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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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DOE OSTI2024

Quantification of Native Lignin Structural Features with Gel–Phase 2D–HSQC 0 Reveals Lignin Structural Changes During Extraction

Our ability to study and valorize the lignin fraction of biomass is hampered by the fundamental and still unmet challenge of precisely quantifying native lignin's structural features. Here, we developed a rapid elevated-temperature 1 H– 13 C Heteronuclear Single-Quantum Coherence Zero (HSQC 0 ) NMR method that enables this precise quantification of native lignin structural characteristics even with whole plant cell wall (WPCW) NMR spectroscopy, overcoming fast spin relaxation in the gel phase. We also formulated a Gaussian fitting algorithm to perform automatic and reliable spectral integration. By combining HSQC 0 measurements with yield measurements following depolymerisation, we can confirm the combinatorial nature of radical coupling reactions during biosynthesis leading to a random sequential organization of linkages within a largely linear lignin chain. Such analyses illustrate how this analytical method can greatly facilitate the study of native lignin structure, which can then be used for fundamental studies or to understand lignin depolymerization methods like reductive catalytic fractionation or aldehyde-assisted fractionation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Quantification of Native Lignin Structural Features with Gel–Phase 2D–HSQC 0 Reveals Lignin Structural Changes During Extraction

09 BIOMASS FUELS↗

DOE OSTI2017

Porous Structure Design of Polymeric Membranes for Gas Separation

High-performance polymeric membranes for gas separation are of interest for molecular-level separations in industrial-scale chemical, energy and environmental processes. To overcome the inherent trade-off relationship between permeability and selectivity, the creation of permanent microporosity in polymeric matrices is highly desirable because the porous structures can provide a high fractional free volume to facilitate gas transport through the dense layer. In this feature article, recent developments in the formation of porous polymeric membranes and potential strategies for pore structure design are reviewed.

36 MATERIALS SCIENCE↗

DOE OSTI2024

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Abstract Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm 2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Upregulated expression of Nogo-A and NgR in an experimental model of focal microgyria regulates the migration, proliferation and self-renewal of subventricular zone neural progenitors

Nogo-A and its receptor (NgR) were first described as myelin-associated inhibitors of neuronal regeneration in response to injury. In recent years, knowledge about the important role of the Nogo-A protein in several neuronal pathologies has grown considerably. Here, we employed a neonatal cortex freeze-lesion (NFL) model in neonatal rats and measured the expression of Nogo-A and NgR in the resulting cerebrocortical microdysgenesis 5–75 days after freezing injury. We observed marked upregulation of Nogo-A and NgR in protein levels. Furthermore, the migration of neural precursor cells (NPCs) derived from the subventricular zone (SVZ) toward the sits of injury was perturbed by treatment of NgR antagonist peptide NEP1-40. In vitro analysis showed that the knockdown of NgR by lentivirus-delivered siRNA promoted in axonal regeneration and SVZ-derived neural stem cell/progenitor cell (SVZ-NPCs) adhesion and migration, findings which were similar to the effects of NEP1-40. Taken together, our results indicate an important role for NgR in regulating the physiological processes of SVZ-NPCs. The observation of upregulated Nogo-A/NgR in lesion sites in the NFL model suggest that the effects of the perturbed Nogo-A are a key feature during the development and/or the progression of cortical malformation. - Highlights: • NFL model is an accurate experimental reproduction of focal microgyria of FCD. • The increase of the Nogo-A Levels occurs in response to freeze-induced focal lesioning. • Nogo-A/NgR may play a critical role for in the pathologic progression of FCD. • Nogo-A is associated with the migration, proliferation and self-renewal of SVZ-NPCs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Synthesis of a novel building block for the preparation of multi-chromophoric sensitizers for panchromatic dye-sensitized solar cells

For this study, a simple 3-step synthesis of a novel pyrazine-containing heteroaromatic building block for use as a scaffold for producing multi-chromophoric sensitizers for use in dye sensitized solar cells has been developed. This moiety features two esterified carboxylic acid groups whose hydrolysis provides moieties for anchoring to nanoparticulate metal oxides and two bromine atoms that facilitate attachment of chromophores via palladium mediated cross-coupling methods. The utility of the building block for the synthesis of complex dye motifs is demonstrated by the preparation of a novel panchromatic dye featuring a BODIPY chromophore and a strongly electron donating porphyrin chromophore using both Suzuki and copper-free Sonogashira cross-coupling methods. The building block could be used either to attach two different or identical dye moieties to the metal oxide, or to attach an electron injection dye plus an auxiliary absorber, photoprotective agent, secondary redox moiety, etc.

14 SOLAR ENERGY↗

DOE OSTI2018

MDSplus yesterday, today and tomorrow

MDSplus is a data acquisition and analysis system used worldwide predominantly in the fusion research community. Development began 31 years ago by a collaboration of software developers who were charged with providing a data acquisition system for three new fusion experiments under construction: CMOD at MIT, ZTH at LANL and RFX at Padova, Italy. The design of MDSplus combined the functionality of MDS (MIT/Model Data System developed at MIT for the Alcator and Tara fusion experiments) with new features suggested by the developers from the other laboratories. The development of MDSplus used a RAD (rapid application development) approach before RAD became a mainstream methodology. MDSplus was implemented and ready for the initial operation of CMOD in 1991. Since that time, many other fusion facilities started using MDSplus for data acquisition and/or for exporting their data to other sites. Today MDSplus is still used around the world for fusion energy research, space exploration and other fields of science and technology. Work on MDSplus continues to enhance its capabilities, support more platforms, and improve its reliability. It is anticipated that MDSplus will continue to provide valuable tools for the fusion energy research community. In conclusion, this paper describes some of the history of the MDSplus software, the work that is currently underway, and the plans to enable MDSplus to continue to be available and supported long into the future.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Extreme-scale workflows: A perspective from the JLESC international community

The Joint Laboratory for Extreme-Scale Computing (JLESC) focuses on software challenges in high-performance computing systems to meet the needs of today’s science campaigns, which often require large resources, consist of multiple tasks, and generate vast amounts of data. In this context, extreme-scale workflows have been the key factor in enabling scientific discoveries by helping scientists automate the dependencies and data exchanges between workflow tasks, instead of managing those manually. Here, in this paper, we present representative extreme-scale workflows and feature workflow systems developed by JLESC participating institutions. We present lessons learned while developing these tools, alongside with the open challenges and future research directions in the field of extreme-scale workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

An extended micromechanics method for probing interphase properties in polymer nanocomposites [An extended micromechanics method for overlapping geometries with application to polymer nanocomposites]

Inclusions comprised on filler particles and interphase regions commonly form complex morphologies in polymer nanocomposites. Addressing these morphologies as systems of overlapping simple shapes allows for the study of dilute particles, clustered particles, and interacting interphases all in one general modeling framework. To account for the material properties in these overlapping geometries, weighted-mean and additive overlapping conditions are introduced and the corresponding inclusion-wise integral equations are formulated. An extended micromechanics method based on these overlapping conditions for linear elastic and viscoelastic heterogeneous material is then developed. An important feature of the proposed approach is that the effect of both the geometric overlapping (clustered particles) and physical overlapping (interacting interphases) on the effective properties can be distinguished. Lastly, we apply the extended micromechanics method to a viscoelastic polymer nanocomposite with interphase regions, and estimate the properties and thickness of the interphase region based on experimental data for carbon-black filled styrene butadiene rubbers.

42 ENGINEERING↗

DOE OSTI2025

Investigating the impact of preparation routes on the properties of copper-decorated silicon particles as anode materials for lithium-ion batteries

In recent years, the calendar life of Si has been recognized as a significant issue that must be addressed prior to technology deployment: The carbon conductive additive is a potential source of parasitic side reactions. However, carbon remains essential due to the low electronic conductivity of Si. In this study, we investigate the use of Cu as a conductive additive and potential alternative to carbon. Some Cu-decorated silicon particles (Si Cu ) were prepared using physical vapor deposition (PVD) via sputtering and high-energy milling. Other Si Cu particles were prepared by using a solution method and examined briefly. The milling method caused Cu to appear as island-like features on the Si surface, whereas the PVD method initially produced similar island-like features that gradually developed into a continuous coating around the Si as sputtering time increased. Electrodes fabricated from Si Cu exhibited lower overall resistivity, demonstrating the beneficial effect of Cu in improving electronic percolation through the electrode. Electrochemical tests showed that the milled SiCu exhibited higher capacity retention, improved rate capability, and lower overpotential. Furthermore, Si Cu coupled with an NMC811 cathode exhibited lower leakage currents compared with the baseline silicon, indicating that incorporating Cu provided an additional advantage of minimizing parasitic currents in the cells.

25 ENERGY STORAGE↗

DOE OSTI2018

mmu-miR-1963 negatively regulates the ameloblast differentiation of LS8 cell line by directly targeting Smoc2 3’UTR

Highlights: • mmu-miR-1963 could downregulate the expression of Smoc2 by binding to its 3’UTR. • Smoc2 could promote the amelogenic differentiation of LS8. • mmu-miR-1963 inhibits the ameloblast differentiation of LS8 via Smoc2. RUNX2 is a key regulator of osteogenic differentiation and odontoblastic differentiation. RUNX2 mutations could cause Cleidocranial dysplasia (CCD; OMIM119600), which is featured by abnormal development of bone and teeth. By using microRNA array, we identified a large number of microRNAs that showed different expression between wild-type Runx2 group and mutant groups. The aim of this study is to find out the effect of mmu-miR-1963, which was downregulated in all mutant Runx2 groups, on the ameloblast differentiation of LS8 cells. qPCR and Western Blot results showed the suppressive effect of mmu-miR-1963 on ameloblast differentiation of LS8 cell line. We further confirmed Smoc2 as one direct target of mmu-miR-1963. For the first time, we showed that mmu-miR-1963 could regulate the ameloblast differentiation of LS8 by targeting Smoc2. This study suggests the suppressive role of mmu-miR-1963 on ameloblast differentiation of LS8 via directly targeting the 3’UTR of Smoc2. We also demonstrated that Smoc2 itself could promote the ameloblast differentiation of LS8 for the first time. Our results indicate a novel explanation to the enamel hypoplasia phenotype in part of CCD patients.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Ligand Desorption and Fragmentation in Oleate-Capped CdSe Nanocrystals under High-Intensity Photoexcitation

Semiconductor nanocrystals (NCs) offer prospective use as active optical elements in photovoltaics, light-emitting diodes, lasers, and photocatalysts due to their tunable optical absorption and emission properties, high stability, and scalable solution processing, as well as compatibility with additive manufacturing routes. Over the course of experiments, during device fabrication, or while in use commercially, these materials are often subjected to intense or prolonged electronic excitation and high carrier densities. The influence of such conditions on ligand integrity and binding remains underexplored. Here, in this study, we expose CdSe NCs to laser excitation and monitor changes in oleate that is covalently attached to the NC surface using nuclear magnetic resonance as a function of time and laser intensity. Higher photon doses cause increased rates of ligand loss from the particles, with upward of 50% total ligand desorption measured for the longest, most intense excitation. Surprisingly, for a range of excitation intensities, fragmentation of the oleate is detected and occurs concomitantly with formation of aldehydes, terminal alkenes, H 2 , and water. After illumination, NC size, shape, and bandgap remain constant although low-energy absorption features (Urbach tails) develop in some samples, indicating formation of substantial trap states. The observed reaction chemistry, which here occurs with low photon to chemical conversion efficiency, suggests that ligand reactivity may require examination for improved NC dispersion stability but can also be manipulated to yield desired photocatalytically accessed chemical species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

A Guide to Using GitHub for Developing and Versioning Data Standards and Reporting Formats

Abstract Data standardization combined with descriptive metadata facilitate data reuse, which is the ultimate goal of the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. Community data or metadata standards are increasingly created through an approach that emphasizes collaboration between various stakeholders. Such an approach requires platforms for collaboration on the development process that centers on sharing information and receiving feedback. Our objective in this study was to conduct a systematic review to identify data standards and reporting formats that use version control for developing data standards and to summarize common practices, particularly in earth and environmental sciences. Out of 108 data standards and reporting formats identified in our review, 32 used GitHub as the version control platform, and no other platforms were used. We found no universally accepted methodology for developing and publishing data standards. Many GitHub repositories did not use key features that could help developers to gather user feedback, or to create and revise standards that build on previous work. We provide guidance for community‐driven standard development and associated documentation on GitHub based on a systematic review of existing practices.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Survey of Thunderstorms That Produce Megaflashes Across the Americas

We previously observed that long-horizontal lightning flashes exceeding 100 km in length, known as “megaflashes,” occur preferentially in certain thunderstorms. In this study, we develop a cluster feature approach for automatically documenting the evolutions of thunderstorm systems from continuous lightning observations provided by the Geostationary Lightning Mapper (GLM) on NOAA's Geostationary Operational Environmental Satellites (GOES). We apply this methodology to GOES-16 GLM observations from 2018 to mid-2022 to improve our understanding of megaflash-producing storms. We find that megaflashes occur in long-lived (median: 14 hr) storms that grow to exceptional sizes (median: 11,984 km 2 ) while they propagate across long distances (median: 622 km) compared to ordinary storms. The first megaflashes are typically produced within 15 min of the storm reaching its peak intensity and extent. However, most megaflashes occur ≥13 hr after the initial megaflash activity, and are sufficiently close to convection to suggest initiation in the thunderstorm core (where GLM has difficulty detecting faint early light sources from megaflashes). Megaflashes generated outside of convection are rare, accounting for 2.7% of the sample using a 50 km convective distance threshold, but also tend to be larger than normal megaflashes, possibly due to having direct access to electrified stratiform clouds through which megaflashes propagate.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Recent advances and applications of deep learning methods in materials science

Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular. In contrast, advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods. In this article, we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation, materials imaging, spectral analysis, and natural language processing. For each modality we discuss applications involving both theoretical and experimental data, typical modeling approaches with their strengths and limitations, and relevant publicly available software and datasets. We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations, challenges, and potential growth areas for DL methods in materials science.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Effective chemical potential for non-equilibrium systems and its application to molecular beam epitaxy of Bi 2 Se 3

First-principles studies often rely on the assumption of equilibrium, which can be a poor approximation, e.g., for growth. Here, an effective chemical potential ( μ ) method for non-equilibrium systems is developed. A salient feature of the theory is that it maintains the equilibrium limits as the correct limit. In application to molecular beam epitaxy, rate equations are solved for the concentrations of small clusters, which serve as feedstock for growth. We find that μ is determined by the most probable, rather than by the lowest-energy, cluster. Finally, in the case of Bi 2 Se 3 , μ is found to be highly supersaturated, leading to a high nucleus concentration in agreement with experiment.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Overcoming fundamental challenges of the electrochemical nitrogen oxidation reaction

Nitric acid and nitrates play a crucial role in modern agriculture and medicine; however, their commercial production through the Haber–Bosch and Ostwald processes is energy-intensive and generates significant greenhouse gas emissions. Additionally, the rapidly growing global population is expected to significantly increase food demand in the coming decades. Therefore, there is a pressing need to explore sustainable alternatives for synthetic fertilizer production to meet increasing global crop demands while reducing the carbon footprint. The electrochemical nitrogen oxidation reaction (NOR) presents a viable alternative which produces nitrates using renewable electricity. However, NOR is still a nascent reaction that has not been studied extensively, and several key challenges must be addressed as the field develops. In this feature article review, we highlight the set of effective approaches that we have employed for studying this complex reaction and overcoming these challenges, as well as discuss future directions for advancing NOR technology.

Energy - Conversion↗