Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 80 tok/s
Gemini 2.5 Pro 60 tok/s Pro
GPT-5 Medium 23 tok/s Pro
GPT-5 High 26 tok/s Pro
GPT-4o 87 tok/s Pro
Kimi K2 173 tok/s Pro
GPT OSS 120B 433 tok/s Pro
Claude Sonnet 4 36 tok/s Pro
2000 character limit reached

Test of low radioactive molecular sieves for radon filtration in SF6 gas-based rare-event physics experiments (2011.06994v2)

Published 13 Nov 2020 in physics.ins-det, hep-ex, and physics.chem-ph

Abstract: Type 5A molecular sieves (MS) have been demonstrated to remove radon from SF$_6$ gas. This is important for ultra-sensitive SF$_6$ gas-based directional dark matter and related rare-event physics experiments, as radon can provide a source of unwanted background events. Unfortunately, commercially available sieves intrinsically emanate radon at levels not suitable for ultra-sensitive physics experiments. A method to produce a low radioactive MS has been developed in Nihon University (NU). In this work, we explore the feasibility of the NU-developed 5A type MS for use in such experiments. A comparison with a commercially available Sigma-Aldrich 5A type MS was made. The comparison was done by calculating a parameter indicating the amount of radon intrinsically emanated by the MS per unit radon captured from SF$_6$ gas. The measurements were made using a specially adapted DURRIDGE RAD7 radon detector. The NU-developed 5A MS emanated radon up to 61$\pm$9$\%$ less per radon captured (2.1$\pm$0.1)$\times 10{-3}$, compared to the commercial Sigma-Aldrich MS (5.4$\pm$0.4)$\times 10{-3}$, making it a better candidate for use in a radon filtration setup for future ultra-sensitive SF$_6$ gas based experiments.

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.