Controllable hierarchical self-assembly: systematic study forming metallosupramolecular frameworks on the basis of helical beta-oligoamides

West, Norton G.; Bamford, Sarah E.; Pigram, Paul J.; Pan, Jisheng; Qi, Dong-Chen; Mechler, Adam

doi:10.1039/d3mh01327h

Mater. Horiz.

2023

DOI: 10.1039/d3mh01327h

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Controllable hierarchical self-assembly: systematic study forming metallosupramolecular frameworks on the basis of helical beta-oligoamides

Norton G. West,

Sarah E. Bamford,

Paul J. Pigram

et al.

Abstract: Metallosupramolecular frameworks are high density two dimensional nanomaterials based on head-to-tail supramolecular assembly of helical units of substituted oligoamides into nanorods that are then further crosslinked by metal coordination.

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Cited by 3 publications

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Peptide-coordination self-assembly: Supramolecular design and biomedical applications

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Light emissions of ratiometric aliphatic redox polymer from canonical, anion, and anion-aggregate: Reduction-associated naked eye detections of Hg(II), Fe(III), and Cu(II)

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Hussain Sanfui

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Self-Organizing Maps for Secondary Ion Mass Spectrometry

Bamford,

Gardner,

Winkler

et al. 2024

J. Am. Soc. Mass Spectrom.

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Secondary ion mass spectrometry (SIMS) is a powerful analytical technique for characterizing the molecular and elemental composition of surfaces. Individual mass spectra can provide information about the mean surface composition, while spatial mapping can elucidate the spatial distributions of molecular species in 2D and 3D with no prior labeling of molecular targets. The data sets produced by SIMS techniques are large and inherently complex, often containing subtle relationships between spatial and molecular features. Machine learning algorithms are well suited to exploring this complexity, making them ideal for data analysis, interpretation, and visualization of SIMS data sets. One such algorithm, the self-organizing map (SOM), is particularly well suited to clustering similar samples and reducing the dimensionality of hyperspectral data sets. Here, we present an introduction to the SOM, a concise mathematical description, and recent examples of its use in SIMS and other related mass spectrometry techniques. These examples demonstrate how SOMs may be used to interpret high volumes of individual mass spectra, imaging, or depth profiling data sets. This review will be useful for specialists in SIMS and other mass spectral techniques seeking to explore self-organizing maps for data analysis.

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Controllable hierarchical self-assembly: systematic study forming metallosupramolecular frameworks on the basis of helical beta-oligoamides

Abstract: Metallosupramolecular frameworks are high density two dimensional nanomaterials based on head-to-tail supramolecular assembly of helical units of substituted oligoamides into nanorods that are then further crosslinked by metal coordination.

Cited by 3 publications

References 44 publications

Peptide-coordination self-assembly: Supramolecular design and biomedical applications

Peptide-coordination self-assembly: Supramolecular design and biomedical applications

Light emissions of ratiometric aliphatic redox polymer from canonical, anion, and anion-aggregate: Reduction-associated naked eye detections of Hg(II), Fe(III), and Cu(II)

Self-Organizing Maps for Secondary Ion Mass Spectrometry

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