149 research outputs found
N-heterocyclic germylenes: structural characterisation of some heavy analogues of the ubiquitous N-heterocyclic carbenes
The X-ray crystal structures of three N-heterocyclic germylenes (NHGes) have been elucidated including the previously unknown 1,3-bis(2,6-dimethylphenyl)diazagermol-2-ylidene (1). In addition, the X-ray crystal structures of the previously synthesised 1,3-bis(2,4,6-trimethylphenyl)diazagermol-2-ylidene (2) and 1,3-bis(2,6-diisopropylphenyl)diazagermol-2-ylidene (3) are also reported. The discrete molecular structures of compounds 1 to 3 are comparable, with Ge-N bond lengths in the range 1.835-1.875 Å, while the N-Ge-N bond angles range between 83.6 and 85.2°. Compound 2 was compared to the analogous N-heterocyclic carbene species, 1,3-bis(2,4,6-trimethylphenyl)imidazol-2-ylidene (IMes). The major geometrical difference observed, as expected, was the bond angle around the divalent group 14 atom. The N-Ge-N bond angle was 83.6° for compound 2 versus the N-C-N bond angle of 101.4° for IMes. The Sn equivalent of (1), 1,3-bis(2,6-dimethylphenyl)diazastannol-2-ylidene (4), has also been synthesised and its crystal structure is reported here. In order to test their suitability as ligands, compounds 1 to 3 were reacted with a wide range of transition metal complexes. No NHGes containing metal complexes were observed. In all cases the NHGe either degraded or gave no reaction
Formation of a nonanuclear copper(II) cluster with 3,5-dimethylpyrazolate starting from an NHC complex of copper(I) chloride
The complete nonanuclear cluster in bis[1,3-bis(2,6-dimethylphenyl)imidazolium] di--chlorido-tetrachloridooctakis(-3,5-dimethylpyrazolato)hexa-3- hydroxido-nonacopper(II) chloroform disolvate, [HIXy]2[Cu9(-pz*)8(3- OH)6(2-Cl)2Cl4]2CHCl3 or (C19H21N2)2[Cu9(C5H7N2)8Cl6(OH)6]2CHCl3, where pz* is the 3,5-dimethylpyrazolyl anion, C5H7N2 , and HIXy is the 1,3- bis(2,6-dimethylphenyl)imidazolium cation, C19H21N2 +, is generated by a crystallographic centre of symmetry with a square-planar CuII ion bound to four 3-OH ions lying on the inversion centre. Of the four remaining unique CuII atoms, three adopt CuN2O2Cl square-pyramidal coordination geometries with the chloride ion in the apical position and one has a distorted CuN2OCl tetrahedral geometry. The dianionic nonanuclear core can be described as a 24-membered [CuNN]8 ring that contains a Cu9O6Cl6 core. The cluster features three intramolecular O—H Cl hydrogen bonds. In the crystal, weak C— H N and C—H Cl interactions link the components. Polynuclear paramagnetic clusters of this type are of considerable interest due to their relevance to both the bioinorganic and single-molecule magnets research fields
Twitter reciprocal reply networks exhibit assortativity with respect to happiness
The advent of social media has provided an extraordinary, if imperfect, 'big
data' window into the form and evolution of social networks. Based on nearly 40
million message pairs posted to Twitter between September 2008 and February
2009, we construct and examine the revealed social network structure and
dynamics over the time scales of days, weeks, and months. At the level of user
behavior, we employ our recently developed hedonometric analysis methods to
investigate patterns of sentiment expression. We find users' average happiness
scores to be positively and significantly correlated with those of users one,
two, and three links away. We strengthen our analysis by proposing and using a
null model to test the effect of network topology on the assortativity of
happiness. We also find evidence that more well connected users write happier
status updates, with a transition occurring around Dunbar's number. More
generally, our work provides evidence of a social sub-network structure within
Twitter and raises several methodological points of interest with regard to
social network reconstructions.Comment: 22 pages, 21 figures, 5 tables, In press at the Journal of
Computational Scienc
Estimation of global network statistics from incomplete data
Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar\u27s hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week
Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter
Individual happiness is a fundamental societal metric. Normally measured
through self-report, happiness has often been indirectly characterized and
overshadowed by more readily quantifiable economic indicators such as gross
domestic product. Here, we examine expressions made on the online, global
microblog and social networking service Twitter, uncovering and explaining
temporal variations in happiness and information levels over timescales ranging
from hours to years. Our data set comprises over 46 billion words contained in
nearly 4.6 billion expressions posted over a 33 month span by over 63 million
unique users. In measuring happiness, we use a real-time, remote-sensing,
non-invasive, text-based approach---a kind of hedonometer. In building our
metric, made available with this paper, we conducted a survey to obtain
happiness evaluations of over 10,000 individual words, representing a tenfold
size improvement over similar existing word sets. Rather than being ad hoc, our
word list is chosen solely by frequency of usage and we show how a highly
robust metric can be constructed and defended.Comment: 27 pages, 17 figures, 3 tables. Supplementary Information: 1 table,
52 figure
An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
Many real world, complex phenomena have underlying structures of evolving
networks where nodes and links are added and removed over time. A central
scientific challenge is the description and explanation of network dynamics,
with a key test being the prediction of short and long term changes. For the
problem of short-term link prediction, existing methods attempt to determine
neighborhood metrics that correlate with the appearance of a link in the next
observation period. Recent work has suggested that the incorporation of
topological features and node attributes can improve link prediction. We
provide an approach to predicting future links by applying the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) to optimize weights which are
used in a linear combination of sixteen neighborhood and node similarity
indices. We examine a large dynamic social network with over nodes
(Twitter reciprocal reply networks), both as a test of our general method and
as a problem of scientific interest in itself. Our method exhibits fast
convergence and high levels of precision for the top twenty predicted links.
Based on our findings, we suggest possible factors which may be driving the
evolution of Twitter reciprocal reply networks.Comment: 17 pages, 12 figures, 4 tables, Submitted to the Journal of
Computational Scienc
Positivity of the English language
Over the last million years, human language has emerged and evolved as a
fundamental instrument of social communication and semiotic representation.
People use language in part to convey emotional information, leading to the
central and contingent questions: (1) What is the emotional spectrum of natural
language? and (2) Are natural languages neutrally, positively, or negatively
biased? Here, we report that the human-perceived positivity of over 10,000 of
the most frequently used English words exhibits a clear positive bias. More
deeply, we characterize and quantify distributions of word positivity for four
large and distinct corpora, demonstrating that their form is broadly invariant
with respect to frequency of word use.Comment: Manuscript: 9 pages, 3 tables, 5 figures; Supplementary Information:
12 pages, 3 tables, 8 figure
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