First Paper of 2024 : JOM – TMS

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Datta, D. Electro-Chemo-Mechanical Modeling of Multiscale Active Materials for Next-Generation Energy Storage: Opportunities and Challenges. JOM (2024).

LINK : https://doi.org/10.1007/s11837-023-06335-y

Although lithium-ion batteries represent the best available rechargeable battery technology, a significant energy and power density gap exists between LIBs and petrol/gasoline. The battery electrodes comprise a mixture of active materials particles, conductive carbon, and binder additives deposited onto a current collector. Although this basic design has persisted for decades, the active material particle’s desired size scale is debated. Traditionally, microparticles have been used in batteries. Advances in nanotechnology have spurred interest in deploying nanoparticles as active materials. However, despite many efforts in nano, industries still primarily use ‘old’ microparticles. Most importantly, the battery industry is unlikely to replace microstructures with nanometer-sized analogs. This poses an important question: Is there a place for nanostructure in battery design due to irreplaceable microstructure? The way forward lies in multiscale active materials, microscale structures with built-in nanoscale features, such as microparticles assembled from nanoscale building blocks or patterned with engineered or natural nanopores. Although experimental strides have been made in developing such materials, computational progress in this domain remains limited and, in some cases, negligible. However, the fields hold immense computational potential, presenting a multitude of opportunities. This perspective highlights the existing gaps in modeling multiscale active materials and delineates various open challenges in the realm of electro-chemo-mechanical modeling. By doing so, it aims to inspire computational research within this field and promote synergistic collaborative efforts between computational and experimental researchers.

New Paper in Annual Review of Heat Transfer

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EXPLORING THERMAL TRANSPORT IN ELECTROCHEMICAL ENERGY STORAGE SYSTEMS UTILIZING TWO-DIMENSIONAL MATERIALS: PROSPECTS AND HURDLES

LINK to Annual Review of Heat Transfer: 10.1615/AnnualRevHeatTransfer.2023049365

Two-dimensional materials (e.g., graphene and transition metal dichalcogenides) and their heterostructures have enormous applications in electrochemical energy storage systems such as batteries. A comprehensive and solid understanding of these materials’ thermal transport and mechanism is essential for practical device design. Several advanced experimental techniques have been developed to measure the intrinsic thermal conductivity of materials. However, experiments have challenges in providing improved control and characterization of complex structures, especially for low-dimensional materials. Theoretical and simulation tools, such as first-principles calculations, Boltzmann transport equations, molecular dynamics simulations, lattice dynamics simulation, and nonequilibrium Green’s function, provide reliable predictions of thermal conductivity and physical insights to understand the underlying thermal transport mechanism in materials. However, doing these calculations requires high computational resources. The development of new materials synthesis technology and fast-growing demand for rapid and accurate prediction of physical properties requires novel computational approaches. The machine learning method provides a promising solution to address such needs. This review details the recent development in atomistic/molecular studies and machine learning of thermal transport in two-dimensional materials. The paper also addresses the latest significant experimental advances. However, designing the best two-dimensional materials-based heterostructures is like a multivariate optimization problem. For example, a particular heterostructure may be suitable for thermal transport but can have lower mechanical strength/stability. For bilayer and multilayer structures, the interlayer distance may influence the thermal transport properties and interlayer strength. Therefore, the last part of this review addresses the future research direction in two-dimensional materials-based heterostructure design for thermal transport in energy storage systems.

New Paper by Dr. Vidushi Sharma in ACS Applied Energy Materials

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New Paper by Dr. Vidushi Sharma :

Effects of Graphene Interface on Potassiation in a Graphene-Selenium Heterostructure Cathode for Potassium-Ion Batteries, ACS Applied Energy Materials, 2023

Selenium (Se) cathodes are an exciting emerging high energy density storage system for potassium-ion batteries (KIB), where potassiation reactions are less understood. Here, we present an atomic-level investigation of a KxSe cathode enclosed in hexagonal lattices of carbon (C) characteristic of a layered graphene matrix and multiwalled carbon nanotubes (MW-CNTs). Microstructural changes directed by the graphene−substrate in the KxSe cathode are contrasted with those in the graphene-free cathode. Graphene’s binding affinity for long-chain polyselenides (Se3 = −2.82 eV and Se2 = −2.646 eV) at low K concentrations and ability to induce enhanced reactivity between Se and K at high K concentrations are investigated. Furthermore, intercalation voltage for graphene-enclosed KxSe cathode reaction intermediates is calculated with K2Se as the final discharged product. Our results indicate a single-step reaction near a voltage of 1.55 V between K and Se cathode. Findings in the paper suggest that operating at higher voltages (∼2 V) could result in the formation of reaction intermediates where intercalation/deintercalation of K could be a challenge, and therefore cause irreversible capacity losses in the battery. The primary issue here is the modulating favorability of graphene surface toward discharging of Se cathode due to its differential preferences for K−Se reaction intermediates. A comparison with a graphene-free cathode highlights the substantial changes a van der Waals (vdW) graphene interface can bring in the atomic structure and electrochemistry of the KxSe cathode.

DIBAKAR won the prestigious National Science Foundation CAREER AWARD

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Exciting News !! Dibakar won the highly prestigious US National Science Foundation CAREER Award.

Program : Mechanics of Materials and Structures (MOMS)

Project : CAREER: Electro-Chemo-Mechanics of Multiscale Active Materials for Next-Generation Energy Storage

Program Manager : Dr. Wendy Crone

SDSC Media Coverage : Graphene, Tin Combo Shows Promise

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San Diego Supercomputer Center (SDSC) highlighted our work: Graphene, Tin Combo Shows Promise for Solar Panels, Artificial Muscles and More

Check the related paper : Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage, ASME Journal of Electrochemical Energy Conversion and Storage, 19(4):041006, 2022

Intercalation Hosts for Multivalent-Ion Batteries

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Wiley Small Structures [LINK]

Among intercalation, alloying, and conversion battery chemistries, the intercalation chemistry is most widely used in commercial applications due to its superior reversibility, round trip efficiency, and stability, albeit at the expense of reduced specific capacity. While intercalation hosts for monovalent ions (e.g., lithium and sodium) are well developed, the jury is still out on the best available intercalation host materials for multivalent ions such as magnesium, zinc, calcium, and aluminum. In multivalent systems, it is challenging to find electrode materials that can act as a durable host, and accommodate large number of ions, while also permitting fast diffusion kinetics. In this perspective, the electrochemical performance of five distinct class of materials (prussian blue analogues, sodium super ionic conductors organic, layered, and open-tunnel oxides) for multivalent ion storage is evaluated. The analysis reveals that open-tunnel oxides show noticeably superior performance in multivalent ion batteries. Herein, the underlying reasons for this are discussed and the case is made for an in-depth machine-learning-driven “materials exploration effort” directed toward discovery of new open-tunneled oxides that could lead to vastly superior multivalent ion batteries.

ASME JEECS ‘Emerging Investigators in Electrochemical Energy Conversion and Storage 2022’

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We recently published this article in ASME Special Issue on “Emerging Investigators in Electrochemical Energy Conversion and Storage 2022” :

V. Sharma, D. Datta, Developing Potential Energy Surfaces for Graphene-Based 2D–3D Interfaces From Modified High-Dimensional Neural Networks for Applications in Energy Storage, ASME Journal of Electrochemical Energy Conversion and Storage, 19(4):041006, 2022

Abstract : Designing a new heterostructure electrode has many challenges associated with interface engineering. Demanding simulation resources and lack of heterostructure databases continue to be a barrier to understanding the chemistry and mechanics of complex interfaces using simulations. Mixed-dimensional heterostructures composed of two-dimensional (2D) and three-dimensional (3D) materials are undisputed next-generation materials for engineered devices due to their changeable properties. The present work computationally investigates the interface between 2D graphene and 3D tin (Sn) systems with density functional theory (DFT) method. This computationally demanding simulation data is further used to develop machine learning (ML)-based potential energy surfaces (PES). The approach to developing PES for complex interface systems in the light of limited data and the transferability of such models has been discussed. To develop PES for graphene-tin interface systems, high-dimensional neural networks (HDNN) are used that rely on atom-centered symmetry function to represent structural information. HDNN are modified to train on the total energies of the interface system rather than atomic energies. The performance of modified HDNN trained on 5789 interface structures of graphene|Sn is tested on new interfaces of the same material pair with varying levels of structural deviations from the training dataset. Root-mean-squared error (RMSE) for test interfaces fall in the range of 0.01–0.45 eV/atom, depending on the structural deviations from the reference training dataset. By avoiding incorrect decomposition of total energy into atomic energies, modified HDNN model is shown to obtain higher accuracy and transferability despite a limited dataset. Improved accuracy in the ML-based modeling approach promises cost-effective means of designing interfaces in heterostructure energy storage systems with higher cycle life and stability.

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