This paper develops an approach for predicting cohesive energy density and solubility parameter of thermoset polymers directly based on atomistic simulations. The cohesive energy density and solubility parameter are obtained from the intermolecular nonbond energy, after excluding the intramolecular nonbond energy from the total nonbond energy by computationally characterizing the detailed network structure of a thermoset polymer. The effects of conversion degree and temperature on the cohesive energy density and solubility parameter are systematically investigated. It is concluded that a thermoset polymer with a higher conversion degree has a lower cohesive energy density and solubility parameter. The cohesive energy density shows a linearly decreasing relationship with the crossslink density. These properties also decrease with increasing temperature and thermoset polymers with lower conversion degrees show a more significant temperature effect.
Polymer – Elsevier
Published: Jan 17, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera