Flame retardancy of flax fibers by pre-irradiation grafting of a phosphonate monomer

In this research contribution, the static behavior and failure mechanisms are developed for a three-dimensional (3D) printed dogbone, auxetic structure and sandwich composite using acoustic emissions (AEs). The skins, core and whole sandwich are manufactured using the same bio-based material which is polylactic acid reinforced with micro-flax fibers. Tensile tests are conducted on the skins and the core while bending tests are conducted on the sandwich composite. Those tests are carried out on four different auxetic densities in order to investigate their effect on the mechanical and damage properties of the materials. To monitor the invisible damage and damage propagation, a highly sensitive AE testing method is used. It is found that the sandwich with high core density displays advanced mechanical properties in terms of bending stiffness, shear stiffness, facing bending stress and core shear stress. In addition, the AE data points during testing present an amplitude range of 40–85[Formula: see text]dB that characterizes visible and invisible damage up to failure.

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Design and preparation of new polypropylene/magnesium oxide micro particles composites reinforced with hydroxyapatite nanoparticles: A study of thermal stability, flame retardancy and mechanical properties

Materials Chemistry and Physics ◽

10.1016/j.matchemphys.2020.123917 ◽

2021 ◽

Vol 258 ◽

pp. 123917

Author(s):

Mohsen Hajibeygi ◽

Masoumeh Mousavi ◽

Meisam Shabanian ◽

Navid Habibnejad ◽

Henri Vahabi

Keyword(s):

Mechanical Properties ◽

Thermal Stability ◽

Magnesium Oxide ◽

Flame Retardancy ◽

Hydroxyapatite Nanoparticles ◽

Micro Particles

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Synthesis and characterization of flame-retardant-wrapped carbon nanotubes and its flame retardancy in epoxy nanocomposites

Polymers and Polymer Composites ◽

10.1177/09673911211024582 ◽

2021 ◽

pp. 096739112110245

Author(s):

Jiangbo Wang

Keyword(s):

Carbon Nanotubes ◽

Heat Release ◽

Flame Retardant ◽

Flame Retardancy ◽

Total Heat Release ◽

Pristine Cnts ◽

Peak Heat Release Rate ◽

Ep Matrix ◽

Better Than

A novel phosphorus-silicon containing flame-retardant DOPO-V-PA was used to wrap carbon nanotubes (CNTs). The results of FTIR, XPS, TEM and TGA measurements exhibited that DOPO-V-PA has been successfully grafted onto the surfaces of CNTs, and the CNTs-DOPO-V-PA was obtained. The CNTs-DOPO-V-PA was subsequently incorporated into epoxy resin (EP) for improving the flame retardancy and dispersion. Compared with pure EP, the addition of 2 wt% CNTs-DOPO-V-PA into the EP matrix could achieve better flame retardancy of EP nanocomposites, such as a 30.5% reduction in peak heat release rate (PHRR) and 8.1% reduction in total heat release (THR). Furthermore, DMTA results clearly indicated that the dispersion for CNTs-DOPO-V-PA in EP matrix was better than pristine CNTs.

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Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

Molecules ◽

10.3390/molecules26041022 ◽

2021 ◽

Vol 26 (4) ◽

pp. 1022

Author(s):

Hoang T. Nguyen ◽

Kate T. Q. Nguyen ◽

Tu C. Le ◽

Guomin Zhang

Keyword(s):

Artificial Intelligence ◽

Flame Retardancy ◽

Material Science ◽

Construction Materials ◽

Engineering Problem ◽

Future Research ◽

Fire Performance ◽

Structural Components ◽

Comprehensive Overview ◽

Fire Engineering

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.

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