Two Borate Supramolecular Frameworks Based on B4O5(OH)4 Cluster Units

Huang, Guang; Pan, Rui; He, Huan; Yang, Biao; Yang, Guo‐Yu

doi:10.1007/s10876-015-0894-6

Cited by 3 publications

(3 citation statements)

References 33 publications

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“…Genetic algorithms have been the common choice to assist predicting metal nanocluster configurations using computational methods. [287][288][289] Experimentally, atomic local environments can be inferred from X-ray adsorption spectroscopy (XAS). Several works have tried to predict the local environments by combining ML models with high-throughput computational or experimental XAS data.…”

Section: Morphologymentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

398

281

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Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Section: Morphologymentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

398

281

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“…These observations are compatible with previous works for some of the compositions addressed here. [32,57] 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 The binding energy (Equation (1)) and the formation energy (Equation (2)) are reported in Figure 2. The sharp increase of the binding energy starting from 7 tin atoms reveals the low SnÀ Sn cohesion as compared to the PtÀ Pt one.…”

Section: Non-supported Pt X Sn 13-x Cluster Modelsmentioning

confidence: 99%

“…[22,23] Experimental findings support these theoretical observations. [23,28] However, only a few theoretical studies deal with bimetallic PtSn nanoclusters, [14,[29][30][31]32] despite their high practical interest. None of these studies includes at the same time support and environment effects.…”

Section: Introductionmentioning

confidence: 99%

Atomistic Models for Highly‐Dispersed PtSn/γ‐Al₂O₃ Catalysts: Ductility and Dilution Affect the Affinity for Hydrogen

et al. 2019

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Supported platinum‐based sub‐nanometric particles play a central role in many catalytic applications. In particular, platinum‐tin active phases supported on γ‐Al2O3 are largely employed for dehydrogenation of alkanes and catalytic reforming of naphtha cuts, although geometric and electronic effects of the active phase in the presence of hydrogen still remains highly debated. Thanks to periodic density functional theory (DFT), we propose structural models of such systems containing thirteen metal atoms (PtxSn13‐x with 0≤x≤13) deposited on the (100) γ‐Al2O3 surface. We thus unravel the intricate effects of the composition (Pt/Sn ratio), of the support (γ‐Al2O3) and the hydrogen coverage on the stability of platinum‐tin sub‐nanometric clusters, in the case where tin is reduced (Sn0). A detailed investigation of the interaction of the supported Pt10Sn3 cluster with hydrogen by velocity‐scaled molecular dynamics provides a mapping of the hydrogen coverage as a function of the operating conditions (T, P(H2)). Our study highlights significant differences between Pt13 and PtxSn13‐x clusters in terms of ductility and dilution (also called ensemble) effects which may be at the origin of the different reactivities usually reported for Pt and PtSn supported catalysts.

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Synthesis, structure, and oxygen reduction reaction of three copper complexes supported by ethylenediamine derivatives with outer-sphere oxoboron clusters

Chen,

Liu,

Pan

2024

Inorganica Chimica Acta

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Two Borate Supramolecular Frameworks Based on B4O5(OH)4 Cluster Units

Cited by 3 publications

References 33 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Atomistic Models for Highly‐Dispersed PtSn/γ‐Al₂O₃ Catalysts: Ductility and Dilution Affect the Affinity for Hydrogen

Synthesis, structure, and oxygen reduction reaction of three copper complexes supported by ethylenediamine derivatives with outer-sphere oxoboron clusters

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Two Borate Supramolecular Frameworks Based on B4O5(OH)4 Cluster Units

Cited by 3 publications

References 33 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Atomistic Models for Highly‐Dispersed PtSn/γ‐Al2O3 Catalysts: Ductility and Dilution Affect the Affinity for Hydrogen

Synthesis, structure, and oxygen reduction reaction of three copper complexes supported by ethylenediamine derivatives with outer-sphere oxoboron clusters

Contact Info

Product

Resources

About

Atomistic Models for Highly‐Dispersed PtSn/γ‐Al₂O₃ Catalysts: Ductility and Dilution Affect the Affinity for Hydrogen