Preparation and Characterization of Carbon Filaments

Abstract

The final report for our contract 'Preparation and Characterization of Carbon Filaments' is presented. We have performed important and extensive research on Catalytic Chemical Vapor deposited fibers with comparative work on ex-polymer fibers. A new diagnostic technique has been developed involving low frequency electrical noise in carbon fibers. The exciting results from this technique infer microstructure properties as inter-ribbon and inter-layers are probed. Mechanical properties including Young's modulus, torsional modulus and compressive properties have been measured also on CCVD filaments. We have proved that Young's modulus of CCVD fibers generally decrease with diameter. However, increases in the Young's modulus of CCVD fibers generally decrease with diameter. However, increases in the Young's modulus for a small range of diameters may be present due to separating entities during the growth of CCVD fibers. Higher torsional moduli were also measured for these annular structures. Typical values were as high as one quarter theoretical maximum for a perfectly ordered system. Temperature dependent piezoresistance was observed for the first time in high moduli fibers.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1991
Accession Number
ADA235633

Entities

People

  • Carol Mcconica
  • Dinesh K. Patel

Organizations

  • Colorado State University

Tags

Communities of Interest

  • Advanced Electronics
  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Carbon Fibers
  • Ceramic Materials
  • Chemical Synthesis
  • Chemistry
  • Crystal Structure
  • Equations Of State
  • Material Degradation Processes
  • Materials
  • Materials Laboratories
  • Materials Processing
  • Materials Science
  • Materials Testing
  • Measurement
  • Mechanical Properties
  • Mechanical Working
  • Modulus Of Elasticity
  • Organic Chemistry

Fields of Study

  • Materials science

Readers

  • Mechanical Engineering/Mechanics of Materials.
  • Reinforced Composite Materials
  • Space/Atmospheric Physics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference