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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Li + Diffusion in Amorphous and Crystalline Al 2 O 3 for Battery Electrode Coatings

Al 2 O 3 is often applied protectively to lithium-ion battery anode and cathode materials to inhibit surface degradation, suppress dendrite formation, and relieve mechanical stresses. Given the very high intrinsic band gap and diffusion barrier of the material, the mechanism that allows Li diffusion through these coatings is not well understood, and widely varying laboratory results indicate that there may be dependencies on morphology and stoichiometry. Using nudged elastic band calculations and ab initio molecular dynamics, we perform a systematic investigation across Al 2 O 3 structures, both crystalline and amorphous, and at various concentrations of Li + to uncover the optimal parameters for maximally diffusive coatings. We find a correlation between the low proximity of Li + to Al 3+ and the low Li + migration barrier. Although barriers are the lowest in the highly diffusive one-dimensional channels of crystalline θ-Al 2 O 3 , the system is structurally delicate and subject to detrimental distortion as the Li + content is increased. The α-Al 2 O 3 lattice is, conversely, highly stable against distortion at all Li + concentrations but disadvantageous for Li + migration. In amorphous systems, unscreened Li + –Li + Coulomb repulsion and pre-emptive occupation of “trapping sites” combine to lower the energy barriers as a function of increasing concentration. One of our most important findings is that Al-deficient materials can sharply increase Li + movement, and we predict that an amorphous material with a combination of high Li + concentration and Al deficiency would enable highly Li + -conductive protective coatings for electrodes.

25 ENERGY STORAGE↗
DOE OSTI2021

Vapor-Fed Cathode Microbial Electrolysis Cells with Closely Spaced Electrodes Enables Greatly Improved Performance

Hydrogen can be electrochemically produced in microbial electrolysis cells (MECs) by current generated from bacterial anodes with a small added voltage. MECs typically use a liquid catholyte containing a buffer or salts. However, anions in these catholytes result in charge being balanced predominantly by ions other than hydroxide or protons, leading to anode acidification. To enhance only hydroxide ion transport to the anode, we developed a novel vapor-fed MEC configuration lacking a catholyte with closely spaced electrodes and an anion exchange membrane to limit the acidification. This MEC design produced a record-high sustained current density of 43.1 ± 0.6 A/m 2 and a H 2 production rate of 72 ± 2 LH 2 /L-d (cell voltage of 0.79 ± 0.00 V). There was minimal impact on MEC performance of increased acetate concentrations, solution conductivity, or anolyte buffer capacity at applied voltages up to 1.1 V, as shown by a nearly constant internal resistance of only 6.8 ± 0.3 mΩ m 2 . At applied external voltages >1.1 V, the buffer capacity impacted performance, with current densities increasing from 28.5 ± 0.6 A/m 2 (20 mM phosphate buffer solution (PBS)) to 51 ± 1 A/m 2 (100 mM PBS). Furthermore, these results show that a vapor-fed MEC can produce higher and more stable performance than liquid-fed cathodes by enhancing transport of hydroxide ions to the anode.

anion exchange membrane↗
DOE OSTI2022

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

25 ENERGY STORAGE↗
DOE OSTI2024

Glassy embedded solid-state electrode assemblies, solid-state batteries and methods of making electrode assemblies and solid-state batteries

Batteries, component structures and manufacturing methods, in particular including a glassy embedded battery electrode assembly having a composite material structure composed of interpenetrating material components including a porous electroactive network including a solid electroactive material, and a continuous glassy medium including a Li ion conducting sulfide glass, can achieve enhanced power output, reduced charging time and/or improved cycle life.

Visco, Steven J.↗
DOE OSTI2021

Textured Electrodes: Manipulating Built-In Crystallographic Heterogeneity of Metal Electrodes via Severe Plastic Deformation

Control of crystallography of metal electrodeposit films has recently emerged as a key to achieving long operating lifetimes in next-generation batteries. It is reported that the large crystallographic heterogeneity, e.g., broad orientational distribution, that appears characteristic of commercial metal foils, results in rough morphology upon plating/stripping. On this basis, an accumulative roll bonding (ARB) methodology—a severe plastic deformation process—is developed. Zn metal is used as a first example to interrogate the concept. It is demonstrated that the ARB process is highly effective in achieving uniform crystallographic control on macroscopic materials. After the ARB process, the Zn grains exhibit a strong (002) texture (i.e., [002] Zn //ND). The texture transitions from a classical bipolar pattern to a nonclassical unipolar pattern under large nominal strain eliminate the orientational heterogeneity of the foil. The strongly (002)-textured Zn remarkably improves the plating/stripping performance by nearly two orders of magnitude under practical conditions. The performance improvements are readily scaled to achieve pouch-type full batteries that deliver exceptional reversibility. The ARB process can, in principle, be applied to any metal chemistry to achieve similar crystallographic uniformity, provided the appropriate temperature and accumulated strains are employed. Here this concept is evaluated using commercial Li and Na foils, which, unlike Zn (HCP), are BCC crystals. The simple process for creating strong textures in both hexagonal and cubic metals and illustrating the critical role such built-in crystallography plays underscores opportunities for developing highly reversible thin metal anodes (e.g., hexagonal Zn, Mg, and cubic Li, Na, Ca, Al).

36 MATERIALS SCIENCE↗
DOE OSTI2022

Electrochemical Stability and Reversibility of Aqueous Polysulfide Electrodes Cycled Beyond the Solubility Limit

Batteries which use dissolved redox-active species, such as redox flow batteries (RFBs), are often considered to be constrained in their operation and energy density by the solubility limit of the redox species. Here, we show that soluble redox active electrolytes can be reversibly cycled deeply into the precipitation regime, permitting higher effective concentrations, energy densities, and lower costs. Using aqueous sodium polysulfide negative electrolytes cycled in the nominal Na 2 S 2 to Na 2 S 4 capacity range as an example, we show that the effective solubility can be increased from 5 M in the fully-dissolved state to as much as 10 M using the precipitation strategy. Stable cycling was observed at 8 M concentration over more than 1600h at room temperature. We also analyze the range of polysulfide electrochemical stability, and characterize the precipitate composition. This enhanced effective concentration approach may be generalized to other redox chemistries that utilize solubilized reactants, and may be especially useful for long-duration storage applications where slow charge-discharge rates allow equilibration of precipitated species with the redox-active solution.

25 ENERGY STORAGE↗