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ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Predicting particle quality attributes of organic crystalline materials using Particle Informatics

Authors: Simone, Elena; Prandini, Emilia; Parisi, Emmanuele; Calì, Eleonora; Maloney, Andrew;

Predicting particle quality attributes of organic crystalline materials using Particle Informatics

Abstract

Dataset related to the publication: "Predicting particle quality attributes of organic crystalline materials using Particle Informatics" published in Powder Technology (Volume 443, 1 July 2024, 119927Volume 443, 1 July 2024, 119927). In this work, a novel quercetin solvate of dimethylformamide (QDMF) was studied. The crystal structure was solved using single crystal X-ray diffraction and analysed using synthon analysis and other particle informatics tools (e.g., solvate analyser). The thermal behaviour and thermodynamic stability of QDMF were studied experimentally using Raman spectroscopy, ATR-FTIR spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. A clear relationship between the two-step desolvation behaviour of QDMF and the type, strength, and directionality of the main bulk synthons characterizing the QDMF structure was observed. Additionally, the attachment energy model was used to predict the QDMF morphology, together with facet-specific topology and chemical nature of each of the dominant {001}, {110}, and {200} facets. The {200} facet was found to be significantly rougher than the other two; whereas, the {110} was characterized by a higher percentage of exposed DMF molecules compared to the other two facets. Specific scanning electron microscopy and contact angle measurements were used to experimentally detect differences among the three facets and validate the modelling results.

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average