Institution

Board of the Swiss Federal Institutes of Technology

CHgovernment

Recent research

  • Physics & SpaceOpen access

    Road transport of trapped antiprotons

    Abstract Low-energy antiprotons confined in ultrahigh-vacuum Penning traps 1 enable precision investigations of charge, parity and time-reversal (CPT) invariance 2 to test the fundamental symmetry between matter and antimatter. These studies are driven by the search for physics b...

    Nature2026-09-161 citationsRead our summary →DOI
  • Physics & Space

    The parent bodies of Ryugu and Ivuna formed before those of other carbonaceous chondrites

    Most carbonaceous chondrite (CC) meteorites contain abundant chondrules, millimeter-scale spherical inclusions of previously molten material. Ivuna-type carbonaceous chondrites (CIs) do not contain chondrules, nor do samples of the carbonaceous asteroid Ryugu. CIs and Ryugu share...

    Science2026-09-101 citationsDOI
  • BiologyOpen access

    Real-time volatilomics reveals microbiota and pathogen fingerprints in the honey bee

    ABSTRACT Understanding the complex relationship between gut microbiota and their hosts often relies on invasive sampling techniques. Honey bees provide a tractable model for host-microbe studies. Here we establish single-bee volatilomics using secondary electrospray ionization (S...

    mBio2026-09-090 citationsDOI
  • AI & ComputingOpen access

    Nonlocal Approximation of Minimal Surfaces: Optimal Estimates From Stability

    ABSTRACT Minimal surfaces in closed 3‐manifolds are classically constructed via the Almgren‐Pitts approach. The Allen‐Cahn approximation has proved to be a powerful alternative, and Chodosh and Mantoulidis (in Ann. Math. 2020) used it to give a new proof of Yau's conjecture for g...

    Communications on Pure and Applied Mathematics2026-09-041 citationsDOI
  • Climate & EnvironmentOpen access

    Interpretable machine learning for urban heat mitigation: Attribution and weighting of multi-scale drivers

    Abstract Strategies to mitigate urban heat are often analysed using complex models. Here, we propose a machine learning method for highly interpretable emulators of such models to efficiently inform actionable levers to mitigate urban heat. We first pre-classify parameters accord...

    Environmental Research Communications2026-08-280 citationsDOI