Developing predictive models for polycyanurates through a comparative study of molecular simulation and empirical thermo-mechanical data

Thermodynamic and mechanical properties of epoxy resin DGEBF crosslinked with DETDA by molecular dynamics

“…Svaneborg et al created crosslinked networks of freely jointed bead-spring chains by superimposing a stochastic linking procedure on a molecular dynamics (MD) trajectory, yielding disordered networks consistent with rubbery polymers [12]. Hamerton et al created a model polycyanurate by constructing fundamental repeat units at the atomistic scale and chemically joining them in a three-dimensional superlattice, www.elsevier.com/locate/JMGM yielding a quasi-crystalline representation [13]. Wu and Xu created a model epoxy using a proximity-based crosslink creation algorithm applied progressively throughout an MD trajectory, starting from a dense fluid of monomer and linker molecules [14].…”

Simulation of the free energy of mixing for blend components in a new family of flexible polycyanurates

“…Initially, a model of the homopolymer of bisphenol A dicyanate was constructed using the builder module within Cerius 2 to test the methodology as extensive studies have been undertaken previously on this polymer using similar techniques [6,7]. The bond lengths and angles within the bisphenyl moieties were based on data collected using single crystal x-ray crystallography experiments [8].…”

“…glass transition temperatures, T g , of between 200 and 300ºC depending on structure and degree of cure) although the very low moisture absorption values that they exhibit are superior to both competitor resins (e.g. this may be as low as 0.6-2.5 weight % depending on backbone structure, compared with figures of 4-4.5 weight % for BMIs or [3][4][5][6] weight % for commercial epoxies) and is backed up by good hot/wet properties. Cured cyanates may find application in a variety of niche technological applications (e.g.…”

Glass transition temperature prediction of polymers through the mass-per-flexible-bond principle

“…Other commonly used methods for T g prediction are: ab initio quantum mechanical calculations [21,22], Monte Carlo [23,24], and molecular dynamics simulations [25][26][27][28][29] and semi-empirical or empirical methods based on group contribution methods, often using the QSPR approach through neural network computation [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. While some of these methods yield good results by predicting glass transition temperatures with errors as good as 3-10 K, most predictive accuracies are on the order of 20-100K.…”