Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2021Large datasets of single carbon and glass fibre mechanical properties obtained with automated testing equipment5citations
  • 2021Single carbon and glass fibre properties characterised using large data sets obtained through automated single fibre tensile testing57citations
  • 2019Further Insights into the Fatigue of Hair Fibres through Statistical Analysis and Relevance to Hair Care Applicationscitations
  • 2019Further Insights into the Fatigue of Hair Fibres through Statistical Analysis and Relevance to Hair Care Applicationscitations

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Swolfs, Yentl
2 / 220 shared
Lomov, Stepan
2 / 67 shared
Leray, Yann
4 / 4 shared
Mesquita, Francisco
2 / 23 shared
Joannès, Sébastien
2 / 37 shared
Lunn, Rebecca
2 / 3 shared
Stringer, Daniel
2 / 2 shared
Islam, Faisal
1 / 6 shared
Chart of publication period
2021
2019

Co-Authors (by relevance)

  • Swolfs, Yentl
  • Lomov, Stepan
  • Leray, Yann
  • Mesquita, Francisco
  • Joannès, Sébastien
  • Lunn, Rebecca
  • Stringer, Daniel
  • Islam, Faisal
OrganizationsLocationPeople

document

Further Insights into the Fatigue of Hair Fibres through Statistical Analysis and Relevance to Hair Care Applications

  • Joannès, Sébastien
  • Lunn, Rebecca
  • Stringer, Daniel
  • Leray, Yann
  • Bucknell, Steve
Abstract

The study of fatigue is a well-established discipline within the field of material science.Understanding the fatigue of materialswhen subjected to repeated load cycles providesinsightful knowledge about fractureandfailuremechanisms.Such information hasbecomeessentialinthedevelopmentand manufacture of products ranging from aeroplanes to car tyres. The fatigue behaviour of fibres is well studied,encompassing bothtextileandtechnicalfibres.Themeritoffatiguetestinghasbeen recognised within the cosmetics industry and in recentyears there has been an increasingusageof cyclic fatigue testing within the hair care sector.Duringdailygroomingpractisesand routines,hairfibresontheheadareexposedtorepeated stresses which contribute to fibre fatigue. Although the applied stresses are much lower than what is requiredtobreakthehairinasingleapplication,overtime,thisleadstoanaccumulationof stress and eventual failure of the fibre.To study this, a repeated stress or strain can be applied to singlehairfibres until they break. Use of Dia-Stron CYC802 modulesin conjunction with anautomated platform allows for high-throughput cyclic fatigue testing of hair fibres. The aim of many cyclic fatigue studies is to make comparisons between different types of damage or to assess the performance of treatments. The number of cycles to break foreach test groupcan be fitted usingeither a Kaplan Meier Estimatora 2-parameter Weibull distribution.InordertoascertainaconfidencefortheestimatedWeibullparameters,itisimperativetocalculate theconfidence intervals[1, 2].ThiscanbeachievedbyapplyingaBootstrapmethodtogenerate confidenceboundsfortheexperimentaldata[3].Also,basedonthedistributionobtainedforthe Weibullparameters,statisticalcomparisonsbetweengroupscanbeconductedusing parametricor non-parametric methodssuch as Kolmogorov Smirnov tests[4]. These statistical analysis techniques,will be illustrated with examples relevant to the hair care applications.References[1]F.Islam,S. Joannès, S.Bucknell,Y.Leray,A.Bunsell andL.Laiarinandrasana.Investigationof tensilestrengthanddimensionalvariationofT700carbonfibresusinganimprovedexperimentalsetup, Journal of Reinforced Plastics and Composites.[2]F. Islam, S. Joannès, and L. Laiarinandrasana.Evaluation of critical parameters in tensile strength measurement of single fibres, Journal of Composites Science, 3 (3), 69.[3]F.Islam,S.Joannès,S. Bucknell,Y.Leray,A.BunsellandL. Laiarinandrasana.Towardsaccurate andefficientsinglefibrecharacterizationtobetterassessfailurestrengthdistribution. ECCM18 -18th European conference on composite materials, Jun 2018, Athenes, Greece. 7 p. (hal-01958211).[4]F.Islam,S.Bucknell,Y.Leray,A.Bunsell,L.LaiarinandrasanaandS.Joannès.Improvementsin determinationofcarbonfibrestrengthdistributionusingautomationandstatisticaldataanalysis. Fiber Society's Spring Conference 2018, Jun 2018, Tokyo, Japan. 3 p. (hal-01959309).

Topics
  • impedance spectroscopy
  • polymer
  • strength
  • fatigue
  • composite
  • tensile strength
  • fatigue testing