Application of life cycle assessment and machine learning for high-throughput screening of green chemical substitutes was written by Zhu, Xinzhe;Ho, Chi-Hung;Wang, Xiaonan. And the article was included in ACS Sustainable Chemistry & Engineering in 2020.Safety of 1,1,1,3,3,3-Hexafluoroisopropylmethacrylate The following contents are mentioned in the article:
The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems because of the use of toxic chem. materials and production infrastructure, energy consumption, and waste treatment. The environmental impacts of the sitagliptin production process were estimated with a life cycle assessment (LCA) method, which suggested that the use of chem. materials provided the major environmental impacts. Both methods of Eco-indicator 99 and ReCiPe endpoint confirmed that chem. feedstock accounted for 83% and 70% of life-cycle impact, resp. Among all the chem. materials used in the sitagliptin production process, trifluoroacetic anhydride was identified as the largest influential factor in most impact categories according to the results of the ReCiPe midpoints’ method. Therefore, high-throughput screening was performed to seek for greener chem. substitutes to replace the target chem. (i.e., trifluoroacetic anhydride) by the following three steps. First, the 30 most similar chems. were obtained from 2 million candidate alternatives in the PubChem database on the basis of their mol. descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chems. in Ecoinvent v3.5 database with known LCA values and corresponding mol. descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chems. were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chem. substitutes and optimize the pharmaceutical manufacturing process. Neural network models were trained using mol. descriptors and life-cycle impact of known chems. and used to search greener substitutes in a huge library of chems. This study involved multiple reactions and reactants, such as 1,1,1,3,3,3-Hexafluoroisopropylmethacrylate (cas: 3063-94-3Safety of 1,1,1,3,3,3-Hexafluoroisopropylmethacrylate).
1,1,1,3,3,3-Hexafluoroisopropylmethacrylate (cas: 3063-94-3) belongs to esters. Esters perform as high-grade solvents for a broad array of plastics, plasticizers, resins, and lacquers, and are one of the largest classes of synthetic lubricants on the commercial market. Esters are more polar than ethers but less polar than alcohols. They participate in hydrogen bonds as hydrogen-bond acceptors, but cannot act as hydrogen-bond donors, unlike their parent alcohols. This ability to participate in hydrogen bonding confers some water-solubility.Safety of 1,1,1,3,3,3-Hexafluoroisopropylmethacrylate
Referemce:
Ester – Wikipedia,
Ester – an overview | ScienceDirect Topics