Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ3DFT-based structural and energetic characterisation of nitrogen-rich tetrazine/triazine derivatives with Kamlet-Jacobs performance predictions
Ekincioğlu, Yavuz; Kepceoğlu, Abdullah; Çakir, Zehra; Şentürk, Şükrü
Nitrogen-rich heterocycles are attractive candidates for next-generation energetic materials because they can deliver high heats of formation while producing relatively benign, N2-rich decomposition products. In this work, three nitrogen-rich frameworks 3,6-bis(tetrazol-5-yl)−1,2,4,5-tetrazine (H2BTTz), 2,4,6-tris(tetrazol-5-yl)−1,3,5-triazine (H3TTT), and 2,3,5,6-tetra(tetrazol-5-yl)pyrazine (H4TTP) were investigated using a combined conformational sampling and density functional theory (DFT) approach. Initial conformational searches were carried out with the MMFF method, and the lowest-energy structures were optimised at the DFT/B3LYP/6-311++G(d,p) level a widely used compromise between accuracy and computational cost for nitrogen-rich energetic molecules. Electronic structure analyses included frontier molecular orbitals, Mulliken and natural population charges, and molecular electrostatic potential maps. Energetic performance was evaluated through heats of formation, detonation velocity and pressure, and impact sensitivity estimates. Topological descriptors such as ELF, LOL, and NCI were analyzed using Multiwfn program. The calculated HOMO–LUMO gaps (3.54-4.90 eV) indicate electronically stable molecular frameworks. Gas-phase formation enthalpies were converted to condensed-phase values using a sublimation correction, and detonation properties were estimated via the Kamlet–Jacobs equations. The predicted performance follows the order H4TTP > H2BTTz > H3TTT. Although their detonation performance is lower than that of FOX-7 and CL-20, these nitrogen-rich systems remain promising candidates for reduced-sensitivity energetic materials and merit further experimental investigation.
AtıfScopus1WoS1OpenAlex1
İncele - Kitap BölümüYayıncıda açık erişim2026Scientific Trends Towards the Use of Generative Artificial Intelligence Tools in Early Childhood STEM Education: A Bibliometric Analysis
Çakir, Zehra; Uğraş, Mustafa
This study aims to analyze scientific publications on the use of productive artificial intelligence tools in STEM education during early childhood between 2021 and 2025 using bibliometric methods. In this context, the international literature published during the relevant period was examined to identify scientific production trends, prominent authors, influential studies, institutional and international collaboration networks, and thematic focuses. A total of 1, 224 documents were included in the analysis. During the data analysis process, the Bibliometrix (Biblioshiny) software running on the R program was used; publication trends, keyword frequencies, citation analyses, author and institution collaborations, and scientific mapping techniques were utilized. The analysis findings show that academic interest in productive artificial intelligence tools has increased significantly between 2021 and 2025. The study also systematically identifies key trends, application areas, and research gaps related to the integration of these technologies into educational processes.