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Please wait a minute... For Selected: Download Citations EndNote Ris BibTeX Toggle Thumbnails Select Nickel-Catalyzed Cyanation of Aryl Triflates Using Acetonitrile as a Cyano Source ZHOU Kun, SHEN Zengming Journal of Shanghai Jiao Tong University    2023, 57 (10): 1245-1249.   DOI: 10.16183/j.cnki.jsjtu.2022.108 Abstract （186）   HTML （20）    PDF（pc） （954KB）（108）       Knowledge map    Save In classic cyanation reactions, toxic metal cyanide sources or complex organic cyanide sources are often used. Therefore, it is particularly important to develop a green and economical cyano source. Initially, 4-biphenylyl trifluoromethanesulfonate is chosen as the model substrate. Through extensive screening of catalysts, ligands, additives, reductant, temperature and other conditions, the optimal conditions are obtained (Ni(OTf)2, 1, 3-bis (diphenyphosphino)propane, Zn(OTf)2, Zn with a mole fraction of 0.1, 0.1, 0.2, 2, respectively, 0.7 mL CH3CN, N2, 60 h, 100 ℃). Subsequently, the generation and limitations of the substrates are studied under optimal conditions. It is found that substrates bearing electron-donating substituents exhibit an excellent efficiency for the cyanation of aryl trifluoromethanesulfonates. The cyanation of aryl trifluoromethanesulfonates is first realized under the catalysis of nickel with acetonitrile as a green and economical cyano source. Table and Figures | Reference | Related Articles | Metrics | Comments（0） Select Application of Machine Learning in Chemical Synthesis and Characterization SUN Jie, LI Zihao, ZHANG Shuyu Journal of Shanghai Jiao Tong University    2023, 57 (10): 1231-1244.   DOI: 10.16183/j.cnki.jsjtu.2023.078 Abstract （429）   HTML （48）    PDF（pc） （4421KB）（364）       Knowledge map    Save Automated chemical synthesis is one of the long-term goals pursued in the field of chemistry. In recent years, the advent of machine learning (ML) has made it possible to achieve this goal. Data-driven ML uses computers to learn relative information in massive chemical data, find objective connections between information, train models by using objective connections, and analyze the actual problems which can be solved according to these models. With its excellent computational prediction capabilities, ML helps chemists solve chemical synthesis problems quickly and efficiently and accelerate the research process. The emergence and development of ML has shown a strong research assistance in the field of chemical synthesis and characterization. However, there is no highly versatile ML model at present, and chemists still need to choose different models for training and learning according to actual situations. This paper aims to show chemists the best cases of common learning methods in chemical synthesis and characterization from the perspective of ML, such as supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, etc., and help them use ML knowledge to further broaden their research ideas. Table and Figures | Reference | Related Articles | Metrics | Comments（0）