Synthesis, structure and characterization of ZrPOF-DEA, a microporous zirconium phosphate framework material

Kubli, Martin; Šišak, Dubravka; Baerlocher, Christian; McCusker, Lynne B.; Liu, Lei; Li, Jinping; Dong, Jinxiang

doi:10.1016/j.micromeso.2012.06.042

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…At 300 °C, most of the material has lost any long-range order. The temperature is significantly lower than reported for zirconium phosphates with 3D connected frameworks, 21,22 which are stable up to 410 °C.…”

Section: ■ Resultsmentioning

confidence: 55%

“…Previously, we have shown how ionothermal synthesis can be performed in the zirconium phosphate system. We used deep eutectic solvents (DES), ,,− consisting of urea or oxalic acid and quaternary ammonium salts. The structure-directing role of the quaternary ammonium cation could be observed in the oxalic-acid-based DES synthesis of ZrPOF-EA, a novel open-framework zirconium phosphate synthesized in the presence of ethylammonium ions.…”

Section: Introductionmentioning

confidence: 99%

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Ionothermal Synthesis and Structure of a New Layered Zirconium Phosphate

et al. 2015

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A new layered zirconium phosphate material has been synthesized ionothermally using N-ethylpyridinium (Epy) bromide as both the solvent and the template, and its structure has been solved from synchrotron X-ray powder diffraction data using the charge-flipping routine implemented in Superflip. Rietveld refinement coupled with difference electron density map analysis was used to locate the organic cations between the layers. In the final stages of refinement, it became clear that not only ethylpyridinium but also pyridinium ions were present between the zirconium phosphate layers. These findings were then corroborated using elemental analysis, TGA, and solid-state (13)C CP/MAS NMR data.

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Section: ■ Resultsmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Ionothermal Synthesis and Structure of a New Layered Zirconium Phosphate

et al. 2015

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“…For example, phase information derived from high-resolution transmission electron microscopy (HRTEM) images can be added in reciprocal space, or a structure envelope (Brenner et al, 1997) can be imposed on the electron density map in real space. Although most applications to date have involved inorganic materials, in particular microporous solids (Dong et al, 2007;Liu et al, 2009Liu et al, , 2011Massü ger et al, 2007;Sun et al, 2009;Xie et al, 2009;Kubli et al, 2012), some organic compounds have also been studied (Baerlocher, McCusker & Palatinus, 2007;Š išak, McCusker, Buckl et al, 2010). Because the program is relatively new, the selection of input parameter values for the powder charge-flipping (pCF) algorithm in Superflip has necessarily been rather subjective and arbitrary.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the input parameters for powder charge flipping

et al. 2012

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Over the past few years, the powder charge‐flipping algorithm has proved to be a useful one for structure solution from powder diffraction data, so a semi‐systematic study of the effect of the different input parameters on its success has been performed. Two data sets were studied in these tests: a zirconium phosphate framework material and D‐ribose. The Superflip input parameters tested were the reflection overlap factor, the intensity repartitioning frequency, the isotropic displacement parameter, the threshold for charge flipping and the number of cycles/runs. By varying the values of these parameters within sensible ranges, an optimized set could be found for the zirconium phosphate case, but no combination of parameters allowed the D‐ribose structure to be solved. Reasoning that starting with nonrandom phases might help, an approximate (but incorrect) structure was generated using the direct‐space global‐optimization method implemented in the program FOX. This structure was then used to calculate initial phase sets for Superflip by allowing the calculated phases to vary in a random fashion by a user‐defined percentage. With such phases and reoptimized input parameters, some fully interpretable solutions with the correct symmetry could be produced, even with fairly low resolution data. Unfortunately, it was not possible to recognize these solutions using the Superflip R values, so other criteria were sought. Both cluster analyses and maximum entropy calculations of the solutions were performed, and the latter, in particular, look very promising. A set of guidelines derived from these two structures could be applied successfully to a further two inorganic and seven organic structures.

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