Density-functional tight-binding approach for the structural analysis and electronic structure of copper hydride metallic nanoparticles

Kurban, Hasan; Kurban, Mustafa; Dalkılıç, Mehmet

doi:10.1016/j.mtcomm.2019.100648

Cited by 9 publications

(3 citation statements)

References 38 publications

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“…Chemical, physical and electronic properties of the nanomaterials may change depending on the size of the nanoparticles [4]. A size change in the nanostructures induces variation in the surface volume ratio of the materials which in turn affects the electronic properties and chemical and catalytic activity of the nanostructures [5,6]. Various types of nanostructures such as nanotubes, nanowires and nanoparticles were reported in the literature [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Structural and Magnetic Investigation of Bi2S3@Fe3O4 Nanocomposites for Medical Applications

Karaçam

Yetim

Koç

2020

J Supercond Nov Magn

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Bi 2 S 3 and Fe 3 O 4 nanostructures as well as Bi 2 S 3 @Fe 3 O 4 nanocomposites were produced using hydrothermal synthesis. Scanning electron microscopy (SEM), energy dispersive spectra (EDS) and X-ray diffraction (XRD) were used in the characterization of the nanocomposites. The effect of heat treatment on chemical, physical and magnetic properties of the nanostructures was investigated in this study. It was determined that the duration of heat treatment affects the size and shape of the nanostructures. Bi 2 S 3 samples produced using less than 12 h of heat treatment formed in nanoflower-like shapes whereas when the heat treatment was extended to 24 h, the samples formed in nanoribbon-like shapes. The study concludes that an increase in the duration of heat treatment enhances the saturation value of magnetization of Fe 3 O 4 nanostructures.

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Section: Introductionmentioning

confidence: 99%

Structural and Magnetic Investigation of Bi2S3@Fe3O4 Nanocomposites for Medical Applications

Karaçam

Yetim

Koç

2020

J Supercond Nov Magn

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“…Figure 2 The RT represents the order parameter and is used to find a stable structure in the NP. One needs to calculate RT [18][19][20][21][22] which helps to investigate the segregation of atoms in the NP. RT is the average distance of a type Ti atoms according to a center of a NP, (2) where nT is the number of Ti type atoms, and ri is the distance of the atoms to the coordinate center .…”

Section: A Structural Analysismentioning

confidence: 99%

Hegzagonal ZnO nano parçacıklarının yapısal ve elektronik özelliklerinin incelenmesi

Kurban

2019

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

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Bu çalışmada, yoğunluk fonksiyonel sıkı bağlama (DFTB) yöntemini kullanarak, ZnO nano parçacığının yapısal ve elektronik özellikleri incelendi. İlk olarak, 258 atom içeren ~ 0.9 nm çaplı olan bir altıgen kristal yapısına dayanan 30x30x30 ZnO NP karakterize edildi. İkinci olarak, ZnO nanoparçacığının HOMO, LUMO elektronik özellikleri, bant boşluğu enerjileri, Fermi seviyeleri ve durum yoğunluğu (DOS) hesaplandı. Bu özellikler ayrıca sıcaklığa bağlı olarak ta analiz edildi (en fazla 1000 K). Zn-Zn, O-O, Zn-O gibi ikili etkileşimlerin bağ sayısı, ayrılma olayları ve radyal dağılım fonksiyonu (RDF) gibi yapısal analiz, yeni algoritmalar kullanılarak incelendi. Sonuçlarımız, Zn-Zn bağlarının sayısının O-O ve Zn-O bağlarından daha fazla olduğunu göstermektedir; bu nedenle, Zn atomlarının Zn atomları ile bağ yapmayı daha çok tercih ettiği görülmektedir. Ayrıca durum yoğunluğunu (DOS) analiz ettik ve ZnO parçacığının yarı iletken benzeri bir karakter gösterdiğini gözlemledik. Sıcaklığa bağlı, HOMO-LUMO enerji boşluğu arttığı görüldü. Ayrıca, sonuçların deneysel verilerle uyumlu olduğu bulundu.

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“…Studies on the usage of ML in the field of MS are found in this survey article [1]. The analysis of atomic data produced in the anatase phase at different temperatures is important since it reveals the structural and electronic properties of nanoparticles (NPs) [2,3], which have excellent features compared with bulk materials. The properties of NPs mentioned above depend on its geometrical properties, herein it is significant to determine or predict locations, which mean the structure of NPs, or atom types in the NPs.…”

Section: Introductionmentioning

confidence: 99%

Predicting Atom Types of Anatase TiO<sub>2</sub> Nanoparticles with Machine Learning

Kurban

Sharma

et al. 2021

KEM

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Machine learning (ML) has recently made a major contribution to the fields of Material Science (MS). In this study, ML algorithms are used to learn atoms types over structural geometrical data of anatase TiO2 nanoparticles produced at different temperature levels with the density-functional tight-binding method (DFTB). Especially for this work, Random Forest (RF), Decision Trees (DT), K-Nearest Neighbor (KNN), Naïve Bayes (NB), which are among the most popular ML algorithms, were run to learn titanium (Ti) and oxygen (O) atoms. RF outperforms other algorithms, almost succeeding in learning this skewed data set close to perfect. The use of ML algorithms with datasets compatible with its mathematical design increases their learning performance. Therefore, we find it remarkable that a certain type of ML algorithm performs almost perfectly. Because it can help material scientists predict the behavior and structural and electronic properties of atoms at different temperatures.

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Density-functional tight-binding approach for the structural analysis and electronic structure of copper hydride metallic nanoparticles

Cited by 9 publications

References 38 publications

Structural and Magnetic Investigation of Bi2S3@Fe3O4 Nanocomposites for Medical Applications

Structural and Magnetic Investigation of Bi2S3@Fe3O4 Nanocomposites for Medical Applications

Hegzagonal ZnO nano parçacıklarının yapısal ve elektronik özelliklerinin incelenmesi

Predicting Atom Types of Anatase TiO<sub>2</sub> Nanoparticles with Machine Learning

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