Showing 1–6 of 6 results for author: Zeng, A

Tuning the surface energy of fluorinated diamond-like carbon coatings via plasma immersion ion implantation plasma-enhanced chemical vapor deposition with 1,1,1,2-tetrafluoroethane

Authors: Yuhan Tong, Maryam Zahedian, Aiping Zeng, Ricardo Vidrio, Mike Efremov, Shenwei Yin, Hongyan Mei, Patrick Heaney, Jennifer T. Choy

Abstract: We demonstrate an environmentally friendly and scalable method to create fluorine-doped diamond-like carbon (F-DLC) coatings using plasma immersion ion implantation plasma-enhanced chemical vapor deposition (PIII-PECVD) with 1,1,1,2-tetrafluoroethane. F-DLC films tend to have low wettability and good mechanical flexibility, which make them suitable for applications in biomedical devices and antibi… ▽ More We demonstrate an environmentally friendly and scalable method to create fluorine-doped diamond-like carbon (F-DLC) coatings using plasma immersion ion implantation plasma-enhanced chemical vapor deposition (PIII-PECVD) with 1,1,1,2-tetrafluoroethane. F-DLC films tend to have low wettability and good mechanical flexibility, which make them suitable for applications in biomedical devices and antibiofouling surfaces. We report on the effects of fluorine incorporation on the surface chemistry, surface energy, and morphology of these coatings, showing that our method is effective in increasing the fluorine content in the F-DLC up to 40%. We show that the addition of fluorine leads to a decrease in surface energy, which is consistent with a reduction in surface wettability. △ Less

Submitted 2 May, 2025; v1 submitted 2 February, 2025; originally announced February 2025.

Simultaneous Measurement of Thermal Conductivity, Heat Capacity, and Interfacial Thermal Conductance by Leveraging Negative Delay-Time Data in Time-Domain Thermoreflectance

Authors: Mingzhen Zhang, Tao Chen, Ao Zeng, Jialin Tang, Ruiqiang Guo, Puqing Jiang

Abstract: Time-domain thermoreflectance (TDTR) is a widely used technique for characterizing the thermal properties of bulk and thin-film materials. Traditional TDTR analyses typically focus on positive delay time data for fitting, often requiring multiple-frequency measurements to simultaneously determine thermal conductivity and heat capacity. However, this multiple-frequency approach is cumbersome and ma… ▽ More Time-domain thermoreflectance (TDTR) is a widely used technique for characterizing the thermal properties of bulk and thin-film materials. Traditional TDTR analyses typically focus on positive delay time data for fitting, often requiring multiple-frequency measurements to simultaneously determine thermal conductivity and heat capacity. However, this multiple-frequency approach is cumbersome and may introduce inaccuracies due to inconsistencies across different frequency measurements. In this study, we propose a novel solution to these challenges by harnessing the often-overlooked negative delay time data in TDTR. By integrating these data points, we offer a streamlined, single-frequency method that simultaneously measures thermal conductivity, heat capacity, and interface thermal conductance for both bulk and thin-film materials, enhancing measurement efficiency and accuracy. We demonstrate the effectiveness of this method by measuring several bulk samples including sapphire, silicon, diamond, and Si0.992Ge0.008, and several thin-film samples including a 1.76-μm-thick gallium nitride (GaN) film epitaxially grown on a silicon substrate, a 320-nm-thick gallium oxide (ε-Ga2O3) film epitaxially grown on a silicon carbide substrate, and a 330-nm-thick tantalum nitride (TaN) film deposited on a sapphire substrate, all coated with an aluminum (Al) transducer layer on the surface. Our results show that the new method accurately determines the thermal conductivity and heat capacity of these samples as well as the Al/sample interface thermal conductance using a single modulation frequency, except for the Si0.992Ge0.008 sample. This study sheds light on the untapped potential of TDTR, offering a new, efficient, and accurate avenue for thermal analysis in material science. △ Less

Submitted 24 November, 2024; originally announced November 2024.

Journal ref: Progress in Natural Science: Materials International, 35 (2025) 375-384

Evolving Powergrids in Self-Organized Criticality: An analogy with Sandpile and Earthquakes

Authors: Ho Fai Po, Chi Ho Yeung, An Zeng, K. Y. Michael Wong

Abstract: The stability of powergrid is crucial since its disruption affects systems ranging from street lightings to hospital life-support systems. Nevertheless, large blackouts are inevitable if powergrids are in the state of self-organized criticality (SOC). In this paper, we introduce a simple model of evolving powergrid and establish its connection with the sandpile model, i.e. a prototype of SOC, and… ▽ More The stability of powergrid is crucial since its disruption affects systems ranging from street lightings to hospital life-support systems. Nevertheless, large blackouts are inevitable if powergrids are in the state of self-organized criticality (SOC). In this paper, we introduce a simple model of evolving powergrid and establish its connection with the sandpile model, i.e. a prototype of SOC, and earthquakes, i.e. a system considered to be in SOC. Various aspects are examined, including the power-law distribution of blackout magnitudes, their inter-event waiting time, the predictability of large blackouts, as well as the spatial-temporal rescaling of blackout data. We verified our observations on simulated networks as well as the IEEE 118-bus system, and show that both simulated and empirical blackout waiting times can be rescaled in space and time similarly to those observed between earthquakes. Finally, we suggested proactive maintenance strategies to drive the powergrids away from SOC to suppress large blackouts. △ Less

Submitted 7 December, 2016; originally announced December 2016.

Journal ref: Phys. Rev. E 96, 052312 (2017)

Identification and modeling of discoverers in online social systems

Authors: Matus Medo, Manuel S. Mariani, An Zeng, Yi-Cheng Zhang

Abstract: The dynamics of individuals is of essential importance for understanding the evolution of social systems. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce, all tend to what is already popular. We develop an analytical time-aware framework which shows that when individuals make choices -- which item to buy, for example -- in online social s… ▽ More The dynamics of individuals is of essential importance for understanding the evolution of social systems. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce, all tend to what is already popular. We develop an analytical time-aware framework which shows that when individuals make choices -- which item to buy, for example -- in online social systems, a small fraction of them is consistently successful in discovering popular items long before they actually become popular. We argue that these users, whom we refer to as discoverers, are fundamentally different from the previously known opinion leaders, influentials, and innovators. We use the proposed framework to demonstrate that discoverers are present in a wide range of systems. Once identified, they can be used to predict the future success of items. We propose a network model which reproduces the discovery patterns observed in the real data. Furthermore, data produced by the model pose a fundamental challenge to classical ranking algorithms which neglect the time of link creation and thus fail to discriminate between discoverers and ordinary users in the data. Our results open the door to qualitative and quantitative study of fine temporal patterns in social systems and have far-reaching implications for network modeling and algorithm design. △ Less

Submitted 4 September, 2015; originally announced September 2015.

Comments: main paper (11 pages, 5 figures) together with supporting information (8 pages, 6 figures)

Journal ref: Scientific Reports 6, 34218 (2016)

Enhancing synchronization by directionality in complex networks

Authors: An Zeng, Seung-Woo Son, Chi Ho Yeung, Ying Fan, Zengru Di

Abstract: We proposed a method called residual edge-betweenness gradient (REBG) to enhance synchronizability of networks by assignment of link direction while keeping network topology and link weight unchanged. Direction assignment has been shown to improve the synchronizability of undirected networks in general, but we find that in some cases incommunicable components emerge and networks fail to synchroniz… ▽ More We proposed a method called residual edge-betweenness gradient (REBG) to enhance synchronizability of networks by assignment of link direction while keeping network topology and link weight unchanged. Direction assignment has been shown to improve the synchronizability of undirected networks in general, but we find that in some cases incommunicable components emerge and networks fail to synchronize. We show that the REBG method can effectively avoid the synchronization failure ($R=λ_{2}^{r}/λ_{N}^{r}=0$) which occurs in the residual degree gradient (RDG) method proposed in Phys. Rev. Lett. 103, 228702 (2009). Further experiments show that REBG method enhance synchronizability in networks with community structure as compared with the RDG method. △ Less

Submitted 1 December, 2010; originally announced December 2010.

Comments: 4 pages, 5 figures

Journal ref: Phys. Rev. E 83, 045101(R) (2011)

Coarse Graining for Synchronization in Directed Networks

Authors: An Zeng, Linyuan Lu

Abstract: Coarse graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve the same statistical properties as well as the dynamic behaviors as the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse graining in directed networks lacks of consideration. In this paper… ▽ More Coarse graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve the same statistical properties as well as the dynamic behaviors as the initial networks. Some methods have been proposed and found effective in undirected networks, while the study on coarse graining in directed networks lacks of consideration. In this paper, we proposed a Topology-aware Coarse Graining (TCG) method to coarse grain the directed networks. Performing the linear stability analysis of synchronization and numerical simulation of the Kuramoto model on four kinds of directed networks, including tree-like networks and variants of Barabási-Albert networks, Watts-Strogatz networks and Erdös-Rényi networks, we find our method can effectively preserve the network synchronizability. △ Less

Submitted 23 March, 2011; v1 submitted 1 December, 2010; originally announced December 2010.

Comments: 9 pages, 7 figures

Journal ref: Physical Review E 83, 056123 (2011)