Fe(II)Ti(IV)O3 mixed oxide monolayer at rutile TiO2(011): Structures and reactivities

Wen, Zhan-Hui; Halpegamage, Sandamali; Gong, Xue‐Qing; Batzill, Matthias

doi:10.1016/j.susc.2016.04.019

Cited by 4 publications

(10 citation statements)

References 43 publications

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“…We used the genetic algorithm (GA) optimization method implemented in the Universal Structure Predictor Evolutionary Xtallography code (USPEX) to search amorphous surface structures, which has been successfully applied for predicting stable structures of bulk crystal, nanoclusters, surface reconstruction, etc. , As a global optimization method, the USPEX code uses four ways, namely hereditary, mutation, transmutation, and random, to explore the configurational space for the thermodynamically stable or metastable structures. For this work, the metastable states were selected to mimic amorphous surfaces.…”

Section: Methods and Computational Detailsmentioning

confidence: 99%

“…Recently, computationally efficient and data-driven machine learning (ML) models are becoming alternative to theoretical catalytic research (especially in HTS), which could combine with specific chemical data sets and experts’ domain knowledge. − Several groups have succeeded in employing traditional computational catalysis and ML models to reveal some intriguing findings about metal or alloy catalysts. Ma et al used a multilayer feed-forward neural network and hand-crafted features (including structural and electronic features) to make good predictions of the adsorption energy of CO on metallic alloy .…”

Section: Introductionmentioning

confidence: 99%

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Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

Zhang

Wang

2020

J. Phys. Chem. C

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The prediction of chemisorption energy to facilitate the high-throughput screening of active catalysts has been long pursued but remains challenging. In particular, amorphous materials usually exhibit superior activity and have drawn everincreasing attention in heterogeneous catalysis. However, the insight into the basic structure−property relation remains far from sufficient owing to their disordered structure and untracked surface state, let alone the effective prediction of adsorption energy. Here, employing the amorphous Ni 2 P catalyst as an example and powerful machine learning (ML) models, we propose an effective strategy that enables fast and quantitative prediction of the adsorption energy of hydrogen on amorphous Ni 2 P surfaces. Specifically, our method decomposes the difficult prediction of adsorption energy on amorphous surfaces into two subproblems: frozen adsorption energy and relaxation energy. By training with a set of ab initio adsorption energies within a wide configuration space, we succeed to predict the adsorption energies with ∼0.1 eV error by adopting the feature only relying on local chemical environment. Our strategy allows us to successfully implement the highthroughput exploration of active sites for hydrogen evolution reaction (HER). This work builds a predictive model of site-specific chemisorption energy, and the related statistical analysis underpins the fundamental understanding of the chemical bond, which could largely facilitate rational design of active amorphous catalysts.

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Section: Methods and Computational Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

Zhang

Wang

2020

J. Phys. Chem. C

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“…XPD patterns were simulated by multiple electron scattering simulations implemented in the EDAC package . The atom clusters were constructed from the atom coordinates determined in DFT simulations for various surface models . The size and the scattering volume of the cluster are defined by R max which is constrained by the computation time.…”

Section: Experimental and Computational Methodsmentioning

confidence: 99%

“…This phase segregation indicates that the two surface compositions formed what is known as line phases in bulk phase diagrams, suggesting that these two compositions form particularly stable, low energy surface phases. The details of our previous experimental studies can be found in ref and for the DFT simulations in ref .…”

Section: Introductionmentioning

confidence: 99%

An Ordered Mixed Oxide Monolayer Formed by Iron Segregation on Rutile-TiO₂(011): Structural Determination by X-ray Photoelectron Diffraction

et al. 2016

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Iron impurities in the rutile-TiO 2 (011) surface can result in the formation of an ordered, ternary iron− titanium oxide monolayer. Here the FeTi 2 O 5 mixed oxide monolayer predicted by DFT simulations is confirmed by synchrotron based angle scanned X-ray photoelectron diffraction (XPD) studies. The ternary oxide monolayer has been synthesized on rutile-TiO 2 (011) substrate via two different experimental pathways: first, by segregation of Fe impurities from the bulk by annealing a clean TiO 2 (011) substrate in 1 × 10 −7 mbar of oxygen at ∼450 °C and, second, by physical vapor deposition of Fe on clean TiO 2 (011) in 1 × 10 −7 mbar of oxygen at ∼450 °C. For both preparation procedures an intermixed surface oxide is formed with Fe and Ti in 2+ and 4+ charge states, respectively. The surface is characterized by high-resolution and fast X-ray photoelectron spectroscopy (XPS), XPD, and low energy electron diffraction (LEED). Multiple electron scattering simulations implemented in the electron diffraction in atomic clusters (EDAC) package were performed for comparing experimental XPD patterns with structural models. The reliability of the approach was tested on XPD pattern of reconstructed clean TiO 2 (011)-2 × 1 surface, for which a structural model exists. The XPD pattern of the monolayer ternary iron titanium oxide is compared to structural models proposed by density functional theory (DFT)-based models.

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“…The 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 20 For each 2×1 surface cell of rutile TiO 2 (011), four TiO 2 units are exposed, and they have the same height on an unreconstructed surface while two of them rise above the other two on a 2×1 reconstructed surface. In a very recent work, we have reported an ordered Fe/Ti mixed oxide surface layer on the rutile TiO 2 (011) surface through combined experimental and computational studies 22 . We performed calculations in a similar way in the current work to search for the mixed oxide surface containing 1/3 M...…”

Section: Calculated Structures Of Mixed Oxide Monolayer Surfacementioning

confidence: 99%

Monolayer Intermixed Oxide Surfaces: Fe, Ni, Cr, and V Oxides on Rutile TiO₂(011)

et al. 2016

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The oxide/oxide interface for monolayer oxides on rutile TiO 2 (011) substrates is investigated for several transition metal (M) oxides, with M= Fe, Ni, Cr, and V. The samples are prepared using ultra-high vacuum sample preparation procedures and are characterized with a combination of photoemission spectroscopy and scanning tunneling microscopy (STM). Furthermore, stable intermixed monolayer oxides are determined by density functional theory (DFT) based simulations and compared to the experimental results. Experimentally we find that for specific oxidation conditions a monolayer intermixed oxide is formed for all M. Although different structures as well as oxides in different oxidation states can be obtained depending on the preparation conditions, one common structure has been identified for all M. This intermixed oxide appears to have a defined composition of MTi 2 O 5 . For very small amounts of M the surface segregates into a pure TiO 2 (011)-2x1 surface and into domains of the MTi 2 O 5 phase, indicating that this intermixed oxide monolayer is a low-energy line phase in a compositional surface phase diagram. The gas-phase oxygen pressure that is required to form the intermixed MTi 2 O 5 surface increases in the order of V

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Fe(II)Ti(IV)O3 mixed oxide monolayer at rutile TiO2(011): Structures and reactivities

Cited by 4 publications

References 43 publications

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

An Ordered Mixed Oxide Monolayer Formed by Iron Segregation on Rutile-TiO₂(011): Structural Determination by X-ray Photoelectron Diffraction

Monolayer Intermixed Oxide Surfaces: Fe, Ni, Cr, and V Oxides on Rutile TiO₂(011)

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Fe(II)Ti(IV)O3 mixed oxide monolayer at rutile TiO2(011): Structures and reactivities

Cited by 4 publications

References 43 publications

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

Amorphous Catalysis: Machine Learning Driven High-Throughput Screening of Superior Active Site for Hydrogen Evolution Reaction

An Ordered Mixed Oxide Monolayer Formed by Iron Segregation on Rutile-TiO2(011): Structural Determination by X-ray Photoelectron Diffraction

Monolayer Intermixed Oxide Surfaces: Fe, Ni, Cr, and V Oxides on Rutile TiO2(011)

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Product

Resources

About

An Ordered Mixed Oxide Monolayer Formed by Iron Segregation on Rutile-TiO₂(011): Structural Determination by X-ray Photoelectron Diffraction

Monolayer Intermixed Oxide Surfaces: Fe, Ni, Cr, and V Oxides on Rutile TiO₂(011)