| 1. |
- Pasquini, Luca, et al.
(författare)
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Magnesium- and intermetallic alloys-based hydrides for energy storage : modelling, synthesis and properties
- 2022
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Ingår i: Progress in Energy. - : Institute of Physics Publishing (IOPP). - 2516-1083. ; 4:3
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Forskningsöversikt (refereegranskat)abstract
- Hydrides based on magnesium and intermetallic compounds provide a viable solution to the challenge of energy storage from renewable sources, thanks to their ability to absorb and desorb hydrogen in a reversible way with a proper tuning of pressure and temperature conditions. Therefore, they are expected to play an important role in the clean energy transition and in the deployment of hydrogen as an efficient energy vector. This review, by experts of Task 40 'Energy Storage and Conversion based on Hydrogen' of the Hydrogen Technology Collaboration Programme of the International Energy Agency, reports on the latest activities of the working group 'Magnesium- and Intermetallic alloys-based Hydrides for Energy Storage'. The following topics are covered by the review: multiscale modelling of hydrides and hydrogen sorption mechanisms; synthesis and processing techniques; catalysts for hydrogen sorption in Mg; Mg-based nanostructures and new compounds; hydrides based on intermetallic TiFe alloys, high entropy alloys, Laves phases, and Pd-containing alloys. Finally, an outlook is presented on current worldwide investments and future research directions for hydrogen-based energy storage.
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| 2. |
- Prasad, Ram R. R., et al.
(författare)
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Modulated Self-Assembly of Catalytically Active Metal-Organic Nanosheets Containing Zr6 Clusters and Dicarboxylate Ligands
- 2024
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Ingår i: ACS Applied Materials and Interfaces. - 1944-8244 .- 1944-8252. ; 16:14, s. 17812-17820
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Tidskriftsartikel (refereegranskat)abstract
- Two-dimensional metal–organic nanosheets (MONs) have emerged as attractive alternatives to their three-dimensional metal–organic framework (MOF) counterparts for heterogeneous catalysis due to their greater external surface areas and higher accessibility of catalytically active sites. Zr MONs are particularly prized because of their chemical stability and high Lewis and Brønsted acidities of the Zr clusters. Herein, we show that careful control over modulated self-assembly and exfoliation conditions allows the isolation of the first example of a two-dimensional nanosheet wherein Zr6 clusters are linked by dicarboxylate ligands. The hxl topology MOF, termed GUF-14 (GUF = Glasgow University Framework), can be exfoliated into monolayer thickness hns topology MONs, and acid-induced removal of capping modulator units yields MONs with enhanced catalytic activity toward the formation of imines and the hydrolysis of an organophosphate nerve agent mimic. The discovery of GUF-14 serves as a valuable example of the undiscovered MOF/MON structural diversity extant in established metal–ligand systems that can be accessed by harnessing the power of modulated self-assembly protocols.
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| 3. |
- Witman, Matthew D., et al.
(författare)
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Towards Pareto optimal high entropy hydrides via data-driven materials discovery
- 2023
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Ingår i: Journal of Materials Chemistry A. - : Royal Society of Chemistry. - 2050-7488 .- 2050-7496. ; 11:29, s. 15878-15888
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Tidskriftsartikel (refereegranskat)abstract
- The ability to rapidly screen material performance in the vast space of high entropy alloys is of critical importance to efficiently identify optimal hydride candidates for various use cases. Given the prohibitive complexity of first principles simulations and large-scale sampling required to rigorously predict hydrogen equilibrium in these systems, we turn to compositional machine learning models as the most feasible approach to screen on the order of tens of thousands of candidate equimolar high entropy alloys (HEAs). Critically, we show that machine learning models can predict hydride thermodynamics and capacities with reasonable accuracy (e.g. a mean absolute error in desorption enthalpy prediction of ∼5 kJ molH2−1) and that explainability analyses capture the competing trade-offs that arise from feature interdependence. We can therefore elucidate the multi-dimensional Pareto optimal set of materials, i.e., where two or more competing objective properties can't be simultaneously improved by another material. This provides rapid and efficient down-selection of the highest priority candidates for more time-consuming density functional theory investigations and experimental validation. Various targets were selected from the predicted Pareto front (with saturation capacities approaching two hydrogen per metal and desorption enthalpy less than 60 kJ molH2−1) and were experimentally synthesized, characterized, and tested amongst an international collaboration group to validate the proposed novel hydrides. Additional top-predicted candidates are suggested to the community for future synthesis efforts, and we conclude with an outlook on improving the current approach for the next generation of computational HEA hydride discovery efforts.
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| 4. |
- Witman, Matthew, et al.
(författare)
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Data-driven discovery and synthesis of high entropy alloy hydrides with targeted thermodynamic stability
- 2021
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Ingår i: Chemistry of Materials. - : American Chemical Society (ACS). - 0897-4756 .- 1520-5002. ; 33:11, s. 4067-4076
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Tidskriftsartikel (refereegranskat)abstract
- Solid-state hydrogen storage materials that are optimized for specific use cases could be a crucial facilitator of the hydrogen economy transition. Yet the discovery of novel hydriding materials has historically been a manual process driven by chemical intuition or experimental trial-and-error. Data-driven materials' discovery paradigms provide an alternative to traditional approaches, whereby machine/statistical learning (ML) models are used to efficiently screen materials for desired properties and significantly narrow the scope of expensive/time-consuming first-principles modeling and experimental validation. Here we specifically focus on a relatively new class of hydrogen storage materials, high entropy alloy (HEA) hydrides, whose vast combinatorial composition space and local structural disorder necessitates a data-driven approach that does not rely on exact crystal structures in order to make property predictions. Our ML model quickly screens hydride stability within a large HEA space and permits down selection for laboratory validation based not only on targeted thermodynamic properties, but also secondary criteria such as alloy phase stability and density. To experimentally verify our predictions, we performed targeted synthesis and characterization of several novel hydrides that demonstrate significant destabilization (70x increase in equilibrium pressure, 20 kJ/molH2 decrease in desorption enthalpy) relative to the benchmark HEA hydride, TiVZrNbHfHx. Ultimately, by providing a large composition space in which hydride thermodynamics can be continuously tuned over a wide range, this work will enable efficient materials selection for various applications, especially in areas such as metal hydride based hydrogen compressors, actuators, and heat pumps.
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