Molecular specificity in neutron imaging: the case of hydrogen adsorption in metal organic frameworks.

Neutron imaging is the technique of choice for a number of in situ and operando applications, where a high penetration power is required. White-beam neutron imaging and energy-resolved Bragg edge imaging are successful techniques, the former for the detection of specific elements characterized by strong neutron attenuation and the latter for studying crystal structures. Here we discuss the capabilities of energy-selective neutron imaging taking advantage of the incoherent and inelastic scattering interactions in hydrogenous materials, as a way to obtain molecular-specific information about the composition of a given sample. While few examples from the available literature are discussed, a worked example is presented based on new experimental data on molecular-hydrogen adsorption and conversion in the HKUST-1 metal organic framework.

Enhanced suppression of otoacoustic emissions by contralateral stimulation in Parkinson's disease.

Dopamine depletion affects several aspects of hearing function. Previous work [Wu, Yi, Manca, Javaid, Lauer, and Glowatzki, eLife 9, e52419 (2020)] demonstrated the role of dopamine in reducing the firing rates of inner ear cells, which is thought to decrease synaptic excitotoxicity. Thus, a lack of dopamine could indirectly increase acoustic stimulation of medial olivocochlear efferents. To investigate that, here we studied contralateral suppression of distortion product otoacoustic emissions in a population of Parkinsonian patients, compared to an age-matched control group, both audiometrically tested. To rule out activation of the acoustic reflex, middle ear impedance was monitored during testing. The results show significantly stronger contralateral suppression in the patient group.

Accelerating Neutron Tomography experiments through Artificial Neural Network based reconstruction.

Neutron Tomography (NT) is a non-destructive technique to investigate the inner structure of a wide range of objects and, in some cases, provides valuable results in comparison to the more common X-ray imaging techniques. However, NT is time consuming and scanning a set of similar objects during a beamtime leads to data redundancy and long acquisition times. Nowadays NT is unfeasible for quality checking study of large quantities of similar objects. One way to decrease the total scan time is to reduce the number of projections. Analytical reconstruction methods are very fast but under this condition generate streaking artifacts in the reconstructed images. Iterative algorithms generally provide better reconstruction for limited data problems, but at the expense of longer reconstruction time. In this study, we propose the recently introduced Neural Network Filtered Back-Projection (NN-FBP) method to optimize the time usage in NT experiments. Simulated and real neutron data were used to assess the performance of the NN-FBP method as a function of the number of projections. For the first time a machine learning based algorithm is applied and tested for NT image reconstruction problem. We demonstrate that the NN-FBP method can reliably reduce acquisition and reconstruction times and it outperforms conventional reconstruction methods used in NT, providing high image quality for limited datasets.

Egyptian Grave Goods of Kha and Merit Studied by Neutron and Gamma Techniques.

Artifacts from the Egyptian grave goods of Kha and Merit preserved at the Museo Egizio in Turin were studied through a combination of non-destructive and non-invasive neutron and gamma techniques (namely neutron imaging, neutron diffraction and prompt gamma activation analysis). The results provide unprecedented morphological reconstructions of the inner parts of the two alabaster and metallic vases and their isotopic and phase composition, thereby extending our knowledge of the hitherto unknown content of the vases and their functions.