Enhancing thermal conductivity of bulk polyethylene along two directions by paved crosswise laminate

Recently, some reports show that the ultra-low thermal conductivity of bulk polymers can be enhanced along one direction, which limits its applications. Here, we proposed paved crosswise laminate methods which can enhance the thermal conductivity of bulk polyethylene (PE) along two directions. We find that the thermal conductivity of paved crosswise polyethylene laminate (PPEL) reaches as high as 181 W/m-K along two in-plane directions, which is three orders of magnitude larger than bulk amorphous polyethylene and even more than two times larger than PE single chain (54 W/m-K). The analyses of mechanism indicated that PPEL is a much more crystal-like structure due to the inter-chain van der Waals interactions. Our study may provide guides on the design and fabrication of polymer structures with high thermal conductivity.Comment: 21 page

Secure network solutions for cloud services

Securing a cloud network is an important challenge for delivering cloud services to cloud users. There are a number of secure network protocols, such as VPN protocols, currently available to provide different secure network solutions for enterprise clouds. For example, PPTP, L2TP, GRE, IPsec and SSL/TLS are the most widely used VPN protocols in today’s securing network solutions. However, there are some significant challenges in the implementation stage. For example, which VPN solution is easy to deploy in delivering cloud services? Which solution can provide the best network throughput in delivering the cloud services? Which solution can provide the lowest network latency in delivering the cloud services? This thesis addresses these issues by implementing different VPNs in a test bed environment set up by the Cisco routers. Open source measurement tools will be utilized to acquire the results. This thesis also reviews cloud computing and cloud services and look at their relationships. It also explores the benefits and the weaknesses of each securing network solution. The results can not only provide experimental evidence, but also facilitate the network implementers in development and deployment of secure network solutions for cloud services.Master of Computing (Research

A linear time algorithm for multiscale quantile simulation

Change-point problems have appeared in a great many applications for example cancer genetics, econometrics and climate change. Modern multiscale type segmentation methods are considered to be a statistically efficient approach for multiple change-point detection, which minimize the number of change-points under a multiscale side-constraint. The constraint threshold plays a critical role in balancing the data-fit and model complexity. However, the computation time of such a threshold is quadratic in terms of sample size $n$, making it impractical for large scale problems. In this paper we proposed an $\mathcal{O}(n)$ algorithm by utilizing the hidden quasiconvexity structure of the problem. It applies to all regression models in exponential family with arbitrary convex scale penalties. Simulations verify its computational efficiency and accuracy. An implementation is provided in R-package "linearQ" on CRAN

Enhancement of interfacial thermal conductance of SiC by overlapped carbon nanotubes and intertube atoms

We proposed a new way, adding intertube atoms, to enhance interfacial thermal conductance (ITC) between SiC-carbon nanotube (CNT) array structure. Non-equilibrium molecular dynamics method was used to study the ITC. The results show that the intertube atoms can significantly enhance the ITC. The dependence of ITC on both the temperature and the number of intertube atoms are shown. The mechanism is analyzed by calculating probability distributions of atomic forces and vibrational density of states. Our study may provide some guidance on enhancing the ITC of CNT-based composites

How Does van der Waals Confinement Enhance Phonon Transport?

The van der Waals (vdW) interactions exist in reality universally and play an important role in physics. Here, we show the study on the mechanism of vdW interactions on phonon transport in atomic scale, which would boost developments in heat management and energy conversion. Commonly, the vdW interactions are regarded as a hindrance in phonon transport. Here, we propose that the vdW confinement will enhance phonon transport. Through molecular dynamics simulations, it shows that the vdW confinement makes more than two-fold enhancement on thermal conductivity of both polyethylene single chain and graphene nanoribbon. The quantitative analyses of morphology, local vdW potential energy and dynamical properties are carried out to reveal the underlying physical mechanism. It is found that the confined vdW potential barriers reduce the atomic thermal displacement magnitudes, thus lead to less phonon scattering and facilitate thermal transport. Our study offers a new strategy to modulate the heat transport

Reduction of interfacial thermal resistance of overlapped graphene by bonding carbon chains

Exploring the mechanism of interfacial thermal transport and reducing the interfacial thermal resistance is of great importance for thermal management and modulation. Herein, the interfacial thermal resistance between overlapped graphene nanoribbons is largely reduced by adding bonded carbon chains by performing molecular dynamics simulations. And the analytical model (cross-interface model, CIM) is utilized to analyze and explain the two-dimensional thermal transport mechanism at cross-interface. An order of magnitude reduction in interfacial thermal resistance is found as the graphene nanoribbons are bonded by just one carbon chain. Interestingly, the decreasing rate of interfacial thermal resistance slows down gradually with the increasing of the number of carbon chains, which can be explained by the proposed theoretical relationship based on CIM. Moreover, by the comparison of CIM and traditional simplified model, the accuracy of CIM is verified and demonstrated in overlapped graphene nanoribbons. This work provides a new way to improve the interfacial thermal transport and reveal the essential mechanism for low-dimensional materials applied in thermal management

Tuning Internal Strain in Metal-Organic Frameworks via Vapor Phase Infiltration for CO2 Reduction.

A gas-phase approach to form Zn coordination sites on metal-organic frameworks (MOFs) by vapor-phase infiltration (VPI) was developed. Compared to Zn sites synthesized by the solution-phase method, VPI samples revealed approximately 2.8 % internal strain. Faradaic efficiency towards conversion of CO2 to CO was enhanced by up to a factor of four, and the initial potential was positively shifted by 200-300 mV. Using element-specific X-ray absorption spectroscopy, the local coordination environment of the Zn center was determined to have square-pyramidal geometry with four Zn-N bonds in the equatorial plane and one Zn-OH2 bond in the axial plane. The fine-tuned internal strain was further supported by monitoring changes in XRD and UV/Visible absorption spectra across a range of infiltration cycles. The ability to use internal strain to increase catalytic activity of MOFs suggests that applying this strategy will enhance intrinsic catalytic capabilities of a variety of porous materials

DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces

Reconstruction of geometry based on different input modes, such as images or point clouds, has been instrumental in the development of computer aided design and computer graphics. Optimal implementations of these applications have traditionally involved the use of spline-based representations at their core. Most such methods attempt to solve optimization problems that minimize an output-target mismatch. However, these optimization techniques require an initialization that is close enough, as they are local methods by nature. We propose a deep learning architecture that adapts to perform spline fitting tasks accordingly, providing complementary results to the aforementioned traditional methods. We showcase the performance of our approach, by reconstructing spline curves and surfaces based on input images or point clouds

Negative symptom dimensions and social functioning in Chinese patients with schizophrenia

ObjectiveNegative symptoms can seriously affect social functioning in patients with schizophrenia. However, the role of various components of negative symptoms in social functioning remains unclear. This study aimed to explore the associations among three different dimensions of negative symptoms (i.e., communication, emotion, and motivation) and social functioning to identify potential therapeutic targets.MethodsThis cross-sectional study enrolled 202 Chinese participants with schizophrenia. Negative symptoms were evaluated using the Negative Symptom Assessment (NSA). Social functioning was represented by the Personal and Social Performance Scale (PSP) total score and employment status. Correlation analysis was conducted to clarify the relationship between negative symptoms and the PSP total score. Regression analysis was performed to explore the determinants of the PSP total score and employment status, considering negative symptoms and possible confounders, such as demographic features, positive symptoms, cognitive symptoms, depressive symptoms, and extrapyramidal side effects.ResultsThe PSP total score was correlated with all three dimensions of negative symptoms (i.e., emotion, motivation, and communication; rs = –0.509, –0.662, and –0.657, respectively). Motivation, instead of emotion or communication, predicted both low PSP total scores and unemployment.ConclusionSocial functioning in patients with schizophrenia was significantly related to motivation. Further studies should focus on motivation and consider it as a therapeutic target to improve patients’ social functioning