ChemSpecNet: A Neural Network for chemical analysis of Sum Frequency Generation Spectroscopic imaging

Shah, Shishir K.; Pikalov, Aleksandr; Baldelli, Steven

doi:10.1016/j.optcom.2021.127691

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…By calculating the mean squared errors of the predicted normalized susceptibility values and the experimentally determined ones, the smallest mean squared error we can obtain is 0.02, whose corresponding predicted tilt angle away from the lab frame z -axis is 23 ± 1.5° (Supporting Information Table S2 and Figure c). To the best of our knowledge, this is the first work using neural network to relate VSFG spectral observables back to physical properties of molecules, while other studies involving machine learning in the SFG field mainly focus on assisting peak fitting/assignment and sole spectral analysis. − …”

Section: Resultsmentioning

confidence: 99%

Imaging Orientation of a Single Molecular Hierarchical Self-Assembled Sheet: The Combined Power of a Vibrational Sum Frequency Generation Microscopy and Neural Network

Wagner

Wang

et al. 2022

J. Phys. Chem. B

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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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Section: Resultsmentioning

confidence: 99%

Imaging Orientation of a Single Molecular Hierarchical Self-Assembled Sheet: The Combined Power of a Vibrational Sum Frequency Generation Microscopy and Neural Network

Wagner

Wang

et al. 2022

J. Phys. Chem. B

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“…A second-order NLO mechanism called sum frequency generation (SFG) works by annihilating two input photons with each frequency

and

while simultaneously generating one photon with frequency

[ 210 ]. When imaging self-assembled thiol monolayers on gold using the SFG spectroscopic method, ANNs are utilized as a substitute for chemical identification [ 211 ]. ANNs are also particularly helpful for solving issues when it is difficult or impossible to provide realistic physical or mathematical models [ 212 ].…”

Section: Nlo Processes Analyzed With MLmentioning

confidence: 99%

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

et al. 2022

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The ability to interpret information through automatic sensors is one of the most important pillars of modern technology. In particular, the potential of biosensors has been used to evaluate biological information of living organisms, and to detect danger or predict urgent situations in a battlefield, as in the invasion of SARS-CoV-2 in this era. This work is devoted to describing a panoramic overview of optical biosensors that can be improved by the assistance of nonlinear optics and machine learning methods. Optical biosensors have demonstrated their effectiveness in detecting a diverse range of viruses. Specifically, the SARS-CoV-2 virus has generated disturbance all over the world, and biosensors have emerged as a key for providing an analysis based on physical and chemical phenomena. In this perspective, we highlight how multiphoton interactions can be responsible for an enhancement in sensibility exhibited by biosensors. The nonlinear optical effects open up a series of options to expand the applications of optical biosensors. Nonlinearities together with computer tools are suitable for the identification of complex low-dimensional agents. Machine learning methods can approximate functions to reveal patterns in the detection of dynamic objects in the human body and determine viruses, harmful entities, or strange kinetics in cells.

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ChemSpecNet: A Neural Network for chemical analysis of Sum Frequency Generation Spectroscopic imaging

Cited by 2 publications

References 30 publications

Imaging Orientation of a Single Molecular Hierarchical Self-Assembled Sheet: The Combined Power of a Vibrational Sum Frequency Generation Microscopy and Neural Network

Imaging Orientation of a Single Molecular Hierarchical Self-Assembled Sheet: The Combined Power of a Vibrational Sum Frequency Generation Microscopy and Neural Network

A Framework for Biosensors Assisted by Multiphoton Effects and Machine Learning

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