Quantitative evaluation of mineral grains using automated SEM–EDS analysis and its application potential in optically stimulated luminescence dating

Abstract Optically stimulated luminescence (OSL) dating allows constraining the depositional age of sediments with good accuracy and precision. A fundamental requirement in OSL dating is to use purified sub-samples (i.e. mono-mineralogic aliquots composed of e.g. quartz or potassium feldspar only), because of the different OSL properties and dosimetry characteristics of each mineral phase. Where multiple mineral phases are present on an aliquot, a mixed OSL signal might be obtained, with potentially adverse effects on the robustness of the resulting optical ages. Detailed evaluation of the mineralogical composition of the hundreds or even thousands of individual mineral particles that constitute an aliquot in OSL dating has – until recently – not been reasonably feasible with current analytical techniques. Here we report on the use of an automated mineralogy system that combines scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) and facilitates ultra-fast analysis of particulate mineral phases with a spatial resolution on the micron scale. The method is applied to mono-mineralogic coarse-grained (100–250 μm) and poly-mineral fine-grained (4–11 μm) OSL samples, respectively and cross-checked with electron probe micro analysis (EPMA). It is shown that (i) some coarse-grained mineral extracts that underwent standard physico-chemical preparation to isolate quartz for OSL dating, still suffer from mineralogical contamination, mainly in the form of feldspar and mica inclusions and, that (ii) polymineral fine-grained samples reveal a complex mineralogical composition with a significant percentage of mica (mainly muscovite). Implications of these quantitative mineralogical observations for OSL dating are discussed. QEMSCAN is further used to examine the efficiency of different physico-chemical preparation strategies to isolate a restricted range of mineralogies and to optimize single preparation steps. We conclude that the clear advantage of automated SEM–EDS systems lies in the rapidity with which accurate high-resolution maps of hundreds or even thousands of mineral particles can be generated, i.e. at a level statistically representative of the bulk OSL sample. Automated SEM–EDS techniques might thus be helpful in OSL dating for quality assurance and investigation of problematic OSL samples.

[1]  M. Williams,et al.  Loess and floods: High-resolution multi-proxy data of Last Glacial Maximum (LGM) slackwater deposition in the Flinders Ranges, semi-arid South Australia , 2010 .

[2]  J. R. Leinum,et al.  Chemical characterization of individual particles (PM10) from ambient air in Guiyang City, China. , 2005, The Science of the total environment.

[3]  Tamsin A. Mather,et al.  Composition-resolved size distributions of volcanic aerosols in the Mt. Etna plumes , 2008 .

[4]  G. Duller.  Distinguishing quartz and feldspar in single grain luminescence measurements , 2003 .

[5]  M. Krbetschek,et al.  Spectral information from minerals relevant for luminescence dating , 1997 .

[6]  G. Rollinson,et al.  Green eye cosmetics of antiquity. , 2009, Pharmaceutical historian.

[7]  D. Sutherland,et al.  Application of automated quantitative mineralogy in mineral processing , 1991 .

[8]  S. Stokes.  Optical dating of young (modern) sediments using quartz: Results from a selection of depositional environments , 1992 .

[9]  Reuven Chen,et al.  Thermoluminescent properties of mica , 1988 .

[10]  A. Benedictus,et al.  Quantitative characterisation of processed phlogopite ore from Silver City Dome District, Kansas, USA, by automated mineralogy , 2008 .

[11]  M. Kortekaas.  A method for the removal of mica from quartz separates , 2005 .

[12]  Rolf Fandrich,et al.  Modern SEM based mineral liberation analysis , 2007 .

[13]  G. Yin,et al.  Luminescence properties of biotite relevant to dating and dosimetry , 2006 .

[14]  D. Sanderson,et al.  Photostimulated luminescence excitation spectroscopy of feldspars and micas , 1994 .

[15]  G. Rollinson,et al.  The spatial distribution and source of arsenic, copper, tin and zinc within the surface sediments of the Fal Estuary, Cornwall, UK , 2003 .

[16]  C. Knappett,et al.  Mineralogical analysis and provenancing of ancient ceramics using automated SEM-EDS analysis (QEMSCAN®): a pilot study on LB I pottery from Akrotiri, Thera , 2011 .

[17]  F. Preusser,et al.  Testing the application of post IR-IRSL dating to fine grain waterlain sediments , 2012 .

[18]  B. Smith,et al.  The Optical Dating of Sediments Using Quartz , 1990 .

[19]  Alan R. Butcher,et al.  The use of QEMSCAN and diagnostic leaching in the characterisation of visible gold in complex ores , 2005 .

[20]  A. Wintle,et al.  Luminescence sensitivity changes of polymineral fine grains during IRSL and [post-IR] OSL measurements , 2003 .

[21]  F. Ogundare,et al.  TL response of muscovite samples from granite pegmatite of Ilesa and Ijero-Ekiti, Southwestern Nigeria , 2006 .

[22]  A. Wintle,et al.  Infrared stimulated luminescence measurements of single grains of K-rich feldspar for isochron dating , 2011 .

[23]  W. Wegscheider,et al.  Qualitative and quantitative determination of micro-inclusions by automated SEM/EDX analysis , 2004, Analytical and bioanalytical chemistry.