Super-oxidation of silicon nanoclusters: magnetism and reactive oxygen species at the surface

Lepeshkin, Sergey; Baturin, Vladimir S.; Tikhonov, E. V.; Matsko, N. L.; Uspenskii, Yu. A.; Naumova, Anastasia S.; Feya, Oleg D.; Schoonen, Martin A. A.; Oganov, Artem R.

doi:10.1039/c6nr07504e

Cited by 12 publications

(10 citation statements)

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“…34,35 The energies of 10 best structures for each composition were refined using the GAUSSIAN code 36 with the B3LYP/6-311+G(2d,p) approach. 37 Comparison of our results with earlier publications 4,6,10,[26][27][28][30][31][32][33] showed that 101 optimal structures of Si n O m clusters were correctly reproduced by us, 17 better ones were found, and 197 clusters were studied for the first time to our best knowledge (see table 1 in SI). Fig.…”

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confidence: 74%

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Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions

Lepeshkin

Baturin

Uspenskii

et al. 2018

J. Phys. Chem. Lett.

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We present a universal method for the largescale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close compositions. The resulting speedup is up to 50 times compared to current methods. This enables the first-principles studies of multi-component clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented firstprinciples global optimization of 315 Si n O m clusters with n ≤ 15 and m ≤ 20. The obtained map of Si-O cluster stability shows the existence of both expected (SiO 2 ) n and unexpected (e.g. Si 4 O 18 ) stable (magic) clusters, which can be important for miscellaneous applications.Graphical TOC Entry 1 arXiv:1812.06568v1 [cond-mat.mtrl-sci] 17 Dec 2018The unique properties of nanoparticles are extensively used in optoelectronics, photovoltaics, photocatalysis, biomedicine, etc. These properties are closely linked to the atomic structure of particles, what is more explicit in the small particles and nanoclusters. 1,2 Despite the importance of knowing the structure, its experimental determination remains very difficult. 3 For this reason the main body of structural information on clusters is obtained via first-principles calculations 4 which were mostly done either for monoatomic clusters or for binary clusters of stoichiometric composition corresponding to the bulk compounds, while clusters of general composition were studied only in few publications. 5,6 Such an accent in ab initio research ignores the fact that the chemistry of clusters is much richer than that of solids because of a large share of surface atoms. Multi-component clusters often have stable compositions, which are far from chemical compounds presented in the bulk x-T phase diagram. This is of interest not only for basic chemistry of clusters. It significantly increases the scope of candidate nanomaterials for practical applications such as: the development of efficient and affordable catalysts 7,8 and magnets, 9 the investigation of complex processes of nucleation and particle growth, 10-12 etc.The bottle-neck of first-principles activity in cluster studies is the computational cost of atomic structure determination, which is a global optimization of the total energy among all possible atomic configurations. There are several methods of structure prediction (basin and minima hopping, 13,14 simulated annealing, 15 evolutionary algorithm, 16 etc.), however they all involve thousands of local optimizations (relaxations) even for finding a structure of one cluster. In the applications mentioned above, the computation of atomic structure and the screening of stability and properties are required in wide regions including hundreds of cluster compositions, therefore such firstprinciples investigations turn out extremely exhausting. To reduce the computational cost, the global optimization is frequently performed ...

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confidence: 74%

“…315 cluster compositions). We note that the earlier investigations of Si n O m were either done for relatively small clusters (n ≤ 7) 6,26,28,30,31 or focused on stoichiometric compounds (SiO) n or (SiO 2 ) n . 4,10,27,32,33 Even for these compounds it is often seen that newer papers report lowerenergy structures than the older ones.…”

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confidence: 99%

Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions

Lepeshkin

Baturin

Uspenskii

et al. 2018

J. Phys. Chem. Lett.

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“…Furthermore, the chemistry of nanoparticles can be very sensitive to the chemical environment and quite unexpected cluster compositions can be 'magic' (that is, particularly stable; for example this was shown for Fe-O and Ce-O clusters [85]). It has been shown that under normal conditions (300 K and 0.21 atmospheres partial pressure of O2) the predominant Si7Om, Fe4Om, and Ce4Om nanoparticles are oxygen-rich magnetic Si7O19, Fe4O8, Ce4O14 clusters rather than 'normal' Si7O14, Fe4O6 and Ce4O8 [85,86]. The presence of reactive oxygen species (such as ozo-nide-groups O3 2-) in such clusters may explain the known carcinogenicity of small silica particles [87].…”

Section: [H1] Introductionmentioning

confidence: 99%

Structure prediction drives materials discovery

et al. 2019

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Progress in the discovery of new materials has been accelerated by the development of reliable quantum-mechanical approaches to crystal structure prediction. The properties of a material depend very sensitively on its structure, therefore structure prediction is the key to computational materials discovery. Structure prediction was considered to be a formidable problem, but the development of new computational tools has allowed the structures of many new and increasingly complex materials to be anticipated. These widely applicable methods, based on global optimisation and relying on little or no empirical knowledge, have been used to study crystalline structures, point defects, surfaces and interfaces. In this Review we discuss structure prediction methods, examining their potential for the study of different materials systems, and present examples of computationally-driven discoveries of new materials -including superhard materials, superconductors and organic materials -that will enable new technologies. Advances in firstprinciple structure predictions also lead to a better understanding of physical and chemical phenomena in materials.

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“…Quartz and cristobalite dust are known as carcinogens, in contrast to stishovite 42 . One hypothesis connects toxicity of silica dust with the presence of reactive oxygen species on the surface, and these were found with electron paramagnetic resonance in cristobalite dust 43 and theoretically predicted on α-quartz (011) surface 44 and in silica nanoclusters 45 . “Dense” surfaces described here have no reactive oxygen species.…”

Section: Discussionmentioning

confidence: 96%

Tetrahedral honeycomb surface reconstructions of quartz, cristobalite and stishovite

Feya

Wang

Lepeshkin

et al. 2018

Sci Rep

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Crystalline silica (SiO2) is a major material used in many technologies, yet the exact surface structures of silica polymorphs are still mostly unknown. Here we perform a comprehensive study of surface reconstructions of α-cristobalite (001), α-quartz (001) and stishovite (110) and (100) using evolutionary algorithm USPEX in conjunction with ab initio calculations. We found the well-known “dense surface” to be among low-energy reconstructions of α-quartz (001), as well as its previously proposed distorted version, which we call “shifted surface”. For cristobalite and stishovite we show the formation of reconstructions without dangling bonds which share common features with well-known “dense surface” of α-quartz (001). We call them “dense cristobalite” and “dense stishovite” – all of these have honeycomb arrangements of corner-sharing SiO4-tetrahedra in the surface layers. These tetrahedral honeycombs have very low surface energies, and such tetrahedral surface pattern is observed even in stishovite (the bulk structure of which has SiO6-octahedra, rather than SiO4-tetrahedra).

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Super-oxidation of silicon nanoclusters: magnetism and reactive oxygen species at the surface

Cited by 12 publications

References 24 publications

Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions

Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions

Structure prediction drives materials discovery

Tetrahedral honeycomb surface reconstructions of quartz, cristobalite and stishovite

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