Trace-element XAFS sensitivity: a stress test for a new XRF multi-detector.
Ilaria CarlomagnoMatias AntonelliGiuliana AquilantiPierluigi BelluttiGiuseppe BertuccioGiacomo BorghiGiuseppe CauteroDaniela CirrincioneGiovanni De GiudiciFrancesco FicorellaMassimo GandolaDario GiuressiDaniela MedasFilippo MeleRalf Hendrik MenkLuca OliviGiulio OrzanAntonino PicciottoFrancesca PoddaAlexandre RachevskiIrina RashevskayaLuigi StebelAndrea VacchiGianluigi ZampaNicola ZampaNicola ZorziCarlo MeneghiniPublished in: Journal of synchrotron radiation (2021)
X-ray absorption fine-structure (XAFS) spectroscopy can assess the chemical speciation of the elements providing their coordination and oxidation state, information generally hidden to other techniques. In the case of trace elements, achieving a good quality XAFS signal poses several challenges, as it requires high photon flux, counting statistics and detector linearity. Here, a new multi-element X-ray fluorescence detector is presented, specifically designed to probe the chemical speciation of trace 3d elements down to the p.p.m. range. The potentialities of the detector are presented through a case study: the speciation of ultra-diluted elements (Fe, Mn and Cr) in geological rocks from a calcareous formation related to the dispersal processes from Ontong (Java) volcanism (mid-Cretaceous). Trace-elements speciation is crucial in evaluating the impact of geogenic and anthropogenic harmful metals on the environment, and to evaluate the risks to human health and ecosystems. These results show that the new detector is suitable for collecting spectra of 3d elements in trace amounts in a calcareous matrix. The data quality is high enough that quantitative data analysis could be performed to determine their chemical speciation.
Keyphrases
- human health
- high resolution
- data analysis
- risk assessment
- image quality
- climate change
- heavy metals
- monte carlo
- single molecule
- living cells
- organic matter
- dual energy
- air pollution
- magnetic resonance imaging
- molecular dynamics
- quantum dots
- deep learning
- heat stress
- machine learning
- metal organic framework
- health information