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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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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Accelerating the discovery of battery electrode materials through data mining and deep learning models

The availability of crystalline materials databases allows for building accurate machine learning (ML) models that can accelerate the exploration of materials chemical space for energy storage applications. In this work, we screen all inorganic materials included in the Materials Project and AFLOW databases as potential metal-ion battery electrodes. We develop an efficient protocol to mine and screen raw data in current databases and provide a new database of electrode materials by considering pairs of charged and discharged electrodes. This effort leads to a new database with over 190,000 instances, in contrast to the original battery database which contains about 5000. The expanded battery data set is then used to build regression-based deep neural network models for predicting average voltages and percentage volume changes upon charging and discharging, which present improvements of at least 28% for target properties with respect to previous models, and are now able to predict anode electrodes (low voltage region) as well as electrodes that will not work in electrochemical cells (negative voltages), overcoming the challenges identified in previous ML models for battery electrodes. Additionally, a further screening of the expanded database itself allowed us to identify 35 novel electrode candidates with excellent battery performance metrics.

25 ENERGY STORAGE↗
DOE OSTI2025

A simple centrifuge cell method for ex situ quantification of electrical conductivity of slurry electrode materials

We present the design, experimental procedure, and experimental evaluation of a system for fast, simple, and ex situ characterization of electrical conductivity of slurry electrode materials. The system uses a custom-designed electrochemical cell compatible with centrifugation in a swing-bucket centrifuge. The cell features cylindrical graphite electrodes that are partially sheathed so as to expose only 2 mm of the electrode surface to the bottom region of the packed particulate pellet. Also presented is a conduction model that provides a shape factor for estimating effective conductivity. We tested aqueous solutions of carbon black (CB), activated carbon (AC), and mixtures thereof. These particles were dispersed in 0.0 and 0.5 M NaCl solutions. Measurements show that the effective conductivity initially increases linearly with pellet mass and then saturates at higher masses. Notably, CB exhibited a fivefold increase in conductivity than AC at equal pellet masses. CB/AC mixtures at a fixed pellet mass were tested with CB mass fractions of 0 to 100%. Interestingly, the mixture conductivity was found to be a non-monotonic function of CB mass fraction, with a maximum conductivity at about 60 % CB mass fraction. At this maximum, the mixture conductivity is approximately 30 % higher than pure CB. NaCl concentration in the slurry solution had no effects on conductivity. These results highlight the interactions between slurry electrode composition and compaction, offering insights for optimizing slurry electrodes. Furthermore, the system and results may also be applicable to evaluation of particulate materials (including slurries) used for Li-ion batteries, capacitive deionization, fuel cells, and flow electrodes.

Capacitive deionization↗
DOE OSTI2021

Synthesis of new two-dimensional titanium carbonitride Ti 2 C 0.5 N 0.5 T x MXene and its performance as an electrode material for sodium-ion battery

Two-dimensional (2D) layered transition metal carbides/nitrides, called MXenes, are attractive alternative electrode materials for electrochemical energy storage. Owing to their metallic electrical conductivity and low ion diffusion barrier, MXenes are promising anode materials for sodium-ion batteries (SIBs). Herein, we report on a new 2D carbonitride MXene, viz ., Ti 2 C 0.5 N 0.5 T x (T x stands for surface terminations), and the only second carbonitride after Ti 3 CNT x so far. A new type of in situ HF (HCl/KF) etching condition was employed to synthesize multilayer Ti 2 C 0.5 N 0.5 T x powders from Ti 2 AlC 0.5 N 0.5 . Spontaneous intercalation of tetramethylammonium followed by sonication in water allowed for large-scale delamination of this new titanium carbonitride into 2D sheets. Multilayer Ti 2 C 0.5 N 0.5 T x powders showed higher specific capacities and larger electroactive surface area than those of Ti 2 CT x powders. Multilayer Ti 2 C 0.5 N 0.5 T x powders show a specific capacity of 182 mAh g -1 at 20 mA g -1 , the highest among all reported MXene electrodes as SIBs with excellent cycling stability.

25 ENERGY STORAGE↗
DOE OSTI2025

A layered Prussian blue analogue as fast-charging negative electrode material for lithium-ion batteries

The simultaneous achievement of fast-charging and high specific capacity remains a critical challenge for lithium-ion battery negative electrodes. Here we report a layered manganese-based Prussian blue analogue, synthesized through vacancy control and subsequent thermal transformation. As a conversion-type negative electrode, this material exhibits high-rate performance, delivering a specific capacity of 510 mAh g −1 at a specific current of 8 A g −1 , and operates at a moderate average voltage of approximately 1.2 V vs. Li/Li + , which mitigates lithium plating risks. This high-rate capability stems from the analogue’s specific linkage configurations, which facilitate a high content of active transition metal and strong Li+ adsorption at nitrogen sites. The high transition metal content enables a high reversible capacity, while strong Li + adsorption promotes an efficient initial crystalline-to-amorphous transformation. This process induces dynamically reversible component migration during subsequent cycling, thereby enhancing conversion reaction kinetics. Our findings provide insights into the application of Prussian blue analogues as fast-charging negative electrode materials. The development of fast-charging and high-capacity negative electrodes is critical for advanced lithium-ion batteries. Here, authors use a vacancy engineering strategy to develop a layered Prussian blue analogue with competitive rate capability, delivering a specific capacity of 510 mAh g −1 at a specific current of 8 A g −1 .

25 ENERGY STORAGE↗
DOE OSTI2022

Extreme Fast Charging: Effect of Positive Electrode Material on Crosstalk

Extreme fast charging (XFC) is a key requirement for the adoption of battery-based electric vehicles by the transportation sector. However, XFC has been shown to accelerate degradation, causing the capacity, life, and safety of batteries to deteriorate. We tested cells containing five positive electrode chemistries, LFP (olivine structure), LMO (spinel), LCO (layered), NMC811 (layered) and NCA (layered), using fast-charging protocols. After testing, the negative electrodes from cells containing positive electrodes crystallizing with a layered structure were found to have more lithium deposited on their surfaces. Further, those crystallizing with a layered structure also tended to have a larger increase in impedance than those crystallizing with a spinel or olivine structure. Characterization of the negative electrodes by X-ray photoelectron spectroscopy showed that using the concentrations of LiF and Li x PO y F z as metrics, the concentration of LiF in the SEI from the cell with different positive electrodes is LFP > LMO > LCO ~ NMC811 > NCA; and for Li x PO y F z , the order is LMO > LFP > NCA > NMC811 > LCO. Clearly, the positive-electrode material was influencing the amounts of these materials formed.

25 ENERGY STORAGE↗
DOE OSTI2024

Combining 3D printing of copper current collectors and electrophoretic deposition of electrode materials for structural lithium-ion batteries

Serving as a proof of concept, additive manufacturing and electrophoretic deposition are leveraged in this work to enable structural lithium-ion batteries with load-bearing and energy storage dual functionality. The preparation steps of a complex 3D printed copper current collector, involving the formulation of a photocurable resin formulation, as well as the vat photopolymerization process followed by a precursors-based solution soaking step and thermal post-processing are presented. Compression and microhardness testing onto the resulting 3D printed copper current collector are shown to demonstrate adequate mechanical performance. Electrophoretic deposition of graphite as a negative electrode active material and other additives was then performed onto the 3D printed copper collector, with the intention to demonstrate energy storage functionality. Half-cell electrochemical cycling of the 3D multi-material current collector/negative electrode versus lithium metal finally demonstrates that structural battery components can be successfully obtained through this approach.

25 ENERGY STORAGE↗
DOE OSTI2023

Doping strategy for layered oxide electrode materials used in lithium-ion batteries

The present invention features a new way of doping layered cathode materials in lithium ion batteries. Using a “high entropy” doping strategy, more than four impurity elements can be introduced to the host materials. The present invention applies this high entropy doping strategy to a high nickel content layered oxide material and a lithium-manganese rich material. This new high entropy doping strategy allows the layered oxide materials used in the positive electrode of lithium ion battery to achieve high energy density, long life cycle and reduced reliance on the expensive and toxic cobalt, all of which are desired attributes for improving the performance of lithium ion batteries and reducing their cost.

Xin, Huolin↗
DOE OSTI2022

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

Performing first-principles calculations to discover electrodes’ properties in the large chemical space is a challenging task. While machine learning (ML) has been applied to effectively accelerate those discoveries, most of the applied methods ignore the materials’ spatial information and only use predefined features: based only on chemical compositions. Here, we propose two attention-based graph convolutional neural network techniques to learn the average voltage of electrodes. Our proposed methods, which combine both atomic composition and atomic coordinates in 3D-space, improve the accuracy in voltage prediction significantly when compared to composition-based ML models. The first model directly learns the chemical reaction of electrodes and metal ions to predict their average voltage, whereas the second model combines electrodes’ ML predicted formation energy (E form ) to compute their average voltage. Our E form -based model demonstrates improved accuracy in transferability from our subset of learned Li ions to Na ions. Moreover, we predicted the theoretical voltage of 10 Na x MPO 4 F (M = Ti, Cr, Fe, Cu, Mn, Co, and Ni) fluorophosphate battery frameworks, which are unavailable in the Material Project database. It could be shown that we can expect average voltages higher than 3.1 V from those Na battery frameworks except from the NaTiPO 4 F and TiPO 4 F pair of electrodes, which offer an average voltage of 1.32 V.

25 ENERGY STORAGE↗
DOE OSTI2024

Process Optimization of Carbon Electrode Materials Manufacturing by Experimental Study and Machine Learning Techniques

Electrospun carbon fibers from coal have been investigated as electrodes for batteries and supercapacitors. Despite the excellent properties of coal-derived carbon fibers (CCNF) for energy storage devices, there still lacks systematic understanding on how various process parameters affect final electrode performances, which poses challenges to scale from pilot to high volume manufacturing. The goals of this project are twofold. First, we focuse on process optimization for converting a new precursor from powder river basin (PRB) coal, referred to as coal-based polyurethane (CPU) to CCNF using electrospinning. Second, different machine learning techniques will be examined using experimental data from this work and open literature. Specifically, for CPU the following process parameters need to be characterized and optimized in order to produce CCNFs with desirable mechanical integrity and physiochemical properties: precursor composition and viscosity, operating voltage and distance, oxidation and carbonization temperature and duration. Consequently, physiochemical properties of the fibers were characterized to correlate these process parameters with desirable electrochemical performance. Given the complex nature of the fiber production process, ML models are assessed for their ability to capture the nonlinear relationship between process parameters and the electrochemical properties in applications including supercapacitors. As such, we applied various machine learning techniques, to determine which technique produces a model that best predicts device function.

Cincotta, Robert E.F.↗
DOE OSTI2022

Oxidation of glucose to glycolic acid using oxygen and pyrolyzed spent Li-ion battery electrode material as catalyst

A search for non-noble catalysts for biomass processing led to the discovery that pyrolyzed electrode coating of spent Li-ion batteries can be used as an excellent catalyst for oxidation of D-glucose to glycolic acid. New no/low-cost catalyst was prepared by pyrolyzing black electrode coatings of 18,650 Li-ion cells from a spent DELL 1525 laptop battery at 600 ºC. Catalyst was characterized using SEM, EDX and X-ray and was shown to contain lithium nickel manganese cobalt oxide (Li a Ni b Mn c Co d O e ) on carbon with Ni: Mn: Co 4.12: 2.10: 1.50. Here, the catalytic activity of this material was evaluated for oxidation of D-glucose in aq. NaOH and water; glycolic, tartaric, malic, succinic and 2-hydroxybutaric acid were identified as key degradation products. The highest glycolic acid yield of 94% was obtained for oxidation of D-glucose under 3.4 Atm. oxygen, 120 °C, 2.0 h in 0.5 M aq. NaOH using 10 g catalyst/mol glucose.

25 ENERGY STORAGE↗
DOE OSTI2021

Machine Learning Screening of Metal-Ion Battery Electrode Materials

Here, in this work we present deep neural network regression machine learning models (ML) for predicting the average voltage and the percentage change in volume of battery electrodes upon charging and discharging with metal ions. Our models exhibit good performance as measured by the average mean absolute error obtained from a 10-fold cross-validation as well as on independent test sets. We further assess the robustness our ML models by investigating their screening potential beyond the training database. We produce novel Na-ion electrodes by systematically replacing Li-ions in the original database by Na-ions, and then selecting a set of 22 electrodes that exhibit a good performance in energy density as well as small volume variations upon charging and discharging, as predicted by the machine learning model. The ML predictions for these new materials are then compared to quantum-mechanics based calculations. Our results reaffirm the significant role of machine learning techniques in the exploration of materials for battery applications.

,electrode volume change↗
DOE OSTI2022

Additive manufacturing of LiNi 1/3 Mn 1/3 Co 1/3 O 2 battery electrode material via vat photopolymerization precursor approach

Additive manufacturing, also called 3D printing, has the potential to enable the development of flexible, wearable and customizable batteries of any shape, maximizing energy storage while also reducing dead-weight and volume. In this work, for the first time, three-dimensional complex electrode structures of high-energy density LiNi 1/3 Mn 1/3 Co 1/3 O 2 (NMC 111) material are developed by means of a vat photopolymerization (VPP) process combined with an innovative precursor approach. This innovative approach involves the solubilization of metal precursor salts into a UV-photopolymerizable resin, so that detrimental light scattering and increased viscosity are minimized, followed by the in-situ synthesis of NMC 111 during thermal post-processing of the printed item. The absence of solid particles within the initial resin allows the production of smaller printed features that are crucial for 3D battery design. The formulation of the UV-photopolymerizable composite resin and 3D printing of complex structures, followed by an optimization of the thermal post-processing yielding NMC 111 is thoroughly described in this study. Based on these results, this work addresses one of the key aspects for 3D printed batteries via a precursor approach: the need for a compromise between electrochemical and mechanical performance in order to obtain fully functional 3D printed electrodes. In addition, it discusses the gaps that limit the multi-material 3D printing of batteries via the VPP process.

25 ENERGY STORAGE↗
DOE OSTI2023

Improving charge transport in integrated MoO 3 /C electrode materials for water-in-salt energy storage systems by incorporating oxygen vacancies

Improvements in the charge storage properties of α-MoO 3 used as an electrode with a 30m ZnCl 2 water-in-salt electrolyte have been achieved by enhancements in electron and ion transport enabled by an inventive synthesis route. Electron transport was improved through the integration of MoO 3 with dopamine-derived carbon via a chemical preintercalation route, and enhanced ion transport was achieved by incorporating oxygen vacancies in MoO 3 structure through ethanol 2 reduction under hydrothermal conditions. Here, the presence of carbon was confirmed by corresponding D and G bands observed in Raman spectroscopy measurements. The presence of oxygen vacancies was proven through correlated XPS, TGA, Raman spectroscopy and XRD analyses, with the introduction of oxygen vacancies leading to an expanded interlayer region. Four-point probe measurements provided evidence of increased electronic conductivity due to the incorporation of carbon, and cyclic voltammetry-based charge storage mechanism analyses revealed increases in ion transport kinetics due to oxygen vacancy formation. Tuning the oxygen vacancy concentration is critical, as excessive concentrations of these point defects leads to structural instability and poor capacity retention. This work demonstrates the combined potential of carbon and oxygen vacancies in moderate concentrations to enhance the charge storage properties of transition metal oxides. The strategies developed in this study offer a path to the development of promising materials for high-rate, high-capacity, and long-duration electrochemical energy storage technologies.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Unraveling the origin of air-stability in single-crystalline layered oxide positive electrode materials

Single-crystalline Ni-rich layered oxides present compelling advantages over conventional polycrystalline counterparts toward large-scale applications, including enhanced mechanical stability and higher energy density. Nevertheless, the deleterious effects of air exposure, which is inevitable in industrial processing, on their structure and electrochemical performance remain poorly understood. Herein, we reveal that air exposure is more detrimental to the electrochemical performance of single-crystalline layered oxide positive electrodes than polycrystalline counterparts. It is found that air-induced surface structural distortions are primarily responsible for the electrochemical performance decay of single-crystalline samples rather than the generally believed surface residual lithium. Leveraging multiscale diffraction and imaging techniques, we identify an undesirable structural transition to a metastable O1* phase, which introduces substantial lattice defects and localized strain concentrations within the layered structure. These adverse structural evolutions compromise structural integrity and promote crack initiation during electrochemical cycling, ultimately accelerating capacity fade. Our findings provide critical insights into the air-induced degradation mechanisms and emphasize the urgent need for developing effective stabilization strategies to facilitate the commercial implementation of single-crystalline Ni-rich positive electrodes.

36 MATERIALS SCIENCE↗