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Strozi RB, Witman M, Stavila V, Cizek J, Sakaki K, Kim H, Melikhova O, Perrière L, Machida A, Nakahira Y, Zepon G, Botta WJ, Zlotea C. Elucidating Primary Degradation Mechanisms in High-Cycling-Capacity, Compositionally Tunable High-Entropy Hydrides. ACS APPLIED MATERIALS & INTERFACES 2023; 15:38412-38422. [PMID: 37540153 DOI: 10.1021/acsami.3c05206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
The hydrogen sorption properties of single-phase bcc (TiVNb)100-xCrx alloys (x = 0-35) are reported. All alloys absorb hydrogen quickly at 25 °C, forming fcc hydrides with storage capacity depending on the Cr content. A thermodynamic destabilization of the fcc hydride is observed with increasing Cr concentration, which agrees well with previous compositional machine learning models for metal hydride thermodynamics. The steric effect or repulsive interactions between Cr-H might be responsible for this behavior. The cycling performances of the TiVNbCr alloy show an initial decrease in capacity, which cannot be explained by a structural change. Pair distribution function analysis of the total X-ray scattering on the first and last cycled hydrides demonstrated an average random fcc structure without lattice distortion at short-range order. If the as-cast alloy contains a very low density of defects, the first hydrogen absorption introduces dislocations and vacancies that cumulate into small vacancy clusters, as revealed by positron annihilation spectroscopy. Finally, the main reason for the capacity drop seems to be due to dislocations formed during cycling, while the presence of vacancy clusters might be related to the lattice relaxation. Having identified the major contribution to the capacity loss, compositional modifications to the TiVNbCr system can now be explored that minimize defect formation and maximize material cycling performance.
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Affiliation(s)
- Renato Belli Strozi
- Univ Paris Est Creteil, CNRS, ICMPE, UMR 7182, 2 Rue Henri Dunant, 94320 Thiais, France
- Department of Materials Engineering, Federal University of São Carlos, DEMa-UFSCar, 13565-905 São Carlos, Brazil
| | - Matthew Witman
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Vitalie Stavila
- Sandia National Laboratories, Livermore, California 94551, United States
| | - Jakub Cizek
- Faculty of Mathematics and Physics, Charles University, V Holesovickach 2, Prague 8 18000, Czech Republic
| | - Kouji Sakaki
- National Institute of Advanced Industrial Science and Technology, AIST West, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan
| | - Hyunjeong Kim
- National Institute of Advanced Industrial Science and Technology, AIST West, 16-1 Onogawa, Tsukuba, Ibaraki 305-8569, Japan
| | - Oksana Melikhova
- Faculty of Mathematics and Physics, Charles University, V Holesovickach 2, Prague 8 18000, Czech Republic
| | - Loïc Perrière
- Univ Paris Est Creteil, CNRS, ICMPE, UMR 7182, 2 Rue Henri Dunant, 94320 Thiais, France
| | - Akihiko Machida
- National Institutes for Quantum Science and Technology (QST), 1-1-1, Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
| | - Yuki Nakahira
- National Institutes for Quantum Science and Technology (QST), 1-1-1, Kouto, Sayo-cho, Sayo-gun, Hyogo 679-5148, Japan
| | - Guilherme Zepon
- Department of Materials Engineering, Federal University of São Carlos, DEMa-UFSCar, 13565-905 São Carlos, Brazil
| | - Walter José Botta
- Department of Materials Engineering, Federal University of São Carlos, DEMa-UFSCar, 13565-905 São Carlos, Brazil
| | - Claudia Zlotea
- Univ Paris Est Creteil, CNRS, ICMPE, UMR 7182, 2 Rue Henri Dunant, 94320 Thiais, France
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Mai H, Le TC, Chen D, Winkler DA, Caruso RA. Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203899. [PMID: 36285802 PMCID: PMC9798988 DOI: 10.1002/advs.202203899] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 09/27/2022] [Indexed: 06/04/2023]
Abstract
Addressing climate change challenges by reducing greenhouse gas levels requires innovative adsorbent materials for clean energy applications. Recent progress in machine learning has stimulated technological breakthroughs in the discovery, design, and deployment of materials with potential for high-performance and low-cost clean energy applications. This review summarizes basic machine learning methods-data collection, featurization, model generation, and model evaluation-and reviews their use in the development of robust adsorbent materials. Key case studies are provided where these methods are used to accelerate adsorbent materials design and discovery, optimize synthesis conditions, and understand complex feature-property relationships. The review provides a concise resource for researchers wishing to use machine learning methods to rapidly develop effective adsorbent materials with a positive impact on the environment.
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Affiliation(s)
- Haoxin Mai
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - Tu C. Le
- School of EngineeringSTEM CollegeRMIT UniversityGPO Box 2476MelbourneVictoria3001Australia
| | - Dehong Chen
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
| | - David A. Winkler
- Monash Institute of Pharmaceutical SciencesMonash UniversityParkvilleVIC3052Australia
- School of Biochemistry and ChemistryLa Trobe UniversityKingsbury DriveBundoora3042Australia
- School of PharmacyUniversity of NottinghamNottinghamNG7 2RDUK
| | - Rachel A. Caruso
- Applied Chemistry and Environmental ScienceSchool of ScienceSTEM CollegeRMIT UniversityMelbourneVictoria3001Australia
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Allendorf MD, Stavila V, Snider JL, Witman M, Bowden ME, Brooks K, Tran BL, Autrey T. Challenges to developing materials for the transport and storage of hydrogen. Nat Chem 2022; 14:1214-1223. [DOI: 10.1038/s41557-022-01056-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 09/02/2022] [Indexed: 11/09/2022]
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Batalović K, Radaković J, Paskaš Mamula B, Kuzmanović B, Medić Ilić M. Predicting the Heat of Hydride Formation by Graph Neural Network ‐ Exploring the Structure–Property Relation for Metal Hydrides. ADVANCED THEORY AND SIMULATIONS 2022. [DOI: 10.1002/adts.202200293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Katarina Batalović
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Jana Radaković
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Bojana Paskaš Mamula
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Bojana Kuzmanović
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
| | - Mirjana Medić Ilić
- Center of Excellence for Hydrogen and Renewable Energy VINCA Institute of Nuclear Sciences – National Institute of the Republic of Serbia University of Belgrade P.O.Box 522 Belgrade 11000 Serbia
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Recent Development in Nanoconfined Hydrides for Energy Storage. Int J Mol Sci 2022; 23:ijms23137111. [PMID: 35806115 PMCID: PMC9267122 DOI: 10.3390/ijms23137111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 06/21/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022] Open
Abstract
Hydrogen is the ultimate vector for a carbon-free, sustainable green-energy. While being the most promising candidate to serve this purpose, hydrogen inherits a series of characteristics making it particularly difficult to handle, store, transport and use in a safe manner. The researchers’ attention has thus shifted to storing hydrogen in its more manageable forms: the light metal hydrides and related derivatives (ammonia-borane, tetrahydridoborates/borohydrides, tetrahydridoaluminates/alanates or reactive hydride composites). Even then, the thermodynamic and kinetic behavior faces either too high energy barriers or sluggish kinetics (or both), and an efficient tool to overcome these issues is through nanoconfinement. Nanoconfined energy storage materials are the current state-of-the-art approach regarding hydrogen storage field, and the current review aims to summarize the most recent progress in this intriguing field. The latest reviews concerning H2 production and storage are discussed, and the shift from bulk to nanomaterials is described in the context of physical and chemical aspects of nanoconfinement effects in the obtained nanocomposites. The types of hosts used for hydrogen materials are divided in classes of substances, the mean of hydride inclusion in said hosts and the classes of hydrogen storage materials are presented with their most recent trends and future prospects.
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Ahmed A, Siegel DJ. Predicting hydrogen storage in MOFs via machine learning. PATTERNS (NEW YORK, N.Y.) 2021; 2:100291. [PMID: 34286305 PMCID: PMC8276024 DOI: 10.1016/j.patter.2021.100291] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/10/2021] [Accepted: 05/26/2021] [Indexed: 11/14/2022]
Abstract
The H2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g cm-3) in combination with high surface areas (>5,300 m2 g-1), void fractions (∼0.90), and pore volumes (>3.3 cm3 g-1). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature.
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Affiliation(s)
- Alauddin Ahmed
- Mechanical Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
| | - Donald J. Siegel
- Mechanical Engineering Department, University of Michigan, Ann Arbor, MI 48109, USA
- Materials Science & Engineering, University of Michigan, Ann Arbor, MI 48109, USA
- Applied Physics Program, University of Michigan, Ann Arbor, MI 48109, USA
- University of Michigan Energy Institute, University of Michigan, Ann Arbor, MI 48109, USA
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Artificial Intelligence Application in Solid State Mg-Based Hydrogen Energy Storage. JOURNAL OF COMPOSITES SCIENCE 2021. [DOI: 10.3390/jcs5060145] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The use of Mg-based compounds in solid-state hydrogen energy storage has a very high prospect due to its high potential, low-cost, and ease of availability. Today, solid-state hydrogen storage science is concerned with understanding the material behavior of different compositions and structure when interacting with hydrogen. Finding a suitable material has remained an elusive idea, and therefore, this review summarizes works by various groups, the milestones they have achieved, and the roadmap to be taken on the study of hydrogen storage using low-cost magnesium composites. Mg-based compounds are further examined from the perspective of artificial intelligence studies, which helps to improve prediction of their properties and hydrogen storage performance. There exist several techniques to improve the performance of Mg-based compounds: microstructure modification, use of catalytic additives, and composition regulation. Microstructure modification is usually achieved by employing different synthetic techniques like severe plastic deformation, high energy ball milling, and cold rolling, among others. These synthetic approaches are discussed herein. In this review, a discussion of key parameters and operating conditions are highlighted in a view to finding high storage capacity and faster kinetics. Furthermore, recent approaches like machine learning have found application in guiding the experimental design. Hence, this review paper also explores how machine learning techniques have been utilized to fasten the materials research. It is however noted that this study is not exhaustive in itself.
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