NEW TECHNIQUES FOR TRACE ELEMENT AND RADIOGENIC ISOTOPE MEASUREMENT OF DIAMONDS: THEIR APPLICATION TO DIAMOND PETROGENESIS AND SOURCE TRACING

To investigate impurities in diamond we have developed an offline laser ablation method to acquire radiogenic isotope compositions and quantitative trace element determinations on diamond. This information has the potential to be used as both a petrogenetic tracer and as a tool in determining the geographic region of origin of particular diamonds. Trace element abundances are determined by sector-field ICPMS and isotope ratios are analysed via TIMS (Sr) and multi-collector ICPMS (Nd-Pb). To report quantitative trace element data the analyte mass we require from a given ablated sample volume is <1 pg for most elements, except for Sr, Zr, Ba which require between 2 – 30 pg, and for Pb ~40 pg. Diamonds show broad LILE and LREE enrichment and HFSE and HREE depletion. Trace element systematics in fibrous diamonds are mimicked in monocrystalline diamonds. Monocrystalline diamonds display 87Sr/86Sr(i) = 0.7014±0.0010 to 0.70864±0.00004 and fibrous diamonds display 87Sr/86Sr(i) = 0.70386±0.00005 to 0.712406±0.00007. The isotope data show no defined isochron systematics that could be used for dating purposes. The parental fluids of fibrous and monocrystalline diamonds are thought to be derived through a similar multi-component mechanism. Diamond formation will result from the interaction between 1) a primitive, volatile and carbonate-rich, silicate liquid with an unradiogenic Sr signature ascending from the asthenosphere and 2) other components with more radiogenic Sr, akin to more ancient, enriched and vein-dominated lower lithosphere e.g. glimmerite/ PIC assemblages and the sources of Group II kimberlites. We have demonstrated, using >100 diamonds from the Ekati, Diavik, Snap Lake and Congo kimberlites, that statistical processing of data using analysis of variance and logistic regression can allow source discrimination of ‘unknown’ samples. For logistic regression the most successful models focus on differences in Nb, Eu, Rb and Th data. This method shows good potential for use in a diamond fingerprinting programme.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

doctoral

Grantor dc:publisher.institution

Durham University

Year dc:date.issued

2011

Author dc:creator

• McNeill, John Charles Robert

Identifier URI

https://etheses.durham.ac.uk/id/eprint/713/3/Appendix_Table_A2.xls
https://etheses.durham.ac.uk/id/eprint/713/4/Appendix_Table_A3.xls
https://etheses.durham.ac.uk/id/eprint/713/5/Appendix_Table_A4.xls
https://etheses.durham.ac.uk/id/eprint/713/6/Appendix_Table_A5_A6.xls
https://etheses.durham.ac.uk/id/eprint/713/7/LOQ_file___Electronic_appendix.xls

OAI identifier oai:identifier

oai:etheses.durham.ac.uk:713