Elemental mapping of large samples by external ion beam analysis with sub-millimeter resolution and its applications

Silva, Tiago Fiorini da; Rodrigues, C. L.; Added, N.; Rizzutto, M. A.; Tabacniks, M.H.; Mangiarotti, A.; Curado, Jessica Fleury; Aguirre, Fernando; Aguero, N.F.; Allegro, P.R.P.; Campos, P.H.O.V.; Restrepo, Jennifer; Trindade, Gustavo F.; Antonio, Mark R.; Assis, R.F.; Leite, Alisson Rodolfo

doi:10.1016/j.nimb.2018.03.006

Cited by 11 publications

(8 citation statements)

References 35 publications

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“…In this section, we present some results obtained using the proposed approach to illustrate its potentialities. The data were obtained using the external beam set‐up built at the University of São Paulo that recently received an update to enable the mapping capability . In this set‐up, the sample surface is divided into a matrix of spots that are sequentially measured to form a matrix of spectra (hyperspectral image).…”

Section: Methodsmentioning

confidence: 99%

Multivariate analysis applied to particle‐induced X‐ray emission mapping

2018

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The use of particle‐induced X‐ray emission (PIXE) for elemental speciation and quantification has gained new attention thanks to mapping capabilities. Microprobes are able to raster a proton beam and produce elemental maps on the micrometre scale. Moreover, recent developments of in‐air PIXE instrumentation have enabled the acquisition of large area elemental maps. However, the amount of data produced is very large, and the data processing is not trivial. In this paper, we propose the use of multivariate analysis to process data of PIXE mapping. First, we apply the non‐negative matrix factorization (NMF), which is a nonsupervised machine‐learning algorithm, to decompose the data into a smaller number of components; then, we use the k‐means algorithm to divide the pixels into categories regarding similarities in the NMF results; finally, we sum the spectra of all pixels in the same category so that the results can be analyzed by standard procedures for PIXE quantification. This last step is important to enable the quantification of the elements found in each component by correctly accounting for matrix self‐absorptions. With the procedure described in this paper, not just, we reduced the number of variables, facilitating the reasoning process on the data by employing the multivariate analysis, but we also increased the counting statistics by summing similar pixels leading to better results concerning the quantification of trace elements. We also propose methods for both, the automatic determination of the optimal number of components to describe the dataset, and for the combined analysis of multiple detectors.

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Section: Methodsmentioning

confidence: 99%

Multivariate analysis applied to particle‐induced X‐ray emission mapping

2018

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“…With this solution, the count rate can be roughly the same for low-and high-energy X-ray detectors. Two-detector PIXE setup has become very popular; see, for example, [65][66][67][68][69][70][71][72][73][74][75]. This is the adopted solution also at INFN-LABEC, within the INFN-CHNet network.…”

Section: X-ray Detectors-pixementioning

confidence: 99%

Detectors and Cultural Heritage: The INFN-CHNet Experience

et al. 2021

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Detectors are a key feature of the contemporary scientific approach to cultural heritage (CH), both for diagnostics and conservation. INFN-CHNet is the network of the Italian National Institute of Nuclear Physics that develops and applies new instrumentation for the study of CH. This process results in both optimized traditional state-of-the-art and highly innovative detection setups for spectrometric techniques. Examples of the former are X-rays, gamma-rays, visible-light and particles spectrometers tailored for CH applications, with optimized performances, reliability, weight, transportability, cost, absorbed power, and complementarity with other techniques. Regarding the latter, examples are ARDESIA, the array of detectors at the DAΦNE-Light facility, the MAXRS detection setup at the Riken-RAL muon beamline and the imaging facilities at the LENA Laboratory. Paths for next-generation instruments have been suggested, as in the case of the X-ray Superconductive Detectors and X-ray Microcalorimeter Spectrometers, allowing astonishing improvement in energy resolution. Many issues in CH can now be addressed thanks to scientific techniques exploiting the existing detectors, while many others are still to be addressed and require the development of new approaches and detectors.

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“…Besides that, the commissioning of the new external beam infrastructure with capabilities for scanning large areas [2] introduced a new primary source of data in the laboratory. It produces a large amount of information for each map that is virtually impossible to be processed by humans.…”

Section: Introductionmentioning

confidence: 99%