Heterogeneously catalyzed asymmetric hydrogenation of C = C bonds directed by surface-tethered chiral modifiers

Asymmetric hydrogenation of C=C bonds is of the highest importance in organic synthesis, and such reactions are currently carried out with organometallic homogeneous catalysts. Achieving heterogeneous metal-catalyzed hydrogenation, a highly desirable goal, necessitates forcing the crucial enantiodifferentiating step to take place at the metal surface. By synthesis and application of six chiral sulfide ligands that anchor robustly to Pd nanoparticles and resist displacement, we have for the first time accomplished heterogeneous enantioselective catalytic hydrogenation of isophorone. High resolution XPS data established that ligand adsorption from solution occurred exclusively on the Pd nanoparticles and not on the carbon support. All ligands contained a pyrrolidine nitrogen to enable their interaction with the isophorone substrate while the sulfide functionality provided the required interaction with the Pd surface. Enantioselective turnover numbers of up to similar to 100 product molecules per ligand molecule were found with a very large variation in asymmetric induction between ligands: observed enantiomeric excesses increased with increasing size of the alkyl group in the sulfide. This likely reflects varying degrees of ligand dispersion on the surface: bulky substituent groups hinder close approach of ligand molecules to each other, inhibiting close-packed island formation, favoring dispersion as separate molecules, and leading to effective asymmetric induction. Conversely, small substituents favor island formation leading to very low asymmetric induction. Enantioselective reaction most likely involves initial formation of an enamine or iminium species, confirmed by use of an analogous tertiary amine, which leads to racemic product. Ligand rigidity and resistance to self-assembled monolayer formation are important attributes that should be designed into improved chiral modifiers.

An extreme value theory approach to calculating minimum capital risk requirements

This paper investigates the frequency of extreme events for three LIFFE futures contracts for the calculation of minimum capital risk requirements (MCRRs). We propose a semiparametric approach where the tails are modelled by the Generalized Pareto Distribution and smaller risks are captured by the empirical distribution function. We compare the capital requirements form this approach with those calculated from the unconditional density and from a conditional density - a GARCH(1,1) model. Our primary finding is that both in-sample and for a hold-out sample, our extreme value approach yields superior results than either of the other two models which do not explicitly model the tails of the return distribution. Since the use of these internal models will be permitted under the EC-CAD II, they could be widely adopted in the near future for determining capital adequacies. Hence, close scrutiny of competing models is required to avoid a potentially costly misallocation capital resources while at the same time ensuring the safety of the financial system.

Integrated palaeoecology and archaeology: a powerful approach for understanding pre-Columbian Amazonia

The old paradigm that Amazonia's tropical ecosystems prevented cultural development beyond small-scale shifting agricultural economies, that had little environmental impact, no longer holds true for much of Amazonia. A diversity of archaeological evidence, including terra preta soils, raised fields, causeways, large habitation mounds, geometric earthworks, and megalithic monuments, all point to considerable cultural complexity and environmental impacts. However, uncertainty remains over the chronology of these cultures, their diet and economy, and the scale of environmental impact and land use associated with them. Here, we argue that a cross-disciplinary approach, closely coupling palaeoecology and archaeology, can potentially resolve these uncertainties. We show how, with careful site selection (pairing small and large lakes, close proximity to archaeological sites, transects of soil pits) and choice of techniques (e.g., pollen, phytoliths, starch grains, charcoal, stable isotopes), these two disciplines can be successfully integrated to provide a powerful tool for investigating the relationship between pre-Columbian cultures and their environment.

Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

The analysis step of the (ensemble) Kalman filter is optimal when (1) the distribution of the background is Gaussian, (2) state variables and observations are related via a linear operator, and (3) the observational error is of additive nature and has Gaussian distribution. When these conditions are largely violated, a pre-processing step known as Gaussian anamorphosis (GA) can be applied. The objective of this procedure is to obtain state variables and observations that better fulfil the Gaussianity conditions in some sense. In this work we analyse GA from a joint perspective, paying attention to the effects of transformations in the joint state variable/observation space. First, we study transformations for state variables and observations that are independent from each other. Then, we introduce a targeted joint transformation with the objective to obtain joint Gaussianity in the transformed space. We focus primarily in the univariate case, and briefly comment on the multivariate one. A key point of this paper is that, when (1)-(3) are violated, using the analysis step of the EnKF will not recover the exact posterior density in spite of any transformations one may perform. These transformations, however, provide approximations of different quality to the Bayesian solution of the problem. Using an example in which the Bayesian posterior can be analytically computed, we assess the quality of the analysis distributions generated after applying the EnKF analysis step in conjunction with different GA options. The value of the targeted joint transformation is particularly clear for the case when the prior is Gaussian, the marginal density for the observations is close to Gaussian, and the likelihood is a Gaussian mixture.

A single-column model ensemble approach applied to the TWP-ICE experiment

Single-column models (SCM) are useful test beds for investigating the parameterization schemes of numerical weather prediction and climate models. The usefulness of SCM simulations are limited, however, by the accuracy of the best estimate large-scale observations prescribed. Errors estimating the observations will result in uncertainty in modeled simulations. One method to address the modeled uncertainty is to simulate an ensemble where the ensemble members span observational uncertainty. This study first derives an ensemble of large-scale data for the Tropical Warm Pool International Cloud Experiment (TWP-ICE) based on an estimate of a possible source of error in the best estimate product. These data are then used to carry out simulations with 11 SCM and two cloud-resolving models (CRM). Best estimate simulations are also performed. All models show that moisture-related variables are close to observations and there are limited differences between the best estimate and ensemble mean values. The models, however, show different sensitivities to changes in the forcing particularly when weakly forced. The ensemble simulations highlight important differences in the surface evaporation term of the moisture budget between the SCM and CRM. Differences are also apparent between the models in the ensemble mean vertical structure of cloud variables, while for each model, cloud properties are relatively insensitive to forcing. The ensemble is further used to investigate cloud variables and precipitation and identifies differences between CRM and SCM particularly for relationships involving ice. This study highlights the additional analysis that can be performed using ensemble simulations and hence enables a more complete model investigation compared to using the more traditional single best estimate simulation only.