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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 1,405 records · Page 78
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DOE OSTI2022

Chemical and Morphological Structure of Transgenic Switchgrass Organosolv Lignin Extracted by Ethanol, Tetrahydrofuran, and $\mathcal{γ}$-Valerolactone Pretreatments

We report the recalcitrance of lignocellulosic biomass is a challenge in biological-based biorefinery systems due to the complex physicochemical structure of plant cell walls. Pretreatment and genetic modification are two approaches in biomass conversion that have succeeded in modifying the structure of lignocellulose to enable better enzymatic deconstruction. However, the structural differences among pretreatment-solubilized lignin isolated from switchgrass genotypes have not been extensively investigated. Here, three organosolv pretreatment systems-ethanol (EtOH), tetrahydrofuran (THF), and γ-valerolactone (GVL)-were used on wild-type (WT) and two transgenic switchgrasses. All organosolv pretreatments caused a significant reduction in the molecular mass of lignins; particularly, up to ~90% decrease was observed in EtOH-pretreated lignin compared to untreated lignin. The WT EtOH lignin also presented the smallest particle size among all WT lignins. THF pretreated transgenic lignins showed a higher molecular mass, β-O-4 linkages, and aliphatic hydroxyl content compared to EtOH and GVL pretreated lignin. The number of hydrogen bonds between lignin and the organic solvents calculated from the molecular dynamics simulations followed the same trend as the experimentally determined reduction in lignin molecular mass. The results revealed the structural changes of solubilized lignin isolated from wild-type and transgenic switchgrass after different organosolv pretreatments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Enhancing Streamflow Forecast and Extracting Insights Using Long-Short Term Memory Networks With Data Integration at Continental Scales

Recent observations with varied schedules and types (moving average, snapshot, or regularly spaced) can help to improve streamflow forecasts, but it is challenging to integrate them effectively. Based on a long short-term memory (LSTM) streamflow model, we tested multiple versions of a flexible procedure we call data integration (DI) to leverage recent discharge measurements to improve forecasts. DI accepts lagged inputs either directly or through a convolutional neural network unit. DI ubiquitously elevated streamflow forecast performance to unseen levels, reaching a record continental-scale median Nash-Sutcliffe Efficiency coefficient value of 0.86. Integrating moving-average discharge, discharge from the last few days, or even average discharge from the previous calendar month could all improve daily forecasts. Directly using lagged observations as inputs was comparable in performance to using the convolutional neural network unit. Importantly, we obtained valuable insights regarding hydrologic processes impacting LSTM and DI performance. Before applying DI, the base LSTM model worked well in mountainous or snow-dominated regions, but less well in regions with low discharge volumes (due to either low precipitation or high precipitation-energy synchronicity) and large interannual storage variability. DI was most beneficial in regions with high flow autocorrelation: it greatly reduced baseflow bias in groundwater-dominated western basins and also improved peak prediction for basins with dynamical surface water storage, such as the Prairie Potholes or Great Lakes regions. However, even DI cannot elevate performance in high-aridity basins with 1-day flash peaks. Despite this limitation, there is much promise for a deep-learning-based forecast paradigm due to its performance, automation, efficiency, and flexibility.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Extracting the femtometer structure of strange baryons using the vacuum polarization effect

Abstract One of the fundamental goals of particle physics is to gain a microscopic understanding of the strong interaction. Electromagnetic form factors quantify the structure of hadrons in terms of charge and magnetization distributions. While the nucleon structure has been investigated extensively, data on hyperons are still scarce. It has recently been demonstrated that electron-positron annihilations into hyperon-antihyperon pairs provide a powerful tool to investigate their inner structure. We present a method useful for hyperon-antihyperon pairs of different types which exploits the cross section enhancement due to the effect of vacuum polarization at theJ/ψresonance. Using the 10 billionJ/ψevents collected with the BESIII detector, this allows a precise determination of the hyperon structure function. The result is essentially a precise snapshot of the$$\bar{\Lambda }{\Sigma }^{0}\,(\Lambda {\bar{\Sigma }}^{0})$$ Λ ¯ Σ 0 ( Λ Σ ¯ 0 ) transition process, encoded in the transition form factor ratio and phase. Their values are measured to beR = 0.860 ± 0.029(stat.) ± 0.015(syst.),$$\Delta {\Phi }_{\bar{\Lambda }{\Sigma }^{0}}=(1.011\pm 0.094({{\rm{stat.}}})\pm 0.010({{\rm{syst.}}}))\,{{\rm{r}}}ad$$ Δ Φ Λ ¯ Σ 0 = ( 1.011 ± 0.094 ( stat. ) ± 0.010 ( syst. ) ) r a d and$$\Delta {\Phi }_{\Lambda {\bar{\Sigma }}^{0}}=(2.128\pm 0.094({{\rm{stat.}}})\pm 0.010({{\rm{syst.}}}))\,{{\rm{r}}}ad$$ Δ Φ Λ Σ ¯ 0 = ( 2.128 ± 0.094 ( stat. ) ± 0.010 ( syst. ) ) r a d . Furthermore, charge-parity (CP) breaking is investigated in this reaction and found to be consistent with CP symmetry.

Science & Technology - Other Topics↗
DOE OSTI2025

ORBITaL-Net: A labeled training library for large-scale building feature extraction

Over the course of several years, nearly 1.5 million building outlines have been created from approximately 128,000 training tiles covering roughly 7,000 km 2 of very high-resolution multispectral overhead imagery, primarily dated between 2010 and 2020. This dataset, dubbed the Oak Ridge Building Image and TrAining Label Net (ORBITaL-Net), is designed for machine learning applications and is global in scope, with samples drawn from 72 countries across North America, South America, Africa, Europe, and Asia. ORBITaL-Net captures a great diversity in geographic setting, structural characteristics, land use (urban and rural), terrain, and imagery conditions. While the labeled building outlines are themselves valuable, the dataset’s true strength lies in the pairing of these labels with corresponding reference imagery, which is being released for open source use. Similar to SpaceNet and Replicable AI For Microplanning (ramp), this building outline dataset will allow the larger computer vision community from academia, government, and industry the opportunity to develop robust, scalable, and generalizable geospatial machine learning techniques. Unlike SpaceNet and ramp, which offer high resolution labels and imagery primarily for large urban cities, ORBITaL-Net is not focused on training samples from heavily populated areas but instead aims to capture the innate variability of conditions present in both the physical environment and imagery collections.

Geography↗
DOE OSTI2026

A procedure for rule extraction from a Self-Organising plasma disruption predictor for JET

In a previous paper, a Self-Organizing Map had proven to be able to identify the regions of the plasma operative space characterizing the pre-disruptive phase at JET without relying on any a priori information. One of the strengths of this disruption predictor lies in its inherent self-organization capability. The Self-Organizing Map discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states directly from the experimental data. Moreover, the provided model allows the visualization of high-dimensional plasma parameters and facilitates easy interrogation of the model to understand the reasons behind its correlations. In this paper, an additional step is taken towards the interpretability of models for predicting disruptions by training a Decision Tree to classify the plasma states according to the interpretation provided by the Self-Organizing Map (stable or at high risk of disruptions). The Decision tree provides a set of rules which describe the transition of the plasma towards the pre-disruptive phase as visualized in the Self-Organizing Map. The obtained rules for the database explored in the study identify four regions in the map, two of which are at risk of disruption. These regions correspond to partitions of a 3D space based on the peaking factors of the core and divertor radiation, as well as the Locked Mode. The agreement between the Self-Organizing Map answers and the rules supplied by the Decision Tree is confirmed by the comparison of the performance exhibited by the two models in the prediction of disruptions.

Setzu, Samuele [Univ. of Cagliari, Monserrato, Cag↗
DOE OSTI2022

Plot2Spectra: an automatic spectra extraction tool

Scientists cannot easily make use of numerical data encoded in plot images, such as of spectroscopy data, in scientific literature. Plot2Spectra was developed to use computer vision tools to automatically digitize plot images.

Jiang, Weixin↗