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Computational molecular engineering for the development of durable and high-perfornamce plasticizers

Computational molecular engineering for the development of durable and high-perfornamce plasticizers
用于开发耐用高性能增塑剂的计算分子工程
批准号:
514051-2017
负责人:
Xi, Li
金额:
$2.91万
依托单位:
依托单位国家:
加拿大
项目类别:
Collaborative Research and Development Grants
财政年份:
2017
资助国家:
加拿大
项目状态:
已结题
起止时间:
2017-01-01 至 2018-12-31

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中文摘要
翻译
塑料材料本质上是聚合物的混合物,加入各种添加剂来调节其性能和外观。增塑剂是一类重要的添加剂,用于使聚合物在加工过程中更容易流动,并使材料在最终产品中更具柔韧性。最广泛使用的增塑剂属于一类被称为邻苯二甲酸盐的化学物质,尽管它们具有很强的增塑剂功效,但随着时间的推移,它们往往会从宿主材料中迁移出来。这不仅会导致材料性能下降,还会污染周围的材料和环境。最近的研究表明邻苯二甲酸盐存在潜在的安全风险,导致全球对这些物质的监管越来越严格。塑料行业正面临着逐步淘汰邻苯二甲酸盐并寻找替代品的压力。作为安大略省领先的塑料织物和薄膜生产商,CGT正在加大力度开发绿色增塑剂。该项目将利用多尺度分子模型和计算机模拟来进一步了解增塑剂的分子机理和增塑剂的稳定性。基于这些知识,将确定对增塑剂效率和稳定性重要的分子特征。预测结果将与CGT的实验结果进行比较,定量模型将用于基于化学结构的增塑剂性能的先验预测。该模型将帮助CGT筛选大量增塑剂设计,以选择有希望的候选产品进行进一步测试,从而降低实验成本,缩短开发周期。通过该项目积累的经验和专业知识也将使我们处于世界范围内正在进行的材料创新范式转变的最前沿,这种转变越来越依赖于计算分子工程和数据科学。
英文摘要
Plastic materials are essentially mixtures of polymers with various additives introduced to adjust theirproperties and appearance. Plasticizers are an important class of additives used to make the polymers easier toflow during its processing and make the material more flexible in the final product. The most widely usedplasticizers belong to a family of chemicals called phthalates which, despite their strong plasticization efficacy,tend to migrate out of the host material over time. This not only causes the material performance to deterioratebut also contaminates the surrounding materials and the environment. Recent research has suggested potentialsafety risks of phthalates, leading to tightening regulation of these substances around the globe. The plasticindustry is under pressure to phase out phthalates and find alternatives. As a leading producer of plastic fabricsand films based in Ontario, CGT is increasing its effort in developing green plasticizers. The proposed projectwill use multiscale molecular modeling and computer simulation to further our understanding of the molecularmechanism of plasticization and plasticizer stability. Based this knowledge, molecular features important forplasticizer efficiency and stability will be identified. Predictions will be compared with experimental resultsfrom CGT and quantitative models will be developed for the a priori prediction of plasticizer performancebased on chemical structures. The model will help CGT screen large pools of plasticizer designs to selectpromising candidates for further testing, with which the experimental cost can be reduced and the developmentcycle shortened. Experience and know-how accumulated through the project will also put us at the forefront ofthe ongoing paradigm shift around the world in material innovation, which increasingly relies on computationalmolecular engineering and data science.
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