231 research outputs found

    Vibrational spectra and structures of bare and Xe-tagged cationic Si<sub>n</sub>O<sub>m</sub><sup>+</sup> clusters

    Get PDF
    Vibrational spectra of Xe-tagged cationic silicon oxide clusters SinOm+ with n = 3–5 and m = n, n ± 1 in the gas phase are obtained by resonant infrared multiple photon dissociation (IRMPD) spectroscopy and density functional theory calculations. The SinOm+ clusters are produced in a laser vaporization ion source and Xe complexes are formed after thermalization to 100 K. The clusters are subsequently irradiated with tunable light from an IR free electron laser and changes in the mass distribution yield size-specific IR spectra. The measured IRMPD spectra are compared to calculated linear IR absorption spectra leading to structural assignments. For several clusters, Xe complexation alters the energetic order of the SinOm+ isomers. Common structural motifs include the Si2O2 rhombus, the Si3O2 pentagon, and the Si3O3 hexagon

    Infrared action spectroscopy of nitrous oxide on cationic gold and cobalt clusters

    Get PDF
    Understanding the catalytic decomposition of nitrous oxide on finely divided transition metals is an important environmental issue. In this study, we present the results of a combined infrared action spectroscopy and quantum chemical investigation of molecular N2O binding to isolated Aun+ (n ≤ 7) and Con+ (n ≤ 5) clusters. Infrared multiple-photon dissociation spectra have been recorded in the regions of both the N=O (1000–1400 cm-1) and N=N (2100–2450 cm-1) stretching modes of nitrous oxide. In the case of Aun+ clusters only the ground electronic state plays a role, while the involvement of energetically low-lying excited states in binding to the Con+ clusters cannot be ruled out. There is a clear preference for N-binding to clusters of both metals but some O-bound isomers are observed in the case of smaller Con(N2O)+ clusters

    Excited States of Proton-bound DNA/RNA Base Homo-dimers: Pyrimidines

    Full text link
    We are presenting the electronic photo fragment spectra of the protonated pyrimidine DNA bases homo-dimers. Only the thymine dimer exhibits a well structured vibrational progression, while protonated monomer shows broad vibrational bands. This shows that proton bonding can block some non radiative processes present in the monomer.Comment: We acknowledge the use of the computing facility cluster GMPCS of the LUMAT federation (FR LUMAT 2764

    Stabilities of nanohydrated thymine radical cations: insights from multiphoton ionization experiments and ab initio calculations

    Get PDF
    Multi-photon ionization experiments have been carried out on thymine-water clusters in the gas phase. Metastable H2O loss from T+(H2O)n was observed at n ≥ 3 only. Ab initio quantum-chemical calculations of a large range of optimized T+(H2O)n conformers have been performed up to n = 4, enabling binding energies of water to be derived. These decrease smoothly with n, consistent with the general trend of increasing metastable H2O loss in the experimental data. The lowest-energy conformers of T+(H2O)3 and T+(H2O)4 feature intermolecular bonding via charge-dipole interactions, in contrast with the purely hydrogen-bonded neutrals. We found no evidence for a closed hydration shell at n = 4, also contrasting with studies of neutral clusters

    The Citation Field of Evolutionary Economics

    Get PDF
    Evolutionary economics has developed into an academic field of its own, institutionalized around, amongst others, the Journal of Evolutionary Economics (JEE). This paper analyzes the way and extent to which evolutionary economics has become an interdisciplinary journal, as its aim was: a journal that is indispensable in the exchange of expert knowledge on topics and using approaches that relate naturally with it. Analyzing citation data for the relevant academic field for the Journal of Evolutionary Economics, we use insights from scientometrics and social network analysis to find that, indeed, the JEE is a central player in this interdisciplinary field aiming mostly at understanding technological and regional dynamics. It does not, however, link firmly with the natural sciences (including biology) nor to management sciences, entrepreneurship, and organization studies. Another journal that could be perceived to have evolutionary acumen, the Journal of Economic Issues, does relate to heterodox economics journals and is relatively more involved in discussing issues of firm and industry organization. The JEE seems most keen to develop theoretical insights

    Size and shape dependent photoluminescence and excited state decay rates of diamondoids

    Get PDF
    Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.We present photoluminescence spectra and excited state decay rates of a series of diamondoids, which represent molecular structural analogues to hydrogen-passivated bulk diamond. Specific isomers of the five smallest diamondoids (adamantane–pentamantane) have been brought into the gas phase and irradiated with synchrotron radiation. All investigated compounds show intrinsic photoluminescence in the ultraviolet spectral region. The emission spectra exhibit pronounced vibrational fine structure which is analyzed using quantum chemical calculations. We show that the geometrical relaxation of the first excited state of adamantane, exhibiting Rydberg character, leads to the loss of Td symmetry. The luminescence of adamantane is attributed to a transition from the delocalized first excited state into different vibrational modes of the electronic ground state. Similar geometrical changes of the excited state structure have also been identified in the other investigated diamondoids. The excited state decay rates show a clear dependence on the size of the diamondoid, but are independent of the particle geometry, further indicating a loss of particle symmetry upon electronic excitation.DFG, FOR 1282, Controlling the electronic structure of semiconductor nanoparticles by doping and hybrid formatio

    Microsecond Isomer at the N=20 Island of Shape Inversion Observed at FRIB

    Full text link
    Excited-state spectroscopy from the first Facility for Rare Isotope Beams (FRIB) experiment is reported. A 24(2)-μ\mus isomer was observed with the FRIB Decay Station initiator (FDSi) through a cascade of 224- and 401-keV γ\gamma rays in coincidence with 32Na^{32}\textrm{Na} nuclei. This is the only known microsecond isomer (1 μsT1/2<1 ms1{\text{ }\mu\text{s}}\leq T_{1/2} < 1\text{ ms}) in the region. This nucleus is at the heart of the N=20N=20 island of shape inversion and is at the crossroads of spherical shell-model, deformed shell-model, and ab initio theories. It can be represented as the coupling of a proton hole and neutron particle to 32Mg^{32}\textrm{Mg}, 32Mg+π1+ν+1^{32}\textrm{Mg}+\pi^{-1} + \nu^{+1}. This odd-odd coupling and isomer formation provides a sensitive measure of the underlying shape degrees of freedom of 32Mg^{32}\textrm{Mg}, where the onset of spherical-to-deformed shape inversion begins with a low-lying deformed 2+2^+ state at 885 keV and a low-lying shape-coexisting 02+0_2^+ state at 1058 keV. We suggest two possible explanations for the 625-keV isomer in 32^{32}Na: a 66^- spherical shape isomer that decays by E2E2 or a 0+0^+ deformed spin isomer that decays by M2M2. The present results and calculations are most consistent with the latter, indicating that the low-lying states are dominated by deformation.Comment: 7 pages, 5 figures, accepted by Physical Review Letter

    Who leads research productivity growth? Guidelines for R&D policy-makers

    Full text link
    [EN] This paper evaluates to what extent policy-makers have been able to promote the creation and consolidation of comprehensive research groups that contribute to the implementation of a successful innovation system. Malmquist productivity indices are applied in the case of the Spanish Food Technology Program, finding that a large size and a comprehensive multi-dimensional research output are the key features of the leading groups exhibiting high efficiency and productivity levels. While identifying these groups as benchmarks, we conclude that the financial grants allocated by the program, typically aimed at small-sized and partially oriented research groups, have not succeeded in reorienting them in time so as to overcome their limitations. We suggest that this methodology offers relevant conclusions to policy evaluation methods, helping policy-makers to readapt and reorient policies and their associated means, most notably resource allocation (financial schemes), to better respond to the actual needs of research groups in their search for excellence (micro-level perspective), and to adapt future policy design to the achievement of medium-long term policy objectives (meso and macro-level).Jiménez Saez, F.; Zabala Iturriagagoitia, JM.; Zofio, JL. (2013). Who leads research productivity growth? Guidelines for R&D policy-makers. Scientometrics. 94(1):273-303. doi:10.1007/s11192-012-0763-0S273303941Abbring, J. H., & Heckman, J. J. (2008). Dynamic policy analysis. In L. Mátyás & P. Sevestre (Eds.), The econometrics of panel data (3rd ed., pp. 795–863). Heidelberg: Springer.Acosta Ballesteros, J., & Modrego Rico, A. (2001). Public financing of cooperative R&D projects in Spain: the concerted projects under the national R&D plan. Research Policy, 30, 625–641.Arbel, A. (1981). Policy evaluation in the dynamic input–output model. International Journal of Systems Science, 12, 255–260.Arnold, E. (2004). Evaluation research and innovation policy: A systems world needs systems evaluations. Research Evaluation, 13, 3–17.Arrow, J. K. (1962). Economic welfare and the allocation of resources for inventions. In R. Nelson (Ed.), The rate and direction of inventive activity: Economic and social factor (pp. 609–625). Princeton: Princeton University Press and NBER.Autio, E. (1997). New, technology-based firms in innovation networks symplectic and generative impacts. Research Policy, 26, 263–281.Balk, B. (2001). Scale efficiency and productivity change. Journal of Productivity Analysis, 15, 153–183.Balzat, M., & Hanusch, H. (2004). Recent trends in the research on national innovation systems. Journal of Evolutionary Economics, 14, 197–210.Berg, S. A., Førsund, F. R., & Jansen, E. S. (1992). Malmquist indices of productivity growth during the deregulation of Norwegian banking. Scandinavian Journal of Economics, 94, S211–S228.Bergek, A., Carlsson, B., Lindmark, S., Rickne, A., & Jacobsson, S. (2008). Analyzing the functional dynamics of technological innovation systems: A scheme of analysis. Research Policy, 37, 407–429.Bonaccorsi, A., & Daraio, C. (2005). Exploring size and agglomeration effects on public research productivity. Scientometrics, 63(1), 87–120.Buisseret, T. J., Cameron, H., & Georghiou, L. (1995). What difference does it make? Additionality in the public support of R&D in large firms. International Journal of Technology Management, 10, 587–600.Bustelo, M. (2006). The potential role of standards and guidelines in the development of an evaluation culture in Spain. Evaluation, 12, 437–453.Chavas, J. P., & Cox, T. M. (1999). A generalized distance function and the analysis of production efficiency. Southern Economic Journal, 66, 295–318.CICYT. (1987). Programa Nacional de Tecnología de los Alimentos. Madrid: Ministerio de Educación y Ciencia.CICYT (1988). Plan Nacional de Investigación Científica y Desarrollo Tecnológico 1988–1991. Ministerio de Educación y Ciencia, Secretaría de Estado de Universidades e Investigación, Madrid.Cooper, W. W., Seiford, L. M., & Tone, K. (2000). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-software. Boston: Kluwer Academic Publishers.David, P., Mowery, D., & Steinmueller, W. E. (1994). Analyzing the economic payoffs from basic research. In D. Mowery (Ed.), Science and technology policy in interdependent economies (pp. 57–78). Boston: Kluwer Academic Publishers.Dopfer, K., Foster, J., & Potts, J. (2004). Micro-meso-macro. Journal of Evolutionary Economics, 14, 263–279.Edquist, C., & Hommen, L. (2008). Comparing national systems of innovation in Asia and Europe: Theory and comparative framework. In C. Edquist & L. Hommen (Eds.), Small country innovation systems: Globalisation, change and policy in Asia and Europe (pp. 1–28). Cheltenham: Edward Elgar.Färe, R., Grosskopf, S., Norris, M., & Zhang, Z. (1994). Productivity growth, technical progress, and efficiency change in industrialized countries. American Economic Review, 84, 66–83.Farrell, M. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, Series A, General, 120(3), 253–281.Førsund, F. R. (1993). Productivity growth in Norwegian ferries. In H. O. Fried, C. A. K. Lovell, & S. S. Schmidt (Eds.), The measurement of productive efficiency: Techniques and applications (pp. 352–373). New York: Oxford University Press.Førsund, F. R. (1997). The Malmquist productivity index, TFP and scale. University of Oslo, Oslo: Working Paper, Department of Economics and Business Administration.Freeman, C. (1987). Technology policy and economic performance: Lessons from Japan. London: Printer Publishers.García-Martínez, M., & Briz, J. (2000). Innovation in the Spanish food & drink industry. International Food and Agribusiness Management Review, 3, 155–176.Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage Publications.Grammatikopoulos, V., Kousteiios, A., Tsigilis, N., & Theodorakis, Y. (2004). Applying dynamic evaluation approach in education. Studies in Educational Evaluation, 30, 255–263.Grifell-Tatjé, E., & Lovell, C. A. K. (1999). A generalized Malmquist productivity index. Top, 7(1), 81–101.Grimpe, C., & Sofka, W. (2007). Search patterns and absorptive capacity: A comparison of low- and high-technology firms from thirteen European countries. Discussion paper no. 07-062. Centre for European Economic Research (ZEW), Mannheim, Germany.Guan, J., & Wang, J. (2004). Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59(1), 131–155.Hekkert, M. P., Suurs, R. A. A., Negro, S. O., Kuhlmann, S., & Smits, R. E. H. M. (2007). Functions of innovation systems: A new approach for analysing technological change. Technological Forecasting and Social Change, 74, 413–432.Jiménez-Sáez, F. (2005). Una Evaluación del Programa Nacional de Tecnología de Alimentos: análisis de la articulación fomentada sobre el Sistema Alimentario de Innovación en España. PhD dissertation, Servicio de Publicaciones de la Universidad Politécnica de Valencia, Valencia.Jiménez-Sáez, F., Zabala-Iturriagagoitia, J. M., Zofío, J. L., & Castro-Martínez, E. (2011). Evaluating research efficiency within National R&D Programmes. Research Policy, 40, 230–241.Kao, C. (2008). Efficiency analysis of university departments: An empirical study. OMEGA, 36, 653–664.Kuhlmann, S. (2003). Evaluation of research and innovation policies: A discussion of trends with examples from Germany. International Journal of Technology Management, 26, 131–149.Laitinen, E. K. (2002). A dynamic performance measurement system: Evidence from small Finnish technology companies. Scandinavian Journal of Management, 18, 65–99.Laranja, M., Uyarra, E., & Flanagan, K. (2008). Policies for science, technology and innovation: Translating rationales into regional policies in a multi-level setting. Research Policy, 37(5), 823–835.Lee, T.-L., & von Tunzelman, N. (2005). A dynamic analytic approach to national innovation systems: The IC industry in Taiwan. Research Policy, 34, 425–440.Lipsey, R., & Carlaw, K. (1998). A structuralist assessment of technology policies: Taking Schumpeter seriously on policy. Ottawa: Industry Canada Research Publications Program.Lipsey, R., Carlaw, K., & Bekar, C. (2005). Economic transformations: General purpose technologies and long term economic growth. Oxford: Oxford University Press.Lundvall, B. Å. (1992). National systems of innovation: Toward a theory of innovation and interactive learning. London: Printer Publishers.Lundvall, B. Å., Johnson, B., Andersen, E. S., & Dalum, B. (2002). National systems of production, innovation and competence building. Research Policy, 31, 213–231.Markard, J., & Truffer, B. (2008). Actor-oriented analysis of innovation systems: Exploring micro-meso level linkages in the case of stationary fuel cells. Technology Analysis & Strategic Management, 20, 443–464.Metcalfe, J. S. (2002). Equilibrium and evolutionary foundations of competition and technology policy: New perspectives on the division of labour and the innovation process. CRIC Working Papers series, University of Manchester.Miettinen, R. (1999). The riddle of things. Activity theory and actor network theory as approaches of studying innovations. Mind, Culture and Activity, 6, 170–195.Molas-Gallart, J., & Davies, A. (2006). Toward theory-led evaluation: The experience of European science, technology, and innovation policies. American Journal of Evaluation, 27, 64–82.Mytelka, L. K., & Smith, K. (2002). Policy learning and innovation theory: An interactive and co-evolving process. Research Policy, 31, 1467–1479.Olazarán, M., Lavía, C., & Otero, B. (2004). ¿Hacia una segunda transición en la ciencia? Política científica y grupos de investigación. Revista Española de Sociología, 4, 143–172.Potts, J. (2007). The innovation system & economic evolution. Productivity commission submission, public support for science & innovation, productivity commission, Camberra.Ray, S., & Desli, E. (1997). Productivity growth, technical progress, and efficiency change in industrialized countries: Comment. American Economic Review, 87(5), 1033–1039.Rip, A., & Nederhof, A. J. (1986). Between dirigism and laissez-faire: Effects of implementing the science policy priority for biotechnology in the Netherlands. Research Policy, 15, 253–268.Schmidt, E. K., Graversen, E. K., & Langberg, K. (2003). Innovation and dynamics in public research environments in Denmark: A research-policy perspective. Science and Public Policy, 30, 107–116.Schmoch, U., & Schubert, T. (2009). Sustainability of incentives for excellent research—The German case. Scientometrics, 81(1), 195–218.Shephard, R. (1970). Theory of cost and production functions. New Jersey: Princeton University Press.Simar, L., & Wilson, P. W. (1998). Productivity growth in industrialized countries. Discussion paper 9810, Universite Catholique de Louvain, Belgium.Van Raan, A. F. J. (2000). R&D evaluation at the beginning of the new century. Research Evaluation, 8, 81–86.Zofio, J. L. (2007). Malmquist productivity index decompositions: A unifying framework. Applied Economics, 39, 2371–2387.Zofio, J. L., & Lovell, C. A. K. (1998). Yet another Malmquist productivity index decomposition. Working paper, Department of Economics, University of Georgia, Athens, GA 30602, USA.Zofio, J. L., & Lovell, C. A. K. (2001). Graph efficiency and productivity measures: An application to US agriculture. Applied Economics, 33(10), 1433–1442.Zofio, J. L., & Prieto, A. M. (2006). Return to dollar, generalized distance function and the Fisher productivity index. Spanish Economic Review, 8, 113–138
    corecore