Mühendislik Fakültesi

Permanent URI for this communityhttps://hdl.handle.net/20.500.12573/30

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Citation - Scopus: 1
Data-Driven Discovery and DFT Modeling of Fe4H on the Atomistic Level
(Elsevier B.V., 2024) Zagorac, Dejan; Zagorac, Jelena; Djukic, Milos B.; Bal, Burak; Schön, Johann Christian; 0000-0002-7389-9155; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Bal, Burak; 01. Abdullah Gül University; 02.06. Makine Mühendisliği; 02. Mühendislik Fakültesi
Since their discovery, iron and hydrogen have been two of the most interesting elements in scientific research, with a variety of known and postulated compounds and applications. Of special interest in materials engineering is the stability of such materials, where hydrogen embrittlement has gained particular importance in recent years. Here, we present the results for the Fe-H system. In the past, most of the work on iron hydrides has been focused on hydrogen-rich compounds since they have a variety of interesting properties at extreme conditions (e.g. superconductivity). However, we present the first atomistic study of an iron-rich Fe4H compound which has been predicted using a combination of data mining and quantum mechanical calculations. Novel structures have been discovered in the Fe4H chemical system for possible experimental synthesis at the atomistic level. © 2024 Elsevier B.V., All rights reserved.
Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets
(Springer Verlag service@springer.de, 2018) Cetiner, Erkan; Güngör, Vehbi Çağrı; Kocak, Taskin; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri; 01. Abdullah Gül University
Hybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches. © 2018 Elsevier B.V., All rights reserved.
Citation - WoS: 23
Citation - Scopus: 23
Pressure-Induced Amorphization of MOF-5: A First Principles Study
(Wiley-VCH Verlag GmbH, 2018) Erkartal, Mustafa; Durandurdu, Murat; Erkartal, Mustafa; Durandurdu, Murat; 0000-0001-5636-3183; AGÜ, Mühendislik Fakültesi, Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü; 01. Abdullah Gül University; 02.07. Malzeme Bilimi ve Nanoteknoloji Mühendisliği; 02. Mühendislik Fakültesi
Amorphous metal-organic frameworks (MOFs) and the amorphization of crystalline MOFs under mechanical stimuli are attracting considerable interest in last few years. However, we still have limited knowledge on their atomic arrangement and the physical origin of crystalline-to-amorphous phase transitions under mechanical stimuli. In this study, ab initio simulations within a generalized gradient approximation are carried out to investigate the high-pressure behavior of MOF-5. Similar to the previous experimental findings, a pressure-induced amorphization is observed at 2 GPa through the simulations. The phase transformation is an irreversible first order transition and accompanied by around 68% volume collapse. Remarkably, the transition arises from local distortions and, contrary to previous suggestions, does not involve any bond breaking and formation. Additionally, a drastic band gap closure is perceived for the amorphous state. This study has gone some way towards enhancing our understanding of pressure-induced amorphization in MOFs.
Citation - WoS: 6
Citation - Scopus: 7
Synergistic Effect of Organic Acid on the Dissolution of Mixed Nickel-Cobalt Hydroxide Precipitate in Sulphuric Acid Solution
(Edp Sciences S A, 2019) Kursunoglu, Sait; Kursunoglu, Sait; 0000-0002-1680-5482; AGÜ, Mühendislik Fakültesi, Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü; 01. Abdullah Gül University
The synergistic effect of an organic acid on the dissolution of nickel and cobalt from a mixed nickel-cobalt hydroxide precipitate (MHP) in sulphuric acid solution was studied. The effects of sulphuric acid concentration, the type of organic acid, leaching time, leaching temperature and stirring speed on the dissolution of the metals were experimentally investigated. It was observed that there is no beneficial effect of leaching temperature and stirring speed on the dissolution of the metals from the used MHP product which contains 37.7% Ni, 2.1% Co and 5.6% Mn. It was found that citric acid was more effective than oxalic acid for the dissolution of nickel and manganese, whereas oxalic acid was more effective than citric acid for the dissolution of cobalt. The addition of oxalic acid into the leaching system, however, affected the dissolution of nickel negatively because nickel precipitate as nickel oxalate. Therefore, the use of citric acid as synergist for sulphuric acid leaching of MHP product is more promising. After 60 min of leaching, 90.9% Ni, 84.2% Co and 98.1% Mn were dissolved under the following conditions: 0.75 M sulphuric acid, 2 g citric acid, 1/10 solid-to-liquid ratio, 400 rpm stirring speed and 30 degrees C temperature. The experimental results demonstrate that the addition of citric acid as a synergist for sulphuric acid leaching of a MHP product provides beneficial effect for the dissolution of nickel, cobalt and manganese.

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