期刊
ACS APPLIED MATERIALS & INTERFACES
卷 13, 期 45, 页码 53425-53438出版社
AMER CHEMICAL SOC
DOI: 10.1021/acsami.1c12767
关键词
machine learning; structural design; flame retardancy; graphene oxide dispersion; hydrogen bonding
资金
- Key Program of Science and Technology of Yunnan Province [202002AB080001-2]
- National Natural Science Foundation of China [52073168]
The research team utilized machine learning to accelerate the development of flame-retardant polymers and explored the relationship between the limit oxygen index and components through data analysis to determine the flame-retardant mechanism and components. Additionally, by wrapping nano graphene oxide on micro zinc hydroxystannate, the flame retardancy of polypropylene composites was successfully enhanced.
Designing flame-retardant polymers with high performance is a long-standing challenge, partly because of the time-consuming traditional approaches based on experiential intuition and trial-and-error screenings. Inspired by the effective new paradigm of data-driven material discovery, we used machine learning to analyze experimental data to accelerate the development of new flame-retardant polymers. To explore the relationship between limit oxygen index (LOI) and components, we prepared 20 composites and then trained a simple equation for the LOI using the method sure independence screening and sparsifying operator (SISSO). The data analysis allows us for a better understanding of the flame-retardant mechanism and components, and the equation has good accuracy in guiding the design of composites with high flame-retardant performance. Meanwhile, the increasing structural design of flame retardants is crucial to flame-retardant polymer composites. We proposed a structure of nano graphene oxide (GO) wrapped micro zinc hydroxystannate (ZHS) in a simple but effective way as a novel flame-retardant agent to enhance the flame retardancy and mechanical properties of polypropylene (PP) composites. The GO sheets were like light yarns wrapped onto the ZHS via hydrogen bonding in an ethanol solution. The selected samples were analyzed to confirm the predictive LOI model. The resultant composites with the substitution of intumescent flame retardant (IFR) by 1.0, 2.0, and 4.0 wt % ZHS@GO conferred better flame retardancy compared with PP composite containing only IFR, reflected by the efficient increase of LOI value and VO rating of UL-94 vertical tests. The analysis principles and facile fabrication strategies proposed in this work could be important for developing highly flame retardant composites.
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