Humboldt Fellow joins HZB for battery research
Dr. Wenxi Wang specialises in the design of organic electrodes for lithium-sulfur and zinc-ion batteries and investigates interactions between ions and active materials. © arö/HZB
Dr. Wenxi Wang is working in the team of Prof. Yan Lu as Humboldt Foundation postdoctoral fellow. He studied at the Southern University of Science and Technology in Shenzhen, China, and completed his doctorate at the King Abdullah University of Science and Technology in Saudi Arabia. He specialises in the precise design of organic electrodes for lithium-sulfur and zinc-ion batteries and the investigation of the interactions between ions and active materials.
"At Helmholtz-Zentrum Berlin I find excellent conditions to deepen my research," says Wenxi Wang. Prof. Yan Lu's group has extensive experience in the synthesis and characterisation of novel electrode materials and state-of-the-art infrastructures for battery research. In addition, the X-ray source BESSY II at HZB offers a variety of spectroscopic methods to analyse electrochemical reactions in real time.
Lithium-sulfur (Li-S) batteries are considered one of the most interesting technologies to replace lithium-ion batteries due to their extremely high energy density and cheap starting materials. However, their performance still falls far short of expectations due, in part due to polysulfide intermediates that form during charging cycles. Porous host materials can trap such polysulfides, improving the energy density and lifetime of Li-S batteries. "My research project focuses on the precise design of organic-based host materials with suitable pore sizes and functional groups (Covalent Organic Frameworks, COF) to enable high-performance Li-S batteries and deepen our understanding of their mechanisms," says Wang.
- 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.
- HZB congratulates Peter Hegemann on winning the Nobel PrizeOur warmest congratulations go to Peter Hegemann, Professor of Neuroscience and Experimental Biophysics at Humboldt University in Berlin. Together with Prof. Georg Nagel (Julius Maximilian University of Würzburg) and Prof. Karl Deisseroth (Stanford University, USA), he has been awarded the 2026 Nobel Prize in Physiology or Medicine. We are delighted for him, his team, Humboldt University and the entire Berlin scientific community.