Evaluation of Conventional Quartz Extraction Protocols for Optically Stimulated Luminescence (OSL) Dating by Scanning Electron Microscopy (SEM) Coupled with Energy Dispersive X-Ray (EDX) Spectroscopy
Abstract
Quartz purity is essential for reliable optically stimulated luminescence (OSL) dating, yet the mineralogical evolution of sediment samples during extraction is rarely documented at each preparation stage. This study uses scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) to characterize two loess samples from the Urluia section, Dobrogea, Romania, across sixteen successive stages of a standard OSL quartz extraction protocol. Four compositional indices - the Quartz Purity Index (QPI), Feldspar Contamination Index (FCI), Heavy Mineral Index (HMI), and Carbonate Index (CI) - are introduced to quantify purification efficiency. Each preparation stage contributed differently: HCl treatment primarily removed carbonates, grain-size separation and density fractionation reduced heavy minerals by ~99%, and feldspar removal resulted from the combined effects of density separation and HF etching. Despite identical protocols, two samples collected from stratigraphically adjacent positions showed markedly different HF etching efficiencies, reaching ~94% and ~66% quartz purity, respectively. However, luminescence measurements showed similarly weak feldspar signals in both samples, with intensities substantially lower than the quartz OSL signal. These results demonstrate that purification efficiency cannot be assumed to be uniform across samples and highlight SEM-EDS as a practical quality-control tool for OSL sample preparation.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.