Helium bubble formation in ultrafine and nanocrystalline tungsten under different extreme conditions

We have investigated the effects of helium ion irradiation energy and sample temperature on the performance of grain boundaries as helium sinks in ultrafine grained and nanocrystalline tungsten. Irradiations were performed at displacement and non-displacement energies and at temperatures above and below that required for vacancy migration. Microstructural investigations were performed using Transmission Electron Microscopy (TEM) combined with either in-situ or ex-situ ion irradiation. Under helium irradiation at an energy which does not cause atomic displacements in tungsten (70 eV), regardless of temperature and thus vacancy migration conditions, bubbles were uniformly distributed with no preferential bubble formation on grain boundaries. At energies that can cause displacements, bubbles were observed to be preferentially formed on the grain boundaries only at high temperatures where vacancy migration occurs. Under these conditions, the decoration of grain boundaries with large facetted bubbles occurred on nanocrystalline grains with dimensions less than 60 nm. We discuss the importance of vacancy supply and the formation and migration of radiation-induced defects on the performance of grain boundaries as helium sinks and the resulting irradiation tolerance of ultrafine grained and nanocrystalline tungsten to bubble formatio

First Direct Mass Measurements of Nuclides around Z=100 with a Multireflection Time-of-Flight Mass Spectrograph

The masses of 246Es, 251Fm, and the transfermium nuclei 249−252Md and 254No, produced by hot- and cold-fusion reactions, in the vicinity of the deformed N=152 neutron shell closure, have been directly measured using a multireflection time-of-flight mass spectrograph. The masses of 246Es and 249,250,252Md were measured for the first time. Using the masses of 249,250Md as anchor points for α decay chains, the masses of heavier nuclei, up to 261Bh and 266Mt, were determined. These new masses were compared with theoretical global mass models and demonstrated to be in good agreement with macroscopic-microscopic models in this region. The empirical shell gap parameter δ2n derived from three isotopic masses was updated with the new masses and corroborates the existence of the deformed N=152 neutron shell closure for Md and Lr

A new study of the N=32N=32 and N=34N=34 shell gap for Ti and V by the first high-precision MRTOF mass measurements at BigRIPS-SLOWRI

The atomic masses of $^{55}$Sc, $^{56,58}$Ti, and $^{56-59}$V have been determined using the high-precision multi-reflection time-of-flight technique. The radioisotopes have been produced at RIKEN's RIBF facility and delivered to the novel designed gas cell and multi-reflection system (ZD MRTOF), which has been recently commissioned downstream of the ZeroDegree spectrometer following the BigRIPS separator. For $^{56,58}$Ti and $^{56-59}$V the mass uncertainties have been reduced down to the order of $10\,\mathrm{keV}$, shedding new light on the $N=34$ shell effect in Ti and V isotopes by the first high-precision mass measurements of the critical species $^{58}$Ti and $^{59}$V. With the new precision achieved, we reveal the non-existence of the $N=34$ empirical two-neutron shell gaps for Ti and V, and the enhanced energy gap above the occupied $\nu p_{3/2}$ orbit is identified as a feature unique to Ca. We perform new Monte Carlo shell model calculations including the $\nu d_{5/2}$ and $\nu g_{9/2}$ orbits and compare the results with conventional shell model calculations, which exclude the $\nu g_{9/2}$ and the $\nu d_{5/2}$ orbits. The comparison indicates that the shell gap reduction in Ti is related to a partial occupation of the higher orbitals for the outer two valence neutrons at $N=34$

Paricle identification at VAMOS++ with machine learning techniques

Multi-nucleon transfer reaction between 136Xe beam and 198Pt target was performed using the VAMOS++ spectrometer at GANIL to study the structure of n-rich nuclei around N=126. Unambiguous charge state identification was obtained by combining two supervised machine learning methods, deep neural network (DNN) and positional correction using a gradient-boosting decision tree (GBDT). The new method reduced the complexity of the kinetic energy calibration and outperformed the conventional method improving the charge state resolution by 8%