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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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High-Capacitance Pseudocapacitors from Li[superscript +] Ion Intercalation in Nonporous, Electricall
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Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks
The formation and stability of the solid-electrolyte interphase (SEI) play central roles in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI’s heterogeneous, dynamic, and multiphase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational−experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electrochemical reaction networks (eCRNs), stochastic algorithms, and laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonatebased electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in the literature and predicted 28 novel SEI species nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectral analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational−experimental approach deepens our molecular-level understanding of SEI chemistry by resolving which species form and through which decomposition mechanisms they emerge. Such knowledge provides the foundation necessary to connect electrolyte composition to the resulting SEI components, a critical step toward a more informed electrolyte development in next-generation lithium-based batteries.
Extreme fast charging of commercial Li-ion batteries via combined thermal switching and self-heating approaches
Abstract The mass adoption of electric vehicles is hindered by the inadequate extreme fast charging (XFC) performance (i.e., less than 15 min charging time to reach 80% state of charge) of commercial high-specific-energy (i.e., >200 Wh/kg) lithium-ion batteries (LIBs). Here, to enable the XFC of commercial LIBs, we propose the regulation of the battery’s self-generated heat via active thermal switching. We demonstrate that retaining the heat during XFC with the switch OFF boosts the cell’s kinetics while dissipating the heat after XFC with the switch ON reduces detrimental reactions in the battery. Without modifying cell materials or structures, the proposed XFC approach enables reliable battery operation by applying <15 min of charge and 1 h of discharge. These results are almost identical regarding operativity for the same battery type tested applying a 1 h of charge and 1 h of discharge, thus, meeting the XFC targets set by the United States Department of Energy. Finally, we also demonstrate the feasibility of integrating the XFC approach in a commercial battery thermal management system.
Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning
Electrolytes play a critical role in designing next-generation battery systems, by allowing efficient ion transfer, preventing charge transfer, and stabilizing electrode-electrolyte interfaces. In this work, we develop a differentiable geometric deep learning (GDL) model for chemical mixtures, DiffMix, which is applied in guiding robotic experimentation and optimization towards fast charging battery electrolytes. In particular, we extend mixture thermodynamic and transport laws by creating GDL-learnable physical coefficients. We evaluate our model with mixture thermodynamics and ion transport properties, where we show improved prediction accuracy and model robustness of Diff-Mix than its purely data-driven variants. Furthermore, with a robotic experimentation setup, Clio, we improve ionic conductivity of electrolytes by over 18.8% within 10 experimental steps, via differentiable optimization built on DiffMix gradients. By combining GDL, mixture physics laws, and robotic experimentation, DiffMix expands the predictive modeling methods for chemical mixtures and enables efficient optimization in large chemical spaces.
Predicting the heat release variability of Li-ion cells under thermal runaway with few or no calorimetry data
Accurate measurement of the variability of thermal runaway behavior of lithium-ion cells is critical for designing safe battery systems. However, experimentally determining such variability is challenging, expensive, and time-consuming. Here, we utilize a transfer learning approach to accurately estimate the variability of heat output during thermal runaway using only ejected mass measurements and cell metadata, leveraging 139 calorimetry measurements on commercial lithium-ion cells available from the open-access Battery Failure Databank. We show that the distribution of heat output, including outliers, can be predicted accurately and with high confidence for new cell types using just 0 to 5 calorimetry measurements by leveraging behaviors learned from the Battery Failure Databank. Fractional heat ejection from the positive vent, cell body, and negative vent are also accurately predicted. We demonstrate that by using low cost and fast measurements, we can predict the variability in thermal behaviors of cells, thus accelerating critical safety characterization efforts.
Artificial generation of representative single Li-ion electrode particle architectures from microscopy data
Accurately capturing the architecture of single lithium-ion electrode particles is necessary for understanding their performance limitations and degradation mechanisms through multi-physics modeling. Information is drawn from multimodal microscopy techniques to artificially generate LiNi 0.5 Mn 0.3 Co 0.2 O 2 particles with full sub-particle grain detail. Statistical representations of particle architectures are derived from X-ray nano-computed tomography data supporting an ‘outer shell’ model, and sub-particle grain representations are derived from focused-ion beam electron backscatter diffraction data supporting a ‘grain’ model. A random field model used to characterize and generate the outer shells, and a random tessellation model used to characterize and generate grain architectures, are combined to form a multi-scale model for the generation of virtual electrode particles with full-grain detail. This work demonstrates the possibility of generating representative single electrode particle architectures for modeling and characterization that can guide synthesis approaches of particle architectures with enhanced performance.
Evolution of redox couples in Li- and Mn-rich cathode materials and mitigation of voltage fade by reducing oxygen release
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First-cycle voltage hysteresis in Li-rich 3d cathodes associated with molecular O[subscript 2] trapped in the bulk
Abstract not provided
Tailoring electrolyte solvation for Li metal batteries cycled at ultra-low temperature
Not provided.
Fictitious phase separation in Li layered oxides driven by electro-autocatalysis
Layered oxides widely used as lithium-ion battery electrodes are designed to be cycled under conditions that avoid phase transitions. Although the desired single-phase composition ranges are well established near equilibrium, operando diffraction studies on many-particle porous electrodes have suggested phase separation during delithiation. Notably, the separation is not always observed, and never during lithiation. These anomalies have been attributed to irreversible processes during the first delithiation or reversible concentration-dependent diffusion. However, these explanations are not consistent with all experimental observations such as rate and path dependencies and particle-by-particle lithium concentration changes. Here, we show that the apparent phase separation is a dynamical artefact occurring in a many-particle system driven by autocatalytic electrochemical reactions, that is, an interfacial exchange current that increases with the extent of delithiation. We experimentally validate this population-dynamics model using the single-phase material L ix (Ni 1/3 Mn 1/3 Co 1/3 )O 2 (0.5 < x < 1) and demonstrate generality with other transition-metal compositions. Operando diffraction and nanoscale oxidation-state mapping unambiguously prove that this fictitious phase separation is a repeatable non-equilibrium effect. We quantitatively confirm the theory with multiple-datastream-driven model extraction. More generally, our study experimentally demonstrates the control of ensemble stability by electro-autocatalysis, highlighting the importance of population dynamics in battery electrodes (even non-phase-separating ones).
Earth-abundant Li-ion cathode materials with nanoengineered microstructures
Manganese-based materials have tremendous potential to become the next-generation lithium-ion cathode as they are Earth abundant, low cost and stable. Here we show how the mobility of manganese cations can be used to obtain a unique nanosized microstructure in large-particle-sized cathode materials with enhanced electrochemical properties. By combining atomic-resolution scanning transmission electron microscopy, four-dimensional scanning electron nanodiffraction and in situ X-ray diffraction, we show that when a partially delithiated, high-manganese-content, disordered rocksalt cathode is slightly heated, it forms a nanomosaic of partially ordered spinel domains of 3–7 nm in size, which impinge on each other at antiphase boundaries. The short coherence length of these domains removes the detrimental two-phase lithiation reaction present near 3 V in a regular spinel and turns it into a solid solution. This nanodomain structure enables good rate performance and delivers 200 mAh g –1 discharge capacity in a (partially) disordered material with an average primary particle size of ~5 µm. The work not only expands the synthesis strategies available for developing high-performance Earth-abundant manganese-based cathodes but also offers structural insights into the ability to nanoengineer spinel-like phases.
Efficacy of atomic layer deposition of Al2O3 on composite LiNi0.8Mn0.1Co0.1O2 electrode for Li-ion batteries
Not Available
Three-dimensional phase evolution and stress-induced non-uniform Li intercalation behavior in lithium iron phosphate
A lithium iron phosphate (LiFePO 4 ) olivine cathode displays rich phase transition behavior during charging/discharging. Despite extensive studies, the evolution of the LiFePO 4 /FePO 4 two-phase coexistence structure and the underlying mechanism are still a point of debate due to the intricate and often disparate phase morphologies observed. Here we apply phase-field simulations to provide detailed insights into the three-dimensional phase evolution process in LiFePO 4 upon delithiation under a large driving force. The simulation reveals that the coherency stress arising from the LiFePO 4 /FePO 4 lattice mismatch destabilizes the initially flat delithiation front and induces filamentary growth of the FePO 4 phase along [010], which is in excellent agreement with a recent operando X-ray imaging study. The combined simulation and experimental results provide the first illustration of the phenomenon of stress-induced instability of the lithium (de)intercalation front in battery electrode compounds. We show that this phenomenon is facilitated by the surface mode of coherent spinodal decomposition in LiFePO 4 and influenced by the anisotropies of misfit strain, elasticity and lithium diffusion. It is detrimental to battery performance and life by causing stress concentration and reducing the lithium (de)intercalation kinetics and thus should be mitigated.