journal article·1990-09

Nondestructive characterization of prepreg ageing using nuclear magnetic resonance techniques

E. Köller, G. Dobmann, W. Kuhn

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Abstract

Abstract The ageing of prepregs of and fiber reinforced duroplastic polymers (glassfiber and carbonfiber reinforced polymers; GFRP and CFRP) during storage before manufacturing and during the manufacturing process at elevated temperatures is an essential technical problem. The resulting mechanical properties of cured components are strongly influenced by the ageing state of the prepregs. Nondestructive techniques up to now are unsufficient or time consuming. Nuclear magnetic resonance (NMR) techniques have been well known for 40 years and applied in analytical chemistry in order to characterize the microstructure and binding state of organic chemical compounds in fluid states. In medical diagnostics NMR-imaging is used for tissue characterization. Publications on solid state NMR-imaging are rare in technical literature because of the fact that the strong spin-spin-interaction in solids results in a broadening of the spectral lines, i.e., very short transversal relaxation times, T2 (some μs), compared to fluids (up to some 100 ms). Therefore special electronics are needed in order to produce short pulses and to switch the gradient fields in short time periods. The emphasis of the following contribution will be on the presentation of first results on the application of NMR techniques at prepegs in order to characterize the crosslink state after exposition at room and elevated (50°C) temperature. The results correlate with results of destructive tests and document the potential of NMR as a NDT tool. The experiments were performed with a MSL-400 Bruker NMR-spectrometer and micro-imaging system which works at 400 MHz (1H resonance frequency at a magnetic field of 9.4 T).
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Research context

OpenAlex topics: NMR spectroscopy and applications · Advanced MRI Techniques and Applications · Spectroscopy and Chemometric Analyses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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