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  • Data report: standard mineral mixtures, normalization factors, and determination of error for quantitative X-ray diffraction analyses of bulk powders and clay-sized mineral assemblages
Title: Data report: standard mineral mixtures, normalization factors, and determination of error for quantitative X-ray diffraction analyses of bulk powders and clay-sized mineral assemblages
X-ray diffraction (XRD) was an important analytical component of International Ocean Discovery Program (IODP) Expeditions 372 and 375 in the Hikurangi subduction zone. This report documents the composition of standard mineral mixtures that we used to calibrate computations of mineral abundance in bulk powder and clay-sized fractions of the sediment. Shipboard analyses of the bulk powders were completed on the R/V JOIDES Resolution using a Bruker D4 Endeavor diffractometer, and reduction of those data utilized two types of software (MacDiff and Bruker DIFFRAC.EVA). To evaluate precision more rigorously, we replicated the bulk powder analyses at the New Mexico Bureau of Geology and Mineral Resources (New Mexico Tech; United States) using a Panalytical X'Pert Pro diffractometer, and MacDiff software was used for data reduction. The relation between peak area (integrated intensity) and weight percent in the bulk powder mixtures is constrained in two ways: a matrix of normalization factors, derived by singular value decomposition (SVD), and polynomial regression equations. Differences in results between the two computational methods are trivial. Absolute errors (known weight percent − computed weight percent) average less than 3 wt%, which is a level of accuracy more than sufficient to satisfy the scientific goals of shipboard XRD. The clay-sized standards were analyzed only at New Mexico Tech, and MacDiff software was used for data reduction. For computations of weight percent using those raw data, we tested three computations: Biscaye weighting factors, SVD normalization factors, and regression equations. Results using the SVD normalization factors and regression equations are significantly more accurate than those using Biscaye weighting factors.  more » « less
Award ID(s):
1326927
NSF-PAR ID:
10228880
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the International Ocean Discovery Program
Volume:
372B/375
ISSN:
2377-3189
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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