Preparation and Properties of Halogen-Free Flame Retardant and High Refractive Index Optical Resin via Click Chemistry

2018 ◽  
Vol 26 (4) ◽  
pp. 346-352 ◽  
Author(s):  
Chaoyun Luo ◽  
Jiandong Zuo ◽  
Fuquan Wang ◽  
Yanchao Yuan ◽  
Feng Lin ◽  
...  
2021 ◽  
Author(s):  
Nicole Ziegenbalg ◽  
Ruth Lohwasser ◽  
Giovanni D’Andola ◽  
Torben Adermann ◽  
Johannes Christopher Brendel

Polyethersulfones are an interesting class of polymers for industrial applications due to their unusual properties such as a high refractive index, flame-retardant properties, high temperature and chemical resistance. The common...


2015 ◽  
Vol 5 (3) ◽  
pp. 462 ◽  
Author(s):  
Chaoyun Luo ◽  
Jiandong Zuo ◽  
Yanchao Yuan ◽  
Xuechun Lin ◽  
Feng Lin ◽  
...  

RSC Advances ◽  
2016 ◽  
Vol 6 (6) ◽  
pp. 4377-4381 ◽  
Author(s):  
Chih-Yuan Hsu ◽  
Wei-Gang Han ◽  
Shu-Jen Chiang ◽  
Wen-Chiung Su ◽  
Ying-Ling Liu

Multi-functional branched polysiloxanes exhibiting good thermal stability, high thermal resistance, self-extinguishing properties, high transparency at ultraviolet to blue light region, and relatively high RI value (1.59).


2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>


2013 ◽  
Vol 28 (6) ◽  
pp. 671-676 ◽  
Author(s):  
Yu-Qing ZHANG ◽  
Li-Li ZHAO ◽  
Shi-Long XU ◽  
Chao ZHANG ◽  
Xiao-Ying CHEN ◽  
...  

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