Modulating the Framework Negative Charge Density in the System [BDT−TTP•+]/[Re6S5Cl91-]/[Re6(S/Se)6Cl82-]/[Re6S7Cl73-]

A series of 2D metals, β-(BDT-TTP)6[Re6Se6Cl8]·(CHCl2−CHCl2)2, 2; β-(ST-TTP)6[Re6S6Cl8]·(CH2Cl−CHCl2)2, 3; β-(BDT-TTP)7[Re6S6Cl8]0.5[Re6S7Cl7]0.5·(CH2Cl2), 4; β-(BDT-TTP)7[Re6Se6Cl8]0.5[Re6S7Cl7]0.5·(CH2Cl2), 5; β-(BDT-TTP)8[Re6S7Cl7]·(CH2Cl2)4, 6 (BDT-TTP and ST-TTP are 2,5-bis(1,3-dithiol-2-ylidene)-1,3,4,6-tetrathiapentalene and 2-(1,3-diselenol-2-ylidene)-5(1,3-dithiol-2-ylidene)-1,3,4,6-tetrathiapentalene, respectively) is reported to have one single β-slab layered topology despite successive increases of the cluster anion negative charge. The charge density within the templating composite inorganic−neutral molecule slab is shown to remain above a threshold of ca. one negative charge per square nanometer, that is, for cluster anions with two negative charges and higher. Conversely, discrete stacks are shown to be stabilized instead in the semiconducting salts (BDT-TTP)2[Re6S5Cl9], 1 where the cluster anion bears one negative charge only. The electronic structure of salts 2−6 is shown to be very stable and kept almost intact across the series. The templating strategy is shown to fulfill its anticipated potential for deliberate installment of incommensurate band fillings in molecular metals. The deliberate admixture of the 6:1 and 8:1 structures yields novel phases with a 7:1 stoichiometry with the anticipated crystal and electronic structures. The action at the organic−inorganic interface triggered by changing the anion charge yet keeping its shape and volume identical, which ultimately governs the shape of the unit cell, is of paramount importance in defining the Fermi surface of these metallic salts. The present BDT-TTP salts thus provide a series of materials with strongly related but subtly different Fermi surfaces worthy of many physical studies. Shubnikov−de Haas measurements are expected to be particularly interesting since they are especially sensitive to the details of the Fermi surface

Surface-induced charge state conversion of nitrogen-vacancy defects in nanodiamonds

We present a study of the charge state conversion of single nitrogen-vacancy (NV) defects hosted in nanodiamonds (NDs). We first show that the proportion of negatively-charged NV$^{-}$ defects, with respect to its neutral counterpart NV$^{0}$, decreases with the size of the ND. We then propose a simple model based on a layer of electron traps located at the ND surface which is in good agreement with the recorded statistics. By using thermal oxidation to remove the shell of amorphous carbon around the NDs, we demonstrate a significant increase of the proportion of NV$^{-}$ defects in 10-nm NDs. These results are invaluable for further understanding, control and use of the unique properties of negatively-charged NV defects in diamondComment: 6 pages, 4 figure

Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years

[EN] Research collaboration is necessary, rewarding, and beneficial. Cohesion between team members is related to their collective efficiency. To assess collaboration processes and their eventual outcomes, agencies need innovative methods-and social network approaches are emerging as a useful analytical tool. We identified the research output and citation data of a network of 61 research groups formally engaged in publishing rare disease research between 2000 and 2013. We drew the collaboration networks for each year and computed the global and local measures throughout the period. Although global network measures remained steady over the whole period, the local and subgroup metrics revealed a growing cohesion between the teams. Transitivity and density showed little or no variation throughout the period. In contrast the following points indicated an evolution towards greater network cohesion: the emergence of a giant component (which grew from just 30 % to reach 85 % of groups); the decreasing number of communities (following a tripling in the average number of members); the growing number of fully connected subgroups; and increasing average strength. Moreover, assortativity measures reveal that, after an initial period where subject affinity and a common geographical location played some role in favouring the connection between groups, the collaboration was driven in the final stages by other factors and complementarities. The Spanish research network on rare diseases has evolved towards a growing cohesion-as revealed by local and subgroup metrics following social network analysis.The Spanish Ministry of Economics and Competitiveness partially supported this research (Grant Number ECO2014-59381-R).Benito Amat, C.; Perruchas, F. (2016). Evolving cohesion metrics of a research network on rare diseases: a longitudinal study over 14 years. Scientometrics. 108(1):41-56. https://doi.org/10.1007/s11192-016-1952-zS41561081Aymé, S., & Schmidtke, J. (2007). Networking for rare diseases: A necessity for Europe. Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz, 50(12), 1477–1483. doi: 10.1007/s00103-007-0381-9 .Barabási, A. L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3–4), 590–614. doi: 10.1016/S0378-4371(02)00736-7 .Bettencourt, L. M. A., Kaiser, D. I., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3(3), 210–221. doi: 10.1016/j.joi.2009.03.001 .Bian, J., Xie, M., Topaloglu, U., Hudson, T., Eswaran, H., & Hogan, W. (2014). Social network analysis of biomedical research collaboration networks in a CTSA institution. Journal of Biomedical Informatics, 52, 130–140. doi: 10.1016/j.jbi.2014.01.015 .Bordons, M., Aparicio, J., González-Albo, B., & Díaz-Faes, A. A. (2015). The relationship between the research performance of scientists and their position in co-authorship networks in three fields. Journal of Informetrics, 9(1), 135–144. doi: 10.1016/j.joi.2014.12.001 .Börner, K., Dall’Asta, L., Ke, W., & Vespignani, A. (2005). Studying the emerging global brain: Analyzing and visualizing the impact of co-authorship teams. Complexity, 10(4), 57–67. doi: 10.1002/cplx.20078 .Casey-Campbell, M., & Martens, M. L. (2009). Sticking it all together: A critical assessment of the group cohesion–performance literature. International Journal of Management Reviews, 11(2), 223–246. doi: 10.1111/j.1468-2370.2008.00239.x .Chiocchio, F., & Essiembre, H. (2009). Cohesion and performance: A meta-analytic review of disparities between project teams, Production teams, and service teams. Small group research, 40(4), 382–420. doi: 10.1177/1046496409335103 .Cho, A. (2011). Particle physicists’ new extreme teams. Science, 333(6049), 1564–1567. doi: 10.1126/science.333.6049.1564 .Cooke, N. J., & Hilton, M. L. (2015). Enhancing the effectiveness of team science. Washington, D.C.: National Academies Press. Recuperado a partir de http://www.nap.edu/catalog/19007/enhancing-the-effectiveness-of-team-science .Cugmas, M., Ferligoj, A., & Kronegger, L. (2015). The stability of co-authorship structures. Scientometrics, 106(1), 163–186. doi: 10.1007/s11192-015-1790-4 .Estrada, E. (2011). The structure of complex networks: Theory and applications. Oxford: University Press.Gallivan, M., & Ahuja, M. (2015). Co-authorship, homophily, and scholarly influence in information systems research. Journal of the Association for Information Systems, 16(12), 980.Ghosh, J., Kshitij, A., & Kadyan, S. (2014). Functional information characteristics of large-scale research collaboration: Network measures and implications. Scientometrics, 102(2), 1207–1239. doi: 10.1007/s11192-014-1475-4 .Heymann, S. (2014). Gephi. In R. Alhajj & J. Rokne (Eds.), Encyclopedia of social network analysis and mining (pp. 612–625). New York: Springer.Himmelstein, D. S., & Powell, K. (2016). Analysis for “the history of publishing delays” blog post v1.0. Zenodo,. doi: 10.5281/zenodo.45516 .Hunt, J. D., Whipple, E. C., & McGowan, J. J. (2012). Use of social network analysis tools to validate a resources infrastructure for interinstitutional translational research: A case study. Journal of the Medical Library Association, 100(1), 48–54. doi: 10.3163/1536-5050.100.1.009 .Kolaczyk, E. D., & Csardi, G. (2014). Statistical analysis of network data with R (Vol. 65). New York: Springer.Kumar, S. (2015). Efficacy of a giant component in co-authorship networks: Evidence from a Southeast Asian dataset in economics. Aslib Journal of Information Management, 68(1), 19–32. doi: 10.1108/AJIM-12-2014-0172 .Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332. doi: 10.1002/asi.23266 .Laudel, G. (2002). What do we measure by co-authorships? Research Evaluation, 11(1), 3–15. doi: 10.3152/147154402781776961 .Liu, X., Bollen, J., Nelson, M. L., & Van de Sompel, H. (2005). Co-authorship networks in the digital library research community. Information Processing and Management, 41(6), 1462–1480. doi: 10.1016/j.ipm.2005.03.012 .Liu, P., & Xia, H. (2015). Structure and evolution of co-authorship network in an interdisciplinary research field. Scientometrics, 103(1), 101–134. doi: 10.1007/s11192-014-1525-y .Ministerio de Sanidad y Consumo. 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Hydrogenation of nanodiamonds using MPCVD: A new route toward organic functionalization

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Surface properties of hydrogenated nanodiamonds: a chemical investigation

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Rayleigh-like instability in the ion-shaping of Au-Ag alloy nanoparticles embedded within a silica matrix

International audienceWe have studied how spherical 23 ± 3 nm Au45Ag55 nanoparticles embedded within a silica matrix transform into prolate nanorods and nanowires by irradiating them with swift heavy ions. Samples were irradiated at room temperature and normal incidence with 74 MeV Kr and 36 MeV S ions for fluences up to 1.0 × 1015 cm−2. We demonstrate the existence of two regimes: (i) below a critical fluence, ∼2.0 × 1014 cm−2, the transformation of the spherical nanoparticle into a nanorod is an individual process, i.e. each nanoparticle transforms into a single nanorod; (ii) for larger fluences the transformation from nanorod to nanowire becomes a collective process, i.e. the break up and dissolution of unstable nanorods contribute to the growth of long nanowires. The passage from the first to the second regime can be interpreted in terms of a Rayleigh-like instability under irradiation. The latter becomes active when the diameter of the nanowire approaches its saturation width under irradiation. Furthermore, we show that the composition of the alloy is only slightly modified during the ion-shaping process. Finally, the energy and the fluence thresholds for deformation and the deformation strain-rate are estimated