BESSY II: Neutralising electronic inhomogeneity in cleaved bulk MoS₂
The illustration shows the MoS2 lattice structure (green: Mo, yellow: S). The material after cleaving is shown in the forefront, the surface is jagged, and the measured surface electronic structure is inhomogeneous (coloured map). In the back is the cleaved material after exposure to atomic hydrogen (represented by the white balls). The measured surface electronic structure, shown in the map, is more homogenous. © Martin Künsting/HZB
Molybdenum disulphide (MoS₂) is a highly versatile material that can function, for example, as a gas sensor or as a photocatalyst in green hydrogen production. Although the understanding of a material usually starts from investigating its bulk crystalline form, for MoS₂ much more studies have been devoted to mono and few layer nanosheets. The few studies conducted thus far show diverse and irreproducible results for the electronic properties of cleaved bulk MoS₂ surfaces, highlighting the need for a more systematic study.
Dr. Erika Giangrisostomi and her team at HZB carried such a systematic study at the LowDosePES end-station of the BESSY II light source. They utilised X-ray photoelectron spectroscopy technique to map the core-level electron energies across extensive surface areas of MoS2 samples. Using this method, they were able to monitor the changes in the surface electronic properties after in-situ ultra-high-vacuum cleaving, annealing and exposure to atomic and molecular hydrogen.
The results from this study point to two main findings. Firstly, the study unambiguously reveals sizeable variations and instabilities in electron energies for the freshly cleaved surfaces, demonstrating how easy it is to come to diverse and irreproducible outcomes. Secondly, the study shows that room temperature atomic hydrogen treatment is remarkably effective in neutralising the surface electronic inhomogeneity and instability. This is rationalised by the ability of hydrogen atoms to either accept or give away an electron, and calls for further characterisations of the functional properties of the hydrogenated material. “We hypothesise that atomic hydrogen helps rearranging sulphur vacancies and excess of sulphur atoms yielding a more ordered structure”. Erika Giangrisostomi says.
This study marks a fundamental step in the investigation of MoS2. Due to the extensive use of MoS2 in all kinds of applications, the findings of this research have the potential to reach a wide audience in the fields of electronics, photonics, sensors and catalysis.
- Only at BESSY II worldwide: IR nanoscopy at very long THz wavelengthsThe team at the IRIS beamline at BESSY II has created a new experimental option: They achieved infrared nanoscopy with a spatial resolution of around 50 nm at very long THz wavelengths. This was possible thanks to a temporal pattern of light pulses known as the low-alpha operating mode at BESSY II. Worldwide, BESSY II is the only synchrotron light source at which users could perform nanoscopy in a single setup from 25 to 4000 cm-1, a unique opportunity for ultra-broadband characterisation of complex materials at the nanoscale in both the life sciences and materials sciences.
- 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)