Physical interpretation of the paraxial estimator

The paraxial estimator (PE) is a parameter quantifying the paraxiality of a light beam. Even if some of its features were previously tackled, key details on its behavior were not fully presented. This paper robustly presents the physical meaning of the PE in a global way, enlarging its interpretation out of the paraxial region what permits to get a first view of the beam propagation dynamics from the value of this parameter. The physical interpretation is given in the spatial domain and in the spectral domain as well. In the first one, the value of PE is related to the competition between the fast oscillations and the remaining oscillations of a propagating field. Looking at spectral domain, the PE deals with the spectral dispersion (or width) of the plane waves forming the field. In this context, a negative value of PE concerns the effective contribution of the evanescent waves what only happens in a strong nonparaxial regime. The PE also accounts the geometric and physical features on the concept of the paraxial approximation in a natural way. An analysis performed for beams propagating through a spherical thin lens reveals that the loss of paraxiality is due to the geometric effect of ray bending by the lens and by another physical effect, concerning the nonideal collimation of the beam.     

Nitrile Synthesis with Aldoxime Dehydratases: A Biocatalytic Platform with Applications in Asymmetric Synthesis,Bulk Chemicals,and Biorefineries

Nitriles comprise a broad group of chemicals that are currently being industrially produced and used in fine chemicals and pharmaceuticals, as well as in bulk applications, polymer chemistry, solvents, etc. Aldoxime dehydratases catalyze the cyanide-free synthesis of nitriles starting from aldoximes under mild conditions, holding potential to become sustainable alternatives for industrial processes. Different aldoxime dehydratases accept a broad range of aldoximes with impressive high substrate loadings of up to >1 Kg L⁻¹ and can efficiently catalyze the reaction in aqueous media as well as in non-aqueous systems, such as organic solvents and solvent-free (neat substrates). This paper provides an overview of the recent developments in this field with emphasis on strategies that may be of relevance for industry and sustainability. When possible, potential links to biorefineries and to the use of biogenic raw materials are discussed.     

Lost topological (Berry) phase factor in electronic structure calculations. Example: the ozone molecule

It is shown that standard computations of electronic structures of polyatomic systems that yield the global minimum configuration and vibrational frequencies may be faulty if the symmetry of this configuration is lower than the highest possible one and the origin of this distortion, which is always due to the Jahn-Teller effect, is neglected; this may lead, in particular, to the loss of the Berry phase factor that changes the vibronic energy level spectrum and which we show to be present even when there are no apparent conical intersections. The general case and the ozone molecule are analyzed.     

Multi‐walled Carbon Nanotubes Non‐covalently Functionalized with Polyarginine: A New Alternative for the Construction of Reagentless NAD+/Dehydrogenase‐based Ethanol Biosensor

This work reports for the first time the development of a reagentless enzymatic amperometric biosensor for ethanol based on the use of a glassy carbon electrode (GCE) modified with multi‐walled carbon nanotubes (MWCNTs) non‐covalently functionalized with polyarginine (Polyarg) as platform for the robust immobilization of alcohol dehydrogenase (ADH) and NAD⁺. The new strategy allows to obtain an integrated GCE/MWCNTs‐Polyarg/NAD⁺‐ADH ethanol biosensor with important advantages compared to the existing ethanol biosensors: avoids the external addition of the cofactor for each measurement, ensures a fast and sensitive quantification of ethanol due to the intimate interaction of the components, and allows the detection at considerably lower potentials due to the catalytic activity of the carbon nanostructures. These unique properties have made possible a very efficient ethanol quantification with a sensitivity of (1487±6) μA M^?1, detection limit of 0.65 μM, response time of 8 s, and reproducibility of 5.5 % with a very successful application for the quantification of ethanol in different commercial beverages.     

On the estimation of the strength of supramolecular complexes of fullerenes

The accurate estimation of the free energy changes in solution for supramolecular complexes is one of the most important challenges for quantum chemistry in this century. In general, there is a good correlation between the association constants determined experimentally and the gas phase binding energies calculated using the most popular density functional methods. However, when the receptors adopt very different structures in the gas and condensed phase, this approach does not work, leading to unreliable predictions. We discuss this problem in detail using three popular fullerene receptors: Aida's dimeric porphyrins, buckycatchers I, and II. We found that it is possible to obtain a reliable order of affinities using gas phase binding energies when a special attention is put on analyzing the structure adopted by the receptor in the gas and condensed phases.     

Structure and fluctuations of the premelted liquid film of ice at the triple point

ABSTRACT

In this paper, we study the structure of the ice/vapour interface in the neighbourhood of the triple point for the TIP4P/2005 model. We probe the fluctuations of the ice/film and film/vapour surfaces that separate the liquid film from the coexisting bulk phases at basal, primary prismatic and secondary prismatic planes. The results are interpreted using a coupled sine Gordon plus Interface Hamiltonian model. At large length scales, the two bounding surfaces are correlated and behave as a single complex ice/vapour interface. For small length, on the contrary, the ice/film and film/vapour surfaces behave very much like independent ice/water and water/vapour interfaces. The study suggests that the basal facet of the TIP4P/2005 model is smooth, the prismatic facet is close to a roughening transition, and the secondary prismatic facet is rough. For the faceted basal face, our fluctuation analysis allows us to estimate the step free energy in good agreement with experiment. Our results allow for a quantitative characterisation of the extent to which the adsorbed quasi-liquid layer behaves as water and explains experimental observations which reveal similar activation energies for crystals grown in bulk vapour or bulk water.     

Error Bounds for Semi-Galerkin Approximations of Nonhomogeneous Incompressible Fluids

We consider spectral semi-Galerkin approximations for the strong solutions of the nonhomogeneous Navier–Stokes equations. We derive an optimal uniform in time error bound in the H¹ norm for approximations of the velocity. We also derive an error estimate for approximations of the density in some spaces L^r. P. Braz e Silva was supported for this work by FAPESP/Brazil, #02/13270-1 and is currently supported in part by CAPES/MECD-DGU Brazil/Spain, #117/06. M. Rojas-Medar is partially supported by CAPES/MECD-DGU Brazil/Spain, #117/06 and project BFM2003-06446-CO-01, Spain.     

Self-correcting networks: function, robustness, and motif distributions in biological signal processing

Statistical properties of large ensembles of networks, all designed to have the same functions of signal processing, but robust against different kinds of perturbations, are analyzed. We find that robustness against noise and random local damage plays a dominant role in determining motif distributions of networks and may underlie their classification into network superfamilies.     

Learning temporal nodes Bayesian networks

Temporal Nodes Bayesian Networks (TNBNs) are an alternative to Dynamic Bayesian Networks for temporal reasoning with much simpler and efficient models in some domains. TNBNs are composed of temporal nodes, temporal intervals, and probabilistic dependencies. However, methods for learning this type of models from data have not yet been developed. In this paper, we propose a learning algorithm to obtain the structure and temporal intervals for TNBNs from data. The method consists of three phases: (i) obtain an initial approximation of the intervals, (ii) obtain a structure using a standard algorithm and (iii) refine the intervals for each temporal node based on a clustering algorithm. We evaluated the method with synthetic data from three different TNBNs of different sizes. Our method obtains the best score using a combined measure of interval quality and prediction accuracy, and a competitive structural quality with lower running times, compared to other related algorithms. We also present a real world application of the algorithm with data obtained from a combined cycle power plant in order to diagnose temporal faults.