New high-performance thermoplastic toughened epoxy thermosets

Pasquale, Giovanna Di; Motto, O.; Rocca, A. La; Carter, J.T.; McGrail, P.T.; Acierno, Domenico

doi:10.1016/s0032-3861(96)01031-2

Cited by 100 publications

(50 citation statements)

References 9 publications

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“…The most effective approach is the introduction of a second component, which is capable of phase separation such as reactive liquid rubbers [5,6], engineering thermoplastics [7,8], or core-shell particles [9,10]. An attraction of liquid rubber like carboxyl-terminated copolymer of butadiene and acrylonitrile (CTBN) as a modifier is their solubility in the base epoxy with the formation of initially a homogeneous solution.…”

Section: Introductionmentioning

confidence: 99%

Reactive acrylic liquid rubber with terminal and pendant carboxyl groups as a modifier for epoxy resin

Ratna

Banthia

2006

Polymer Engineering & Sci

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Section: Introductionmentioning

confidence: 99%

Reactive acrylic liquid rubber with terminal and pendant carboxyl groups as a modifier for epoxy resin

Ratna

Banthia

2006

Polymer Engineering & Sci

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“…Other advantages coming from the use of polymeric composites are the improvement of the resistance to both corrosion and chemicals. The standard thermal cure process induces high quality and performance in these materials and it has been extensively studied and optimized for each specific application (Ellis, 1994;Di Pasquale et al, 1997;Mimura et al, 2000). On the other hand radiation processing has become more and more promising for advanced structures for the several advantages offered.…”

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confidence: 99%

Radiation Curing of Thermosetting-Thermoplastic Blends as Matrices for Structural Carbon Fibre Composites

Alessi¹,

Dispenza²,

Pitarresi³

et al. 2011

Advances in Composite Materials - Ecodesign and Analysis

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“…In order to enhance the fracture toughness, thermoplastic toughening agents are added 11,20,43,61,72 . Cure-induced phase separation of toughener from reacting epoxy and amine suggests that thermoset morphology depends on how fast polymerization-induced phase separation occurs in relation to glassy vitrification 117 .…”

Section: Introductionmentioning

confidence: 99%

“…At 20% PES weight fraction a continuous interpenetrating network with domain sizes of 1 µm was formed and the fracture toughness was observed to be 90% greater than the neat epoxy 71 . The differences in the fracture toughness of toughened thermosets have been attributed to the phase separated morphology 11,20,43,72 . Domains ranging from 5 nm to 12 µm have been observed and a number of studies have found that the largest increase in fracture corresponded to a co-continuous interpenetrating network morphology of thermoset and thermoplastic 13,52,71,117,121 .…”

mentioning

confidence: 99%

“…In [20] (R2,3))) print(cure_percents_ss) alphas = np.linspace(0,1) fit_ydata = DiBenedetto(alphas,T1,T0=Tgs_ss [0],inter_param=inter_parm) plt.plot(alphas,fit_ydata,label='$R^2$:{}'.format(round (R2,3) (R2,3))) print(cure_percents_ss) alphas = np.linspace(0,1) fit_ydata = DiBenedetto(alphas,T1,T0=Tgs_ss [0],inter_param=inter_parm) plt.plot(alphas,fit_ydata,label='$R^2$:{}'.format(round (R2,3) (R2,3))) print(cure_percents_subset) plt.plot(cure_percents_subset,fit_Tgs,label='$R^2$:{}'.format(round (R2,3) , loc='lower right') ax1.ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) ax2.ticklabel_format(axis='y', style='sci', scilimits=(-2,2),size=1) ax2.tick_params(axis = 'both', which = 'major',labelsize=15) ax1.tick_params(axis = 'both', which = 'major',labelsize=25) Tgs = np.asarray(Tgs) cure_percents = np.asarray(cure_percents) data= [ , loc='lower right') ax1.ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) ax2.ticklabel_format(axis='y', style='sci', scilimits=(-2,2),size=1) ax2.tick_params(axis = 'both', which = 'major',labelsize=15) ax1.tick_params(axis = 'both', which = 'major',labelsize=20) Tgs = np.asarray(Tgs) cure_percents = np.asarray(cure_percents) data= [ Tgs.append(tgx) ax1.legend(fontsize=15,loc='lower right') ax1.ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) ax2.ticklabel_format(axis='y', style='sci', scilimits=(-2,2),size=1) ax2.tick_params(axis = 'both', which = 'major',labelsize=15) ax1.tick_params(axis = 'both', which = 'major',labelsize=20) Tgs = np.asarray ( Tgs.append(tgx) ax1.legend(fontsize=15,loc='lower right') ax1.ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) ax2.ticklabel_format(axis='y', style='sci', scilimits=(-2,2),size=1) ax2.tick_params(axis = 'both', which = 'major',labelsize=15) ax1.tick_params(axis = 'both', which = 'major',labelsize=20) Tgs = np.asarray(Tgs) cure_percents = np.asarray(cure_percents) data= [ (R2,3))) #print(cure_percents_ss) alphas = np.linspace(0,1) fit_ydata = DiBenedetto(alphas,T1,T0=T0,inter_param=inter_parm) ax1.plot(alphas,fit_ydata,label='$R^2$:{}'.format(round (R2,3) Tgs.append(tgx) ax1.legend(fontsize=10,loc='lower right') ax1.ticklabel_format(axis='y', style='sci', scilimits=(-2,2)) ax2.ticklabel_format(axis='y', style='sci', scilimits=(-2,2),size=1) ax2.tick_params(axis = 'both', which = 'major',labelsize=15) ax1.tick_params(axis = 'both', which = 'major',labelsize=20) Tgs = np.asarray(Tgs) cure_percents = np.asarray(cure_percents) data= [ …”

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New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics

Thomas¹

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Graduate College. dedicated to curiosity, tenacity and our families. iv ACKNOWLEDGMENTS I would like to thank my advisor Dr. Eric Jankowski for giving me the golden opportunity to work with him and setting me up on a path to success from day one. I want to especially thank him for showing me by example, how to be a good scientist, communicator and a strategist. I am grateful to Dr. Carla Reynolds for guiding me through this project and helping me get the practical perspective on ideas that I came up with. I am also thankful for her for helping me run many of the simulations on the Garcia cluster computer at Boeing and being the first beta tester for epoxpy. I also want to thanks our "American" cousins Sanju, Reshma, Vinu, Megha and our "American" aunt and uncle, Geetha and Dr. Mathew for being our Santa through our college life. ABSTRACTThis research aims at developing new computational methods to understand the molecular self-assembly of reacting systems whose complex structures depend on the thermodynamics of mixing, reaction kinetics, and diffusion kinetics. The specific reacting system examined in this study is epoxy, cured with linear chain thermoplastic tougheners whose complex microstructure is known from experiments to affect mechanical properties and to be sensitive to processing conditions. Mesoscale simulation techniques have helped to bridge the length and time scales needed to predict the microstructures of cured epoxies, but the prohibitive computational cost of simulating experimentally relevant system sizes has limited their impact. In this workwe develop an open-source plugin for the molecular dynamics code HOOMD-Blue that permits epoxy crosslinking simulations of millions of particles to be routinely performed on a single modern graphics card. Using these capabilities, we are able to use ensembles of epoxy processing pathways to obtain realistic bond kinetics and relaxation times that sensitively depend on stochastic bonding rates and a diffusive drag parameter respectively. This work also demonstrates the first implementation of fully customizable temperature-time curing profiles and the largest cross-linked structures obtained using molecular dynamics simulation. We evaluate coarse-grained models based on Dissipative Particle Dynamics (DPD) and compare with Lennard-Jones(LJ) models for their suitability to study glassy dynamics which is important for modeling epoxies or any other glassy material. We find that "hard" particle potentials such as the LJ potential are necessary to model glassy materials and characterize multiple viii methods for measuring the glass transition temperature (T g ) in simulations. We find that variations in temperature-time curing profiles result in significant differences in the final cured morphologies. Finally, we apply our general techniques to the specific DGEBA/DDS/PES system and validate our predicted glass transition temperatures against experiment.ix

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New high-performance thermoplastic toughened epoxy thermosets

Cited by 100 publications

References 9 publications

Reactive acrylic liquid rubber with terminal and pendant carboxyl groups as a modifier for epoxy resin

Reactive acrylic liquid rubber with terminal and pendant carboxyl groups as a modifier for epoxy resin

Radiation Curing of Thermosetting-Thermoplastic Blends as Matrices for Structural Carbon Fibre Composites

New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics

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