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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Screening of redox couples and electrode materials

Electrochemical parameters of selected redox couples that might be potentially promising for application in bulk energy storage systems were investigated. This was carried out in two phases: a broad investigation of the basic characteristics and behavior of various redox couples, followed by a more limited investigation of their electrochemical performance in a redox flow reactor configuration. In the first phase of the program, eight redox couples were evaluated under a variety of conditions in terms of their exchange current densities as measured by the rotating disk electrode procedure. The second phase of the program involved the testing of four couples in a redox reactor under flow conditions with a varity of electrode materials and structures.

Giner, J.↗
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↗
NASA NTRS1971

Investigation of electrode materials for alkaline batteries

A number of amalgam electrode systems were investigated for possible use as high rate anodes and cathodes. The systems examined include: lithium, sodium, and potassium in Group 1, magnesium, calcium, and barium in Group 2, aluminum in Group 3, lead in Group 4, copper in Group 1b, and zinc and cadmium in Group 2b. The K(Hg) and Na(Hg) anodes in 10 VF and 15 VF (an unambiguous expression of concentration that indicates the number of formula weights of solute dissolved in a liter of solution) hydroxide solutions have proven satisfactory; some of these have produced current densities of more than 8 A/sq cm. None of the amalgam cathodes have approached this performance although the TI(Hg) has delivered 1 A/sq cm. Se(Hg) and Te(Hg) cathodes have given very stable discharges. Zn(Hg) and Cd(Hg) electrodes did not show good high rate characteristics, 200 to 300 mA/sq cm being about the maximum current densities obtainable. Both anodes are charged through a two-step process in which M(Hg) is first formed electrochemically and subsequently reduces Zn(II or Cd(II) to form the corresponding amalgam. The second step is extremely rapid for zinc and very slow for cadmium.

Arcand, G. M.↗
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↗
NASA NTRS2012

Evaluation of Niobium as Candidate Electrode Material for DC High Voltage Photoelectron Guns

The field emission characteristics of niobium electrodes were compared to those of stainless steel electrodes using a DC high voltage field emission test apparatus. A total of eight electrodes were evaluated: two 304 stainless steel electrodes polished to mirror-like finish with diamond grit and six niobium electrodes (two single-crystal, two large-grain, and two fine-grain) that were chemically polished using a buffered-chemical acid solution. Upon the first application of high voltage, the best large-grain and single-crystal niobium electrodes performed better than the best stainless steel electrodes, exhibiting less field emission at comparable voltage and field strength. In all cases, field emission from electrodes (stainless steel and/or niobium) could be significantly reduced and sometimes completely eliminated, by introducing krypton gas into the vacuum chamber while the electrode was biased at high voltage. Of all the electrodes tested, a large-grain niobium electrode performed the best, exhibiting no measurable field emission (< 10 pA) at 225 kV with 20 mm cathode/anode gap, corresponding to a field strength of 18:7 MV/m.

BastaniNejad, M.↗
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↗
NASA NTRS2005

Nickel-Tin Electrode Materials for Nonaqueous Li-Ion Cells

Experimental materials made from mixtures of nickel and tin powders have shown promise for use as the negative electrodes of rechargeable lithium-ion electrochemical power cells. During charging (or discharging) of a lithium-ion cell, lithium ions are absorbed into (or desorbed from, respectively) the negative electrode, typically through an intercalation or alloying process. The negative electrodes (for this purpose, designated as anodes) in state-of-the-art Li-ion cells are made of graphite, in which intercalation occurs. Alternatively, the anodes can be made from metals, in which alloying can occur. For reasons having to do with the electrochemical potential of intercalated lithium, metallic anode materials (especially materials containing tin) are regarded as safer than graphite ones; in addition, such metallic anode materials have been investigated in the hope of obtaining reversible charge/discharge capacities greater than those of graphite anodes. However, until now, each of the tin-containing metallic anode formulations tested has been found to be inadequate in some respect.

Ehrlich, Grant M.↗
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↗