Field-induced diastereomers for chiral separation

A novel approach for the state-specific enantiomeric enrichment and the spatial separation of enantiomers is presented. Our scheme utilizes techniques from strong-field laser physics, specifically an optical centrifuge in conjunction with a static electric field, to create a chiral field with defined handedness. Molecular enantiomers experience unique rotational excitation dynamics and this can be exploited to spatially separate the enantiomers using electrostatic deflection. Notably, the rotational-state-specific enantiomeric enhancement and its handedness is fully controllable. To explain these effects, we introduce the conceptual framework of $field\text{-}induced~diastereomers$ of a chiral molecule and perform robust quantum mechanical simulations on the prototypical chiral molecule propylene oxide (C$_3$H$_6$O), for which ensembles with an enantiomeric excess of up to $30~\%$ were obtained

Knife edge skimming for improved separation of molecular species by the deflector

A knife edge for shaping a molecular beam is described to improve the spatial separation of the species in a molecular beam by the electrostatic deflector. The spatial separation of different molecular species from each other as well as from atomic seed gas is improved. The column density of the selected molecular-beam part in the interaction zone, which corresponds to higher signal rates, was enhanced by a factor of 1.5, limited by the virtual source size of the molecular beam.Comment: 3 pages, 2 figure

The transformation of earth-system observations into information of socio-economic value in GEOSS

The Group on Earth Observations System of Systems, GEOSS, is a co-ordinated initiative by many nations to address the needs for earth-system information expressed by the 2002 World Summit on Sustainable Development. We discuss the role of earth-system modelling and data assimilation in transforming earth-system observations into the predictive and status-assessment products required by GEOSS, across many areas of socio-economic interest. First we review recent gains in the predictive skill of operational global earth-system models, on time-scales of days to several seasons. We then discuss recent work to develop from the global predictions a diverse set of end-user applications which can meet GEOSS requirements for information of socio-economic benefit; examples include forecasts of coastal storm surges, floods in large river basins, seasonal crop yield forecasts and seasonal lead-time alerts for malaria epidemics. We note ongoing efforts to extend operational earth-system modelling and assimilation capabilities to atmospheric composition, in support of improved services for air-quality forecasts and for treaty assessment. We next sketch likely GEOSS observational requirements in the coming decades. In concluding, we reflect on the cost of earth observations relative to the modest cost of transforming the observations into information of socio-economic value

Ultrafast light-induced dynamics in solvated biomolecules: The indole chromophore with water

Interactions between proteins and their solvent environment can be studied in a bottom-up approach using hydrogen-bonded chromophore-solvent clusters. The ultrafast dynamics following UV-light-induced electronic excitation of the chromophores, potential radiation-damage, and their dependence on solvation are important open questions. The microsolvation effect is challenging to study due to the inherent mix of the produced gas-phase aggregates. We used the deflector to spatially separate different molecular species in combination with pump-probe velocity-map-imaging experiments. We demonstrated that this powerful experimental approach reveals intimate details of the UV-induced dynamics in the near-UV-absorbing prototypical biomolecular indole-water system. We determined the time-dependent appearance of the different reaction products and disentangled the occurring ultrafast processes. This novel approach ensures that the reactants are well-known and that detailed characteristics of the specific reaction products are accessible -- paving the way for the complete chemical-reactivity experiment

Cultuur als antwoord

Indonesi

A Deep Learning Approach for Digital Color Reconstruction of Van Gogh’s Paintings Using Unpaired Areas Under the Frame

Factors such as dirt accumulation, aging of paint materials and chemical reactions with the surrounding atmosphere lead to alteration and degradation of paintings. As a result, colors in the paintings can change. Van Gogh’s paintings are no exception to this process, and virtual reconstruction of the original colors is a very challenging problem. In this work we propose a novel approach for color reconstruction that does not require any pre-existing digital reconstructions, physical reproductions or artificial aging experiments, and relies purely on the data within the painting. We exploit the fact that areas of the painting located under the frame are typically well-preserved (mainly due to the protection from light exposure and dirt accumulation offered by the frame) and contain colors which are relatively close to their original look. Inspired by the recent advances in machine learning techniques for unpaired image-to-image translation, a practical weakly supervised approach for digital color reconstruction is formulated. Moreover, its performance is demonstrated for paintings by Vincent van Gogh. To our knowledge, this is the first time that a method for color reconstruction that relies purely on the data available within one painting is described in literature.</p

Collecting and utilising crowdsourced data for numerical weather prediction: propositions from the meeting held in Copenhagen, 4–December 5, 2018

In December 2018, the Danish Meteorological Institute organised an international meeting on the subject of crowdsourced data in numerical weather prediction (NWP) and weather forecasting. The meeting, spanning 2 days, gathered experts on crowdsourced data from both meteorological institutes and universities from Europe and the United States. Scientific presentations highlighted a vast array of possibilities and progress being made globally. Subjects include data from vehicles, smartphones, and private weather stations. Two groups were created to discuss open questions regarding the collection and use of crowdsourced data from different observing platforms. Common challenges were identified and potential solutions were discussed. While most of the work presented was preliminary, the results shared suggested that crowdsourced observations have the potential to enhance NWP. A common platform for sharing expertise, data, and results would help crowdsourced data realise this potential

Spatial separation of pyrrole and pyrrole-water clusters

We demonstrate the spatial separation of pyrrole and pyrrole(H$_2$O) clusters from the other atomic and molecular species in a supersonically-expanded beam of pyrrole and traces of water seeded in high-pressure helium gas. The experimental results are quantitatively supported by simulations. The obtained pyrrole(H$_2$O) cluster beam has a purity of ~100 %. The extracted rotational temperature of pyrrole and pyrrole(H$_2$O) from the original supersonic expansion is $T_\text{rot}=0.8\pm0.2$ K, whereas the temperature of the deflected, pure-pyrrole(H$_2$O) part of the molecular beam corresponds to $T_\text{rot}\approx0.4$ K

Scientific challenges of convective-scale numerical weather prediction

Numerical weather prediction (NWP) models are increasing in resolution and becoming capable of explicitly representing individual convective storms. Is this increase in resolution leading to better forecasts? Unfortunately, we do not have sufficient theoretical understanding about this weather regime to make full use of these NWPs. After extensive efforts over the course of a decade, convective–scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three–dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km) with slowly–evolving semi–geostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial–differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues: the Liouville principle and Bayesian probability for probabilistic forecasts; and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided