Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Pham, Duong Dinh | - |
dc.contributor.author | Vo, Dat-Nguyen | - |
dc.contributor.author | Phong, Mai Thanh | - |
dc.contributor.author | Nguyen, Huu Hieu | - |
dc.contributor.author | Nguyen-Thoi, Trung | - |
dc.contributor.author | Pham, Thuy-Phuong T. | - |
dc.contributor.author | Phuong, Duy Ha Le | - |
dc.contributor.author | Pham, Le Kim Hoang | - |
dc.contributor.author | Won, Da Hye | - |
dc.contributor.author | Nguyen, Dang Le Tri | - |
dc.contributor.author | Nguyen, Tung M. | - |
dc.date.accessioned | 2024-06-20T02:30:08Z | - |
dc.date.available | 2024-06-20T02:30:08Z | - |
dc.date.created | 2024-06-20 | - |
dc.date.issued | 2024-06 | - |
dc.identifier.issn | 1385-8947 | - |
dc.identifier.uri | https://pubs.kist.re.kr/handle/201004/150090 | - |
dc.description.abstract | The relentless growth of plastic has emerged as a significant environmental and human health concern. Catalytic glycolysis of polyethylene terephthalate (PET) has proven to be an effective solution. This study investigated a series of M-BDC (M = Ni, Co, Cu, and Zn) metal -organic frameworks as catalysts for PET glycolysis. Zn-BDC exhibited the best experimental performance. Subsequently, the effects of operating conditions were optimized for the first time through a comprehensive investigation using a model based on deep neural networks (DNNs). The guidance of the DNN model resulted in a BHET selectivity as high as 0.95, outperforming most current heterogeneous catalysts for PET depolymerization. Furthermore, the apparent activation energy was also estimated by kinetic study. In addition, our designed system exhibits high durability after five consecutive runs, reflecting its promising escalation at the industrial level. Notably, the fabrication of the M-BDC framework can utilize the organic linker terephthalic acid (TPA) derived from the BHET monomer. By applying this strategy, we have achieved a significant advancement towards closed-loop PET recycling, contributing to a more comprehensive definition of sustainable development. | - |
dc.language | English | - |
dc.publisher | Elsevier BV | - |
dc.title | A new circulation in glycolysis of polyethylene terephthalate using MOF-based catalysts for environmental sustainability of plastic | - |
dc.type | Article | - |
dc.identifier.doi | 10.1016/j.cej.2024.151667 | - |
dc.description.journalClass | 1 | - |
dc.identifier.bibliographicCitation | Chemical Engineering Journal, v.490 | - |
dc.citation.title | Chemical Engineering Journal | - |
dc.citation.volume | 490 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.identifier.wosid | 001241053300001 | - |
dc.identifier.scopusid | 2-s2.0-85193904110 | - |
dc.relation.journalWebOfScienceCategory | Engineering, Environmental | - |
dc.relation.journalWebOfScienceCategory | Engineering, Chemical | - |
dc.relation.journalResearchArea | Engineering | - |
dc.type.docType | Article | - |
dc.subject.keywordPlus | COORDINATION | - |
dc.subject.keywordPlus | METAL-ORGANIC FRAMEWORK | - |
dc.subject.keywordPlus | POLY(ETHYLENE-TEREPHTHALATE) | - |
dc.subject.keywordPlus | NANOPARTICLES | - |
dc.subject.keywordPlus | WASTE | - |
dc.subject.keywordAuthor | PET glycolysis | - |
dc.subject.keywordAuthor | Metal -organic frameworks | - |
dc.subject.keywordAuthor | Deep Neural Networks | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Plastic circulation | - |
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