Photoinduced Mo−CN Bond Breakage in Octacyanomolybdate Leading to Spin Triplet Trapping

The photoinduced properties of the octacoordinated complex K₄Mo^IV(CN)₈⋅2 H₂O were studied by theoretical calculations, crystallography, and optical and magnetic measurements. The crystal structure recorded at 10 K after blue light irradiation reveals an heptacoordinated Mo(CN)₇ species originating from the light-induced cleavage of one Mo−CN bond, concomitant with the photoinduced formation of a paramagnetic signal. When this complex is heated to 70 K, it returns to its original diamagnetic ground state, demonstrating full reversibility. The photomagnetic properties show a partial conversion into a triplet state possessing significant magnetic anisotropy, which is in agreement with theoretical studies. Inspired by these results, we isolated the new compound [K(crypt-222)]₃[Mo^IV(CN)₇]⋅3 CH₃CN using a photochemical pathway, confirming that photodissociation leads to a stable heptacyanomolybdate(IV) species in solution.     

Multi-component PZT ceramics obtained by mechanochemical activation and conventional ceramic technology

Journal of Thermal Analysis and Calorimetry - Numerical predictions of flow boiling in a square tube rotating in vertical direction at a uniform rotating speed are presented. There-dimensional...     

Synthesis of aryliminoacetonitriles under FVT conditions or by dehydrogenation of arylaminoacetonitriles: an NMR and UV-photoelectron spectroscopy study

The synthesis of [(E)-arylimino]-acetonitriles 3 has been described. It was found that the title compounds can be obtained on the three ways, namely by: (i) dehydrogenation of arylaminoacetonitriles 1, (ii) thermal fragmentation of 1-aryl-4-cyano-β-lactams 4 and (iii) retro-ene reaction of (allyl-p-methoxyphenyl-amino)-acetonitrile (7a) under FVT conditions. ¹H and ¹³C NMR spectra of compounds 3, 5 and 6, and all their precursors 1 and 4, were recorded and analysed in detail using chemical shifts δ_H and δ_C [from GIAO DFT B3LYP/6-31(d) calculations] and J-couplings predicted at the DFT B3LYP/IGLO-II level. Also, UV-photoelectron spectra of 4a,d and 3a,d were measured and analysed considering the theoretical evaluation of their ionisation potentials.     

Protein secondary structure prediction with SPARROW

A first step toward predicting the structure of a protein is to determine its secondary structure. The secondary structure information is generally used as starting point to solve protein crystal structures. In the present study, a machine learning approach based on a complete set of two-class scoring functions was used. Such functions discriminate between two specific structural classes or between a single specific class and the rest. The approach uses a hierarchical scheme of scoring functions and a neural network. The parameters are determined by optimizing the recall of learning data. Quality control is performed by predicting separate independent test data. A first set of scoring functions is trained to correlate the secondary structures of residues with profiles of sequence windows of width 15, centered at these residues. The sequence profiles are obtained by multiple sequence alignment with PSI-BLAST. A second set of scoring functions is trained to correlate the secondary structures of the center residues with the secondary structures of all other residues in the sequence windows used in the first step. Finally, a neural network is trained using the results from the second set of scoring functions as input to make a decision on the secondary structure class of the residue in the center of the sequence window. Here, we consider the three-class problem of helix, strand, and other secondary structures. The corresponding prediction scheme "SPARROW" was trained with the ASTRAL40 database, which contains protein domain structures with less than 40% sequence identity. The secondary structures were determined with DSSP. In a loose assignment, the helix class contains all DSSP helix types (α, 3-10, π), the strand class contains β-strand and β-bridge, and the third class contains the other structures. In a tight assignment, the helix and strand classes contain only α-helix and β-strand classes, respectively. A 10-fold cross validation showed less than 0.8% deviation in the fraction of correct structure assignments between true prediction and recall of data used for training. Using sequences of 140,000 residues as a test data set, 80.46% ± 0.35% of secondary structures are predicted correctly in the loose assignment, a prediction performance, which is very close to the best results in the field. Most applications are done with the loose assignment. However, the tight assignment yields 2.25% better prediction performance. With each individual prediction, we also provide a confidence measure providing the probability that the prediction is correct. The SPARROW software can be used and downloaded on the Web page http://agknapp.chemie.fu-berlin.de/sparrow/ .     

Normed Topological Pseudovector Groups

A normed topological pseudovector group (NTPVG for short) is a valued topological group (V,?+?,||·||) (not necessarily Abelian) endowed with a continuous scalar multiplication \({\mathbb R}_+ \times V \ni (t,x) \mapsto t \cdot x \in V\) such that 0 ·x?=?e (e denotes the neutral element of V), 1 ·x?=?x, (st) ·x?=?s ·(t ·x), t ·(x?+?y)?=?(t ·x)?+?(t ·y) and ||t ·x||?=?t ||x|| for each t, \(s \in {\mathbb R}_+\) and x, y?∈?V. It is shown that every valued topological group can be isometrically and group-homomorphically embedded in a NTPVG as a closed subset by means of a functor. Locally compact NTPV groups are fully classified. It is shown that the (unbounded) Urysohn universal metric space can be endowed with a structure of a NTPV group of exponent 2.     

Normal systems over ANR’s, rigid embeddings and nonseparable absorbing sets

Most of results of Bestvina and Mogilski [Characterizing certain incomplete infinite-di-mensional absolute retracts. Michigan Math. J., 33, 291-313 (1986)] on strong Z-sets in ANR's and absorbing sets is generalized to nonseparable case. It is shown that if an ANR X is locally homotopy dense embeddable in infinite-dimensional Hilbert manifolds and w(U ) = w(X ) (where "w"is the topological weight) for each open nonempty subset U of X , then X itself is homotopy dense embeddable in a Hilbert manifold. It is also demonstrated that whenever X is an AR, its weak product W (X, ) = {(x n ) ∞ n=1 ∈ X ω : x n = for almost all n} is homeomorphic to a pre-Hilbert space E with E ～ = ΣE. An intrinsic characterization of manifolds modelled on such pre-Hilbert spaces is given.     

On a universal best choice algorithm for partially ordered sets

For the only known universal best choice algorithm for partially ordered sets with known cardinality and unknown order (proposed by J. Preater) we improve the estimation of the lower bound of its chance of success from the hitherto known constant 1/8 to 1/4. We also show that this result is the best possible for this algorithm, i.e., the 1/4 bound cannot be further improved. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2008