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Molecular Spectroscopy: Absorption and Emission01:14

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Molecules possess discrete energy levels called quantum states. Unlike atoms, which have simpler energy levels, molecules possess additional rotational and vibrational energy levels.  Each energy level is separated by an energy gap, with the gaps between adjacent electronic, vibrational, and rotational levels varying significantly. The three types of energy levels in a diatomic molecule are shown in Figure 1.
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Upon ionization, aromatic compounds generate a molecular ion that is observed as a prominent peak in their mass spectra. For example, the molecular ion peak for benzene appears at a mass-to-charge ratio of 78, while toluene is observed at a mass-to-charge ratio of 92. The molecular ion benzene is highly stable and does not readily undergo further fragmentation due to the significant amount of energy required to disrupt the aromatic stability of the benzene ring. In contrast, the molecular ion...
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A covalently bonded heteronuclear diatomic molecule can be modeled as two vibrating masses connected by a spring. The vibrational frequency of the bond can be expressed using an equation derived from Hooke's law, which describes how the force applied to stretch or compress a spring is proportional to the displacement of the spring. In this case, the atoms behave like masses, and the bond acts like a spring.
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IR Spectroscopy: Molecular Vibration Overview01:24

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When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
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Identical bonds within a polyatomic group can stretch symmetrically (in-phase) or asymmetrically (out-of-phase). Similar to hydrogen bonding, these vibrations also influence the shape of the IR peak. Generally, asymmetric stretching frequencies are higher than symmetric stretching frequencies. For example, primary amines exhibit two distinct IR peaks between 3300–3500 cm−1 corresponding to the symmetric and asymmetric N-H stretching, while secondary amines exhibit a single...
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In Ultraviolet–Visible (UV–Vis) spectroscopy, the absorption of electromagnetic radiation is used to probe the electronic structure of molecules. This technique provides insights into molecular electronic transitions, particularly the movement of electrons between different molecular orbitals. Radiation is absorbed if the energy of the electromagnetic radiation passing through the molecule is precisely equal to the energy difference between the excited and ground states. During this...
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Measurement and Analysis of Atomic Hydrogen and Diatomic Molecular AlO, C2, CN, and TiO Spectra Following Laser-induced Optical Breakdown
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Spectroscopy learning: A machine learning method for study diatomic vibrational spectra including dissociation

Shanshan Long1, Jia Fu1, Jun Jian1

  • 1College of Science, Xihua University, Chengdu 610039, China.

Methodsx
|November 30, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a Spectroscopy Learning Method, combining data and models to predict molecular vibrational spectra and dissociation energies using heat capacity. The approach accurately predicts spectra for CO and Br2 systems.

Keywords:
Dissociation energyMachine learningSpectral prediction

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Area of Science:

  • Physical Chemistry
  • Quantum Chemistry
  • Computational Chemistry

Background:

  • Molecular spectroscopy is crucial for atomic-level physical and chemical studies.
  • Accurate prediction of vibrational spectra, especially near dissociation energy, remains challenging.
  • Previous work utilized the Variational Algebraic Method for ro-vibrational spectra prediction.

Purpose of the Study:

  • To develop a more rigorous, combined model-driven and data-driven machine learning approach for molecular spectroscopy.
  • To predict reliable vibrational spectra and dissociation energy using readily available data like heat capacity.
  • To validate the enhanced method's reliability using diatomic systems like CO and Br2.

Main Methods:

  • Expansion of the Variational Algebraic Method into a Spectroscopy Learning Method (SLM).
  • Integration of model-driven and data-driven machine learning techniques.
  • Utilizing diverse data sources, including heat capacity, for spectral prediction.

Main Results:

  • Successful prediction of reliable vibrational spectra and dissociation energy.
  • Demonstrated accuracy of the Spectroscopy Learning Method for ground states of CO and Br2.
  • Validation of the method's effectiveness by correlating heat capacity data with spectral properties.

Conclusions:

  • The Spectroscopy Learning Method offers a robust approach for predicting molecular vibrational spectra and dissociation energies.
  • Heat capacity serves as a viable data source for enhancing spectral predictions.
  • The method provides a reliable tool for computational spectroscopy, particularly for challenging systems near dissociation.