Synthesis of destabilized nanostructured lithium hydride via hydrogenation of lithium electrochemically inserted into graphite

Huang, Liwu; Bonnet, Jean-Pierre; Zlotea, Claudia; Bourgon, Julie; Latroche, M.; Courty, Matthieu; Aymard, L.

doi:10.1016/j.ijhydene.2015.07.130

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…250 Lithium hydride NPs have also been synthesized electrochemically by reductive hydrogenation of metallic lithium intercalated into graphite. 251 TGA-MS measurements reveal that the onset of hydrogen desorption for the sub-100 nm LiH NPs is >500 °C lower compared to bulk LiH and starts at around 200 °C.…”

Section: Thin Film Synthesesmentioning

confidence: 94%

“…The electrochemical reaction results in formation of a composite containing Mg NPs embedded in a LiH matrix, which can be converted electrochemically into MgH 2 NPs of 10 to 40 nm . Lithium hydride NPs have also been synthesized electrochemically by reductive hydrogenation of metallic lithium intercalated into graphite . TGA-MS measurements reveal that the onset of hydrogen desorption for the sub-100 nm LiH NPs is >500 °C lower compared to bulk LiH and starts at around 200 °C.…”

Section: Synthetic Routesmentioning

confidence: 99%

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Nanostructured Metal Hydrides for Hydrogen Storage

et al. 2018

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Knowledge and foundational understanding of phenomena associated with the behavior of materials at the nanoscale is one of the key scientific challenges toward a sustainable energy future. Size reduction from bulk to the nanoscale leads to a variety of exciting and anomalous phenomena due to enhanced surface-to-volume ratio, reduced transport length, and tunable nanointerfaces. Nanostructured metal hydrides are an important class of materials with significant potential for energy storage applications. Hydrogen storage in nanoscale metal hydrides has been recognized as a potentially transformative technology, and the field is now growing steadily due to the ability to tune the material properties more independently and drastically compared to those of their bulk counterparts. The numerous advantages of nanostructured metal hydrides compared to bulk include improved reversibility, altered heats of hydrogen absorption/desorption, nanointerfacial reaction pathways with faster rates, and new surface states capable of activating chemical bonds. This review aims to summarize the progress to date in the area of nanostructured metal hydrides and intends to understand and explain the underpinnings of the innovative concepts and strategies developed over the past decade to tune the thermodynamics and kinetics of hydrogen storage reactions. These recent achievements have the potential to propel further the prospects of tuning the hydride properties at nanoscale, with several promising directions and strategies that could lead to the next generation of solid-state materials for hydrogen storage applications.

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Section: Thin Film Synthesesmentioning

confidence: 94%

Section: Synthetic Routesmentioning

confidence: 99%

Nanostructured Metal Hydrides for Hydrogen Storage

et al. 2018

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“…We chose this for the simplicity of having only two element types. Lithium hydride clusters have been studied in several theoretical − and experimental , works and have implications in various applications including hydrogen storage. − …”

Section: Resultsmentioning

confidence: 99%

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Xie

Persson

Small

2020

J. Chem. Theory Comput.

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Machine learning (ML) approximations to density functional theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present, they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new machine learning approach called “BpopNN” for obtaining efficient approximations to DFT PESs. Conceptually, the methodology is based on approaching the true DFT energy as a function of electron populations on atoms; in practice, this is realized with available density functionals and constrained DFT (CDFT). The new approach creates approximations to this function with neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on Li n H n clusters.

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“…Lithium hydride clusters have been studied in several theoretical [114][115][116][117][118][119] and experimental 120,121 works, and have implications in various applications including hydrogen storage. [122][123][124][125][126] In the calculations below, we employ the descriptor described above, but, as mentioned above, we modified it so that the total electron population on each atom, rather than the 2 spin populations, is used for the electronic terms.…”

Section: Resultsmentioning

confidence: 99%

Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations

Xie

Persson

Small

2020

Preprint

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Machine Learning (ML) approximations to Density Functional Theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new Machine Learning approach called "bpopNN" for obtaining efficient approximations to DFT PESs. The methodology is based on approaching the true DFT energy as a function of electron populations on atoms, which may be realized in practice with constrained DFT (CDFT). The new approach creates approximations to this function with deep neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on Li n H n clusters.

show abstract

Synthesis of destabilized nanostructured lithium hydride via hydrogenation of lithium electrochemically inserted into graphite

Cited by 6 publications

References 20 publications

Nanostructured Metal Hydrides for Hydrogen Storage

Nanostructured Metal Hydrides for Hydrogen Storage

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations

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