16 research outputs found

    Visualizing Ribbon‐to‐Ribbon Heterogeneity of Chemically Unzipped Wide Graphene Nanoribbons by Silver Nanowire‐Based Tip‐Enhanced Raman Scattering Microscopy

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    Graphene nanoribbons (GNRs), a quasi-one-dimensional form of graphene, have gained tremendous attention due to their potential for next-generation nanoelectronic devices. The chemical unzipping of carbon nanotubes is one of the attractive fabrication methods to obtain single-layered GNRs (sGNRs) with simple and large-scale production. The authors recently found that unzipping from double-walled carbon nanotubes (DWNTs), rather than single- or multi-walled, results in high-yield production of crystalline sGNRs. However, details of the resultant GNR structure, as well as the reaction mechanism, are not fully understood due to the necessity of nanoscale spectroscopy. In this regard, silver nanowire-based tip-enhanced Raman spectroscopy (TERS) is applied for single GNR analysis and investigated ribbon-to-ribbon heterogeneity in terms of defect density and edge structure generated through the unzipping process. The authors found that sGNRs originated from the inner walls of DWNTs showed lower defect densities than those from the outer walls. Furthermore, TERS spectra of sGNRs exhibit a large variety in graphitic Raman parameters, indicating a large variation in edge structures. This work at the single GNR level reveals, for the first time, ribbon-to-ribbon heterogeneity that can never be observed by diffraction-limited techniques and provides deeper insights into unzipped GNR structure as well as the DWNT unzipping reaction mechanism

    Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: Experimental evaluation by the PHENIX collaboration

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    Extensive experimental data from high-energy nucleus-nucleus collisions were recorded using the PHENIX detector at the Relativistic Heavy Ion Collider (RHIC). The comprehensive set of measurements from the first three years of RHIC operation includes charged particle multiplicities, transverse energy, yield ratios and spectra of identified hadrons in a wide range of transverse momenta (p_T), elliptic flow, two-particle correlations, non-statistical fluctuations, and suppression of particle production at high p_T. The results are examined with an emphasis on implications for the formation of a new state of dense matter. We find that the state of matter created at RHIC cannot be described in terms of ordinary color neutral hadrons.Comment: 510 authors, 127 pages text, 56 figures, 1 tables, LaTeX. Submitted to Nuclear Physics A as a regular article; v3 has minor changes in response to referee comments. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (or will be) publicly available at http://www.phenix.bnl.gov/papers.htm

    Using Risk Factors to Predict Drug Use in a College Population

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    The purpose of this study was to provide evidence for the model that the greater number of risk factors experienced by an individual, the greater the drug use. A series of questionnaires including a parental nurturance scale, the PRIDE College Drug Use Prevalence Questionnaire, a delinquency scale, the Zuckerman Sensation Seeking Scale, a modification of the Social Readjustment Rating Scale, and the Johns Hopkins Symptom Checklist were administered to 235 college students. The series of questionnaires measured ten risk factors used in the study that included high perceived distance from parents, early tobacco use, low religiosity, low academic motivation, sensation seeking, stressful life events, psychological distress, peer drug use, parental drug use, and disregard for rules. Pearson product moment correlation coefficients indicated relationships of r=.33 between number of risk factors and marijuana use to r=.48 between number of risk factors and beer use. Regression analyses indicated that sensation seeking and peer drug use accounted for the most variance in drug use. The relationships provide evidence for the predictive utility of the model in predicting those individuals at risk for drug use. Implications for further research are discussed
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