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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Battery aging mode identification across NMC compositions and designs using machine learning

A comprehensive understanding of lithium-ion battery (LiB) lifespan is the key to designing durable batteries and optimizing use protocols. Although battery lifetime prediction methods are flourishing, diagnosis of the root causes of aging and degradation have not yet been well developed nor studied for a broad mixture of designs and use cases. Here, we create a machine-learning (ML)-based framework that distinguishes aging modes using multiple electrochemical signatures recorded cycle-by-cycle. The predominant aging behaviors include a combination of loss of active materials in cathode (LAMPE) and a loss of Li inventory (LLI) in Li plating or solid electrolyte interphase (SEI) formation, manifested from 44 batteries representing two cathode chemistries, two electrode loadings, and five charging rates. Here, the aging mode classification accuracy is 86% using features within the first 50 cycles and increases to 88% beyond 225 cycles. The same features can quantify the percentage of end-of-life LAMPE with only 4.3% of error.

25 ENERGY STORAGE↗
DOE OSTI2020

Effect of reacting gas flowrates and hydration on the carbonation of anion exchange membrane fuel cells in the presence of CO 2

Anion exchange membrane fuel cells (AEMFCs) have been widely touted as a low-cost alternative to existing proton exchange membrane fuel cells. However, one of the limitations of this technology has been the severe performance penalty related to the introduction of CO 2 to the cell – typically in the air cathode feed. Introduction of CO 2 into AEMFCs results in cell carbonation, which imparts thermodynamic, kinetic and Ohmic overpotentials that can add up to hundreds of millivolts. Therefore, it is important to find strategies and operational protocols for AEMFCs that minimize these overpotentials. In this paper, we reserach the impacts of the anode and cathode flowrate, as well as the cell hydration level, on the extent of cell carbonation and cell polarization. Key findings include: (1) decreasing the cathode flowrate generally decreases the total CO 2 -related voltage loss while changing the anode flowrate has a minimal effect; (2) increasing cell hydration helps to mitigate the performance loss in the presence of CO 2 ; and (3) operational combinations are found that significantly reduce the CO 2 penalty compared to the present literature.

25 ENERGY STORAGE↗
DOE OSTI2020

Correlating the influence of porosity, tortuosity, and mass loading on the energy density of LiNi 0.6 Mn 0.2 Co 0.2 O 2 cathodes under extreme fast charging (XFC) conditions

Extreme fast charging capabilities along with high energy density of Li-ion batteries are the key factors to increase the adoption of electric vehicles while eliminating the problem of range anxiety. The U.S Department of Energy has a goal of <12 min charging time with energy density of > 200 Wh kg -1 . A combined improvement in the electrode architecture, electrolyte properties, and separator membrane is necessary to achieve this goal. Cells with thin electrodes are capable of extreme fast charging at the expense of low energy density and high cost. Electrode engineering can maximize energy density. In this work, the influence of porosity, mass loading and charging protocols on capacity and energy density and electrode kinetics are investigated under extreme fast charging conditions. Increasing the mass loading from 11.5 mg cm -2 to 25 mg cm -2 compromises the rate performance due to the mass transport limitation and underutilization of thick electrodes. Furthermore, reducing the electrode porosity from 50% to 35% improves the rate performance ascribed to shorter Li ion diffusion length. Symmetric cells are cycled to verify the performance of the half cells, suggesting that Li metal plating is the rate limiting step under high current density.

25 ENERGY STORAGE↗
DOE OSTI2021

Revealing causes of macroscale heterogeneity in lithium ion pouch cells via synchrotron X-ray diffraction

Heterogeneous battery performance is a critical issue for maximization of cell lifetime capacity and safety. Using high energy synchrotron X-ray diffraction, the influence of charge rate, voltage limit, uneven stack pressure, and gas generation on the lithium transport properties was quantified in single-layer graphite/LiNi 0.5 Mn 0.3 Co 0.2 O 2 pouch cells. A freshly formatted cell tracked in operando during initial fast charge cycles indicated variable position-dependent performances, while lateral mapping showed a significant fast charge (6C) heterogeneity compared to slow charge (C/2). Pressure effects were non-dominant compared to charge rate. Maps of previously aged and rested cells indicate that lateral heterogeneity slowly equilibrates at rest, but regenerates upon further cycling at fast charge rate. Furthermore, an unformatted cell was mapped at charge and discharge during its first formation cycle to analyze the effect of byproduct gases on the heterogeneous lithium transport. Gas was observed as randomly interspersed “bubbles” which locally hindered lithium intercalation and caused significant heterogeneity. Electrode architectures and charging protocols that promote homogeneous intercalation are critical for predictable high-performance and long-life batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

Feature engineering for machine learning enabled early prediction of battery lifetime

Accurate battery lifetime estimates enable accelerated design of novel battery materials and determination of optimal use protocols for longevity in deployments. Unfortunately, traditional battery testing may take years to reach thousands of cycles. Recent studies have shown that machine learning (ML) tools can predict lithium-ion battery lifetimes from 100 or fewer preliminary cycles, representing only a few weeks of cycling. Until now, conclusions about the efficacy and broad applicability of these predictions across a variety of cathode chemistries have been limited by available experimental information. In this work, we leverage a battery cycling dataset representing six cathode chemistries (NMC111, NMC532, NMC622, NMC811, HE5050, and 5Vspinel), multiple electrolyte/anode compositions, and 300 total carefully prepared pouch batteries to explore feature selection and battery chemistry's role in ML battery lifetime predictions. Here, a mean absolute error (MAE) of 78 cycles in prediction was seen for a chemistry-spanning test set from 100 preliminary cycles. Furthermore, an MAE of 103 cycles was seen when using only the first cycle. This study represents an in-depth investigation of strategies for feature selection for battery lifetime prediction, ML models' generalization across multiple battery chemistries, and predictions beyond the training set in the chemical space.

25 ENERGY STORAGE↗
DOE OSTI2022

The influence of temperature on area-specific impedance and capacity of Li-ion cells with nickel-containing positive electrodes

The temperature-dependent behaviors of five nickel-containing positive electrodes (NCA, NMC811, NMC622, NMC532, and NMC111) in lithium-ion batteries are investigated using an electrochemical protocol involving rate studies, mild aging (~100 cycles), and hybrid pulse power characterization (HPPC). Tests are conducted using coin-cells with graphite negative electrodes at -20 °C, 0 °C, 20 °C, and 40 °C. Three techniques are compared for determining the area-specific impedance (ASI): i) fits to the rate study average voltages, ii) fitting to the entire voltage curves using a regularization scheme, and iii) HPPC. When fit to an Arrhenius-type equation, all methods yield similar apparent activation energies (±2 kJ/mol) for the impedance, which range from -20 to -31 kJ/mol for the electrodes. Impedance growth increases with temperature but remains at less than 0.2% per cycle for most electrodes and temperatures. NCA and NMC811 are the exceptions, which yield 0.5% and 1.5% increases in ASI per cycle, respectively, at 40 °C. For cells with the same electrodes, the capacities are similar at 20 and 40 °C but reduce at lower temperatures, with up to a 52% reduction at -20 °C and 2C. The fade in energy of the cells during C/3 cycling is attributed to decreasing capacity as opposed to increasing ASI.

25 ENERGY STORAGE↗
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 OSTI2024

Impact of cycling conditions on lithium-ion battery performance for electric vertical takeoff and landing applications

The development of better electrochemical energy storage systems has sparked significant interest in using Li-ion batteries for electric vertical takeoff and landing (eVTOL) applications. To ensure the optimal performance and safety of onboard batteries, their behavior under different charging/discharging protocols and environmental conditions must be understood. Here, this paper presents a comprehensive evaluation of commercial Li-ion batteries for eVTOL applications, focusing on their responses to varying charging/discharging strategies and mechanical vibrations experienced during flight. Through controlled experiments, the effects of rapid cycling on battery performance were investigated, including effects on lifespan, capacity, and internal resistance. Additionally, the impact of mechanical vibrations on battery behavior was assessed to identify potential challenges for onboard batteries. The results of this study revealed intriguing insights into the interplay between temperature, vibration, and battery performance. This work contributes to the broader adoption of electric aerial transportation, promising a greener and safer future for urban mobility.

18650 cells↗
DOE OSTI2025

Predicting Si-Anode Calendar Life Using Machine Learning: Correlating Electrolyte Properties and Electrochemical Signals

This study evaluates novel electrolytes tailored for Si-containing anodes to promote calendar-life. Drawing inspiration from advancements in electrolytes for Li-metal cells, the work investigates correlations between predicted electrolyte properties and measured electrochemical performance using several machine-learning models. By leveraging machine learning and advanced modeling techniques, this study aims to establish predictive frameworks that accelerate calendar-aging experiments and inform rational electrolyte design for Si-containing cells. In the present study, fifteen different electrolytes are evaluated in a Si-containing cell using an accelerated calendar-life protocol. For each electrolyte considered, 87 properties (features) from the Advanced Electrolyte Model were produced to identify key property/performance relationships. In this study, the best performing electrolytes were generally those formulations that included non-coordinating fluoroether solvents, and the most predictive features for long-term calendar-life were features related to salt concentration and electrolyte viscosity as well as early capacity, ionic conductivity, and Coulombic efficiency measurements. The framework developed in this study correlating electrolyte properties to measured electrochemical performance is expected to accelerate electrolyte design for Si-containing anodes and ultimately enable high-energy-density, long-life Li-ion batteries.

25 - ENERGY STORAGE↗
DOE OSTI2026

An Accelerated Testing and Analysis Framework for Qualification of Battery Materials Part I: A Case Study with LFP Cathodes

The growing demand for batteries used within automotive, aviation, and grid applications has exacerbated the need to supplement critical battery material feedstocks, such as those for anode and cathode active materials. New or supplementary material sources, however, universally comprise unique properties that can affect the lifetime and performance of resultant batteries. Even minor differences between new sources and established supplies can delay qualification, making it difficult for new suppliers to commercialize and resulting in a less resilient supply chain. Accordingly, the influence of composition, microstructure, and morphology on electrochemical performance should be characterized quickly and accurately to accelerate commercialization of new sources. This work introduces a tiered framework to assess new material viability and understand the influence of physicochemical properties on battery performance. The Tier 1 testing described here is rapid and low-effort to recognize materials with fundamental flaws and potentially disqualify them. Later testing would require more effort but provide higher-fidelity information with a goal of application-based validation. A case study examining commercial sources of LiFePO4 (LFP) is presented, using Tier 1 of the protocol to identify rapid electrochemical and physicochemical signals that correlate with performance and provide early go/no-go decisions for LFP materials without requiring long-term cycling.

25 ENERGY STORAGE↗
DOE OSTI2023

An infrared spectral database for gas-phase quantitation of volatile per- and polyfluoroalkyl substances (PFAS)

We report the construction of a database of vetted infrared spectra specifically targeting volatile fluorocarbon gases that may be emitted during thermal treatment of per- and polyfluoroalkyl substances (PFAS) to assist understanding of treatment processes and improve quantification. To populate this database, protocols derived from the Pacific Northwest National Laboratory (PNNL) infrared spectral database are used, curtailing the species selection for this data set. Each spectrum in the database is a weighted average derived from 10 or more individual measurements at different partial pressures (static method) or flow rates (dissemination method) to yield good fidelity of both strong and weak infrared signatures, with each composite spectrum ranging from = 6500 cm-1 to = 600 cm-1 with an apodized resolution of 0.112 cm-1. This resolution was chosen to fully resolve all spectral features, recognizing that atmospheric pressure broadening results in nearly all ro-vibrational lines having linewidths = 0.1 cm-1. As an example case, application of the database is demonstrated via identification and quantification of dominant 1H-perfluoroheptane and perfluorohept-1-ene fluorocarbon products resulting from thermal decomposition of perfluorooctanoate (PFOA) below 450 °C.

Infrared, Gas-phase spectra, FTIR, Spectral databa↗
DOE OSTI2022

Custom-designed heat treatment simultaneously resolves multiple challenges facing 3D-printed single-crystal superalloys

Single-crystal Ni-based superalloys are currently the material of choice for turbine blade applications, especially with the emerging additive manufacturing (AM) that facilitates the manufacture/repair of these single crystals. This promising AM route, however, comes with a dilemma: in the fusion and heat affected zones after e-beam or laser induced melting, one needs a solutionizing annealing to relieve the residual stresses and homogenize the chemical/microstructure. The super-solvus solutionizing temperature is usually adopted from the protocol for the cast superalloys, which almost always causes recrystallization and stray grain growth, resulting in a polycrystalline microstructure and degrading the high-temperature mechanical performance. Here, in this paper, we demonstrate a custom-designed post-printing heat treatment to replace the conventional super-solvus one. The recovery and relatively low temperature diminish the driving force for recrystallization and the movement of stray grain boundaries, without suffocating the chemical/microstructural homogenization thanks to the narrow dendrite width and short element segregation distance. The optimal duration of the heat treatment is proposed to achieve atomic-diffusion mediated chemical homogenization while limiting γ'-particle coarsening in the interdendritic regions. Our strategy makes it practically feasible to resolve several bottleneck problems with one processing/treatment, removing a seemingly formidable obstacle to effective additive manufacturing of superalloy single crystal products.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Robust natural nanocomposites realizing unprecedented ultrafast precise molecular separations

Synthetic polymer membranes can potentially reduce the large energy and carbon footprints that are typically associated with traditional chemical separation technologies. Unfortunately, current production protocols negate the green benefits of membrane separation. To address this bottleneck, here we report the use of natural materials monosaccharide – glucose and polydopamine and Zr-based metal organic frameworks (MOFs) to fabricate ultrathin nanocomposite membranes via interfacial polymerization reaction. The synergistic effect of these three materials on angstrom-scale molecular transport both in organic solvent and aqueous environment was elucidated using a series of complementary techniques. We demonstrate such nature-inspired nanocomposite membranes enable structural stability even in polar aprotic solvents, and unparalleled ultra-fast, low-pressure, precise separations in both nanofiltration modes, which easily surpass state-of-the-art membranes relying on unsustainable materials. Finally, the multi-functionality of saccharide nanocomposites was elegantly harnessed to impact separation applications that contribute towards a better living environment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Catalysts by pyrolysis: Direct observation of chemical and morphological transformations leading to transition metal-nitrogen-carbon materials

Transition metal-nitrogen-carbon materials (M-N-C catalysts) are promising electrocatalysts in polymer electrolyte fuel cells (PEFCs) and electrolyzer applications. High temperature treatment in inert atmosphere (pyrolysis) is the essential, most common method for the synthesis of M-N-C catalysts and critical to achieve high electrocatalytic activity and electronic conductivity. To this day, despite many uses and successful implementations in materials manufacturing, pyrolysis has been an entirely empirical technology, with process control and optimization relying exclusively on “Edisonian” approach. The knowledge gap in the mechanism about how the precursor is being transformed into catalysts hinders further development of the M-N-C catalysts regardless of the precursor class and processing protocols. In this work, we probed the morphological evolution and chemical transformation of a nitrogen-containing charge transfer organic salt, mixed with transition metal (iron) salt and amorphous silica powder (precursor) during the pyrolysis process via a combination of in situ synchrotron and laboratory-based diagnostic techniques. The pyrolysis process is found to be divided into three stages. During a controlled temperature ramp, the selected organic N-C precursor (nicarbazin) began melting and decomposing just below 400 °C, forming a certain number of micrometer-scale pores and pathways. With increase in temperature from 400 °C to 900 °C, amorphous carbon domains started forming, and reduced (metallic) iron nanoclusters appeared, being dispersed uniformly throughout the carbonaceous matrix. When temperature advanced above 900 °C, graphitization of carbon commenced, associated with appearance and evolution of atomically dispersed metal-nitrogen moieties in the carbonaceous matrix. As the graphitization advanced further, a secondary process of agglomeration of metal nanoparticles occurred. Multi-analytical technique observations conducted here provide a base for rational design and optimization of M-N-C electrocatalysts via pyrolysis.

36 MATERIALS SCIENCE↗
DOE OSTI2024

ToF-SIMS in material research: A view from nanoscale hydrogen detection

Hydrogen in materials has attracted tremendous interest as its incorporation leads to significant alterations in nanoscale structure, composition, and chemistry, impacting functional properties. It has also been integral to nuclear fusion reactors and is considered a future clean energy source. However, nanoscale characterization and manipulation of hydrogen in materials are challenging as only a selected few analytical techniques can readily detect hydrogen, among which time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a unique and powerful one due to its excellent detection limit along with decent depth and lateral resolutions. Here, in this review, we discuss, using selected examples, how to detect and quantify hydrogen in materials by ToF-SIMS and its impact on revealing the hydrogenation/protonation-induced novel functional states in different classes of materials. In addition, we present our protocols on sample preparation and experimental conditions optimization, allowing us to achieve the best possible results. Finally, we highlight future research directions that can lead to the discovery of novel functional states and ultimately provide a deeper understanding of scientific questions in materials science.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Orientation matters: Measuring the correct surface of polyamide membranes with quartz crystal microbalance

The surface of polyamide reverse osmosis (RO) membranes which regulates interface-dominated phenomena, such as partitioning and fouling, is the one facing the feed during operation. However, the opposite surface of the polyamide selective layer, the one facing the permeate and in contact with the polysulfone porous support, is commonly analyzed in quartz crystal microbalance (QCM) measurements due to limitations of state-of-the-art transfer methodologies. Such measurements on the back surface cannot be generalized because the polyamide layer is chemically and morphologically asymmetric. Herein, we introduce a simple method to coat QCM sensors with polyamide active layers in the correct orientation (i.e., exposing their front surface) and show that interface-dominated phenomena differ significantly between orientations. We start by describing a transfer protocol to coat any surface with a polyamide layer on its front surface orientation. We then systematically analyze the chemical and morphological differences between the two surfaces of the polyamide layer of a commercial RO membrane. Finally, we demonstrate that interface-dominated phenomena depend on the orientation by showing that NaCl partitioning at pH 6 was 1.3 to 2.3-fold higher on the front surface and that organic fouling with humic acid occurred at a lower rate on this surface. The new method presented herein enables measurements on the front surface of polyamide RO membranes, which should be the standard in any future QCM studies.

Fouling behavior↗
DOE OSTI2022

Machine learning prediction on the fractional free volume of polymer membranes

Fractional free volume (FFV) characterizes the microstructural level features of polymers and affects their properties including thermal, mechanical, and separation performance. Experimental measurements and theoretical analyses have been used to quantify the FFV of polymers, but challenges remain because of their limitations. Experimental measurements are laborious and based on semi empirical equations, while Bondi’s group contribution theory involves ambiguities like the determination of van der Waals volume and the choice of factor values in the theoretical equation. To efficiently evaluate the FFV of polymers, this study utilizes high-throughput molecular dynamics (MD) simulations to build a large dataset regarding polymer’s FFV. Based on this large dataset, we further build machine learning (ML) models to establish the composition-structure relation. Inspired by group contribution theory which correlates polymer’s functional groups to FFV, our ML models correlate polymer’s substructures or physico-chemical indexes to FFV. Here, our study first benchmarks the MD simulation protocol to obtain reliable FFV of polymers and then carries out high-throughput MD simulations for more than 6,500 homopolymers and 1,400 polyamides. Such a large and diverse dataset makes the well-trained ML models more generalizable, compared with the group contribution theory. The efficiency of a feed forward neural network model is further demonstrated by applying it to a hypothetical polyimide dataset of more than 8 million chemical structures. The predicted FFVs of hypothetical polyimides are further validated by MD simulations. The obtained FFVs of the 8 million polymers, plus their previously reported gas separation performances, demonstrate the promising capability of ML virtual screening for the discovery of polymer membranes with exceptional permeability/selectivity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Rechargeable alkaline zinc–manganese oxide batteries for grid storage: Mechanisms, challenges and developments

Rechargeable alkaline Zn–MnO 2 (RAM) batteries are a promising candidate for grid-scale energy storage owing to their high theoretical energy density rivaling lithium-ion systems (~400 Wh/L), relatively safe aqueous electrolyte, established supply chain, and projected costs below $100/kWh at scale. In practice, however, many fundamental chemical and physical processes at both electrodes make it difficult to achieve commercially competitive energy density and cycle life. In this paper, we present a detailed and timely analysis of the constituent materials, current commercial status, electrode processes, and performance-limiting factors of RAM batteries. We also examine recently reported strategies in RAM and related systems to address these issues through additives and modifications to the electrode materials and electrolyte, special ion-selective separators and/or coatings, and unconventional cycling protocols. We conclude with a critical summary of these developments and discussion of how future studies should be focused toward the goal of energy-dense, scalable, and cost-effective RAM systems.

25 ENERGY STORAGE↗