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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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Related Experiment Video

Updated: Nov 5, 2025

Integration of Animal Behavioral Assessment and Convolutional Neural Network to Study Wasabi-Alcohol Taste-Smell Interaction
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Physics-guided neural network for predicting chemical signatures.

Cara P Murphy, John P Kerekes

    Applied Optics
    |May 13, 2021
    PubMed
    Summary

    A new physics-guided neural network (PGNN) improves chemical residue classification accuracy by generating more accurate spectral data for training. This approach significantly boosts performance on spectroscopic sensing tasks.

    Area of Science:

    • Spectroscopy
    • Machine Learning
    • Chemical Sensing

    Background:

    • Accurate classification of trace chemical residues using active spectroscopic sensing is hindered by limited training data.
    • Existing physics-based models for generating training data often lack accuracy compared to real-world measurements.

    Purpose of the Study:

    • To develop a more accurate method for generating spectral libraries for training chemical classifiers.
    • To enhance the classification accuracy of trace chemical residues in spectroscopic sensing applications.

    Main Methods:

    • Developed a physics-guided neural network (PGNN) to predict chemical reflectance spectra.
    • Generated a spectral library using the PGNN for classifier training.
    • Compared classification accuracy using PGNN-generated data versus data from traditional physics-based models.

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    Main Results:

    • The PGNN demonstrated higher accuracy in predicting chemical reflectance than state-of-the-art physics-based models.
    • Using the PGNN-generated library, average classification accuracy increased from 0.623 to 0.813 on real chemical reflectance data.
    • The improved accuracy was observed even for chemicals not included in the PGNN training set.

    Conclusions:

    • Physics-guided neural networks offer a superior approach to generating training data for spectroscopic chemical sensing.
    • The PGNN method significantly enhances classifier performance, addressing limitations of traditional physics-based models.
    • This advancement holds promise for improving the reliability and accuracy of trace chemical detection.