Breaking the trade-off between mechanical properties and fire safety of epoxy resins based on phosphaphenanthrene derivatives by covalent crosslinking DOI

Xianghui Gan,

Jun Wang, Shuang Yang

et al.

Polymer Degradation and Stability, Journal Year: 2023, Volume and Issue: 220, P. 110634 - 110634

Published: Dec. 14, 2023

Language: Английский

2D flame temperature and soot concentration reconstruction from partial discrete data via machine learning: A case study DOI Creative Commons
Mingfei Chen,

R. Zheng,

Xuan Zhao

et al.

Case Studies in Thermal Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 106005 - 106005

Published: March 1, 2025

Language: Английский

Citations

0
Design and Development of Fire-Safety Materials in Artificial Intelligence Era DOI
Teng Fu,

Yu-Zhong Wang

Accounts of Materials Research, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

0
Machine learning for expediting next-generation of fire-retardant polymer composites DOI Creative Commons

Pooya Jafari,

Ruoran Zhang,

Siqi Huo

et al.

Composites Communications, Journal Year: 2023, Volume and Issue: 45, P. 101806 - 101806

Published: Dec. 31, 2023

Machine learning algorithms have emerged as an effective and popular decision-making tool for solving complicated engineering problems challenges. Although introducing these can accelerate the optimization of fire retardants polymeric materials by replacing traditional tedious time-consuming trial-and-error methods, this remains at elementary stage designing materials, thus to date there is a lack insightful yet review on topic. Herein, we most practical accurate used predict flame retardancy features, such limiting oxygen index (LOI) cone calorimetry results, their materials. We highlight merits some current algorithms, including artificial neural network (ANN), Lasso, Ridge, ANN (L-ANN), extreme gradient boosting (XGB). Finally, key challenges with existing predicting next-generation retardants, followed proposed solution future directions. This will help expedite development optimized accelerated machine learning.

Language: Английский

Citations

10
Recent advances in flame retardant polymers via thiol-ene click chemistry DOI
Emrah Çakmakçı

Journal of Macromolecular Science Part A, Journal Year: 2023, Volume and Issue: 60(12), P. 817 - 840

Published: Nov. 10, 2023

Within the toolbox of click chemistry, utilization thiol-ene reactions for polymer synthesis and modification is a current area intense attention. Thiol-ene are used broad range applications. One main that needs particular attention, where immensely employed, fabrication coatings. Especially, when light to trigger reactions, coatings can be prepared within seconds. This method known as photopolymerization (TEP) it marvelous advancement among light-induced crosslinking systems. TEP powerful tool preparation The phosphorous monomers has prominent importance improved thermal properties flame retardancy. Here, existing literature on retardant systems reactive in summarized. review mainly highlights studies thermosets yet some linear examples also included. While this mini-review focuses mostly TEP, relevant works involving other polymerization routes (i.e. polymerization) rather than presented. Finally, utilize synthesize retardants given.

Language: Английский

Citations

9
Breaking the trade-off between mechanical properties and fire safety of epoxy resins based on phosphaphenanthrene derivatives by covalent crosslinking DOI

Xianghui Gan,

Jun Wang, Shuang Yang

et al.

Polymer Degradation and Stability, Journal Year: 2023, Volume and Issue: 220, P. 110634 - 110634

Published: Dec. 14, 2023

Language: Английский

Citations

9