ArticleThe journal of physical chemistry. B2022
Imaging Orientation of a Single Molecular Hierarchical Self-Assembled Sheet: The Combined Power of a Vibrational Sum Frequency Generation Microscopy and Neural Network.
Article in The journal of physical chemistry. B, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed.
- Nanoscale-Sensitive VSFG Microscopy for Identifying Breast Tumor in FFPE Samples.Chemical & biomedical imaging · 2026Article
- Mode-Specific Coherent Interference of Vibrational Sum-Frequency Generation Imaging: An Approach to Differentiate Lung Tumors through Collagen Interfibrillar Distances.Journal of the American Chemical Society · 2025Article
- Spiral packing and chiral selectivity in model membranes probed by phase-resolved sum-frequency generation microscopy.Nature communications · 2024Article
- Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy.Journal of visualized experiments : JoVE · 2023Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
In this work, we determined the tilt angles of molecular units in hierarchical self-assembled materials on a single-sheet level, which were not available previously. This was achieved by developing a fast line-scanning vibrational sum frequency generation (VSFG) hyperspectral imaging technique in combination with neural network analysis. Rapid VSFG imaging enabled polarization resolved images on a single sheet level to be measured quickly, circumventing technical challenges due to long-term optical instability. The polarization resolved hyperspectral images were then used to extract the supramolecular tilt angle of a self-assembly through a set of spectra-tilt angle relationships which were solved through neural network analysis. This unique combination of both novel techniques offers a new pathway to resolve molecular level structural information on self-assembled materials. Understanding these properties can further drive self-assembly design from a bottom-up approach for applications in biomimetic and drug delivery research.
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Registered trials
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