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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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122 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Modeling Electrochemical and Rheological Characteristics of Suspension-Based Electrodes for Redox Flow Cells

Flowable suspension-based electrodes (FSEs) have gained attention in recent years, as the integration of solid materials into electrochemical flow cells can offer improved performance and flexible operation. However, under conditions that engender favorable electrochemical properties (e.g., high particle loading, high conductivity, high surface area), FSEs can exhibit non-Newtonian characteristics that impose large pumping losses and flow-dependent transport rates. These multifaceted trade-offs motivate the use of models to broadly explore scaling relationships and better understand design rules for electrochemical devices. To this end, we present a one-dimensional model, integrating porous electrode theory with FSE rheology as well as flow-dependent electron and mass transport under pressure-driven flow. We study FSE behavior as a function of material properties and operating conditions, identifying key dimensionless groups that describe the underlying physical processes. We assess flow cell performance by quantifying electrode polarization and relative pumping losses, establishing generalized property-performance relationships for FSEs. Importantly, we expound relevant operating regimes—based on a subset of dimensionless groups—that inform practical operating envelopes, ultimately helping to guide FSE and cell engineering for electrochemical systems.

25 ENERGY STORAGE↗
DOE OSTI2021

Deletion of a non-canonical regulatory sequence causes loss of Scn1a expression and epileptic phenotypes in mice

Abstract Background Genes with multiple co-active promoters appear common in brain, yet little is known about functional requirements for these potentially redundant genomic regulatory elements. SCN1A, which encodes the Na V 1.1 sodium channel alpha subunit, is one such gene with two co-active promoters. Mutations in SCN1A are associated with epilepsy, including Dravet syndrome (DS). The majority of DS patients harbor coding mutations causing SCN1A haploinsufficiency; however, putative causal non-coding promoter mutations have been identified. Methods To determine the functional role of one of these potentially redundant Scn1a promoters, we focused on the non-coding Scn1a 1b regulatory region, previously described as a non-canonical alternative transcriptional start site. We generated a transgenic mouse line with deletion of the extended evolutionarily conserved 1b non-coding interval and characterized changes in gene and protein expression, and assessed seizure activity and alterations in behavior. Results Mice harboring a deletion of the 1b non-coding interval exhibited surprisingly severe reductions of Scn1a and Na V 1.1 expression throughout the brain. This was accompanied by electroencephalographic and thermal-evoked seizures, and behavioral deficits. Conclusions This work contributes to functional dissection of the regulatory wiring of a major epilepsy risk gene, SCN1A . We identified the 1b region as a critical disease-relevant regulatory element and provide evidence that non-canonical and seemingly redundant promoters can have essential function.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

SPANet: Generalized permutationless set assignment for particle physics using symmetry preserving attention

The creation of unstable heavy particles at the Large Hadron Collider is the most direct way to address some of the deepest open questions in physics. Collisions typically produce variable-size sets of observed particles which have inherent ambiguities complicating the assignment of observed particles to the decay products of the heavy particles. Current strategies for tackling these challenges in the physics community ignore the physical symmetries of the decay products and consider all possible assignment permutations and do not scale to complex configurations. Attention based deep learning methods for sequence modelling have achieved state-of-the-art performance in natural language processing, but they lack built-in mechanisms to deal with the unique symmetries found in physical set-assignment problems. We introduce a novel method for constructing symmetry-preserving attention networks which reflect the problem's natural invariances to efficiently find assignments without evaluating all permutations. This general approach is applicable to arbitrarily complex configurations and significantly outperforms current methods, improving reconstruction efficiency between 19% - 35% on typical benchmark problems while decreasing inference time by two to five orders of magnitude on the most complex events, making many important and previously intractable cases tractable. A full code repository containing a general library, the specific configuration used, and a complete dataset release, are available at https://github.com/Alexanders101/SPANet

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Full event particle-level unfolding with variable-length latent variational diffusion

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic t\bar{t} t t ‾ production at the Large Hadron Collider.

Shmakov, Alexander↗
DOE OSTI2021

Silicon Consortium Project: No-Go on Moir Interferometry for Measuring SEI Strain as a Probe for Calendar Life Testing

Silicon is a promising candidate as a next generation anode to replace or complement graphite electrodes due to its high energy density and low lithiation potential. When silicon is lithiated, it experiences over 300% expansion which stresses the silicon as well as its solid electrolyte interphase (SEI) leading to poor performance. The use of nano-sized silicon has helped to mitigate volume expansion and stress in the silicon, yet the silicon SEI is still both mechanically and chemically unstable. Identifying the mechanical failure mechanism of the SEI will help enhance calendar and cycle life performance through improved SEI design. In situ moiré interferometry was investigated to try and track the in-plane strain in the SEI and silicon electrode for this purpose. Moiré can detect on the order of 10 nm changes in displacement and is therefore a useful tool in the measurement of strain. As the sample undergoes small deformations, large changes in the moiré fringe allow for measurements of displacement below the diffraction limit of light. Figure 1a shows how the moiré fringe changes as the sample grating deforms. As the sample contracts or expands, the frequency of the moiré fringe changes, and this change is proportional to the strain in the sample.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Catalytic Conversion of Biogenic and Synthetic Polymers into Carbon-Negative and -Neutral Chemicals and Fuels

We investigated a technology that enables the distributed decomposition of biogenic (lignin, cellulose) and synthetic (plastic) polymers into renewable or low-carbon-emission chemicals and fuel intermediates that can substitute fossil hydrocarbons for energy, chemical, and fuel production. The specific goal of this project is to (1) selectively convert biogenic polymers such as lignin and synthetic polymers such as polyethylene into hydrocarbons via electrocatalytic and thermocatalytic processes, and (2) optimize (electro)catalyst composition and reaction conditions to mitigate deactivation and control product selectivity.

09 BIOMASS FUELS↗
DOE OSTI2022

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Anion binding agent lithium salts for battery electrolytes

A method for synthesizing a purified lithium (Li)+ anion binding agent (ABA-F)− salt and the corresponding Li+(ABA-F)− are disclosed. The method includes dissolving a boron-based acid in a polar solvent to form a solution. The solution is refluxed to form an anion binding agent. A stoichiometric amount of a small fluorinated salt, such as LiF, is added to the anion binding agent to form a mixture. The mixture is subsequently crystallized to obtain a substantially pure Li+(ABA-F)− salt. Example purified Li+(ABA-F)− salts include Ox-Li+(ABA-F), m-Li+(ABA-F), and BF3—Li+(ABA-F)−. These purified Li+(ABA-F)− salts provide the benefits of increased battery thermal safety without loss of electrochemical performance.

Orendorff, Christopher↗