Real-Time Observation of “Soft” Magic-Size Clusters during Hydrolysis of the Model Metallodrug Bismuth Disalicylate

Szczerba, Daniel; Tan, Davin; Do, Jean‐Louis; Mouhtadi, Siham; Chaumont, Denis; Lucas, M.C. Marco de; Geoffroy, Nicolas; Meyer, Michel; Rousselin, Yoann; Hudspeth, Jessica; Schwanen, Valérie; Spoerk-Erdely, Petra; Dippel, Ann‐Christin; Ivashko, Oleh; Gutowski, Olof; Glaevecke, Philipp; Bazhenov, Vasilii V.; Arhangelskis, Mihails; Halász, Ivan; Friščić, Tomislav; Kimber, Simon A. J.

doi:10.1021/jacs.1c07186

Cited by 6 publications

(6 citation statements)

References 47 publications

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“…We caution future investigators that all solvents were used directly from the 'bench', and that we previously discovered strong water-content effects, investigating solvolysis in the BiSal 2 system. 38 Finally, using ILAG with 1-2% of NH 4 NO 3 further minimizes the amount of leover Bi 2 O 3 , and repeatably yields good results. We also implemented the heating of the jars with reaction mixture to ensure reproducibility.…”

Section: Bismuth Gallatementioning

confidence: 96%

Mechanochemical synthesis of bismuth active pharmaceutical ingredients, bismuth(iii) gallate and bismuth citrate

Szczerba,

Do,

Tan

et al. 2024

RSC Mechanochem.

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Section: Bismuth Gallatementioning

confidence: 96%

Mechanochemical synthesis of bismuth active pharmaceutical ingredients, bismuth(iii) gallate and bismuth citrate

Szczerba,

Do,

Tan

et al. 2024

RSC Mechanochem.

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“…Therefore, Bi 38 is certainly a targeted node since it is more broadly related to tetravalent metal-oxo clusters (Zr/Hf/Ce/Th/U/Np/Pu IV ), ,− as well as topologically and compositionally similar metal oxides and viable MOF families. Intriguingly, Bi 38 was also recently described as a major species in Pepto Bismol via X-ray scattering studies.…”

Section: Introductionmentioning

confidence: 93%

Bismuth-Polyoxocation Coordination Networks: Controlling Nuclearity and Dimension-Dependent Photocatalysis

Amiri

Lulich

Chiu

et al. 2023

ACS Appl. Mater. Interfaces

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Bismuth-oxocluster nodes for metal−organic frameworks (MOFs) and coordination networks/polymers are less prolific than other families featuring zinc, zirconium, titanium, lanthanides, etc. However, Bi 3+ is non-toxic, it readily forms polyoxocations, and its oxides are exploited in photocatalysis. This family of compounds provides opportunity in medicinal and energy applications. Here, we show that Bi node nuclearity depends on solvent polarity, leading to a family of Bi x -sulfonate/carboxylate coordination networks with x = 1−38. Larger nuclearity-node networks were obtained from polar and strongly coordinating solvents, and we attribute the solvent's ability to stabilize larger species in solution. The strong role of the solvent and the lesser role of the linker in defining node topologies differ from other MOF syntheses, and this is due to the Bi 3+ intrinsic lone pair that leads to weak node−linker interactions. We describe this family by single-crystal X-ray diffraction (eleven structures), obtained in pure forms and high yields. Ditopic linkers include NDS (1,5naphthalenedisulfonate), DDBS (2,2′-[biphenyl-4,4′-diylchethane-2,1-diyl] dibenzenesulphonate), and NH 2 -benzendicarboxylate (BDC). While the BDC and NDS linkers yield more open-framework topologies that resemble those obtained by carboxylate linkers, topologies with DDBS linkers appear to be in part driven by association between DDBS molecules. An in situ small-angle X-ray scattering study of Bi 38 -DDBS reveals stepwise formation, including Bi 38 -assembly, pre-organization in solution, followed by crystallization, confirming the less important role of the linker. We demonstrate photocatalytic hydrogen (H 2 ) generation with select members of the synthesized materials without the benefit of a co-catalyst. Band gap determination from X-ray photoelectron spectroscopy (XPS) and UV−vis data suggest the DDBS linker effectively absorbs in the visible range with ligand-to-Bi-node charge transfer. In addition, materials containing more Bi (larger Bi 38 -nodes or Bi 6 inorganic chains) exhibit strong UV absorption, also contributing to effective photocatalysis by a different mechanism. All tested materials became black with extensive UV−vis exposure, and XPS, transmission electron microscopy, and X-ray scattering of the black Bi 38 -framework suggest that Bi 0 is formed in situ, without phase segregation. This evolution leads to enhanced photocatalytic performance, perhaps due to increased light absorption.

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“…In recent years PDF analysis has been shown to be a powerful technique for characterization of disordered materials (Christiansen et al, 2020b;Yang et al, 2013;Billinge & Kanatzidis, 2004;Keen & Goodwin, 2015), amorphous materials (Christiansen et al, 2020a;Juelsholt et al, 2021;Bennett & Cheetham, 2014), clusters in solution (Anker et al, 2021;Jensen et al, 2016;Szczerba et al, 2021) and nanomaterials (Billinge & levin, 2007;Cooper et al, 2020) where conventional crystallographic approaches are challenged (Billinge & Kanatzidis, 2004;Keen & Goodwin, 2015).…”

Section: Introductionmentioning

confidence: 99%

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning

Anker,

Kjær,

Juelsholt

et al. 2024

J Appl Crystallogr

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Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

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Real-Time Observation of “Soft” Magic-Size Clusters during Hydrolysis of the Model Metallodrug Bismuth Disalicylate

Cited by 6 publications

References 47 publications

Mechanochemical synthesis of bismuth active pharmaceutical ingredients, bismuth(iii) gallate and bismuth citrate

Mechanochemical synthesis of bismuth active pharmaceutical ingredients, bismuth(iii) gallate and bismuth citrate

Bismuth-Polyoxocation Coordination Networks: Controlling Nuclearity and Dimension-Dependent Photocatalysis

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning

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