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Related Concept Videos

IR Spectrum Peak Intensity: Amount of IR-Active Bonds00:55

IR Spectrum Peak Intensity: Amount of IR-Active Bonds

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When infrared radiation is passed through a molecule, absorption occurs if the molecule's vibration leads to a substantial change in its bond dipole moment. Transitions between vibrational energy levels, typically corresponding to infrared frequencies (4000–400 cm−1), allow absorption if the vibration significantly alters the dipole moment, making the molecule infrared active. The molecular bonds have different stretching and bending vibrations, resulting in various peaks with...
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IR Spectrum Peak Broadening: Hydrogen Bonding01:23

IR Spectrum Peak Broadening: Hydrogen Bonding

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The vibrational frequency of a bond is directly proportional to its bond strength. As a result, stronger bonds vibrate at higher frequencies, while weaker bonds vibrate at lower frequencies. The stretching vibration of the strong O–H bond in alcohols and phenols (very dilute solution or gas phase) appears as a sharp peak at 3600–3650 cm−1.
However, the extent of hydrogen bonding influences the observed stretching frequency and band broadening. Intermolecular or intramolecular...
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IR Spectrum Peak Intensity: Dipole Moment01:20

IR Spectrum Peak Intensity: Dipole Moment

1.4K
The dipole moment of a bond is the product of the partial charge on either atom and the distance between them. Dipole moments influence the efficiency of IR absorption and the peak intensity. When a bond with a dipole moment is placed in an electric field, the direction of the field determines if the bond is compressed or stretched. Electromagnetic radiation consists of an electric field component that rapidly reverses direction. It follows that polar bonds are alternately stretched and...
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IR Spectrum Peak Splitting: Symmetric vs Asymmetric Vibrations01:08

IR Spectrum Peak Splitting: Symmetric vs Asymmetric 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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Expected Value01:15

Expected Value

7.4K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
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The Electromagnetic Spectrum02:37

The Electromagnetic Spectrum

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The electromagnetic spectrum consists of all the types of electromagnetic radiation arranged according to their frequency and wavelength. Each of the various colors of visible light has specific frequencies and wavelengths associated with them, and you can see that visible light makes up only a small portion of the electromagnetic spectrum. Because the technologies developed to work in various parts of the electromagnetic spectrum are different, for reasons of convenience and historical...
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High-throughput Screening for Broad-spectrum Chemical Inhibitors of RNA Viruses
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Spectrum adapted the expectation-maximization algorithm for high-throughput peak shift analysis.

Tarojiro Matsumura1, Naoka Nagamura2,3, Shotaro Akaho4

  • 1Research Center for Computational Design of Advanced Functional Materials (CD-FMat), National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan.

Science and Technology of Advanced Materials
|July 6, 2019
PubMed
Summary
This summary is machine-generated.

A new spectrum-adapted expectation-maximization (EM) algorithm offers rapid, high-throughput analysis of spectral data. This method accurately detects peak shifts in materials like graphene and MoS2, enabling faster scientific discovery.

Keywords:
502 Electron spectroscopy60 New topics / OthersEM algorithmXPS analysismachine learningpeak separationspectral data

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Area of Science:

  • Materials Science
  • Spectroscopy
  • Computational Chemistry

Background:

  • High-throughput analysis of spectral datasets is crucial for materials characterization.
  • Existing methods may face challenges with large data volumes and computational time.

Purpose of the Study:

  • To develop a novel spectrum-adapted expectation-maximization (EM) algorithm for efficient spectral data analysis.
  • To evaluate the algorithm's accuracy and speed for analyzing large spectral datasets.

Main Methods:

  • Introduced a spectrum-adapted EM algorithm incorporating intensity weights based on measurement energy steps.
  • Applied the algorithm to synthetic data for performance evaluation.
  • Tested the algorithm on spectral data from graphene and MoS2 field-effect transistors.

Main Results:

  • The algorithm processed spectral data in under 13.4 seconds per set.
  • Successfully identified systematic peak shifts in C 1s (graphene) and S 2p (MoS2) spectral peaks.
  • Demonstrated high accuracy in analysis of synthetic and real-world spectral data.

Conclusions:

  • The proposed EM algorithm enables high-speed processing of large spectral datasets.
  • Provides stable and automatic calculations for reliable peak shift investigation.
  • Supports efficient characterization of materials like graphene and MoS2.