Hyper crosslinked polymers based hydrogen generation: A combined mechanistic, statistical and machine learning approach
Yazarlar (8)
Doç. Dr. Kutalmış GÖKKUŞ Kastamonu Üniversitesi, Türkiye
Dr. Öğr. Üyesi Selim ÜNAL Kastamonu Üniversitesi, Türkiye
Zeynep Gokkus Kastamonu Üniversitesi, Türkiye
Arş. Gör. Aysegul Ozbal Eskişehir Osmangazi Üniversitesi, Türkiye
Prof. Dr. Mahmut GÜR Kastamonu Üniversitesi, Türkiye
Prof. Dr. Muhammet Serdar ÇAVUŞ Kastamonu Üniversitesi, Türkiye
Alper Akalin Dokuz Eylül Üniversitesi, Türkiye
Prof. Dr. Vural Bütün Eskişehir Osmangazi Üniversitesi, Türkiye
| Makale Türü | Özgün Makale (SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | International Journal of Hydrogen Energy (Q1) | ||
| Dergi ISSN | 0360-3199 Wos Dergi Scopus Dergi | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2026 |
| Cilt / Sayı / Sayfa | 206 / 1 / 153387–0 | DOI | 10.1016/j.ijhydene.2026.153387 |
| Makale Linki | https://doi.org/10.1016/j.ijhydene.2026.153387 | ||
| UAK Araştırma Alanları |
Mühendislik
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| Özet |
| This study aimed to i) determine the effects of different functional groups, both individually and synergistically, on catalytic performance in hydrogen production, ii) elucidate the pathways through which the catalytic mechanism occurs according to functional groups, and iii) successfully integrate machine learning algorithms, Adaptive Neuro-Fuzzy Inference System (ANFIS) and ANalysis of Covariance (ANCOVA) methods into catalyst studies. In this respect, this study is a pioneering study as one of the most comprehensive studies on catalysts in the literature. To achieve these objectives, four new hypercross-linked polymers (HCPs) were designed and synthesized with resorcinol, 1-naphthol, and diphenylamine. HCPs were used as catalysts in hydrogen production by methanolysis of NaBH4. Under optimum conditions, a maximum of 11571 mL H2.min−1∙g−1 of hydrogen gas was produced. The functional group … |
| Anahtar Kelimeler |
| ANCOVA | ANFIS | Catalytic mechanism | Hydrogen production | Machine learning modeling | NaBH4 methanolysis | Structure–property relationship |
Science Direct
Hyper crosslinked polymers based hydrogen generation: A combined mechanistic, statistical and machine learning approach
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |