Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group
Journal Home About Current Issue All Issues
  • Applied Spectroscopy
  • Vol. 27,
  • Issue 1,
  • pp. 43-44
  • (1973)

Sampling Techniques for Raman Spectroscopy of Minerals

Leo E. Makovsky

Not Accessible

Your library or personal account may give you access

Abstract

When using laser Raman spectroscopy to study heterogeneous mineral samples such as ores and rocks, it is frequently necessary to isolate small homogeneous particles embedded in the heterogeneous matrix without mechanically destroying the matrix. For this purpose a simple masking device was constructed which, if necessary, can be purged with inert gas during illumination.

PDF Article
More Like This
Spatially-offset Raman spectroscopy for monitoring mineralization of bone tissue engineering scaffolds: feasibility study based on phantom samples

Max Dooley, Aruna Prasopthum, Zhiyu Liao, Faris Sinjab, Jane McLaren, Felicity R. A. J. Rose, Jing Yang, and Ioan Notingher
Biomed. Opt. Express 10(4) 1678-1690 (2019)

Application of laser-induced breakdown spectroscopy technique to hair tissue mineral analysis

Michela Corsi, Gabriele Cristoforetti, Montserrat Hidalgo, Stefano Legnaioli, Vincenzo Palleschi, Azenio Salvetti, Elisabetta Tognoni, and Chiara Vallebona
Appl. Opt. 42(30) 6133-6137 (2003)

Remote Raman spectroscopy of natural rocks

Genesis Berlanga, Tayro E. Acosta-Maeda, Shiv K. Sharma, John N. Porter, Przemyslaw Dera, Hannah Shelton, G. Jeffrey Taylor, and Anupam K. Misra
Appl. Opt. 58(32) 8971-8980 (2019)

View More...

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
Top
       
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.
Privacy | Terms of Use