Adsorption of 3d, 4d, and 5dtransition metal atoms onβ12—Borophene

Alvarez-Quiceno, J. C.; Schleder, Gabriel R.; Marinho, Enesio; Fazzio, A.

doi:10.1088/1361-648x/aa75f0

Cited by 18 publications

(13 citation statements)

References 40 publications

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“…The χ 3 borophene characterizes in a planar structure with periodic hexagon boron vacancies . As a start, we investigate the adsorption behavior of a single Cr atom on χ 3 borophene and find the hexagon boron vacancy is the only stable adsorption site, consistent with previous studies. − Initial adsorptions on other sites are automatically relaxed to the hexagon boron vacancy after fully structural optimization. Because of the relatively low diffusion barriers (the vertical and lateral diffusion barrier is 0.59 and 0.77 eV, respectively; see Figure S1), transition metal adatoms are expected to diffuse easily between different hexagon boron vacancies on borophene surface.…”

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confidence: 71%

“…As an analogue of graphene, borophene has been successfully grown in experiment. − However, owing to the electron deficiency of boron, 2D borophene exhibits much richer bonding chemistry and possesses several tens of allotropes as a result of different concentrations and locations of hexagon boron vacancies, leading to versatile mechanical, electronic, and optical properties. − Unfortunately, all borophenes are intrinsically nonmagnetic, which severely hinders their application in spintronics. Note previous works suggest to introduce ordered spin in borophenes by adsorption of magnetic transition metals (TM); − however, TM adatoms are rather easy to diffuse between different adsorption sites, which would cause serious stability problems in information storage. Inducing stable spin order in borophenes, especially room-temperature ferromagnetism, still challenges the physical chemistry world.…”

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confidence: 99%

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Room-Temperature Ferromagnetism in Transition Metal Embedded Borophene Nanosheets

Yang

2019

J. Phys. Chem. Lett.

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Exploring two-dimensional (2D) materials with room-temperature ferromagnetism and large perpendicular magnetic anisotropy is highly desirable but challenging. Here, through first-principles calculations, we propose a viable strategy to achieve such materials based on transition metal (TM) embedded borophene nanosheets. Due to electron deficiency, the commonly existent hexagon boron vacancies in various borophene phases serve as intrinsic anchor points for electron-rich transition metals, which not only adsorb strongly upon the vacancies but also favor to be embedded into the vacancies, forming 2D planar hybrid nanosheets. The adsorption-to-embedding transition is feasible thermodynamically and kinetically, owing to its exothermic nature and relatively small kinetic barriers. After embedding, phase transition is further proposed to obtain diverse structures of TM embedded borophenes with versatile magnetic properties. Based on the example of χ3 phase borophene, several ferromagnetic TM embedded borophene nanosheets with high Curie temperature and large perpendicular magnetic anisotropy have been predicted.

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confidence: 71%

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confidence: 99%

Room-Temperature Ferromagnetism in Transition Metal Embedded Borophene Nanosheets

Yang

2019

J. Phys. Chem. Lett.

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“…Graphene has shown how quantum confinement can significantly alter the 2D allotrope in comparison with its 3D counterpart. Posterior to discovery of graphene a profusion of 2D materials have been proposed and synthesized: transition metal dichalcogenides (TMDC), h-BN, silicene, germanene, stanene, borophene, II-VI semiconductors, metal oxides, MXenes, and many others, including recently non van der Waals materials [367][368][369][370]. The first approach using data-mining and HT calculations to discover novel 2D materials was performed by Björkman et al [371,372].…”

Section: D Materialsmentioning

confidence: 99%

From DFT to machine learning: recent approaches to materials science–a review

et al. 2019

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Recent advances in experimental and computational methods are increasing the quantity and complexity of generated data. This massive amount of raw data needs to be stored and interpreted in order to advance the materials science field. Identifying correlations and patterns from large amounts of complex data is being performed by machine learning algorithms for decades. Recently, the materials science community started to invest in these methodologies to extract knowledge and insights from the accumulated data. This review follows a logical sequence starting from density functional theory as the representative instance of electronic structure methods, to the subsequent high-throughput approach, used to generate large amounts of data. Ultimately, data-driven strategies which include data mining, screening, and machine learning techniques, employ the data generated. We show how these approaches to modern computational materials science are being used to uncover complexities and design novel materials with enhanced properties. Finally, we point to the present research problems, challenges, and potential future perspectives of this new exciting field. characterized by its diverse and huge volume, usually ranging from terabytes to petabytes of data, being created in or near real-time. Such data is found either structured and unstructured in nature, and is exhaustive, usually aiming to capture entire populations in a scalable manner [7]. Simple tasks represent challenges in this scale: capture, curation, storage, search, sharing, analysis, and visualization of the data cannot be accomplished without the proper tools. Thus, it can be effectively summarized by the popular 'five V's': volume, velocity, variety, veracity, and value, shown in figure 3(right). A related sixth V is visualization, although not exclusive to Big Data, which requires different techniques to handle data with various characteristics.Striving to tackle the challenges imposed by Big Data, the field of Data Science has arisen. It is largely interdisciplinary being a combination of mathematics and statistics, computer science and programming, and domain knowledge for problem definition and solving, as shown in figure 3(left). Its objective is, roughly speaking, to deal with the whole process of data production, cleaning, preparation, and finally, analysis. Data science encompasses areas such as Big Data, which deals with large volumes of data, and data mining, which relates to analysis processes to discover patterns and extract knowledge from data, part of the so-called Knowledge Discovery in Databases (KDD).The analysis process within Data Science is challenging, as the techniques are very different from traditional static and rigid datasets, generated and analyzed under a predetermined hypothesis. The distinction from traditional data is based on the larger abundance, exhaustivity, and variety of Big Data. It is also much more dynamic, messy and uncertain, being highly relational [7]. Recently, the possibility of overcoming such a challenge slowly sta...

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“…Since the discovery of graphene, the prototypical two-dimensional (2D) material, great efforts have been employed into the investigation of this class of low-dimensional materials. Their applications in photocatalysis, spintronic devices, and topological insulators − have made 2D materials one of the most active areas of research in condensed matter physics and materials science in the last decade.…”

Section: Introductionmentioning

confidence: 99%

Theoretical and Experimental Investigation of 2D Hematite

et al. 2019

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Novel two-dimensional non-van der Waals materials have been reported, boosting efforts to probe their properties and identify key applications. In this work, we report the synthesis, by means of a novel route via sonication of synthetic hematite, and characterization by transmission electron and atomic force microscopy of samples composed of two-dimensional hematite ([001]-cut layered α-Fe2O3). Microscopy images show a layered material with a handful of possible crystalline orientations, of which the [001] is the most abundant, presenting thickness of up to approximately 100 nm. Next, we employed first-principles calculations to study their structural stability and evaluate their thickness distribution. The stability of single, double, and triple layered structures is confirmed by phonon spectra and the formation energy is obtained, pointing out to the possibility of few layers, freestanding, stable samples. Further statistical modeling suggests that even though such thin samples are stable, their abundance is very small in comparison to thicker layers. We show that the antiferromagnetic ordering of the bulk phase is preserved in the nanostructured material, from the double-layered sample onward; however, a nonzero magnetization arises due to distinct localized moments in surface Fe atoms. Finally, our calculated band structures present narrower gaps in the layered structures in comparison to the bulk, and a charge-trapping acceptor level is identified at the surface Fe atoms.

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Adsorption of 3d, 4d, and 5dtransition metal atoms onβ₁₂—Borophene

Cited by 18 publications

References 40 publications

Room-Temperature Ferromagnetism in Transition Metal Embedded Borophene Nanosheets

Room-Temperature Ferromagnetism in Transition Metal Embedded Borophene Nanosheets

From DFT to machine learning: recent approaches to materials science–a review

Theoretical and Experimental Investigation of 2D Hematite

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