Synthesis of uranium-in-cryptand complexes
Uranium-in-cryptand complexes can be readily synthesized by reacting UI 3 with 2.2.2-cryptand.
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Uranium-in-cryptand complexes can be readily synthesized by reacting UI 3 with 2.2.2-cryptand.
Four novel Pu( iv ) nitrato chloro complexes were crystallographically characterized and the Pu–ligand bond nature was delineated computationally.
Structure-based simulations and pH-dependent spectra have been used to clarify the protonation preferences of bilin pigments in cryptophyte photosynthetic complexes.
Complexation of selenomaltol to transition metal ions promotes a zwitterionic resonance form.
The first uranium( iii ) phosphido complex is reported.
We report the solution and structural chemistry of nickel( ii ) complexes of the phosphine-oxime Ph 2 PC 6 H 4 -2-CH₌NOH (PCH₌NOH).
Reaction of CoCO 3 with α,α,α′,α′-tetramethyl-1,3-benzenedipropionic acid (H 2 esp) in EtOH forms the paddlewheel-type dimer Co 2 (esp) 2 (EtOH) 2 , which hydrolyzes in MeCN to give a bowl-shaped heptacobalt coordination cluster complex; both compounds display antiferromagnetic interactions between high-spin Co( ii ) ions.
Steric control of dioxygen activation is demonstrated through O 2 titration and isotopic labeling experiments for two Mn( ii ) complexes.
The ligand substituents have a major effect on the redox potentials, catalytic efficiency and robustness of the complexes in HER.
Metalloenzymes are remarkably active, specific, and efficient catalysts.Mimicking the active sites with organometallic complexes based on crystal structures has been a focus for many synthetic chemists. These surrogates have provided significant mechanistic insight into both the molecular complexes as well as the enzymes, however, one of the principle findings has been that just reproducing the active site is not adequate in reproducing the catalytic function. Specifically, the scaffold surrounding the active site plays a critical role in many aspects of catalysis, from positioning reactants optimally to positioning functional groups for reaction to creating the ideal environment for catalysis. Each of these can be controlled with both static positioning as well as small and large dynamics that can activate or control a specific part of the reaction. In this review, we will discuss the effect of organic/peptidic scaffolds, along with medium as an extension of the scaffold, and their varying impacts on catalytic reactivity.
Organic photovoltaics have reached high power conversion efficiencies (PCE) using non-fullerene acceptors (NFAs). However, the best NFAs tend to have complex syntheses, limiting scalability. Among polymer donors, regioregular poly(3-hexylthiophene) (P3HT) has the greatest potential for commercialization due to its simple synthesis and good stability, but PCEs have been limited. It is thus imperative to find scalable NFAs that give high PCE with P3HT. Herein we report a zinc( II ) complex of di(naphthylethynyl)azadipyrromethene (Zn(L2) 2 ) as a non-planar NFA that can be synthesized on the gram scale using inexpensive starting materials without chromatography column purification. The NFA has strong absorption in the 600–800 nm region. Time-dependent density-functional theory calculations suggest that the low-energy absorptions can be understood within a four-orbital model involving transitions between π-orbitals on the azadipyrromethene core. OPVs fabricated from P3HT:Zn(L2) 2 blends reached a PCE of 5.5%, and the PCE was not very sensitive to the P3HT:Zn(L2) 2 weight ratio. Due to its shape, Zn(L2) 2 shows isotropic charge transport and its potential as an electron donor is also demonstrated. The combination of simple synthesis, good PCE and photostability, and tolerance to the active material weight ratio demonstrates the potential of Zn(L2) 2 as an active layer material in OPVs.
A detailed photophysical study of binuclear complexes was performed using steady-state and time-resolved photoluminescence measurements at variable temperature. The results were compared with the prototypical [Ru(bpy) 3 ] 2+ .
A cobalt complex supported by the 2-(diisopropylphosphaneyl)benzenethiol ligand was synthesized and its electronic structure and reactivity were explored.
The synthesis, characterization, emission, and electronic spectroscopy for complexes of derivatives of 2-(1 H -imidazo[4,5- b ]phenazin-2-yl)phenol with uranyl (UO 2 2+ ) and copper (Cu 2+ ) are detailed.
Hydrogen bonding networks are vital for metallo-enzymes to function; however, modeling these systems is non-trivial. The development of 1st-row transition metal chloride complexes with intramolecular hydrogen-bonding interactions are detailed herein.
In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.
Following the lines of the recent paper of J.-P. Gazeau and F. H. Szafraniec [J. Phys. A: Math. Theor. 44, 495201 (2011)], we construct here three types of coherent states, related to the Hermite polynomials in a complex variable which are orthogonal with respect to a non-rotationally invariant measure. We investigate relations between these coherent states and obtain the relationship between them and the squeezed states of quantum optics. We also obtain a second realization of the canonical coherent states in the Bargmann space of analytic functions, in terms of a squeezed basis. All this is done in the flavor of the classical approach of V. Bargmann [Commun. Pure Appl. Math. 14, 187 (1961)].
The simplest power curves model wind power only as a function of the wind speed at the turbine hub height. While the latter is an essential predictor of power output, wind speed information in other parts of the vertical profile, as well as additional atmospheric variables, are also important determinants of power. The goal of this work was to determine the gain in predictive ability afforded by adding wind speed information at other heights, as well as other atmospheric variables, to the power prediction model. Using data from a wind farm with a moderately complex terrain in the Altamont Pass region in California, we trained three statistical models—a neural network, a random forest and a Gaussian process model—to predict power output from various sets of aforementioned predictors. The comparison of these predictions to the observed power data revealed that considerable improvements in prediction accuracy can be achieved both through the addition of predictors other than the hub-height wind speed and the use of statistical models. To our knowledge, the use of the Gaussian process model in this context is new, in contrast to neural networks and random forests. The advantage of this model over the other two models is that it provides a much more natural way to estimate the uncertainty associated with its predictions. In addition, this study presents one of the most comprehensive analyses to date of the relative importance of various wind power curve inputs.