Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Journal of Lightwave Technology
  • Vol. 42,
  • Issue 9,
  • pp. 3338-3345
  • (2024)

Fabrication of Mid-IR As-Se Chalcogenide Glass and Fiber With Low Scattering Loss

Not Accessible

Your library or personal account may give you access

Abstract

The addition of metallic deoxidizer is effective in reducing oxygen-containing impurities during the preparation of the chalcogenide glass. However, excessive metal can introduce undesirable background scattering in the glass host, and increase the optical fiber loss. In this paper, we propose an effective approach to address this issue by introducing micron-sized filters into a multi-stage dynamic distillation process. Experimental results show that, after removing these particles, the 20-mm-thick As40Se60 glass exhibits a transmittance of 60%, which is close to the theoretical value. Further study reveals a transmittance of up to 60.6% at wavelength of 2.94 μm laser using a G1 glass with low hydroxyl group content. Additionally, an AsSe/GeAsSe fiber with core-size of 200-μm diameter was fabricated via isolated-extrusion method, the fiber exhibits a minimum loss of 0.37 dB/m at 5.7 μm and maintains a low scattering loss of about 0.5 dB/m at the wavelength range from 2.5 to 3.5 μm. The fiber demonstrates an ability of enduring an incident CO2 laser power of 7.2 W and outputting power density of 2.33 ± 0.02 kW/cm2 at a wavelength of 10.6 μm, which is higher than the record of 2.1 kW/cm2 with a water-cooled Ge-As-Se fiber in previous work.

PDF Article

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.