Supercritical CO2 as a refrigerant

The present deliverable documents the achievement of Task 10.4 towards a universal adoption of natural refrigerants for future generations of detectors, covering in particular situations where water or boiling CO2 flows cannot be used. The programme announced in the submitted proposal has been extended thanks to synergies with internal funds to the case of ultra-cold detectors, requiring operation below the limit temperatures achievable with boiling CO2. Two natural fluids have been considered: supercritical carbon dioxide and Krypton. Based on the outcome of scientific literature review and theoretical studies, two new dedicated test setups have been designed, built and launched in operation.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-14
DOI
https://doi.org/10.5281/zenodo.15212146
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
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article

Supercritical CO2 as a refrigerant

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Bioinformatics
article

Supercritical CO2 as a refrigerant

article en

Abstract

The present deliverable documents the achievement of Task 10.4 towards a universal adoption of natural refrigerants for future generations of detectors, covering in particular situations where water or boiling CO2 flows cannot be used. The programme announced in the submitted proposal has been extended thanks to synergies with internal funds to the case of ultra-cold detectors, requiring operation below the limit temperatures achievable with boiling CO2. Two natural fluids have been considered: supercritical carbon dioxide and Krypton. Based on the outcome of scientific literature review and theoretical studies, two new dedicated test setups have been designed, built and launched in operation.

Zenodo (CERN European Organization for Nuclear Research)
European Organization for Nuclear Research (CH)
Openalex Percentile: Top 57%
Machine Learning in Bioinformatics
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