The plasticizer is an important polymer material additive.Non-toxic and environmentally friendly plasticizers are developed recently in order to decrease fossil fuel reserves,serious environmental pollution and the to...The plasticizer is an important polymer material additive.Non-toxic and environmentally friendly plasticizers are developed recently in order to decrease fossil fuel reserves,serious environmental pollution and the toxicity of phthalate esters.In this study,a new,efficient and environmentally friendly plasticizer of hydrogenated rosin dodecyl ester was prepared by an esterification reaction of hydrogenated rosin and dodecanol.The influences of different reaction conditions(including different catalysts,the catalyst concentration,the ratio of the reactants,reaction temperature,and reaction time)on the esterification yield are examined and discussed.Hydrogenated rosin dodecyl ester with 71.8%yield was synthesized under the optimized reaction conditions(1:0.8 molar ratio of rosin to dodecanol,1 mol%tetrabutyl titanate concentration,and 210℃for 6 h).The esterification reaction is a second-order reaction,and kinetic calculations showed that the activation energy is 39.77 KJ·mol^(−1).The structure of the hydrogenated rosin dodecyl ester was confirmed by FT-IR spectroscopy and^(13)C NMR spectrum.Besides,the thermal stability of target product(hydrogenated rosin dodecyl ester)was also tested by thermal gravimetric analysis(TGA),which showed a good thermal stability.展开更多
The development of environmentally friendly plastics has received renewed attention for a sustainable society.Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers,...The development of environmentally friendly plastics has received renewed attention for a sustainable society.Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers,the design of biodegradable polymers to overcome these issues is often difficult.In this study,we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 andα-amino acid segments that are mechanically tough and degradable.Multi-objective optimization based on Gaussian process regression for the degradation rate,strain at break,and Young’s modulus(the last two parameters correspond to toughness)suggested appropriateα-amino acid sequences for polyamides endowed with both properties.Ridge regression revealed that the physical factors associated with the sequences,as well as the higher-order multiblock-derived structures(such as the crystal lattice structure,melting points,and hydrogen bonding),were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data.Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques.展开更多
基金the financial support From the Open Fund Project of Key Lab.of Biomass Energy and Material,Jiangsu Province(JSBEM201907)the Ordinary University Young Innovative Talents Project of Guangdong Province(2018KQNCX119)+4 种基金Provincial Science and Technology Planning Projects of Guangdong Province(2017A040405055)Guangdong-Hong Kong Cooperation Project(2017A050506055)Guangdong Provincial Education Department Project(Natural Science,2017KZDXM045)Guangzhou major special project for collaborative innovation of industry,University and research(201604020074)the fund project of Yele Science and Technology Innovation(YL201807).
文摘The plasticizer is an important polymer material additive.Non-toxic and environmentally friendly plasticizers are developed recently in order to decrease fossil fuel reserves,serious environmental pollution and the toxicity of phthalate esters.In this study,a new,efficient and environmentally friendly plasticizer of hydrogenated rosin dodecyl ester was prepared by an esterification reaction of hydrogenated rosin and dodecanol.The influences of different reaction conditions(including different catalysts,the catalyst concentration,the ratio of the reactants,reaction temperature,and reaction time)on the esterification yield are examined and discussed.Hydrogenated rosin dodecyl ester with 71.8%yield was synthesized under the optimized reaction conditions(1:0.8 molar ratio of rosin to dodecanol,1 mol%tetrabutyl titanate concentration,and 210℃for 6 h).The esterification reaction is a second-order reaction,and kinetic calculations showed that the activation energy is 39.77 KJ·mol^(−1).The structure of the hydrogenated rosin dodecyl ester was confirmed by FT-IR spectroscopy and^(13)C NMR spectrum.Besides,the thermal stability of target product(hydrogenated rosin dodecyl ester)was also tested by thermal gravimetric analysis(TGA),which showed a good thermal stability.
基金supported by the Cabinet Office, Government of Japan, Cross-ministerial Strategic Innovation Promotion Program (SIP), and “Technologies for Smart Bio-industry and Agriculture” (funding agency: Bio-oriented Technology Research Advancement Institution, NARO)This study is based on the results obtained from project JPNP18016, commissioned by the New Energy and Industrial Technology Development Organization (NEDO)+4 种基金This work was also supported by JSPS Grant-in-Aid for Scientific Research on Innovative Areas, Discrete Geometric Analysis for Materials Design: 20H04644, Grant-in-Aid for Scientific Research (B): 20H02800Data Creation and Utilization Type Material Research and Development Project Grant Numbers JPMXP1122683430 and JPMXP1122714694by Institute of Mathematics for Industry, Joint Usage/Research Center in Kyushu University (Workshop (II), Reference No. 2023a011 and 2024a011)Y.A. and K.T. acknowledge the financial support from the Grant-in-Aid for the RIKEN-Kyushu University Science and Technology Hub Collaborative Research ProgramSynchrotron radiation experiments were performed at the BL40XU and BL05XU beamlines of SPring-8 with the approval of the Japan Synchrotron Radiation Research Institute (JASRI) (Proposal Nos. 2020A1525, 2021B1476, and 2022B1029).
文摘The development of environmentally friendly plastics has received renewed attention for a sustainable society.Although the trade-off between toughness and degradability is a common challenge in biodegradable polymers,the design of biodegradable polymers to overcome these issues is often difficult.In this study,we demonstrated that machine learning techniques can contribute to the development of multiblock polyamides composed of Nylon6 andα-amino acid segments that are mechanically tough and degradable.Multi-objective optimization based on Gaussian process regression for the degradation rate,strain at break,and Young’s modulus(the last two parameters correspond to toughness)suggested appropriateα-amino acid sequences for polyamides endowed with both properties.Ridge regression revealed that the physical factors associated with the sequences,as well as the higher-order multiblock-derived structures(such as the crystal lattice structure,melting points,and hydrogen bonding),were essential for endowing these polymers with satisfactory properties among the multimodal measurement/calculation data.Our method provides a useful approach for designing and understanding environment-friendly plastics and other materials with multiple properties based on machine learning techniques.
