4.6 Article

Enhanced Electrochemical Storage Properties of Na- and Mg- Intercalated B-Doped-Graphene Based Heterostructures and Bilayers

Journal

JOURNAL OF PHYSICAL CHEMISTRY C
Volume 124, Issue 2, Pages 1260-1268

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/acs.jpcc.9b07180

Keywords

-

Funding

  1. University of North Dakota [20622-4000-02624]
  2. ND EPSCoR through NSF [01A-1355466]
  3. U.S. Department of Energy Office of Science [DE-ACO2-06CH11357]

Ask authors/readers for more resources

Modern technology requires novel materials to develop efficient storage systems, which offer high storage capacities and charging/discharging rates while maintaining good mechanical stability and cyclic lifetime. The extended surface areas in the lightweight two-dimensional (2D) materials are useful to achieve a high gravimetric capacity. Among various 2D materials, Ti2CO2 and B-doped graphene (approximate to 8%) were selected because of their low molecular weight and good electrical conductivity. Highly abundant Na and Mg are convenient to lower the production cost of ion storage. In this study, we performed first-principles calculations to examine the suitability of Na and Mg intercalation in Ti2CO2/B-doped-graphene (B-Gr) heterostructures and B-Gr bilayers. Even though Na- and Mg-intercalated bare graphene bilayers are not energetically stable, our studies reveal that (B-Gr) bilayers facilitate the storing of those ions. As a consequence of smaller atomic size, Mg-intercalated systems show low structural deformations and interlayer distance change (<= 0.5 angstrom) and low in-plane lattice constant change (<= 0.1%), indicating good mechanical stability during the charging/discharging process. The considered bilayers provide higher capacities than MXene-based heterostructures. Na- and Mg-intercalated Ti2CO2/B-Gr systems allow 240.4 and 295.1 mA h/g storage capacities, respectively. In comparison, the calculated gravimetric storage capacities for Na- and Mg-intercalated B-Gr bilayers are 283.8 and 320.6 mA h/g, respectively. All ion-intercalated systems provide average voltages greater than 0.75 V. Our diffusion barrier calculations revealed that very low diffusion barriers, as small as 0.18 eV, are expected for the Na-intercalated systems, offering fast charging/discharging rates for battery applications.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Physics, Condensed Matter

Engineering magnetic anisotropy and exchange couplings in double transition metal MXenes via surface defects

Edirisuriya M. D. Siriwardane, Pragalv Karki, Yen Lee Loh, Deniz Cakir

Summary: This study investigates the magnetic and electronic properties of magnetic double transition metal MXene alloys using density functional theory and Monte Carlo methods. Surface defects such as oxygen vacancies and hydrogen adatoms can modify electronic structures and magnetic properties. Defects can change magnetic anisotropy and enhance exchange couplings, leading to improved Curie temperatures.

JOURNAL OF PHYSICS-CONDENSED MATTER (2021)

Article Chemistry, Physical

Crystal Structure Prediction Using an Age-Fitness Multiobjective Genetic Algorithm and Coordination Number Constraints

Wenhui Yang, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu

Summary: Crystal structure prediction is an important method for discovering new materials, but existing algorithms are inefficient and have limitations in scalability. This paper proposes a contact-map-based crystal structure prediction method that optimizes three objectives to improve prediction accuracy and avoid premature convergence.

JOURNAL OF PHYSICAL CHEMISTRY A (2022)

Article Chemistry, Inorganic & Nuclear

TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery

Lai Wei, Nihang Fu, Edirisuriya M. D. Siriwardane, Wenhui Yang, Sadman Sadeed Omee, Rongzhi Dong, Rui Xin, Jianjun Hu

Summary: Fast and accurate crystal structure prediction algorithms and web servers are important for the discovery of new materials, but currently not accessible for most researchers. This study develops a template-based prediction algorithm and web server that uses various factors to select structure templates and return multiple predictions. Benchmark tests show high accuracy of the algorithm and successful application in material discovery.

INORGANIC CHEMISTRY (2022)

Article Chemistry, Physical

MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art

Jianjun Hu, Stanislav Stefanov, Yuqi Song, Sadman Sadeed Omee, Steph-Yves Louis, Edirisuriya M. D. Siriwardane, Yong Zhao, Lai Wei

Summary: The availability and easy access to large-scale experimental and computational materials data have accelerated the development of algorithms and models for materials property prediction and generative design. However, the lack of user-friendly materials informatics web servers hampers the adoption of such tools in daily materials screening and design by scientists. MaterialsAtlas.org is an online toolbox that aims to address this issue and provides various user-friendly tools for materials discovery.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Nanoscience & Nanotechnology

Accurate Prediction of Voltage of Battery Electrode Materials Using Attention-Based Graph Neural Networks

Steph-Yves Louis, Edirisuriya M. Dilanga Siriwardane, Rajendra P. Joshi, Sadman Sadeed Omee, Neeraj Kumar, Jianjun Hu

Summary: Proposed attention-based graph convolutional neural network techniques significantly improve the accuracy in voltage prediction by combining atomic composition and atomic coordinates, compared to composition-based machine learning models.

ACS APPLIED MATERIALS & INTERFACES (2022)

Article Chemistry, Physical

Generative design of stable semiconductor materials using deep learning and density functional theory

Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Indika Perera, Jianjun Hu

Summary: In this study, a computational pipeline combining generative adversarial networks, classifiers, and first-principles calculations was developed to discover stable semiconductors. It was found that AA ' MH6 semiconductors are wide-bandgap semiconductors, with BaSrZnH6 and KNaNiH6 being direct-bandgap semiconductors.

NPJ COMPUTATIONAL MATERIALS (2022)

Article Materials Science, Multidisciplinary

Nanolaminated Fe2AB2 and Mn2AB2 (A = Al, Si, Ga, In) materials and the assessment of their electronic correlations

Edirisuriya M. Dilanga Siriwardane, Turan Birol, Onur Erten, Deniz Cakir

Summary: This study investigated the properties, stability, and bonding characteristics of ternary layered transition metal borides like Fe(2)AB(2) and Mn(2)AB(2) using first-principles density functional theory. Various calculations revealed electronic and magnetic properties, potential structural features, and bonding characteristics of these compounds.

PHYSICAL REVIEW MATERIALS (2022)

Article Chemistry, Physical

Physics guided deep learning for generative design of crystal materials with symmetry constraints

Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, Nihang Fu, Mohammed Al-Fahdi, Ming Hu, Jianjun Hu

Summary: We propose a deep learning based Physics Guided Crystal Generative Model (PGCGM) for efficient crystal material design. Our model increases the generation validity by more than 700% compared to FTCP, one of the latest structure generators and by more than 45% compared to our previous CubicGAN model. The generated structures are validated using Density Functional Theory (DFT) calculations, with 1869 out of 2000 materials successfully optimized and deposited into the Carolina Materials Database, showing thermodynamic stability and potential synthesizability with negative formation energy and energy-above-hull less than 0.25 eV/atom for 39.6% and 5.3% of the materials, respectively.

NPJ COMPUTATIONAL MATERIALS (2023)

Correction Chemistry, Physical

Physics guided deep learning for generative design of crystal materials with symmetry constraints (vol 9, 38, 2023)

Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, Nihang Fu, Mohammed Al-Fahdi, Ming Hu, Jianjun Hu

NPJ COMPUTATIONAL MATERIALS (2023)

Article Materials Science, Multidisciplinary

Defect formation and migration in MAlB (M = Mo, W) and N2AlB2 (N = Cr, Fe): A first-principles study

Salawu Omotayo Akande, Deniz cakir

Summary: Using density functional theory calculations, we investigated the stability and mobility of point and extended defects in MAlB and N2AlB2 MAB phases. Our findings indicate that VB/NAl defect is the easiest to form under M/N-rich conditions. We also observed that vacancy and antisite defects are more easily formed than interstitial ones. Additionally, we revealed the stability of compositional defects and tilt/rotational boundaries in MAlB and N2AlB2.

PHYSICAL REVIEW MATERIALS (2023)

Article Chemistry, Multidisciplinary

Deep Learning-Based Prediction of Contact Maps and Crystal Structures of Inorganic Materials

Jianjun Hu, Yong Zhao, Qin Li, Yuqi Song, Rongzhi Dong, Wenhui Yang, Edirisuriya M. D. Siriwardane

Summary: Crystal structure prediction is a major unresolved issue in materials science. This study introduces AlphaCrystal, an algorithm that combines a deep residual neural network model and genetic algorithms for predicting the atomic contact map and reconstructing the 3D structure of a target material, respectively. Experimental results demonstrate that AlphaCrystal can accurately predict crystal structures and handle large systems, offering a faster approach to crystal structure prediction.

ACS OMEGA (2023)

Article Computer Science, Artificial Intelligence

Material transformers: deep learning language models for generative materials design

Nihang Fu, Lai Wei, Yuqi Song, Qinyang Li, Rui Xin, Sadman Sadeed Omee, Rongzhi Dong, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu

Summary: Pre-trained transformer language models have shown great potential in generative design of materials compositions, with high enrichment and generation novelty. Different models have different preferences and time complexity, and can be trained with selected sets to tailor the properties of generated compositions.

MACHINE LEARNING-SCIENCE AND TECHNOLOGY (2023)

Article Chemistry, Physical

Exploring the biointerfaces: ab initio investigation of nano-montmorillonite clay, and its interaction with unnatural amino acids

Ashan Fernando, Desmond Khan, Mark R. Hoffmann, Deniz Cakir

Summary: We investigated the interaction between biomimetic Fe and Mg co-doped montmorillonite nanoclay and eleven unnatural amino acids. Employing three different functionals, we examined the clay's structural, electronic, and magnetic properties. Our results revealed the necessity of using specific functional to accurately describe the clay properties. We identified amino acids that strongly interacted with the clay surface, with steric orientation playing a crucial role in facilitating binding. Our DFT calculations highlighted significant electrostatic interactions between the amino acids and the clay slab, with the amino group's predominant role in this interaction. These findings hold promise for designing amino acids for clay-amino acid systems, leading to innovative bio-material composites for various applications. Additionally, our ab-initio molecular dynamics simulations confirmed the stability of clay-amino acid systems under ambient conditions, and the introduction of an implicit water solvent enhanced the binding energy of amino acids on the clay surface.

PHYSICAL CHEMISTRY CHEMICAL PHYSICS (2023)

Article Chemistry, Multidisciplinary

Contact map based crystal structure prediction using global optimization

Jianjun Hu, Wenhui Yang, Rongzhi Dong, Yuxin Li, Xiang Li, Shaobo Li, Edirisuriya M. D. Siriwardane

Summary: Crystal structure prediction using global optimization algorithms and atomic contact maps has shown potential for reconstructing crystal structures, but additional constraints are needed for successful predictions of other materials.

CRYSTENGCOMM (2021)

No Data Available