Exfoliation of a non-van der Waals material from iron ore hematite

Balan, Aravind Puthirath; Radhakrishnan, Shankar; Woellner, Cristiano F.; Sinha, Shyam Kanta; Deng, Liangzi; Reyes, Carlos De Los; Rao, B. Manmadha; Paulose, Maggie; Neupane, Ram; Apte, Amey; Kochat, Vidya; Vajtai, Róbert; Harutyunyan, Avetik R.; Chu, C. W.; Costin, Gelu; Galvão, Douglas S.; Martí, Ángel A.; Aken, Peter A. van; Varghese, Oomman K.; Tiwary, Chandra Sekhar; Iyer, Anantharaman Malie Madom Ramaswamy; Ajayan, Pulickel M.

doi:10.1038/s41565-018-0134-y

Cited by 303 publications

(256 citation statements)

References 53 publications

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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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Section: D Materialsmentioning

confidence: 99%

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

et al. 2019

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“…Subsequent to the discovery of Graphene, a plethora of 2D materials were exfoliated. Among them, the notable 2D materials belonging to the “beyond graphene” regime are hexagonal boron nitrides (h‐BN), transition metal dichalcogenides (TMDs), elemental analogues of graphene such as silicene, germanene, stanene and phosphorene, hematene, ilmenene, and transition metal oxides . These 2D materials exhibit superlative physical and chemical properties when compared to their bulk counterparts and they find innumerable applications in the area of sensors, battery electrodes, and electrocatalysis.…”

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confidence: 99%

Two‐Dimensional Amorphous Cr₂O₃ Modified Metallic Electrodes for Hydrogen Evolution Reaction

Narayanan

Thakur

Balan

et al. 2019

Physica Rapid Research Ltrs

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The surface modification of benchmarked metal catalysts using nanostructured non‐metallic materials for improved performance and stability is an active area of research and is interesting from both a fundamental and an applied perspective. Amorphous few layered nanosheets of Cr2O3 (3–5 nm) are synthesized by rapid thermal exfoliation of CrCl3 · 6H2O precursors and are characterized. The hydrogen evolution reaction (HER) studies on alkaline medium conducted with platinum and gold electrodes modified with amorphous sheets of Cr2O3 show augmented HER activity compared to the pristine ones while Cr2O3 alone is not HER active. The role of amorphous Cr2O3 as a co‐catalyst is established and the synergistic charge transfer effects while coupling Cr2O3 with metal catalysts are studied using electrochemical impedance spectroscopy. Large‐scale processability of amorphous Cr2O3 by rapid thermal treatment along with its high electrochemical stability (>2000 cycles or >50 h) in harsh alkaline conditions, where benchmarked metals fail, open new avenues in designing novel scalable catalysts by protecting the surface of noble metal catalysts without sacrificing the electrochemical performance.

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“…For example, the idea of 2D magnetism in diluted semiconductors such as GaAs, GaN, ZnO, AlN, SiC, MoS 2 , and oxides has been explored . Recently, the exfoliation of a non‐vdW material (Fe 2 O 3 ) was reported, along with room temperature order . In certain cases, interfaces between two disparate materials can also become magnetic, which may be considered as another class of 2D magnet .…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Two‐Dimensional Magnets: Forgotten History and Recent Progress towards Spintronic Applications

Cortie

Causer

Rule

et al. 2019

Adv Funct Materials

158

119

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The recent discovery of 2D magnetic order in van der Waals materials has stimulated a renaissance in the field of atomically thin magnets. This has led to promising demonstrations of spintronic functionality such as tunneling magnetoresistance. The frantic pace of this emerging research, however, has also led to some confusion surrounding the underlying phenomena of phase transitions in 2D magnets. In fact, there is a rich history of experimental precedents beginning in the 1960s with quasi-2D bulk magnets and progressing to the 1980s using atomically thin sheets of elemental metals. This review provides a holistic discussion of the current state of knowledge on the three distinct families of low-dimensional magnets: quasi-2D, ultrathin films, and van der Waals crystals. It highlights the unique opportunities presented by the latest implementation in van der Waals materials. By revisiting the fundamental insights from the field of low-dimensional magnetism, this review highlights factors that can be used to enhance material performance. For example, the limits imposed on the critical temperature by the Mermin-Wagner theorem can be escaped in three separate ways: magnetocrystalline anisotropy, long-range interactions, and shape anisotropy. Several recent experimental reports of atomically thin magnets with Curie temperatures above room temperature are highlighted.

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Exfoliation of a non-van der Waals material from iron ore hematite

Cited by 303 publications

References 53 publications

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

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

Two‐Dimensional Amorphous Cr₂O₃ Modified Metallic Electrodes for Hydrogen Evolution Reaction

Two‐Dimensional Magnets: Forgotten History and Recent Progress towards Spintronic Applications

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Exfoliation of a non-van der Waals material from iron ore hematite

Cited by 303 publications

References 53 publications

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

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

Two‐Dimensional Amorphous Cr2O3 Modified Metallic Electrodes for Hydrogen Evolution Reaction

Two‐Dimensional Magnets: Forgotten History and Recent Progress towards Spintronic Applications

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Two‐Dimensional Amorphous Cr₂O₃ Modified Metallic Electrodes for Hydrogen Evolution Reaction