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.

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 linescanning vibrational sum frequency generation (VSFG) hyperspectral imaging technique in combination with neural network analysis. Rapid VSFG imaging enables polarization resolved images on a single sheet level to be measured within a short time period, circumventing technical challenges due to long term optical setup 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 techniques. This unique combination of both novel techniques offers a new pathway to resolve molecular level structural knowledge of self-assembled materials. Understanding these properties can further drive self-assembly design from a bottom-up approach for applications in biomimetic and drug delivery researches.

Physical properties are commonly represented by tensors, such as optical susceptibilities. Conventional approach of deriving non-vanishing tensor elements of symmetric systems relies on the intuitive consideration of positive/negative sign flipping after symmetry operations, which could be tedious and prone to miscalculation. Here we present a matrix-based approach which gives a physical picture centered on Neumann's principle. The principle states that symmetries in geometric systems are adopted by their physical properties. We mathematically apply the principle to the tensor expressions and show a procedure with clear physical intuitions to derive non-vanishing tensor elements based on eigensystems. The validity of the approach is demonstrated by examples on commonly known 2nd and 3rd-order nonlinear susceptibilities of chiral/achiral surfaces, together with complicated scenarios involving symmetries such as and symmetries. We then further applied this method to higher-rank tensors that are useful for 2D and high-order spectroscopy. We also extended our approach to derive nonlinear tensor elements with magnetization, critical for measuring spin polarization on surfaces for quantum information technologies. A Mathematica code based on this generalized approach is included that can be applied to any symmetry and higher order nonlinear processes.

Linescanning vibrational sum-frequency generation (VSFG) hyperspectral microscopy was developed into an inverted microscope design. The geometry enables seamless collection of brightfield, second-harmonic generation (SHG), and VSFG images of a given sample area. The new vertical configuration also enables future application to biologically relevant environments. The instrument is capable of simultaneously reporting on spatially resolved chemical and geometric specific sample characteristics. This capability is demonstrated with three samples: lyophilized collagen, a molecular self-assembly of sodium dodecyl sulfate and -cyclodextrin (SDS@2-BCD), and a L-phenylalanyl-L-phenylalanine (FF) self-assembly. Hyperspectral analysis showed that the FF samples have anisotropic structural alignment, which is uniform along the long axis and structurally evolving along the short radial axis. Because all three samples represent protein and molecular hierarchically organized materials in the biomaterial and biomimetic fields, this work highlights the chemical-physical information VSFG microscopy can reveal to help in the bottom-up design and characterization of biomaterials.

This paper aims to disclose the law of fish migration trajectories at different water depths. For this purpose, the grass carps in a reservoir in southwestern China were taken as the targets, outdoor experiments were performed to monitor their behaviours and environmental factors in the reservoir. Then, the Hydroacoustic Technology, Inc. (HTI) acoustic tracking system and backpropagation neural network (BPNN) were introduced to simulate and analyse the migration of the fish in the natural state. Meanwhile, the vertical distribution of fish was discussed at different temperatures and dissolved oxygen contents. The results show that the BPNN algorithm has a good fitting effect on the planar migration trajectories of the fish, but fails to achieve a desirable fitting result concerning the migration trajectories in the Z direction. Fortunately, the fitting effect of migration trajectories was greatly enhanced by normalization. The fish were distributed differently in spring and summer across the different water depths, under the influence of water temperature and dissolved oxygen content. Overall, the fish obeyed the normal distribution in the vertical direction, and selected water depth mainly based on dissolved oxygen content. The research findings lay a scientific basis for fish resource protection, river ecology assessment and water environment restoration.