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Low-Density Materials for Particle Accelerator Applications: Challenges, Capabilities, and Future Directions

  • CERN
  • University of Bordeaux
  • Paul Scherrer Institute

Research output: Contribution to journal › Article › peer-review

Abstract

Low-density materials are emerging as a critical enabling technology for next-generation particle accelerator systems, offering unique combinations of low areal density, high thermal resilience, and tuneable functional properties. To address the challenges associated with their development and deployment, an international, multidisciplinary community spanning accelerator physics, materials science, and engineering convened to assess the current state of the field and define future research directions. The discussions highlighted significant progress in the development of carbon-based materials, including carbon nanotube fibres, graphene, and related nanostructured systems, alongside advances in fabrication, characterisation, and modelling capabilities. However, key challenges remain in achieving reproducible material quality, controlling impurities and microstructure, and bridging the gap between laboratory-scale demonstrations and reliable operation in accelerator environments. Limitations in predictive modelling and the availability of validated material data were also identified as critical barriers. The workshop underscored the importance of coordinated efforts in standardisation, testing under realistic conditions, and integration of experimental and simulation approaches. Emerging opportunities were identified in advanced beam instrumentation, high-performance targets, and novel functional materials. Overall, the outcomes emphasise the need for sustained collaboration and strategic investment to realise the full potential of low-density materials in future accelerator applications.
Original languageEnglish
Article number4008
Number of pages25
JournalMaterials
Volume19
Issue number18
Early online date20 Sept 2026
DOIs
Publication statusPublished - 20 Sept 2026

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Acknowledgements

The authors sincerely thank all speakers, participants, and contributors for their valuable contributions to the workshop. The authors are particularly grateful to Thibault Lefèvre, for providing internal CERN funding that enabled the workshop to take place. The authors also acknowledge the University of Bath for hosting the workshop. During the preparation of this manuscript, the authors used Microsoft 365 Copilot (GPT-5 Chat) for graph refinement, language editing, spelling correction, and proofreading. The authors reviewed and edited the output and accept full responsibility for the content of this publication.

Funding

This research received no external funding

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