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Hang Zhang

Bio: Hang Zhang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Graphene & Thermal conductivity. The author has an hindex of 23, co-authored 71 publications receiving 3323 citations. Previous affiliations of Hang Zhang include California Institute of Technology & University of California.


Papers
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Journal ArticleDOI
TL;DR: The first direct observation and controlled creation of one- and two-dimensional periodic ripples in suspended graphene sheets, using both spontaneously and thermally generated strains are reported, elucidate the ripple formation process and can be understood in terms of classical thin-film elasticity theory.
Abstract: Graphene is nature's thinnest elastic material and displays exceptional mechanical and electronic properties Ripples are an intrinsic feature of graphene sheets and are expected to strongly influence electronic properties by inducing effective magnetic fields and changing local potentials The ability to control ripple structure in graphene could allow device design based on local strain and selective bandgap engineering Here, we report the first direct observation and controlled creation of one- and two-dimensional periodic ripples in suspended graphene sheets, using both spontaneously and thermally generated strains We are able to control ripple orientation, wavelength and amplitude by controlling boundary conditions and making use of graphene's negative thermal expansion coefficient (TEC), which we measure to be much larger than that of graphite These results elucidate the ripple formation process, which can be understood in terms of classical thin-film elasticity theory This should lead to an improved understanding of suspended graphene devices, a controlled engineering of thermal stress in large-scale graphene electronics, and a systematic investigation of the effect of ripples on the electronic properties of graphene

1,281 citations

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TL;DR: It is demonstrated that the application of covalent bond-forming chemistry modifies the periodicity of the graphene network thereby introducing a band gap (∼0.4 eV), which is observable in the angle-resolved photoelectron spectroscopy of aryl-functionalized graphene.
Abstract: In order to engineer a band gap into graphene, covalent bond-forming reactions can be used to change the hybridization of the graphitic atoms from sp2 to sp3, thereby modifying the conjugation length of the delocalized carbon lattice; similar side-wall chemistry has been shown to introduce a band gap into metallic single-walled carbon nanotubes. Here we demonstrate that the application of such covalent bond-forming chemistry modifies the periodicity of the graphene network thereby introducing a band gap (∼0.4 eV), which is observable in the angle-resolved photoelectron spectroscopy of aryl-functionalized graphene. We further show that the chemically-induced changes can be detected by Raman spectroscopy; the in-plane vibrations of the conjugated π-bonds exhibit characteristic Raman spectra and we find that the changes in D, G, and 2D-bands as a result of chemical functionalization of the graphene basal plane are quite distinct from that due to localized, physical defects in sp2-conjugated carbon.

504 citations

Journal ArticleDOI
TL;DR: This work reports the development of a nonvolatile memory element based on graphene break junctions, and demonstrates information storage based on the concept of rank coding, in which information is stored in the relative conductance of graphene switches in a memory cell.
Abstract: Graphene's remarkable mechanical and electrical properties, combined with its compatibility with existing planar silicon-based technology, make it an attractive material for novel computing devices. We report the development of a nonvolatile memory element based on graphene break junctions. Our devices have demonstrated thousands of writing cycles and long retention times. We propose a model for device operation based on the formation and breaking of carbon atomic chains that bridge the junctions. We demonstrate information storage based on the concept of rank coding, in which information is stored in the relative conductance of graphene switches in a memory cell.

355 citations

Journal ArticleDOI
TL;DR: This work chemically grafted nitrophenyl groups onto exfoliated single-layer graphene sheets in the form of substrate-supported or free-standing films, paving the way for CMOS-compatible band gap engineering of graphene electronic devices.
Abstract: Chemical functionalization is a promising route to band gap engineering of graphene. We chemically grafted nitrophenyl groups onto exfoliated single-layer graphene sheets in the form of substrate-supported or free-standing films. Our transport measurements demonstrate that nonsuspended functionalized graphene behaves as a granular metal, with variable range hopping transport and a mobility gap ∼0.1 eV at low temperature. For suspended graphene that allows functionalization on both surfaces, we demonstrate tuning of its electronic properties from a granular metal to a semiconductor in which transport occurs via thermal activation over a transport gap ∼80 meV from 4 to 300 K. This noninvasive and scalable functionalization technique paves the way for CMOS-compatible band gap engineering of graphene electronic devices.

137 citations

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TL;DR: In this article, the authors presented a lithography-free technique for fabrication of clean, high quality graphene devices, which is based on evaporation through hard Si shadow masks, and eliminates contaminants introduced by lithographical processes.
Abstract: We present a lithography-free technique for fabrication of clean, high quality graphene devices. This technique is based on evaporation through hard Si shadow masks, and eliminates contaminants introduced by lithographical processes. We demonstrate that devices fabricated by this technique have significantly higher mobility values than those obtained by standard electron beam lithography. To obtain ultra-high mobility devices, we extend this technique to fabricate suspended graphene samples with mobilities as high as 120 000 cm 2 /(V·s).

95 citations


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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
19 Jun 2009-Science
TL;DR: This review analyzes recent trends in graphene research and applications, and attempts to identify future directions in which the field is likely to develop.
Abstract: Graphene is a wonder material with many superlatives to its name. It is the thinnest known material in the universe and the strongest ever measured. Its charge carriers exhibit giant intrinsic mobility, have zero effective mass, and can travel for micrometers without scattering at room temperature. Graphene can sustain current densities six orders of magnitude higher than that of copper, shows record thermal conductivity and stiffness, is impermeable to gases, and reconciles such conflicting qualities as brittleness and ductility. Electron transport in graphene is described by a Dirac-like equation, which allows the investigation of relativistic quantum phenomena in a benchtop experiment. This review analyzes recent trends in graphene research and applications, and attempts to identify future directions in which the field is likely to develop.

12,117 citations

Journal ArticleDOI
TL;DR: An overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.
Abstract: There is intense interest in graphene in fields such as physics, chemistry, and materials science, among others. Interest in graphene's exceptional physical properties, chemical tunability, and potential for applications has generated thousands of publications and an accelerating pace of research, making review of such research timely. Here is an overview of the synthesis, properties, and applications of graphene and related materials (primarily, graphite oxide and its colloidal suspensions and materials made from them), from a materials science perspective.

8,919 citations