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Abstract Two-dimensional materials (2DM) and their heterostructures (2D + nD, where n = 0, 1, 2, 3) hold significant promise for electrochemical energy storage systems (EESS), such as batteries. 2DM can act as van der Waals (vdW) slick interfaces between conventional active materials (e.g., silicon) and current collectors, enhancing interfacial adhesion and mitigating stress-induced fractures. They can also serve as alternatives to traditional polymer binders (e.g., MXenes), highlighting the importance of interfacial mechanics between 2DM and active materials. During charge/discharge cycles, intercalation and deintercalation processes substantially affect the mechanical behavior of 2DM used as binders, collectors, or electrodes. For example, porous graphene networks have demonstrated capacities up to five times greater than traditional graphite anodes. However, modeling 2DM in EESS remains challenging due to the complex coupling between electrochemistry and mechanics. Defective graphene, for instance, promotes strong adatom adsorption (e.g., Li⁺), which can hinder desorption during discharge, thereby influencing mechanical properties. Despite the promise of 2DM, most current studies fall short in capturing these critical chemo-mechanical interactions. This perspective provides a comprehensive overview of recent advances in understanding the mechanical behavior of 2DM in EESS. It identifies key modeling challenges and outlines future research directions. Multiscale modeling approaches—including atomistic and molecular simulations, continuum mechanics, machine learning, and generative artificial intelligence—are discussed. This work aims to inspire deeper exploration of the chemo-mechanics of 2DM and offer valuable guidance for experimental design and optimization of 2DM-based EESS for practical applications. 

The development of next-generation energy storage systems relies on discovering new materials that support multivalent-ion transport. Transition metal oxides (TMOs) are promising due to their structural versatility, high ionic conductivity, and ability to accommodate multiple charge carriers. However, their vast compositional and structural diversity makes traditional exploration inefficient. This work presents a generative AI framework combining a crystal diffusion variational autoencoder (CDVAE) and a fine-tuned large language model (LLM) to discover porous oxide materials. Thousands of candidate structures are generated and screened for structural validity, thermodynamic stability, and electronic properties using a graph-based machine learning model and density functional theory (DFT) calculations. CDVAE identifies a broader variety of structures, including five novel TMO-based candidates, while LLM excels in generating highly stable structures near equilibrium. This approach demonstrates the power of generative AI in accelerating the discovery of advanced battery materials for multivalent-ion storage. 

Understanding the solvation structure of electrolytes is crucial for optimizing the performance and stability of lithium-ion batteries. Novel electrolytes are essential for enhancing electrolyte structure and ensuring better integration with modern electrode systems. Herein, we report a novel weakly solvated ether electrolyte (WSEE) composed of a pure fluorinated ether solvent, which results in an anion-rich solvation structure even at a low salt concentration of 1 M. To explore this, we selected the advanced fluorinated solvent 2,2-difluoroethyl methyl ether (FEME) and compared it with dipropyl ether (DPE), ethylene carbonate (EC), and diethyl carbonate (DEC). The prepared electrolyte systems include DPE with 1 M, 1.8 M, and 4 M LiFSI; FEME with 1 M, 1.8 M, and 4 M LiFSI; and a 1[thin space (1/6-em)]:[thin space (1/6-em)]1 vol% EC/DEC mixture containing 1 M LiPF6. In this work, we comprehensively investigate the Li+ solvation structures using molecular dynamics (MD) simulations and density functional theory (DFT) calculations. Our computational findings indicate the presence of large ion aggregates (AGGs) in each DPE- and FEME-based electrolyte, while SSIPs (68%) are the dominant species in the mixed EC/DEC electrolyte. Notably, the formation of large ion aggregates is more pronounced in FEME-based electrolytes. The dominant solvation structures in the ether-based electrolytes are the anion-rich complexes Li+(FSI−)3(DPE)1 and Li+(FSI−)3(FEME)1. We find that, similar to DPE, the FEME solvent also exhibits weak solvating power across all examined salt concentrations. More specifically, we find that FEME has weaker solvating power than DPE. This behavior is predicted by MD simulations, which indicate a strong preference for Li+ ions to coordinate with FSI− anions within the primary solvation shell. We also observe that the number of unique solvation structures in the ether-based electrolytes increases with salt concentration, with FEME + LiFSI showing slightly more unique solvation structures than DPE + LiFSI. Furthermore, the quantum mechanical features of the Li+ solvation structures in DPE + 1.8 M LiFSI, FEME + 1.8 M LiFSI, and EC/DEC + 1 M LiPF6 electrolytes are analyzed in detail using DFT calculations. We anticipate that this study will provide valuable insights into the Li+ solvation structures in DPE, FEME, and EC/DEC electrolytes, where the ether-based electrolytes exhibit closely similar properties. 

Lithium-ion batteries (LIBs) are ubiquitous in everyday applications. However, Lithium (Li) is a limited resource on the planet and, therefore, not sustainable. As an alternative to lithium, earth-abundant and cheaper multivalent metals such as aluminum (Al) and calcium (Ca) have been actively researched in battery systems. However, finding suitable intercalation hosts for multivalent-ion batteries is urgently needed. Open-tunneled oxides represent a specific category of microparticles distinguished by the presence of integrated one-dimensional channels or nanopores. This work focuses on two promising open-tunnel oxides: Niobium Tungsten Oxide (NTO) and Molybdenum Vanadium Oxide (MoVO). The MoVO structure can accommodate a larger number of multivalent ions than NTO due to its larger surface area and different shapes. Specifically, the MoVO structure can adsorb Ca, Li, and Al ions with adsorption potentials ranging from around 4 to 5 eV. However, the adsorption potential for hexagonal channels of Al ion drops to 1.73 eV due to the limited channel area. The NTO structure exhibits an insertion/adsorption potential of 4.4 eV, 3.4 eV, and 0.9 eV for one Li, Ca, and Al, respectively. Generally, Ca ions are more readily adsorbed than Al ions in both MoVO and NTO structures. Bader charge analysis and charge density plots reveal the role of charge transfer and ion size in the insertion of multivalent ions such as Ca and Al into MoVO and NTO systems. Exploring open-tunnel oxide materials for battery applications is hindered by vast compositional possibilities. The execution of experimental trials and quantum-based simulations is not viable for addressing the challenge of locating a specific item within a large and complex set of possibilities. Therefore, it is imperative to conduct structural stability testing to identify viable combinations with sufficient pore topologies. Data mining and machine learning techniques are employed to discover innovative transitional metal oxide materials. This study compares two machine learning algorithms, one utilizing descriptors and the other employing graphs to predict the synthesizability of new materials inside a laboratory setting. The outcomes of this study offer valuable insights into the exploration of alternative naturally occurring multiscale particles. 

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. 

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. 

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.