AI Article Synopsis
- Metallo-supramolecular self-assembly offers a promising method for creating economically valuable materials but faces challenges in characterizing complex mixtures efficiently.
- A large library of transition metals and ligands was robotically prepared and analyzed using advanced techniques, bypassing the need for individual structural assignments.
- This automated approach simplifies the assessment of self-assembly activities, enabling quicker and more efficient screening of supramolecular processes.
Article Abstract
Embracing complexity in design, metallo-supramolecular self-assembly presents an opportunity for fabricating materials of economic significance. The array of accessible supramolecules is alluring, along with favourable energy requirements. Implementation is hampered by an inability to efficiently characterise complex mixtures. The stoichiometry, size, shape, guest binding properties and reactivity of individual components and combinations thereof are inherently challenging to resolve. A large combinatorial library of four transition metals (Fe, Cu, Ni and Zn), and six β-diketonate ligands at different molar ratios and pH was robotically prepared and directly analysed over multiple timepoints with electrospray ionisation travelling wave ion mobility-mass spectrometry. The dataset was parsed for self-assembling activity without first attempting to structurally assign individual species. Self-assembling systems were readily categorised without manual data-handling, allowing efficient screening of self-assembly activity. This workflow clarifies solution phase supramolecular assembly processes without manual, bottom-up processing. The complex behaviour of the self-assembling systems was reduced to simpler qualities, which could be automatically processed.
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| http://dx.doi.org/10.1002/anie.202313892 | DOI Listing |
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