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Open AccessArticle

Semi-Automated Heavy-Mineral Analysis by Raman Spectroscopy

Department of Sedimentology and Environmental Geology, Geoscience Center, Georg-August University Göttingen, Goldschmidtstraße 3, D-37077 Göttingen, Germany
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Minerals 2019, 9(7), 385; https://doi.org/10.3390/min9070385
Received: 30 May 2019 / Revised: 20 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Heavy Minerals)
A significant amount of information on sedimentary provenance is encoded in the heavy minerals of a sediment or sedimentary rock. This information is commonly assessed by optically determining the heavy-mineral assemblage, potentially followed by geochemical and/or geochronological analysis of specific heavy minerals. The proposed method of semi-automated heavy-mineral analysis by Raman spectroscopy (Raman-HMA) aims to combine the objective mineral identification capabilities of Raman spectroscopy with high-resolution geochemical techniques applied to single grains. The Raman-HMA method is an efficient and precise tool that significantly improves the comparability of heavy-mineral data with respect to both overall assemblages and individual compositions within solid solution series. Furthermore, the efficiency of subsequent analysis is increased due to identification and spatial referencing of the heavy minerals in the sample slide. The method is tested on modern sediments of the Fulda river (central Germany) draining two Miocene volcanic sources (Vogelsberg, Rhön) resting on top of Lower Triassic siliciclastic sediments. The downstream evolution of the volcanic detritus is documented and the capability to analyze silt-sized grains has revealed an additional eolian source. This capability also poses the possibility of systematically assessing the heavy-mineral assemblages of shales, which are often disregarded in sedimentary provenance studies. View Full-Text
Keywords: Raman spectroscopy; heavy minerals; sedimentary provenance; automatization Raman spectroscopy; heavy minerals; sedimentary provenance; automatization
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Lünsdorf, N.K.; Kalies, J.; Ahlers, P.; Dunkl, I.; von Eynatten, H. Semi-Automated Heavy-Mineral Analysis by Raman Spectroscopy. Minerals 2019, 9, 385.

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