X-ray scattering pattern

ioviso

Advanced Characterization for Complex Materials

The advancement of key technologies—from biomedical implants to energy storage—relies on the precise understanding of material properties across hierarchical and nanoscale structures. These properties often change dynamically under real-world conditions such as mechanical stress, humidity, or temperature variations. Capturing these time‑resolved structure‑property relationships therefore demands in-operando experiments that combine high-brilliance X-ray probes with rigorously controlled sample environments.

ioviso GmbH is a research-driven spin-off from the MPICI supported by the German Federal Ministry for Economic Affairs and Energy (EXIST programme). Originating from its founders’ long-standing expertise in the Biomaterials Department and at BESSY II, ioviso enables precise, customized characterization of complex materials under realistic conditions—from structural probing to data interpretation.

It combines:

  • Synchrotron-based small- and wide-angle X-ray scattering (SAXS/WAXS)

  • X-ray fluorescence (XRF)

  • Confocal and environmental scanning electron microscopy (ESEM)

  • In-operando mechanical testing and customized measurement cells

  • Additive manufacturing

  • Multimodal technique integration and real-time data acquisition


Beyond instrumentation, ioviso also provides comprehensive scientific support across the entire research pipeline:

  • Experimental design and feasibility assessment

  • Beamline selection and proposal preparation

  • On-site measurement support and data acquisition

  • Advanced data analysis, interpretation, and visualization

  • Publication and funding proposal assistance

ioviso’s modular service model responds to the growing demand for specialized synchrotron-based methods by bridging the gap between scientific inquiry and technical execution. The aim is to lower the barriers to advanced X-ray techniques through integrated instrumentation and end-to-end scientific support—addressing the key bottlenecks from experiment conception to data interpretation.

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