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3D nanopetrography and chemical imaging of datable zircons by synchrotron multimodal X-ray tomography.

ABSTRACT: Zircon is the most widely used mineral in petrochronology and provides key information about magmatic and crustal differentiation history of plutonic rocks, transport paths of clastic material 'from source to sink' and significantly contributes in the reconstruction of enigmatic planetary-scale tectonic episodes since the Archaean. However, detailed textural analysis of this accessory mineral has always been hampered by two-dimensional (2D) analytical limitations. With the advancements in X-ray nanotomography technology, it is now possible to non-destructively, yet digitally, cut, visualize, compare and quantify internal textures within zircons, their growth and zoning patterns and chemical distribution of trace elements in three dimensions (3D). We present a novel multimodal approach of using a synchrotron radiation nanobeam to perform 3D nanopetrography of

SUBMITTER: Suuronen JP

PROVIDER: S-EPMC5856831 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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3D nanopetrography and chemical imaging of datable zircons by synchrotron multimodal X-ray tomography.

Suuronen J-P JP Sayab M M

Scientific reports 20180316 1

Zircon is the most widely used mineral in petrochronology and provides key information about magmatic and crustal differentiation history of plutonic rocks, transport paths of clastic material 'from source to sink' and significantly contributes in the reconstruction of enigmatic planetary-scale tectonic episodes since the Archaean. However, detailed textural analysis of this accessory mineral has always been hampered by two-dimensional (2D) analytical limitations. With the advancements in X-ray ...[more]

PMID: 29549336

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Project description:Computed tomography remains strongly underused in plant sciences despite its high potential in delivering detailed 3D phenotypical information because of the low X-ray absorption of most plant tissues. Existing protocols to study soft tissues display poor performance, especially when compared to those used on animals. More efficient protocols to study plant material are therefore needed. Flowers of Arabidopsis thaliana and Marcgravia caudata were immersed in a selection of contrasting agents used to treat samples for transmission electron microscopy. Grayscale values for floral tissues and background were measured as a function of time. Contrast was quantified via a contrast index. The thick buds of Marcgravia were scanned to determine which contrasting agents best penetrate thick tissues. The highest contrast increase with cytoplasm-rich tissues was obtained with phosphotungstate, whereas osmium tetroxide and bismuth tatrate displayed the highest contrast increase with vacuolated tissues. Phosphotungstate also displayed the best sample penetration. Furthermore, infiltration with phosphotungstate allowed imaging of all plants parts at a high resolution of 3 µm, which approaches the maximum resolution of our equipment: 1.5 µm. The high affinity of phosphotungstate for vasculature, cytoplasm-rich tissue, and pollen causes these tissues to absorb more X-rays than the surrounding tissues, which, in turn, makes these tissues appear brighter on the scan data. Tissues with different brightness can then be virtually dissected from each other by selecting the bracket of grayscale to be visualized. Promising directions for the future include in silico phenotyping and developmental studies of plant inner parts (e.g., ovules, vasculature, pollen, and cell nuclei) via virtual dissection as well as correlations of quantitative phenotypes with omics datasets. Therefore, this work represents a crucial improvement of previous methods, allowing new directions of research to be undertaken in areas ranging from morphology to systems biology.

Dataset Information

3D nanopetrography and chemical imaging of datable zircons by synchrotron multimodal X-ray tomography.

Publications

3D nanopetrography and chemical imaging of datable zircons by synchrotron multimodal X-ray tomography.

Similar Datasets

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