Traditional HZB Neutron School will be continued at ANSTO in Australia
The neutron school at ANSTO in cooperation with HZB experts could transfer knowledge and skills in neutron research. © ANSTO
This summer, researchers at the Australian neutron source ACNS organised a joint neutron school at Australia's Nuclear Science and Technology Organisation ANSTO. The HZB-ANSTO neutron school will take place every two years.
The first joint HZB-ANSTO neutron school took place from 23 to 28 June 2019 at ANSTO. HZB experts Prof. Dr. Bella Lake and Prof. Dr. Susan Schorr held several lectures. The interest in the neutron school was high, 24 participants were selected from 60 applications. In addition to lectures, hands on trainings on three instruments of the neutron source ACNS at ANSTO have been offered.
“Certainly we were inspired by the all-encompassing nature of the Berlin school - I believe that our plan going forward would be to run a 'general' neutron school every two years and a more specific school in between," said Dr. Helen Maynard-Casely, one of the organisers at ANSTO.
Shortly before the start of the neutron school, the SPATZ neutron reflectometer at ACNS was inaugurated. SPATZ, formerly known as BioRef at the BER II at HZB, is ideally suited for studies of soft matter in biomedicine, energy and materials. It was donated by HZB to ANSTO since the BER II neutron source will be shut down end of 2019.
In a short video, ANSTO presents SPATZ and its virtues.
- Ultrafast demagnetisation - Thinner iron, stronger effectThe thinner an iron film, the more easily it can be demagnetised by a laser pulse. Researchers at the University of Duisburg-Essen, working with Helmholtz-Zentrum Berlin and other partners, have discovered why: the key lies in the magnetic order at the material's interfaces. In the long term, these findings could contribute to the development of new technologies for magnetic data storage. The results have been published in the scientific journal Advanced Science.
- Blind spot for the arrangement of atomsArtificial intelligence is increasingly being used to predict the properties of new materials. A research team from Jena and the United States now shows in a study published in the journal Nature Computational Science that when language models are used for this purpose, they reliably identify which types of atoms a material contains. However, they make insufficient use of information about how those atoms are arranged in space. Neither larger models nor additional training data resolve this weakness. For material properties that depend strongly on structure, AI models specifically designed to process spatial relationships are therefore the more reliable choice. (DOI: 10.1038/s43588-026-01045-2)
- Patterns of polar vortices in lead-free superlattices on siliconA team at HZB has, for the first time, reported polar vortices in (BaTiO3/SrTiO3)10 superlattices on silicon. These results expand the range of materials and substrates on which polar topological textures can occur and open up new options for incorporating complex ferroelectric states into silicon-based technologies.