Abstract
Hydrogen normally occurs as hydroxyl ions related to defects at specific crystallographic sites in the structures, and is normally characterized by infrared spectroscopy (FTIR). For quantification purposes the FTIR technique has proven to be less precise since calibrations against independent methods are needed. Hydrogen analysis by the NMP technique can solve many of the problems, due to the low detection limit, high lateral resolution, insignificant matrix effects and possibility to discriminate surface-adsorbed water. The technique has been shown to work both on thin samples and on thicker geological samples. To avoid disturbance from surface contamination the hydrogen is analyzed inside semi-thick geological samples. The technique used is an elastic recoil technique where both the incident projectile (proton) and the recoiled hydrogen are detected in coincidence in a segmented detector. Both the traditional annular system with the detector divided in two halves and the new double-sided silicon strip detector (DSSSD) has been used. In this work we present an upgraded version of the technique, studying two sets of mineral standards combined with pre-sample charge normalization. To improve the processing time of data we suggest a very simple semi-empirical approach to be used for data evaluation. The advantages and drawbacks with the approach are discussed and a possible extension of the model is suggested. (C) 2013 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 253-256 |
Journal | Nuclear Instruments & Methods in Physics Research. Section B: Beam Interactions with Materials and Atoms |
Volume | 306 |
DOIs | |
Publication status | Published - 2013 |
Bibliographical note
The information about affiliations in this record was updated in December 2015.The record was previously connected to the following departments: Nuclear Physics (Faculty of Technology) (011013007)
Subject classification (UKÄ)
- Subatomic Physics
Free keywords
- Hydrogen analysis
- pp-Scattering
- Depth profiling
- DSSSD