TR Dizin İndeksli Yayınlar / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7149
Browse
Search Results
Now showing 1 - 3 of 3
Article Citation - WoS: 1Citation - Scopus: 2Lacoo3 Is a Promising Catalyst for the Dry Reforming of Benzene Used as a Surrogate of Biomass Tar(Tubitak Scientific & Technological Research Council Turkey, 2024) Çağlar, Başar; Üner, DenizTar build-up is one of the bottlenecks of biomass gasification processes. Dry reforming of tar is an alternative solution if the oxygen chemical potential on the catalyst surface is at a sufficient level. For this purpose, an oxygen-donor perovskite, $LaCoO_3$, was used as a catalyst for the dry reforming of tar. To circumvent the complexity of the tar and its constituents, the benzene molecule was chosen as a model compound. Dry reforming of benzene vapor on the $LaCoO_3$ catalyst was investigated at temperatures of 600, 700, and 800 °C; at $CO_2/C_6H_6$ ratios of 3, 6, and 12; and at space velocities of 14,000 and 28,000 h–1. The conventional Ni(15 wt.%)/$Al_2O_3$ catalyst was also used as a reference material to determine the relative activity of the $LaCoO_3$ catalyst. Different characterization techniques such as X-ray diffraction, $N_2$ adsorption-desorption, temperature-programmed reduction, and oxidation were used to determine the physicochemical characteristics of the catalysts. The findings demonstrated that the $LaCoO_3$ catalyst has higher $CO_2$ conversion, higher $H_2$ and CO yields, and better stability than the Ni(15 wt.%)/γ-$Al_2O_3$ catalyst. The improvement in activity was attributed to the strong capacity of $LaCoO_3$ for oxygen exchange. The transfer of lattice oxygen from the surface of the $LaCoO_3$ catalyst facilitates the oxidation of carbon and other surface species and leads to higher conversion and yields.Article Citation - WoS: 3Citation - Scopus: 3Β-Ketoenamine-linked covalent organic framework for efficient iodine capture(Tubitak Scientific & Technological Research Council Turkey, 2024) Büyükçakır, OnurExploring the materials that effectively capture radioactive iodine is crucial in managing nuclear waste produced from nuclear power plants. In this study, a β-ketoenamine-linked covalent organic framework (bCOF) is reported as an effective adsorbent to capture iodine from both vapor and solution. The bCOF’s high porosity and heteroatom-rich skeleton offer notable iodine vapor uptake capacity of up to 2.51g $g^{–1}$ at 75 °C under ambient pressure. Furthermore, after five consecutive adsorption-desorption cycles, the bCOF demonstrates high reusability performance with significant iodine vapor capacity retention. The adsorption mechanism was also investigated using various ex situ structural characterization techniques, and these mechanistic studies revealed the existence of a strong chemical interaction between the bCOF and iodine. The bCOF also showed good iodine uptake performance of up to 512 mg $g^{–1}$ in cyclohexane with high removal efficiencies. The bCOF’s performance in adsorbing iodine from both vapor and solution makes it a promising material to be used as an effective adsorbent in capturing radioactive iodine emissions from nuclear power plants.Article Citation - WoS: 2Citation - Scopus: 2Enrichment of Turkish Question Answering Systems Using Knowledge Graphs(Tubitak Scientific & Technological Research Council Turkey, 2024) Ciftci, Okan; Soygazi, Fatih; Tekir, SelmaRecent capabilities of large language models (LLMs) have transformed many tasks in Natural Language Processing (NLP), including question answering. The state-of-the-art systems do an excellent job of responding in a relevant, persuasive way but cannot guarantee factuality. Knowledge graphs, representing facts as triplets, can be valuable for avoiding errors and inconsistencies with real-world facts. This work introduces a knowledge graph-based approach to Turkish question answering. The proposed approach aims to develop a methodology capable of drawing inferences from a knowledge graph to answer complex multihop questions. We construct the Beyazperde Movie Knowledge Graph (BPMovieKG) and the Turkish Movie Question Answering dataset (TRMQA) to answer questions in the movie domain. We evaluate our proposed question answering pipeline against a baseline study. Furthermore, we compare it with a question answering system built upon GPT-3.5 Turbo to answer the 1-hop questions from TRMQA. The experimental results confirm that link prediction on a knowledge graph is quite effective in answering questions that require reasoning paths. Finally, we provide insights into the pros and cons of the provided solution through a qualitative study.