Literature DB >> 17326603

Principal component analysis of X-ray diffraction patterns to yield morphological classification of brucite particles.

Charlene R S Matos1, Maria José Xavier, Ledjane S Barreto, Nivan B Costa, Iara F Gimenez.   

Abstract

Principal component analysis was applied to XRD data from a series of Mg(OH)2 samples prepared under different hydrothermal conditions from bischofite (MgCl2.6H2O) and carnallite (KCl.MgCl2.6H2O), owing to differences in full width at half-maximum (fwhm) as well as in the intensity ratio I001/I101 of the respective diffraction peaks. According to the PCA results, the four principal components are able to explain 93% of the total variance and the samples can be classified into four main groups. For instance, the principal component PC1 can be interpreted as the crystallite size along the 101 direction since it is related to the fwhm of this peak. On the other hand, PC3 is related to orientation effects along 001 and 101 directions as it is dominated by the relative intensities of the two peaks. Finally, a comparison of the scanning electron microscopy images of the samples classified in each group revealed that in most of the cases a distinct morphology predominates within each group, which can be explained on the basis of the brucite growth mechanism.

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Year:  2007        PMID: 17326603     DOI: 10.1021/ac061991n

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  4 in total

Review 1.  Colorimetric Sensor Arrays for the Detection and Identification of Chemical Weapons and Explosives.

Authors:  Michael J Kangas; Raychelle M Burks; Jordyn Atwater; Rachel M Lukowicz; Pat Williams; Andrea E Holmes
Journal:  Crit Rev Anal Chem       Date:  2016-09-16       Impact factor: 6.535

Review 2.  Adaptive responses of sterically confined intramolecular chalcogen bonds.

Authors:  Karuthapandi Selvakumar; Harkesh B Singh
Journal:  Chem Sci       Date:  2018-07-25       Impact factor: 9.825

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

Authors:  Mattia Lopresti; Beatrice Mangolini; Marco Milanesio; Rocco Caliandro; Luca Palin
Journal:  J Appl Crystallogr       Date:  2022-07-05       Impact factor: 4.868

4.  A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns.

Authors:  Jin-Woong Lee; Woon Bae Park; Jin Hee Lee; Satendra Pal Singh; Kee-Sun Sohn
Journal:  Nat Commun       Date:  2020-01-03       Impact factor: 14.919

  4 in total

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