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Related Concept Videos

Polymer Classification: Stereospecificity01:26

Polymer Classification: Stereospecificity

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Polymerization generates chiral centers along the entire backbone of a polymer chain. Accordingly, the stereochemistry of the substituent group has a significant effect on polymer properties. Polymers formed from monosubstituted alkene monomers feature chiral carbons at every alternate position in the polymer backbone. Relative to the predominant orientation of substituents at the adjacent chiral carbons, the polymer can exist in three different configurations: isotactic, syndiotactic, and...
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Unlike ionic or small covalent molecules, polymers do not form crystalline solids due to the diffusion limitations of their long-chain structures. However, polymers contain microscopic crystalline domains separated by amorphous domains.
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Polymers: Molecular Weight Distribution01:10

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For any given polymer, the weight average molecular weight (Mw) is higher than, if not equal to, the number average molecular weight (Mn). The only situation in which the weight average molecular weight and the number average molecular weight are equal is when a polymer consists only of chains with equal molecular weight. However, this never happens in a synthetic polymer, since it is difficult to control the polymerization process up to a molecular level with accuracy to a hundred percent.
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Polymer Classification: Architecture01:14

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Polymers are classified as linear or branched on the basis of their chain architecture. The polymer chains in linear polymers have a long chain-like structure with minimal to no branching at all. Even if a polymer features large substituent groups on the monomer, which appear as branches to the skeleton, it is not considered a branched polymer. A branched polymer contains secondary polymer chains that arise from the main polymer chain. The branching occurs when the polymer growth shifts from...
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Polymers: Defining Molecular Weight01:01

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Unlike small molecules with definite molecular weights, polymers are a mixture of individual polymer chains of varying lengths, each with a unique molecular weight.  So, the molecular weight of a polymer is expressed as an average value based on the average size of the polymer chains. The two most common forms of averages used for polymers are the number average molecular weight and weight average molecular weight.
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Organic compounds with conjugated double bonds show strong absorption features in the UV–visible region of the electromagnetic spectrum attributed to π → π* electronic excitations. Generally, a UV–vis absorption spectrum is recorded as a plot of absorbance vs wavelength. The wavelength of maximum absorbance, which manifests as a peak in the absorption spectrum, is denoted as λmax.
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A Component Content Measurement Method Modified Using Indirect Hard Modeling for Polymer Blends Based on Raman

Linlin Huang1,2,3, Yuan Fang1,2,3, Zenan Lin1,2,3

  • 1National Engineering Research Center of Novel Equipment for Polymer Processing, 26467South China University of Technology, Guangzhou, China.

Applied Spectroscopy
|January 27, 2022
PubMed
Summary

This study introduces a modified indirect hard modeling (IHM) method for accurate polymer blend component analysis using Raman spectroscopy. The IHM approach requires fewer training samples than traditional soft modeling, saving materials and time.

Keywords:
2D-COSComponent contentIHMRaman spectroscopyVoigt functioncharacteristic peaksindirect hard modelingpolymer blendssoft modelingtwo-dimensional correlation spectroscopy

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Area of Science:

  • Analytical Chemistry
  • Materials Science
  • Spectroscopy

Background:

  • Raman spectroscopy is valuable for polymer blend component analysis.
  • Current soft modeling methods require extensive training data, leading to material and time inefficiencies.

Purpose of the Study:

  • To develop a more efficient method for measuring component content in polymer blends using Raman spectroscopy.
  • To reduce the number of training samples needed for quantitative analysis.

Main Methods:

  • A modified indirect hard modeling (IHM) method was employed.
  • Two-dimensional correlation spectroscopy was used to screen characteristic spectral parameters.
  • Parameterized spectral models were built and used to establish a linear regression model for content prediction.

Main Results:

  • The IHM model achieved a high coefficient of determination (R² = 0.9931) and low root mean squared error (0.4367 wt%).
  • The IHM method demonstrated superior prediction accuracy compared to partial least squares and artificial neural network methods.
  • Fewer training samples were required for the IHM model.

Conclusions:

  • The modified IHM method offers a highly accurate and efficient approach for quantitative analysis of polymer blends via Raman spectroscopy.
  • This method overcomes the limitations of traditional soft modeling techniques by reducing sample requirements.