Determination of the surface area of mesoporous silicates by X-ray diffraction patterns using partial least squares and multiple linear regressions

Mahani, Mohamad; Sepehrian, Hamid; ShaikhGhomi, Homa; Fasihi, Javad

doi:10.1080/02726351.2015.1085937

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…58 However, challenges arise with these approaches because of the small interaction volume between the two components relative to the bulk of the material and care must be taken during data interpretation. 45,57 Here, multiple linear regression (MLR) analysis was performed on the total scattering data, which has previously been applied to X-ray diffraction patterns to assess the surface area of mesoporous materials 59 and on X-ray uorescence spectrometry data. 60 The blend samples were tted using the two PDFs of the starting materials.…”

Section: Deciphering the Interfacementioning

confidence: 99%

Structural insights into hybrid immiscible blends of metal–organic framework and sodium ultraphosphate glasses

Chester,

Castillo-Blas,

Sajzew

et al. 2023

Chem. Sci.

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Section: Deciphering the Interfacementioning

confidence: 99%

Structural insights into hybrid immiscible blends of metal–organic framework and sodium ultraphosphate glasses

Chester,

Castillo-Blas,

Sajzew

et al. 2023

Chem. Sci.

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show abstract

“…54 However, challenges arise with these approaches because of the small interaction volume between the two components relative to the bulk of the material and care must be taken during data interpretation. 44,53 Here, multiple linear regression (MLR) analysis was performed on the total scattering data, which has previously been applied to X-ray diffraction patterns to assess the surface area of mesoporous materials 55 and on X-ray fluorescence spectrometry data. 56 The blend samples were fitted using the two PDFs of the starting materials.…”

Section: Deciphering the Interfacementioning

confidence: 99%

Synthesis and characterisation of metal-organic framework-inorganic glass hybrid blends

Chester,

Castillo-Blas,

Sajzew

et al. 2023

Preprint

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Recently, increased attention has been focused on amorphous metal-organic frameworks (MOFs) and, more specifically, MOF glasses, the first new glass category discovered since the 1970s. In this work, we explore the fabrication of a compositional series of hybrid blends that combine ZIF-62(Zn) glass and an inorganic glass, 30Na2O-70P2O5, in an effort to combine the chemical versatility of the MOF glass with the mechanical properties of the inorganic glass. We investigate the interfacial interactions between the two components using pair distribution function analysis and solid state NMR spectroscopy, and suggest potential interactions between the two phases. Thermal analysis of the blend samples indicated that they were less thermally stable than the starting materials, and had a Tg shifted relative to the pristine materials. Annular dark field scanning transmission electron microscopy, X-ray energy dispersive spectroscopy (EDS), nanoindentation and 31P NMR all indicated close mixing of the two phases, suggesting that immiscible blends had formed.

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“…BP neural network is suitable for analyzing sample data of SAW micro-pressure sensors. The model topology of the BP neural network consists of the output Layer, the input Layer, and the hidden Layer [19]. Fig.…”

Section: Network Trainingmentioning

confidence: 99%

Fitting analysis and research of measured data of SAW micro-pressure sensor based on BP neural network

Huang

et al. 2020

Measurement

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Determination of the surface area of mesoporous silicates by X-ray diffraction patterns using partial least squares and multiple linear regressions

Cited by 5 publications

References 23 publications

Structural insights into hybrid immiscible blends of metal–organic framework and sodium ultraphosphate glasses

Structural insights into hybrid immiscible blends of metal–organic framework and sodium ultraphosphate glasses

Synthesis and characterisation of metal-organic framework-inorganic glass hybrid blends

Fitting analysis and research of measured data of SAW micro-pressure sensor based on BP neural network

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