Abstract: The radiation damage created in silicon materials by Mn-57 -> Fe-57 ion implantation has been studied and characterized by Mossbauer spectroscopy showing four main lines, assigned to: substitutional, interstitial and damaged configuration sites of the implanted ions. Nevertheless, the Mossbauer spectrum of Fe-57 in this materials remains with some ambiguous identification regarding the implantation configurations before and after annealing, specially the damaged configurations and its evolution. In the present work some possible implantation configurations are suggested and evaluated using a multiscale approach by Monte Carlo ion transport and electronic structure calculations within DFT. The proposed implantation environments were evaluated in terms of stability and the Fe-57 hyperfine parameters were calculated to establish the connections with the experimental observations. Good agreement was found between the experimental and the calculated hyperfine parameters for some configurations; suggesting which ones could be the implantation environments before and after sample annealing.

Abstract: The aim of the present work is to study the radiation damage induced in LYSO and LuYAP crystals by the gamma radiation and the secondary electrons/positrons generated. The displacements per atom (dpa) distributions inside each material were calculated following the Monte Carlo assisted Classical Method (MCCM) introduced by the authors. As gamma sources were used Sc-44, Na-22 and V-48. Also the energy of gammas from the annihilation processes (511 keV) was included in the study. This procedure allowed studying the in-depth dpa distributions inside each crystal for all four sources. It was also possible to obtain the separate contribution from each atom to the total dpa. The LYSO crystals were found to receive more damage, mainly provoked by the displacements of silicon and oxygen atoms.

Abstract: Bridging the gap between complex signal data output and clear interpretation by non-expert end-users is a major challenge many scientists face when converting their scientific technology into a real-life application. Currently, pattern recognition algorithms are the most frequently encountered signal data interpretation algorithms to close this gap, not in the least because of their straight-forward implementation via convenient software packages. Paradoxically, just because their implementation is so straight-forward, it becomes cumbersome to integrate the expert's domain-specific knowledge. In this work, a novel signal data interpretation approach is presented that uses this domain-specific knowledge as its fundament, thereby fully exploiting the unique expertise of the scientist. The new approach applies data preprocessing in an innovative way that transcends its usual purpose and is easy to translate into a software application. Multiple case studies illustrate the straight-forward application of the novel approach. Ultimately, the approach is highly suited for integration in various (bio)analytical applications that require interpretation of signal data.