Direction for the Future - Successive Acceleration of Positive and Negative Ions Applied to Space Propulsion

Electrical space thrusters show important advantages for applications in outer space compared to chemical thrusters, as they allow a longer mission lifetime with lower weight and propellant consumption. Mature technologies on the market today accelerate positive ions to generate thrust. The ion beam is neutralized by electrons downstream, and this need for an additional neutralization system has some drawbacks related to stability, lifetime and total weight and power consumption. Many new concepts, to get rid of the neutralizer, have been proposed, and the PEGASES ion-ion thruster is one of them. This new thruster concept aims at accelerating both positive and negative ions to generate thrust, such that additional neutralization is redundant. This chapter gives an overview of the concept of electric propulsion and the state of the development of this new ion-ion thruster.Comment: 10 pages, contribution to the CAS-CERN Accelerator School: Ion Sources, Senec, Slovakia, 29 May - 8 June 2012, edited by R. Bailey. appears in CERN Yellow Report CERN-2013-007, pp.575-58

Calculation of Raman optical activity spectra for vibrational analysis

By looking back on the history of Raman Optical Activity (ROA), the present article shows that the success of this analytical technique was for a long time hindered, paradoxically, by the deep level of detail and wealth of structural information it can provide. Basic principles of the underlying theory are discussed, to illustrate the technique's sensitivity due to its physical origins in the delicate response of molecular vibrations to electromagnetic properties. Following a short review of significant advances in the application of ROA by UK researchers, we dedicate two extensive sections to the technical and theoretical difficulties that were overcome to eventually provide predictive power to computational simulations in terms of ROA spectral calculation. In the last sections, we focus on a new modelling strategy that has been successful in coping with the dramatic impact of solvent effects on ROA analyses. This work emphasises the role of complementarity between experiment and theory for analysing the conformations and dynamics of biomolecules, so providing new perspectives for methodological improvements and molecular modelling development. For the latter, an example of a next-generation force-field for more accurate simulations and analysis of molecular behaviour is presented. By improving the accuracy of computational modelling, the analytical capabilities of ROA spectroscopy will be further developed so generating new insights into the complex behaviour of molecules

Distinguishing epimers through raman optical activity

The Raman optical activity spectra of the epimers β-d-glucose and β-d-galactose, two monosaccharides of biological importance, have been calculated using molecular dynamics combined with a quantum mechanics/molecular mechanics approach. Good agreement between theoretical and experimental spectra is observed for both monosaccharides. Full band assignments have been carried out, which has not previously been possible for carbohydrate epimers. For the regions where the spectral features are opposite in sign, the differences in the vibrational modes have been noted and ascribed to the band sign changes

The Raman optical activity of β-D-xylose: where experiment and theory meet

Besides its applications in bioenergy and biosynthesis, β-D-xylose is a very simple monosaccharide that exhibits relatively high rigidity. As such, it provides the best basis to study the impact of different solvation shell radii on the computation of its Raman optical activity (ROA) spectrum. Indeed, this chiroptical spectroscopic technique provides exquisite sensitivity to stereochemistry, and benefits much from theoretical support for interpretation. Our simulation approach combines density functional theory (DFT) and molecular dynamics (MD) in order to efficiently account for the crucial hydration effects in the simulation of carbohydrates and their spectroscopic response predictions. Excellent agreement between the simulated spectrum and the experiment was obtained with a solvation radius of 10 Å. Vibrational bands have been resolved from the computed ROA data, and compared with previous results on different monosaccharides in order to identify specific structure–spectrum relationships and to investigate the effect of the solvation environment on the conformational dynamics of small sugars. From the comparison with ROA analytical results, a shortcoming of the classical force field used for the MD simulations has been identified and overcome, again highlighting the complementary role of experiment and theory in the structural characterisation of complex biomolecules. Indeed, due to unphysical puckering, a spurious ring conformation initially led to erroneous conformer ratios, which are used as weights for the averaging of the spectral average, and only by removing this contribution was near perfect comparison between theory and experiment achieved

Calibration of uncertainty in the active learning of machine learning force fields

FFLUX is a machine learning force field that uses the maximum expected prediction error (MEPE) active learning algorithm to improve the efficiency of model training. MEPE uses the predictive uncertainty of a Gaussian process (GP) to balance exploration and exploitation when selecting the next training sample. However, the predictive uncertainty of a GP is unlikely to be accurate or precise immediately after training. We hypothesize that calibrating the uncertainty quantification within MEPE will improve active learning performance. We develop and test two methods to improve uncertainty estimates: post-hoc calibration of predictive uncertainty using the CRUDE algorithm, and replacing the GP with a student- t process. We investigate the impact of these methods on MEPE for single sample and batch sample active learning. Our findings suggest that post-hoc calibration does not improve the performance of active learning using the MEPE method. However, we do find that the student- t process can outperform active learning strategies and random sampling using a GP if the training set is sufficiently large

Deciphering the Curly Arrow Representation and Electron Flow for the 1,3-Dipolar Rearrangement between Acetonitrile Oxide and (1S,2R,4S)‑2-Cyano-7-oxabicyclo[2.2.1]hept-5-en-2-yl Acetate Derivatives

This study is focused on describing the molecular mechanism beyond the molecular picture provided by the evolution of molecular orbitals, valence bond structures along the reaction progress, or conceptual density functional theory. Using bonding evolution theory (BET) analysis, we have deciphered the mechanism of the 1,3-dipolar rearrangement between acetonitrile oxide and (1S,2R,4S)-2-cyano-7-oxabicyclo[2.2.1]hept-5-en-2-yl acetate derivatives. The BET study revealed that the formation of the C−C bond takes place via a usual sharing model before the O−C one that is also formed in the halogenated species through a not very usual sharing model. The mechanism includes depopulation of the electron density at the N−C triple bond and creation of the V(N) and V(C) monosynaptic basins, depopulation of the former C−C double bond with the creation of V(C,C) basins, and final formation of the V(O,C) basin associated with the O−C bond. The topological changes along the reaction pathway take place in a highly synchronous way. BET provides a convenient quantitative method for deriving curly arrows and electron flow representation to unravel molecular mechanisms

On Dipole Moments and Hydrogen Bond Identification in Water Clusters

It is demonstrated that the localized orbitals calculated for a water cluster have small delocalization tails along the hydrogen bonds, that are crucial in determining the resulting dipole moments of the system. (By cutting them, one gets much smaller dipole moments for the individual monomersclose to the values one obtains by using a Bader-type analysis.) This means that the individual water monomers can be delimited only in a quite fuzzy manner, and the electronic charge density in a given point cannot be assigned completely to that or another molecule. Thus, one arrives to the brink of breaking the concept of a water cluster consisting of individual molecules. The analysis of the tails of the localized orbitals can also be used to identify the pairs of water molecules actually forming hydrogen bonds

Solution structure of Mannobioses unravelled by means of Raman optical activity

Structural analysis of carbohydrates is a complicated endeavour, due to the complexity and diversity of the samples at hand. Herein, we apply a combined computational and experimental approach, employing molecular dynamics (MD) and density functional theory (DFT) calculations together with NMR and Raman optical activity (ROA) measurements, in the structural study of three mannobiose disaccharides, consisting of two mannoses with varying glycosidic linkages. The disaccharide structures make up the scaffold of high mannose glycans and are therefore important targets for structural analysis. Based on the MD population analysis and NMR, the major conformers of each mannobiose were identified and used as input for DFT analysis. By systematically varying the solvent models used to describe water interacting with the molecules and applying overlap integral analysis to the resulting calculational ROA spectra, we found that a full quantum mechanical/molecular mechanical approach is required for an optimal calculation of the ROA parameters. Subsequent normal mode analysis of the predicted vibrational modes was attempted in order to identify possible marker bands for glycosidic linkages. However, the normal mode vibrations of the mannobioses are completely delocalised, presumably due to conformational flexibility in these compounds, rendering the identification of isolated marker bands unfeasible

Towards the simulation of biomolecules: optimisation of peptide-capped glycine using FFLUX

YesThe optimisation of a peptide-capped glycine using the novel force field FFLUX is presented. FFLUX is a force field based on the machine-learning method kriging and the topological energy partitioning method called Interacting Quantum Atoms. FFLUX has a completely different architecture to that of traditional force fields, avoiding (harmonic) potentials for bonded, valence and torsion angles. In this study, FFLUX performs an optimisation on a glycine molecule and successfully recovers the target density-functional theory energy with an error of 0.89 ± 0.03 kJ mol−1. It also recovers the structure of the global minimum with a root-mean-squared deviation of 0.05 Å (excluding hydrogen atoms). We also show that the geometry of the intra-molecular hydrogen bond in glycine is recovered accurately.EPSRC Established Career Fellowship [grant number EP/K005472