2023
DOI: 10.1021/acssuschemeng.2c06090
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Simple and Mild Method for the Recycling of Carbon-Fiber-Reinforced Bismaleimide Resin Composite Waste

Abstract: Recycling of high-performance carbon-fiber-reinforced polymer (CFRP) composite waste has been significantly challenging due to its insoluble and infusible thermosetting matrix. In this study, a simple and efficient strategy for the closed-loop recycling of a commercial bismaleimide (BMI)-based composite was described. The mild conditions utilized for the decomposition of CFRP composites circumvented the severe conditions required for the recycling of traditional composites. The BMI resin polymer was successful… Show more

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Cited by 6 publications
(4 citation statements)
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“…In recent years, researchers in the field of resin recovery have explored a new approach by using organic strong bases and highly soluble organic solvents to recover materials such as CFRP [52] and plastics [73]. Zhang et al [53] investigated the recovery of anhydride-cured epoxy resin by using a mixture of 10 different organic bases with ethylene glycol (EG).…”
Section: Organobase/organosolvent Methodsmentioning
confidence: 99%
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“…In recent years, researchers in the field of resin recovery have explored a new approach by using organic strong bases and highly soluble organic solvents to recover materials such as CFRP [52] and plastics [73]. Zhang et al [53] investigated the recovery of anhydride-cured epoxy resin by using a mixture of 10 different organic bases with ethylene glycol (EG).…”
Section: Organobase/organosolvent Methodsmentioning
confidence: 99%
“…DETA acted as both a catalyst and a reactant, while microwave heating not only rapidly heated the reaction mixture and resin particles but also enhanced the penetration of the reaction solution. Additionally, monoisopropanolamine was used to recover double maleimide-based resins with a T g of approximately 98 • C at 160 • C [52]. The average molecular weight of the degradation product was 4181 Da and was utilized to produce new resins.…”
Section: Organobase/organosolvent Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…Currently, the rapid development of machine learning (ML) provides a powerful tool to achieve the efficient discovery of high-performing materials. [33][34][35][36] So far, ML models have been widely used to predict the gas adsorption performance of porous materials and the performance of energy-related systems. Pardakhti et al predicted the methane uptake of ∼130 000 hypothetical MOFs (hMOFs) at 298 K, 35 bar by using ML models including decision tree (DT), support vector machine (SVM) and random forest (RF) models, which are demonstrated to be several orders of magnitude faster than a GCMC simulation.…”
Section: Introductionmentioning
confidence: 99%