767 research outputs found
Molecular dynamics simulation of graphene sinking during chemical vapor deposition growth on semi-molten Cu substrate
Copper foil is the most promising catalyst for the synthesis of large-area, high-quality monolayer graphene. Experimentally, it has been found that the Cu substrate is semi-molten at graphene growth temperatures. In this study, based on a self-developed C-Cu empirical potential and density functional theory (DFT) methods, we performed systematic molecular dynamics simulations to explore the stability of graphene nanostructures, i.e., carbon nanoclusters and graphene nanoribbons, on semi-molten Cu substrates. Many atomic details observed in the classical MD simulations agree well with those seen in DFT-MD simulations, confirming the high accuracy of the C-Cu potential. Depending on the size of the graphene island, two different sunken-modes are observed: (i) graphene island sinks into the first layer of the metal substrate and (ii) many metal atoms surround the graphene island. Further study reveals that the sinking graphene leads to the unidirectional alignment and seamless stitching of the graphene islands, which explains the growth of large single-crystal graphene on Cu foil. This study deepens our physical insights into the CVD growth of graphene on semi-molten Cu substrate with multiple experimental mysteries well explained and provides theoretic references for the controlled synthesis of large-area single-crystalline monolayer graphene
First-order Policy Optimization for Robust Markov Decision Process
We consider the problem of solving robust Markov decision process (MDP),
which involves a set of discounted, finite state, finite action space MDPs with
uncertain transition kernels. The goal of planning is to find a robust policy
that optimizes the worst-case values against the transition uncertainties, and
thus encompasses the standard MDP planning as a special case. For
-rectangular uncertainty sets, we develop a
policy-based first-order method, namely the robust policy mirror descent
(RPMD), and establish an and
iteration complexity for finding an
-optimal policy, with two increasing-stepsize schemes. The prior
convergence of RPMD is applicable to any Bregman divergence, provided the
policy space has bounded radius measured by the divergence when centering at
the initial policy. Moreover, when the Bregman divergence corresponds to the
squared euclidean distance, we establish an complexity of RPMD with any constant stepsize .
For a general class of Bregman divergences, a similar complexity is also
established for RPMD with constant stepsizes, provided the uncertainty set
satisfies the relative strong convexity. We further develop a stochastic
variant, named SRPMD, when the first-order information is only available
through online interactions with the nominal environment. For general Bregman
divergences, we establish an and
sample complexity with two increasing-stepsize
schemes. For the euclidean Bregman divergence, we establish an
sample complexity with constant stepsizes. To the
best of our knowledge, all the aforementioned results appear to be new for
policy-based first-order methods applied to the robust MDP problem
Direct sampling method to inverse wave-number-dependent source problems (part I): determination of the support of a stationary source
This paper is concerned with a direct sampling method for imaging the support
of a frequency-dependent source term embedded in a homogeneous and isotropic
medium. The source term is given by the Fourier transform of a time-dependent
source whose radiating period in the time domain is known.
The time-dependent source is supposed to be stationary in the sense that its
compact support does not vary along the time variable.
Via a multi-frequency direct sampling method, we show that the smallest strip
containing the source support and perpendicular to the observation direction
can be recovered from far-field patterns at a fixed observation angle. With
multiple but sparse observation directions, the shape of the convex hull of the
source support can be recovered. The frequency-domain analysis performed here
can be used to handle inverse time-dependent source problems.
Our algorithm has low computational overhead and is robust against noise.
Numerical experiments in both two and three dimensions have proved our
theoretical findings
Construction of T cell exhaustion model for predicting survival and immunotherapy effect of bladder cancer based on WGCNA
IntroductionThe prognosis of bladder cancer (BLCA) and response to immune checkpoint inhibitors (ICIs) are determined by multiple factors. Existed biomarkers for predicting the effect of immunotherapy cannot accurately predict the response of BLCA patients to ICIs.MethodsTo further accurately stratify patients’ response to ICIs and identify potential novel predictive biomarkers, we used the known T cell exhaustion (TEX)-related specific pathways, including tumor necrosis factor (TNF), interleukin (IL)-2, interferon (IFN)-g, and T- cell cytotoxicpathways, combined with weighted correlation network analysis (WGCNA) to analyze the characteristics of TEX in BLCA in detail, constructed a TEX model.ResultsThis model including 28 genes can robustly predict the survival of BLCA and immunotherapeutic efficacy. This model could divide BLCA into two groups, TEXhigh and TEXlow, with significantly different prognoses, clinical features, and reactivity to ICIs. The critical characteristic genes, such as potential biomarkers Charged Multivesicular Body Protein 4C (CHMP4C), SH2 Domain Containing 2A (SH2D2A), Prickle Planar Cell Polarity Protein 3 (PRICKLE3) and Zinc Finger Protein 165 (ZNF165) were verified in BLCA clinical samples by real-time quantitative chain reaction (qPCR) and immunohistochemistry (IHC).DiscussionOur findings show that the TEX model can serve as biological markers for predicting the response to ICIs, and the involving molecules in the TEX model might provide new potential targets for immunotherapy in BLCA
A simple approach to synthesize novel sulfur/graphene oxide/multiwalled carbon nanotube composite cathode for high performance lithium/sulfur batteries
A sulfur/graphene oxide/multiwalled carbon nanotube
(S/GO/MWNT) composite was synthesized via a simple
ultrasonic mixing method followed by heat treatment. By taking
advantage of this solution-based self-assembly synthesis
route, poisonous and noxious reagents and complicated fabrication
processes are rendered unnecessary, thereby simplifying
its manufacturing and decreasing the cost of the final
product. Transmission and scanning electronic microscopy
observations indicated the formation of the threedimensional
interconnected S/GO/MWNTcomposite through
the environmentally friendly process..
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