JOURNAL ARTICLE
Physical design of a compact electron linear accelerator.
Published In: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics, 2025, v. 40, n. 17. P. 1 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Liao, W.; Luo, X.; Wan, X.; Ren, Z.; Chang, X.; Li, Z. 3 of 3
Abstract
Recent years have seen a rising demand for effective treatments driven by heightened concerns about cancer and other diseases. Among various types of accelerators, the electron linear accelerator has emerged as a promising option for miniaturization. In response to this need, we have completed a comprehensive physical design of a compact side-coupled electron linear accelerator operating at 9.3 GHz. This design encompasses the development of the electron gun, optimization of the accelerating cavity, tuning of the cavity chain, design of the power coupler and final dynamic verification. The resulting electron linear accelerator operates at an energy level of 6 MeV, with the main accelerating structure not exceeding 0.3 m in length. It generates an exit beam current of over 100 mA, with the beam spot radius measuring less than 1.5 mm, and achieves a high transmission efficiency of 78.7%. These specifications fully satisfy the requirements for contemporary medical applications and other fields. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 2025/06, Vol. 40, Issue 17, p1
- Document Type:Article
- Subject Area:Science
- Publication Date:2025
- ISSN:0217-751X
- DOI:10.1142/S0217751X25500526
- Accession Number:185394182
- Copyright Statement:Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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