Investigating temperature-induced structural changes of lead halide perovskites by <i>in situ</i> X-ray powder diffraction

Caliandro, Rocco; Altamura, Davide; Belviso, Benny Danilo; Rizzo, Aurora; Masi, Sofia; Giannini, Cinzia

doi:10.1107/s160057671901166x

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…MSA applied to X-ray measurements has started to develop in recent decades and is still a relatively novel field, as described in a recent review (Guccione et al, 2021). PCA, in particular, has been applied to both single-crystal and powder X-ray diffraction for in situ experiments (Lopresti et al, 2021;Conterosito et al, 2020;Palin et al, 2019;Matos et al, 2007;Guccione et al, 2018), and when combining different techniques such as XRPD and Raman spectroscopy (Urakawa et al, 2011) or XRPD and pair distribution function (PDF)/UV-Vis (Caliandro, Altamura et al, 2019;Caliandro, Toson et al, 2019). Concerning XRF, the use of MSA is already a consolidated practice.…”

Section: Multivariate Statistical Analysismentioning

confidence: 99%

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Lopresti¹,

Mangolini²,

Milanesio³

et al. 2022

J Appl Crystallogr

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In materials and earth science, but also in chemistry, pharmaceutics and engineering, the quantification of elements and crystal phases in solid samples is often essential for a full characterization of materials. The most frequently used techniques for this purpose are X-ray fluorescence (XRF) for elemental analysis and X-ray powder diffraction (XRPD) for phase analysis. In both methods, relations between signal and quantity do exist but they are expressed in terms of complex equations including many parameters related to both sample and instruments, and the dependence on the active element or phase amounts to be determined is convoluted among those parameters. Often real-life samples hold relations not suitable for a direct quantification and, therefore, estimations based only on the values of the relative intensities are affected by large errors. Preferred orientation (PO) and microabsorption (MA) in XRPD cannot usually be avoided, and traditional corrections in Rietveld refinement, such as the Brindley MA correction, are not able, in general, to restore the correct phase quantification. In this work, a multivariate approach, where principal component analysis is exploited alone or combined with regression methods, is used on XRPD profiles collected on ad hoc designed mixtures to face and overcome the typical problems of traditional approaches. Moreover, the partial or no known crystal structure (PONKCS) method was tested on XRPD data, as an example of a hybrid approach between Rietveld and multivariate approaches, to correct for the MA effect. Particular attention is given to the comparison and selection of both method and pre-process, the two key steps for good performance when applying multivariate methods to obtain reliable quantitative estimations from XRPD data, especially when MA and PO are present. A similar approach was tested on XRF data to deal with matrix effects and compared with the more classical fundamental-parameter approach. Finally, useful indications to overcome the difficulties of the general user in managing the parameters for a successful application of multivariate approaches for XRPD and XRF data analysis are given.

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Section: Multivariate Statistical Analysismentioning

confidence: 99%

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Lopresti¹,

Mangolini²,

Milanesio³

et al. 2022

J Appl Crystallogr

Self Cite

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“…In this context, covariance maps have been used to identify correlated features present in diffraction and spectroscopic profiles. Examples include investigations of temperature-induced phase changes in spin crossover materials by XRPD/Raman [36] perovskites by combining XRPD and pair distribution function (PDF) measurements [62], characterization of hybrid composite material known as Maya Blue performed by XRPD/UV-Vis [63] and metabolite-crystalline phase correlation in wine leaves by XRPD coupled with mass spectrometry and nuclear magnetic resonance [64].…”

Section: Kinetic Studies By Single or Multi-probe Experimentsmentioning

confidence: 99%

Multivariate Analysis Applications in X-ray Diffraction

et al. 2020

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Multivariate analysis (MA) is becoming a fundamental tool for processing in an efficient way the large amount of data collected in X-ray diffraction experiments. Multi-wedge data collections can increase the data quality in case of tiny protein crystals; in situ or operando setups allow investigating changes on powder samples occurring during repeated fast measurements; pump and probe experiments at X-ray free-electron laser (XFEL) sources supply structural characterization of fast photo-excitation processes. In all these cases, MA can facilitate the extraction of relevant information hidden in data, disclosing the possibility of automatic data processing even in absence of a priori structural knowledge. MA methods recently used in the field of X-ray diffraction are here reviewed and described, giving hints about theoretical background and possible applications. The use of MA in the framework of the modulated enhanced diffraction technique is described in detail.

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“…A further application of PCA to the XRPD and PDF data has recently been reported [10]. PCA output was compared with conventional structural analysis.…”

Section: Introductionmentioning

confidence: 99%

Principal Component Analysis (PCA) for Powder Diffraction Data: Towards Unblinded Applications

Chernyshov

Dovgaliuk²,

Dyadkin

et al. 2020

Crystals

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We analyze the application of Principal Component Analysis (PCA) for untangling the main contributions to changing diffracted intensities upon variation of site occupancy and lattice dimensions induced by external stimuli. The information content of the PCA output consists of certain functions of Bragg angles (loadings) and their evolution characteristics that depend on external variables like pressure or temperature (scores). The physical meaning of the PCA output is to date not well understood. Therefore, in this paper, the intensity contributions are first derived analytically, then compared with the PCA components for model data; finally PCA is applied for the real data on isothermal gas uptake by nanoporous framework γ –Mg(BH 4 ) 2 . We show that, in close agreement with previous analysis of modulation diffraction, the variation of intensity of Bragg lines and the displacements of their positions results in a series of PCA components. Every PCA extracted component may be a mixture of terms carrying information on the average structure, active sub-structure, and their cross-term. The rotational ambiguities, that are an inherently part of PCA extraction, are at the origin of the mixing. For the experimental case considered in the paper, the extraction of the physically meaningful loadings and scores can only be achieved with a rotational correction. Finally, practical recommendations for non-blind applications, i.e., what boundary conditions to apply for the the rotational correction, of PCA for diffraction data are given.

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Investigating temperature-induced structural changes of lead halide perovskites by in situ X-ray powder diffraction

Cited by 4 publications

References 43 publications

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Multivariate versus traditional quantitative phase analysis of X-ray powder diffraction and fluorescence data of mixtures showing preferred orientation and microabsorption

Multivariate Analysis Applications in X-ray Diffraction

Principal Component Analysis (PCA) for Powder Diffraction Data: Towards Unblinded Applications

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