Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
  • Applied Spectroscopy
  • Vol. 47,
  • Issue 11,
  • pp. 1780-1783
  • (1993)

High-Performance Raman Spectroscopic System Based on a Single Spectrograph, CCD, Notch Filters, and a Kr+ Laser Ranging from the Near-IR to Near-UV Regions

Not Accessible

Your library or personal account may give you access

Abstract

We describe the performance of a high-throughput system for detecting Raman spectra excited in the range from 320 to 750 nm. The system utilizes notch filters to reject Rayleigh scattering, a single polychromator as the dispersive element, and photon detection via a charge-coupled device. The filters, mirrors, and grating are changed to maximize performance in each excitation region. The excitation source consists of a Kr<sup>+</sup> ion laser. Good-quality Raman data are reported for rhodamine 6G at 0.1 mM in methanol with the use of 752.5-nm excitation, and 0.2 mM flavin adenine dinucleotide (FAD) in tris buffer with the use of 647.1-nm excitation. High-quality resonance Raman data for 0.1 mM N-acetylglycine ethyl dithio ester in 5% CH<sub>3</sub>CN/H<sub>2</sub>O are also reported with the use of 324.0-nm excitation.

PDF Article
More Like This
Modified Šolc notch filter for deep ultraviolet applications

Sergei Nikitin, Charles Manka, and Jacob Grun
Appl. Opt. 48(6) 1184-1189 (2009)

Distinguishing between coherent and incoherent signals in excitation-emission spectroscopy

Daniel C. Lünemann, Anitta R. Thomas, Jingjing Xu, Rabea Bartölke, Henrik Mouritsen, Antonietta De Sio, and Christoph Lienau
Opt. Express 29(15) 24326-24337 (2021)

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.