Visualizing mineralization processes and fossil anatomy using synchronous synchrotron X-ray fluorescence and X-ray diffraction mapping

Abstract

Fossils, including those that occasionally preserve decay-prone soft tissues, are mostly made of minerals. Accessing their chemical composition provides unique insight into their past biology and/or the mechanisms by which they preserve, leading to a series of developments in chemical and elemental imaging. However, the mineral composition of fossils, particularly where soft tissues are preserved, is often only inferred indirectly from elemental data, while X-ray diffraction that specifically provides phase identification received little attention. Here, we show the use of synchrotron radiation to generate not only X-ray fluorescence elemental maps of a fossil, but also mineralogical maps in transmission geometry using a two-dimensional area detector placed behind the fossil. This innovative approach was applied to millimetre-thick cross-sections prepared through three-dimensionally preserved fossils, as well as to compressed fossils. It identifies and maps mineral phases and their distribution at the microscale over centimetre-sized areas, benefitting from the elemental information collected synchronously, and further informs on texture (preferential orientation), crystallite size and local strain. Probing such crystallographic information is instrumental in defining mineralization sequences, reconstructing the fossilization environment and constraining preservation biases. Similarly, this approach could potentially provide new knowledge on other (bio)mineralization processes in environmental sciences. We also illustrate that mineralogical contrasts between fossil tissues and/or the encasing sedimentary matrix can be used to visualize hidden anatomies in fossils.

Human brains preserve in diverse environments for at least 12 000 years

“…In addition, non-destructive spectroscopic methods are well-suited for rapid, in situ characterization of molecular fingerprints [ 69 ] and their diagenetic alteration [ 41 ] in fossil organic matter, and constitute a valuable corroborative approach for precious, ancient samples. Finally, a range of largely X-ray-based methods might be employed to refine the identity [ 70 ], oxidation state [ 71 ], and tissue distribution [ 72 ] of metals and minerals intimately associated with preserved nervous tissues.…”

Microbially mediated fossil concretions and their characterization by the latest methodologies: a review

“…Synchrotron exploration of the X-ray and matter interactions on a range of geological materials can provide insights on morphology, elemental composition, oxidation states, crystalline structure, magnetic properties, and others, which can measurably contribute to the investigation of biogenicity of putative biosignatures ( Callefo et al, 2019 ). Gueriau et al (2020) utilized synchrotron radiation to generate X-ray fluorescence elemental maps of fossils representative of different taxonomic groups (arthropods, sarcopterygians and actinopterygians), types of preservation (compressed and three-dimensional fossils, including the ones with extensive soft-tissue mineralization), geological ages and depositional environments. Mineralogical maps were also generated in transmission geometry using a two-dimensional area detector placed behind the fossil.…”

Combinatorial Screening of Cuprate Superconductors by Drop-On-Demand Inkjet Printing

“…Rapid on-the-flight measurements through compositionally graded samples should allow the accessibility and characterization of larger heating ramps and crystallization growth rates. 71,72 All-in-all, the results obtained show that the combinatorial and high-throughput approach can be now moved forward to the fabrication of continuous compositional gradients. This will enable enhanced mapping of growth conditions, a fast screening of functional properties, the construction of libraries that include, for instance, other RE ions, liquid composition changes, nanoparticle percentages in nanocomposites, and the implementation of machine learning algorithms for a swift and smart optimization of TLAG-CSD REBCO film growth.…”