Development of Fiber Bragg Grating Sensors for Measurements in Extreme Environments
A special issue of Photonics (ISSN 2304-6732). This special issue belongs to the section "Lasers, Light Sources and Sensors".
Deadline for manuscript submissions: 15 October 2024 | Viewed by 48
Special Issue Editors
Interests: optical sensor; fiber optic sensor; radiation sensor; wearable sensor; fiber Bragg grating; image and signal processing; biomedical optics; medical optics; space technology
Interests: optical materials; wide-bandgap semiconductors; radiation damage; fibre optic sensors; optical memory; photonics
Special Issue Information
Dear Colleagues,
Fiber Bragg grating (FBG)-based optical sensors have attracted great attention in a variety of fields due to their inherent advantages, such as being electrically passive, being immune to electromagnetic interference (EMI), being small and having multiplexing capability. Although the applications have been successful, they have been mainly limited to conventional industrial environments. The application of FBG sensors in harsh and extreme environments is still under exploration. These harsh environments include but are not limited to the following:
- Nuclear power plants and furnaces, where the temperatures vary from a few hundred to a few thousand degrees Celsius.
- Cryogenic environments, where the FBG sensors could become less sensitive or insensitive.
- Fusion reactors, where the significantly high dose of radiation can cause light attenuation in the fiber.
- Corrosive environments.
- Space.
This Special Issue focuses on developments in the FBG fibre material, sensor fabrication and implementing methods, signal processing algorithm and instruments. Experimental investigations are strongly encouraged to be submitted to this Special Issue.
We look forward to receiving your contributions.
Dr. Xiyong Huang
Dr. Joseph J. Schuyt
Guest Editors
Manuscript Submission Information
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Keywords
- Fiber Bragg grating
- fiber optic sensor
- extreme environmental sensing
- signal processing
- machine learning
- interrogator
