Chemical synthesis of a new polysaccharide. Ring-opening polymerization of 1,6-anhydro-2,3,4-tri-O-benzyl-.beta.-D-allopyranose and preparation of stereoregular 1→ 6)-α-D-allopyranan

1984 ◽  
Vol 17 (7) ◽  
pp. 1307-1312 ◽  
Author(s):  
Toshiyuki Uryu ◽  
Yoshihiro Sakamoto ◽  
Kenichi Hatanaka ◽  
Kei Matsuzaki
1991 ◽  
Vol 24 (25) ◽  
pp. 6797-6799 ◽  
Author(s):  
Masahiko Okada ◽  
Yoshitaka Yamakawa ◽  
Hiroshi Sumitomo

1983 ◽  
Vol 16 (6) ◽  
pp. 853-858 ◽  
Author(s):  
Toshiyuki Uryu ◽  
Kenichi Hatanaka ◽  
Kei Matsuzaki ◽  
Hiroyoshi Kuzuhara

1986 ◽  
Vol 18 (8) ◽  
pp. 601-611 ◽  
Author(s):  
Masahiko Okada ◽  
Hiroshi Sumitomo ◽  
Takahito Hirasawa ◽  
Kiyomichi Ihara ◽  
Yoshikazu Tada

2020 ◽  
Author(s):  
Nathaniel Park ◽  
Dmitry Yu. Zubarev ◽  
James L. Hedrick ◽  
Vivien Kiyek ◽  
Christiaan Corbet ◽  
...  

The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.<br>


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