Fitting in a complex χ(2) landscape using an optimized hypersurface sampling

Non-invasive detection of hazardous materials with a thermal-to-epithermal neutron station: a feasibility study towards practical application

The growing scale of the devastation that even a single terrorist attack can cause requires more effective methods for the detection of hazardous materials. In particular, there are no solutions for effectively monitoring threats at sea, both for the off-shore infrastructure and ports. Currently, state-of-the-art detection methods determine the density distribution and the shapes of tested subjects but only allow for a limited degree of substance identification. This work aims to present a feasibility study of the possible usage of several methods available on the thermal-to-epithermal neutron station, VESUVIO, at the ISIS neutron and muon spallation source, UK, for the detection of hazardous materials. To this end, we present the results of a series of experiments performed concurrently employing neutron transmission and Compton scattering using melamine, a commonly used explosive surrogate, in order to determine its signal characteristics and limits of detection and quantitation. The experiments are supported by first-principles modelling, providing detailed scrutiny of the material structure and the nuclear dynamics behind the neutron scattering observables.

Mutually Beneficial Combination of Molecular Dynamics Computer Simulations and Scattering Experiments

We showcase the combination of experimental neutron scattering data and molecular dynamics (MD) simulations for exemplary phospholipid membrane systems. Neutron and X-ray reflectometry and small-angle scattering measurements are determined by the scattering length density profile in real space, but it is not usually possible to retrieve this profile unambiguously from the data alone. MD simulations predict these density profiles, but they require experimental control. Both issues can be addressed simultaneously by cross-validating scattering data and MD results. The strengths and weaknesses of each technique are discussed in detail with the aim of optimizing the opportunities provided by this combination.

On the microscopic mechanism behind the purely orientational disorder–disorder transition in the plastic phase of 1-chloroadamantane

The microscopic mechanism behind the disorder–disorder phase transition in 1-chloroadamantane is related to changes both in structure and dynamics, as revealed by QENS and neutron diffraction experiments.

Quantum Metrology Enhanced by Repetitive Quantum Error Correction

We experimentally demonstrate the protection of a room-temperature hybrid spin register against environmental decoherence by performing repeated quantum error correction whilst maintaining sensitivity to signal fields. We use a long-lived nuclear spin to correct multiple phase errors on a sensitive electron spin in diamond and realize magnetic field sensing beyond the time scales set by natural decoherence. The universal extension of sensing time, robust to noise at any frequency, demonstrates the definitive advantage entangled multiqubit systems provide for quantum sensing and offers an important complement to quantum control techniques.

A robust comparison of dynamical scenarios in a glass-forming liquid

We use Bayesian inference methods to provide fresh insights into the sub-nanosecond dynamics of glycerol, a prototypical glass-forming liquid.

Insights into the determination of molecular structure from diffraction data using a Bayesian algorithm

The determination of the molecular ordering in a liquid is still a controversial subject. There is no general consensus either on the methods to obtain reliable liquid structures or on the way to analyze them. Regardless of the method, it is very important to have a realistic molecular structure available that allows simulations to faithfully reproduce the sample features, and that minimizes the computing time in structure refinements. However, attention is not always paid to this point and molecular models coming from general force-fields are frequently used to undertake many of the analyses. We propose in this work to use a Bayesian scheme to fit the experimental data and produce reliable molecular models that can be used as the starting point of any simulation or refinement. The algorithm behind the proposed method is based on a Markov chain Monte Carlo procedure, as many other refinement programs such as reverse Monte Carlo or empirical potential structure refinement.