Oxygen- and Lithium-Doped Hybrid Boron-Nitride/Carbon Networks for Hydrogen Storage

Shayeganfar, Farzaneh; Shahsavari, Rouzbeh

doi:10.1021/acs.langmuir.6b02997

Cited by 64 publications

(42 citation statements)

References 73 publications

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“…focused on a unit cell of BN consisting of a 3×3 BN primitive surface monolayer; with pristine BN, the adsorption energy was calculated to be −6.9 kJ mol −1 whereas it improved to 8.4 kJ mol −1 for O‐doped BN . Shayeganfar and Shahsavari used DFT and molecular dynamics simulations to compare pristine and O‐doped three‐dimensional pillared BN . It was found that O favors the chemisorbed state of H 2 by polarizing it.…”

Section: Avenues For Improvementsmentioning

confidence: 99%

“…[61] Shayeganfara nd Shahsavari used DFT and molecular dynamics simulations to compare pristine and O-doped three-dimensional pillared BN. [62] It was found that Of avors the chemisorbed state of H 2 by polarizing it. It is theoretically able to favor the adsorption of am aximum of six H 2 moleculesa round it;s pecifically, three of the H 2 molecules are adsorbed onto the Oa tom and the three otherm olecules are far away from O.…”

Section: Substitutional Doping Of Bnmentioning

confidence: 99%

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Boron Nitride for Hydrogen Storage

2018

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Boron nitride, BN, for hydrogen storage emerged in the beginning of the millennium and there swiftly followed more than 15 years of efforts combining experimental laboratory works and to a greater extent computational predictions. BN has been considered mainly for the storage of molecular hydrogen, H2, by physisorption and/or chemisorption over a wide range of temperatures, that is, between −196 °C and 300 °C. Yet its potential has gone beyond the sorption of H2 as it has been also, although less extensively, involved in chemical H storage. The present review aims at giving a comprehensive overview of the main experimental results and findings as well as of the different avenues worth being explored. A key lesson of this survey is that boron nitride may turn out to be a promising material for hydrogen storage at room conditions provided all the predictions come true. The “ball” is now in the lab experimenters’ court.

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Section: Avenues For Improvementsmentioning

confidence: 99%

Section: Substitutional Doping Of Bnmentioning

confidence: 99%

Boron Nitride for Hydrogen Storage

2018

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“…We use the data from the radiation cascade MD simulations on multilayer hexagonal boron nitride (h‐BN), and graphene (graphite) microstructures conducted for wide radiation doses and high temperatures by our group . h‐BN and graphene are among the most promising multifunctional 2D materials that hold great promises for various applications in electronics, powers, energy, etc., including exposure to extreme conditions such as concurrent radiation and high temperature . Two sequential stages of MD simulations were applied to the models: first, a primary knock‐on atom (PKA) was initiated with a specific radiation dose energy when the system was at specific temperature (stage I), followed by a tensile simulation along [100]—the zigzag edge—to obtain the strength of the radiation‐induced damaged structures at the same temperature.…”

Section: General Methods and Strategiesmentioning

confidence: 99%

Deep Learning to Speed up the Development of Structure–Property Relations For Hexagonal Boron Nitride and Graphene

Hundi

Shahsavari

2019

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Structure–property maps play a key role in accelerated materials discovery. The current norm for developing these maps includes computationally expensive physics‐based simulations. Here, the capabilities of deep learning agents are explored such as convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) to predict structure–property relations and reduce dependence on simulations. This study contains simulated hexagonal boron nitride (h‐BN) microstructures damaged by various levels of radiation and temperature, with the objective to predict their residual strengths from the final atomic positions. By developing low dimensional physical descriptors to statistically describe the defects, these results show that purpose‐specific microstructure representation can help in achieving a good prediction accuracy at low computational cost. Furthermore, the adaptability of the trained deep learning agents is explored to predict structure–property maps of other 2D materials using transfer learning. It is shown that in order to achieve good predictions accuracy (≈95% R2), an agent that is training for the first time (“learning from scratch”) requires 23–45% of simulated data, whereas an agent adapting to a different material (“transfer learning”) requires only about 10% or less. This suggests that transfer learning is a potential game changer in material discovery and characterization approaches.

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“…Hydrogen storage in solids is preferred to other methods, and porous materials are particularly favored due to their high surface area, including zeolites, MOFs, and carbon materials. [171] One of the present challenges of hydrogen storage is the fact that other gas molecules like oxygen can be incorporated into hydrogen storage media during the hydrogen adsorption, [172] which forces to limit the access of oxygen to the material and increases the costs. [170] Another high-performance material is pillared boron nitride-graphene, which integrates oxygen-doped boron nitride nanotubes and graphene into a single 3D structure.…”

Section: Hydrogen Production By Water Splittingmentioning

confidence: 99%

Research Frontiers in Energy‐Related Materials and Applications for 2020–2030

Blay

Galian

Mureşan

et al. 2020

Advanced Sustainable Systems

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structure was found for the first time in a mineral composed by CaTiO 3 in 1839. Since then, multiple metal oxide perovskites (AMO 3 ) were developed, which have interesting properties for ferroelectric, superconductive, and pyroelectric applications. Metal halide perovskites (X is a halide anion such as Cl − , Br − , or I − ), which were developed later, display promising semiconducting properties. In particular, lead halide perovskites (APbX 3 ) are the most widely studied perovskite composition and are classified according to the A cation into hybrid perovskites (methylammonium bromide, MA or formamidinium, FA) and all-inorganic perovskites (cesium, Cs). [2] The electronic properties of the perovskites are related to the M-X bond, while the size of the A cation can introduce crystal distortion and change the symmetry of the ideal cubic crystal structure. [3] Interestingly, by using a large A organic cation, low-dimensional halide perovskites can be formed, including 2D sheets, 1D chains, or 0D clusters. [4] Lead halide perovskites are revolutionizing photovoltaic technology thank to their outstanding optical and electronic properties, low cost, and simple preparation. Compared to silicon-based technology, perovskites are prepared from more abundant and low-cost precursors. The superior performance and high efficiency of perovskite-based solar cells (PSC) are due to i) high optical absorption coefficient, ii) long carrier diffusion length This article delineates the state of the art for several materials used in the harvest, conversion, and storage of energy, and analyzes the challenges to be overcome in the decade ahead for them to reach the market and benefit society. The materials covered have had a special interest in recent years and include perovskites, materials for batteries and supercapacitors, graphene, and materials for hydrogen production and storage. Looking at the common challenges for these different systems, scientists in basic research should carefully consider commercial requirements when designing new materials. These include cost and ease of synthesis, abundance of precursors, recyclability of spent devices, toxicity, and stability. Improvements in these areas deserve more attention, as they can help bridge the gap for these technologies and facilitate the creation of partnerships between academia and industry. These improvements should be pursued in parallel with the design of novel compositions, nanostructures, and devices, which have led most interest during the past decade. Research groups are encouraged to adopt a cross-disciplinary mindset, which may allow more efficient use of existing knowledge and facilitate breakthrough innovation in both basic and applied research of energy-related materials.

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Oxygen- and Lithium-Doped Hybrid Boron-Nitride/Carbon Networks for Hydrogen Storage

Cited by 64 publications

References 73 publications

Boron Nitride for Hydrogen Storage

Boron Nitride for Hydrogen Storage

Deep Learning to Speed up the Development of Structure–Property Relations For Hexagonal Boron Nitride and Graphene

Research Frontiers in Energy‐Related Materials and Applications for 2020–2030

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