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Related Concept Videos

Polymers02:34

Polymers

41.6K
The word polymer is derived from the Greek words “poly” which means “many” and “mer” which means “parts”. Polymers are long chains of molecules composed of repeating units of smaller molecules, known as monomers. They either occur naturally, such as DNA and proteins, or can be constructed synthetically, like plastics. They have varied structural characteristics, such as linear chains, branched chains, or complex networks, that contribute to the...
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Polymers02:34

Polymers

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Polymers: Molecular Weight Distribution01:10

Polymers: Molecular Weight Distribution

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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

Polymer Classification: Architecture

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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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Step-Growth Polymerization: Overview01:03

Step-Growth Polymerization: Overview

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Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
Many natural and synthetic polymers are produced by...
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Polymer Classification: Crystallinity01:21

Polymer Classification: Crystallinity

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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.
Crystalline domains are the regions where polymer chains are aligned in an orderly manner and held together in proximity by intermolecular forces. For example, chains in the crystalline domains of polyethylene and nylon are bound together by van der Waals...
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Polymer Microarrays for High Throughput Discovery of Biomaterials
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Polymer Informatics: Opportunities and Challenges.

Debra J Audus1, Juan J de Pablo2,3

  • 1Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, Maryland, 20899 USA.

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|December 5, 2017
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Summary
This summary is machine-generated.

Materials informatics accelerates new material development. However, polymer informatics faces challenges in data reporting, hindering advanced machine learning applications for polymer design.

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Area of Science:

  • Materials Science
  • Polymer Science
  • Computational Chemistry

Background:

  • Scientific data and computational advances are revolutionizing materials development.
  • Materials informatics is established for metals and oxides.
  • Polymer science lags due to unique data reporting challenges.

Purpose of the Study:

  • To highlight the opportunities and challenges of polymer informatics.
  • To discuss the integration of machine learning in polymer design.
  • To address data standardization needs in polymer science.

Main Methods:

  • Viewpoint discussion on current state of polymer informatics.
  • Analysis of data reporting practices in polymer science.
  • Identification of barriers to machine learning adoption.

Main Results:

  • Significant potential exists for polymer informatics.
  • Inconsistent data reporting is a major obstacle.
  • Standardized data is crucial for machine learning.

Conclusions:

  • Overcoming data challenges is key to realizing polymer informatics.
  • Advanced machine learning can drive innovation in polymer design.
  • Standardized reporting will enable data-driven polymer discovery.