Applying multivariate analysis to X-ray diffraction computed tomography: the study of medieval applied brocades

Bordet, Pierre; Kergourlay, Florian; Pinto, Ariane; Blanc, Nils; Martinetto, Pauline

doi:10.1039/d1ja00143d

Cited by 7 publications

(10 citation statements)

References 32 publications

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“…Using the same alignment and adjustment of the singlecrystal-comb + Soller-collimator blocks of this MAD system, we collected powder diffractograms of several complex heterogeneous samples from cultural heritage studies of the 'Patrimalp' project (Patrimalp, https://patrimalp.univ-grenoble-alpes.fr; Martinetto et al, 2021;Bordet et al, 2021;Autran, 2021;Autran et al, 2021). The analysis of these cultural heritage materials is a real challenge for crystallography and, in the following experiment, illustrates the power of our MAD filter system, as they are very hetero- 100), ( 210), ( 320) and ( 321).…”

Section: Comparison Ofmentioning

confidence: 99%

A compact-rigid multi-analyser for energy and angle filtering of high-resolution X-ray experiments. Part 2. Efficiency of a single-crystal-comb

Hodeau¹,

Prat²,

Boudet³

et al. 2023

J Synchrotron Radiat

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Diffraction instruments using filtering by one or several analyser crystals exist since the 1980s and 1990s at synchrotron radiation sources, but, due to its low efficiency, this filtering is little used on laboratory sources. In order to overcome this limitation, the efficiency of a small diffraction filtering multi-analyzer block (MAD block) realized with a `single-crystal-comb' curved on a rigid support is demonstrated here. The geometry of this curved surface is logarithmic spiral and is optimized to allow multi-filtering over a relatively important diffraction angular range and to be also applicable over an X-ray spectral range. The efficiency of such a small rigid-compact MAD block consisting of this single-crystal-comb generating 20–50 Si(111) single-crystal blades, associated with a block of Soller collimators, is demonstrated. The angle between each crystal is 0.1°, so the measurement range of the comb is 2–5°. The geometry of this system has been optimized for operation with a synchrotron X-ray source over an energy range of 22 keV to 46 keV and could be used with laboratory X-ray sources (Ag Kα1, 22.1 keV). This MAD block complements and exploits the qualities of the `photon-counting' detectors which have very low intrinsic noise. Their joint efficacy is supported by powder pattern measurements of a LaB6 reference sample and of several heterogeneous samples of cultural heritage materials, carried out at 22 keV on the D2AM beamline at the ESRF. Their signal-to-noise ratio is excellent (1000/1) and allows the detection thresholds of the measurements (from 3–1% to 0.1%) to detect minor phases in the studies of `real' heterogeneous materials to be drastically improved.

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Section: Comparison Ofmentioning

confidence: 99%

A compact-rigid multi-analyser for energy and angle filtering of high-resolution X-ray experiments. Part 2. Efficiency of a single-crystal-comb

Hodeau¹,

Prat²,

Boudet³

et al. 2023

J Synchrotron Radiat

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“…The XRD-CT data were collected at the French CRG beamline BM02 of the ESRF (Grenoble, France). The experimental setup and the data preprocessing are detailed by Bordet et al (2021) and partially summarized in Fig. 1.…”

Section: Sample and Xrd-ct Measurementsmentioning

confidence: 99%

Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data

Poline,

Purushottam Raj Purohit,

Bordet

et al. 2024

J Appl Cryst

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Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called `applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with TOPAS, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the R-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.

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“…XRD-CT has also recently been applied to fifteenth-century French brocade samples incorporating gold and silver yarn [ 138 ]. As it was known the samples contained a range of unidentified phases, the study uses supervised multivariate curve resolution—alternating least-squares (MCR-ALS) analysis to automatically decompose data for more efficient phase identification.…”

Section: Recent Insights From Xds-ctmentioning

confidence: 99%

Recent developments in X-ray diffraction/scattering computed tomography for materials science

Omori,

Bobitan,

Vamvakeros

et al. 2023

Phil. Trans. R. Soc. A.

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X-ray diffraction/scattering computed tomography (XDS-CT) methods are a non-destructive class of chemical imaging techniques that have the capacity to provide reconstructions of sample cross-sections with spatially resolved chemical information. While X-ray diffraction CT (XRD-CT) is the most well-established method, recent advances in instrumentation and data reconstruction have seen greater use of related techniques like small angle X-ray scattering CT and pair distribution function CT. Additionally, the adoption of machine learning techniques for tomographic reconstruction and data analysis are fundamentally disrupting how XDS-CT data is processed. The following narrative review highlights recent developments and applications of XDS-CT with a focus on studies in the last five years. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 2)'.

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Applying multivariate analysis to X-ray diffraction computed tomography: the study of medieval applied brocades

Abstract: X-ray diffraction tomography is a well-developed technique to study the structure of heterogeneous materials which makes it a tool of choice for the non-destructive investigation of cultural heritage microsamples. Characterizing...

Cited by 7 publications

References 32 publications

A compact-rigid multi-analyser for energy and angle filtering of high-resolution X-ray experiments. Part 2. Efficiency of a single-crystal-comb

A compact-rigid multi-analyser for energy and angle filtering of high-resolution X-ray experiments. Part 2. Efficiency of a single-crystal-comb

Neural networks for rapid phase quantification of cultural heritage X-ray powder diffraction data

Recent developments in X-ray diffraction/scattering computed tomography for materials science

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