10,000 bp. DNase I enzyme digestion, SYBR Green I staining, and TapeStation system were employed for characterization. Whole genome profiling analysis revealed a high concordance between EV DNA and the mutant spectrum of parent cells, particularly regarding single nucleotide polymorphisms (SNPs). EVs contained evolutionary relevant mutation profile of melanoma cells with different metastatic potentials and had a comparable evolutionary relationship with the parent cells. Results: (1) EVs derived from B16 melanoma cells contained stable dsDNA fragments ranging from 15 to 10,000 bp. (2) EVs DNA comprehensively covered the entire genome of parent cells. (3) EVs DNA exhibited strong consistency with small fragment mutations (SNPs, Inserts/Deletions) of parent cells, with decreasing consistency as mutation length increased. (4) EVs carried mutant gene profiles associated with melanoma cell progression and had similar evolutionary relationships with parent cells. Conclusions: This study underscores the ability of EVs DNA to reflect the mutation status of parental cells and emphasizes their potential as biomarkers for monitoring tumor evolution. These findings offer a theoretical foundation for the dynamic monitoring of tumor progression using EVs DNA

Reverse Microemulsion Synthesis of Sulfur/Graphene Composite for Lithium/Sulfur Batteries

Due to its high theoretical capacity, high energy density, and easy availability, the lithium-sulfur (Li-S) system is considered to be the most promising candidate for electric and hybrid electric vehicle applications. Sulfur/carbon cathode in Li-S batteries still suffers, however, from low Coulombic efficiency and poor cycle life when sulfur loading and the ratio of sulfur to carbon are high. Here, we address these challenges by fabricating a sulfur/carboxylated-graphene composite using a reverse (water-in-oil) microemulsion technique. The fabricated sulfur-graphene composite cathode, which contains only 6 wt % graphene, can dramatically improve the cycling stability as well as provide high capacity. The electrochemical performance of the sulfur-graphene composite is further enhanced after loading into a three-dimensional heteroatom-doped (boron and nitrogen) carbon-cloth current collector. Even at high sulfur loading (8 mg/cm 2 ) on carbon cloth, this composite showed 1256 mAh/g discharge capacity with more than 99% capacity retention after 200 cycles