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(2026) Journal of Analytical and Applied Pyrolysis_Pyrolysis mechanism of engineering plastic waste under carbon dioxide

(2026) Journal of Analytical and Applied Pyrolysis_Pyrolysis mechanism of engineering plastic waste under carbon dioxide

 

Kwon D.; Kim Y.; Kim J.Y.; Lee D.; Lee J.; Kwon E.E.

 

(Elsevier B.V.) Journal of Analytical and Applied Pyrolysis ISSN: 1652370 Vol.196 Issue. Article No.107733 DOI: 10.1016/j.jaap.2026.107733

 

This study explored carbon CO2-assisted catalytic pyrolysis as a sustainable method for converting polybutylene terephthalate (PBT) waste into syngas under moderate-temperature conditions (≤700 ˚C), providing a more energy-efficient alternative to conventional gasification. CO2 was adopted as a reactive medium to enhance gas-phase carbon conversion and reduce the environmental footprint. The results showed that CO2-assisted catalytic pyrolysis effectively suppressed the formation of condensable pyrogenic products, reducing the liquid yields from 67.91 wt% to 10 wt%, and boosted CO production by up to 65-fold compared to that of one-stage pyrolysis under inert conditions. To elucidate the fundamental reaction pathways, terephthalic acid (TPA) was utilized as a model compound representing the core aromatic backbone of PBT. Mechanistic analysis showed that CO2 activation on the Ni catalyst surface generated reactive intermediates (CO2•⁻ and M-COO⁻), which promoted selective bond cleavage and deoxygenation reactions, efficiently converting heavy oxygenates into syngas. Moreover, CO2 played a dual role by also suppressing coke formation, thereby preserving the catalyst performance. An environmental assessment indicated that the catalytic pyrolysis of 1.8 million tonnes of PBT waste could lead to a net reduction of 6 million tonnes of CO2 emissions compared to that under incineration, which would release nearly 4 million tonnes. These findings highlight the use of CO2-assisted catalytic pyrolysis as a carbon-negative and circular solution that transforms plastic waste and CO2 into valuable products. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

 

This work was supported by the National Research Foundation of Korea (NRF), funded by the Korean Government (MSIT) (grant number No. RS-2023\u201300219667 ). Dohee Kwon also acknowledges financial support from the Hyundai Motor Chung Mong-Koo Foundation.  

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