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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Association Mapping of Cell Wall Synthesis Regulatory Genes and Cell Wall Quality in Switchgrass

Inefficient conversion of biomass to biofuels is one of the main barriers for biofuel production from such materials. Approximately half of polysaccharides in biomass remain unused by typical biochemical conversion methods. Conversion efficiency is influenced by the composition and structure of cell walls of biomass. Grasses such as wheat, maize, and rice, as well as dedicated perennial bioenergy crops, like switchgrass, make up ~55% of biomass that can be produced in the United States. Grass cell walls have a different composition and patterning compared with dicotyledonous plants, including the well-studied model plant, Arabidopsis. This project identified genetic determinants of cell wall composition in grasses using both naturally occurring genetic variation of switchgrass and gene network reconstruction and functional assays in rice. In addition, the project linked functional data in rice and other species to switchgrass improvement efforts through curation of the most abundant class of regulators in the switchgrass genome. Characterizing natural diversity of switchgrass for variation in cell wall composition and properties, also known as quality, provides an unbiased avenue for identifying biologically viable diversity in switchgrass cell walls. To characterizing natural diversity, this project generated cell wall composition and enzymatic deconstruction data for ~450 genotypes of the Switchgrass Southern Association Collection (SSAC), a diverse collection composed of 36 switchgrass accessions from the southern U.S. distribution of switchgrass. Comparing these data with other measures of cell wall quality for the same samples demonstrated the complementary nature of the diverse characterization platforms now being used for biomass characterization. Association of the composition data with ~3.2K single nucleotide variant markers identified six significant single nucleotide variant markers co-associated with digestibility and another compositional trait. These markers might be used to select switchgrass genotypes with improved composition in breeding programs for biofuel and forage production. Because the SSAC continues to be characterized by collaborators in the bioenergy community, the data generated will be used to identify additional markers in higher resolution genotyping data to approach identifying the genes and alleles that cause natural variation in switchgrass cell wall quality. For example, these markers can be surveyed in the 2100-member Oklahoma Southern and Northern Lowland switchgrass collections that this project also characterized. An orthogonal approach to biodiversity studies, using comparative functional genomics permits systematic querying of how much regulatory information is likely to be transferable from dicots to grasses and use of accumulated functional genomics resources for better-characterized grass species, such as rice, itself a biomass source in global agriculture and in certain regions. The project generated and tested a number of specific hypotheses regarding cell wall transcription factors and enzymes of grasses. To aid identification of cell wall regulators, the project assembled a novel, highdepth and -quality gene association network using a general linearized model scoring system to combine rice gene network data. Using known or putative orthologs of Arabidopsis cell wall biosynthesis genes and regulators, the project pulled from this network a cell wall sub-network that includes 96 transcription factors. Reverse genetics of a co-ortholog of the Arabidopsis MYB61 transcription factor in rice revealed that this regulatory node has evolved the ability to regulate grass-specific cell wall synthesis enzymes. A transcription factor with such activity has not been previously characterized to our knowledge, representing a major conclusion of this work. Changes in gene expression in a protoplast-based assay demonstrated positive or negative roles in cell wall regulation for eleven other transcription factors from the rice gene network. Eight of fifteen (53%) of these have not previously been examined for this function. Some of these may represent novel grass-diverged cell wall regulators, while others are likely to have this function across angiosperms. A parallel effort of this project to expand knowledge of enzymes that have evolved to function in grass cell wall synthesis, revealed that a grass-diverged enzyme in rice, OsAT 5 , ferulates monolignols that are naturally incorporated into grass cell walls. This finding opens potential natural selection avenues for improving biomass composition for downstream processing by weak base pretreatment. Thus, this project has significantly expanded knowledge of cell wall synthesis and regulation in rice, information that can be used in reverse genetics and synthetic biology approaches to re-engineer cell walls for improved production of biofuel and high-value products. To lay the foundation for translating these results directly for switchgrass improvement, the project employed a comparative phylogenetic analysis of the major group of cell wall transcription factors that have been found to function in cell wall regulation, the R 2 R 3 MYBs. This analysis concluded that known cell wall regulators are largely conserved across switchgrass, rice, maize, poplar, and Arabidopsis. This interpretation is also largely consistent with the gene network analysis described above, though both approaches provide evidence that some co-orthologs of Arabidopsis regulators have diminished or increased in importance based on gene expression patterns. Also, several clades containing dicot cell wall regulators have expanded, consistent with the evolution of new cell wall regulators. This latter result is supported by functional analysis of the R 2 R 3 MYB protein SWAM 1 in a collaboration between this project and the DOE-funded group of Dr. S. Hazen at the University of Massachusettes. The curation of the switchgrass genome through this project provides specific targets for future engineering of switchgrass cell wall regulation and may also facilitate identification of regulators that underlie the molecular markers that are genetically linked to differences in cell wall quality. With the goal of spurring further research and technological developments in lignocellulosic biofuel production, this work has been communicated to the bioenergy and cell wall communities though various presentations and publications. To date, three manuscripts have been published, two others are near to publication, three others are in an advanced state, and two to four more are likely to be written based on analyses still in progress. In addition, project participants have presented thirteen posters and talks at regional, national, and international meetings about aspects of this project. In sum, the work supported by this funding has made and communicated significant progress in identifying the genes that grasses use for cell wall synthesis and regulation, information that will be used by project participants and others to improve the efficiency of conversion of lignocellulosic biomass to biofuels.

09 BIOMASS FUELS↗
NASA NTRSDate not supplied

Curating AI-Ready Datasets for Equity and Environmental Justice: A Data-Centric AI Case Study

An equitable and environmentally just community is essentialin order to avoid disproportionate burden borne by vulnerablecommunities. This need becomes pressing in the aftermathof an extreme event such as disaster or hazard when it is diffi-cult for the governing bodies to implement resource allocationas per the need. Artificial Intelligence (AI) algorithms canhelp surface Equity and Environmental Justice (EEJ) issueswhen trained on EEJ datasets. However, curating AI-readyEEJ training datasets is challenging due to differences in fac-tors such as heterogeneity, resolution, modality, and level ofexpertise in labeling. Additionally, EEJ issues involve sensi-tive information where uncertainties and errors could degradethe performance of AI algorithms. For eg. Error in seasonalcrop yield information can highly affect the prediction of an-nual crop yield. To address these challenges, Data-centricAI (DCAI) methods are employed, which enhance AI algo-rithm performance even with limited training samples. DCAIprioritizes data quality, thereby reducing the adverse effectsof uncertainties and errors during the model training process.This research proposes a novel dataset and benchmark for an-alyzing the effect of the Maui Wildfire of 2023 for Equityand Environmental Justice (EEJ) issues. The proposed datasetaligns with the concepts of DCAI such as annotation quality,data preprocessing, privacy, feature engineering, governanceand provenance. We firmly believe that the proposed datasetwould lay a foundation to implement robust and reliable mod-ern AI algorithms for addressing EEJ issues.

Paridhi Parajuli↗
DOE OSTI2025

Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation

Scanning Electron Microscopes (SEMs) are widely used in experimental science laboratories, often requiring cumbersome and repetitive user analysis. Automating SEM image analysis processes is highly desirable to address this challenge. In particle sample analysis, Machine Learning (ML) has emerged as the most effective approach for particle segmentation. However, the time-intensive process of manually annotating thousands of SEM images limits the applicability of supervised learning approaches. Self-Supervised Learning (SSL) offers a promising alternative by enabling knowledge extraction from raw, unlabeled data. This study presents a framework for evaluating SSL techniques in SEM image analysis, focusing on novel methods leveraging the ConvNeXtV2 architecture for particle detection. A dataset comprising 25,000 SEM images is curated to benchmark these proposed SSL methods. The results demonstrate that ConvNeXtV2 models, with varying parameter counts, consistently outperform other techniques in particle detection across different length scales, achieving up to a 34% reduction in relative error compared to established SSL methods. Furthermore, an ablation study explores the relationship between dataset size and SSL performance, providing actionable insights for practitioners regarding model selection and resource efficiency. This research advances the integration of SSL into autonomous analysis pipelines and supports its application in accelerating materials science discovery.

Rettenberger, Luca↗
DOE OSTI2021

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Anomalous and Ungrouped Carbonaceous Chondrites in the US Antarctic Meteorite Collection and their Potential Relevance to Ryugu and Bennu

With two different carbonaceous asteroid sample return missions in full swing, attention has focused on what connections can be made between the asteroid samples and the wide range of carbonaceous chondrite meteorites in worldwide collections. The US Antarctic meteorite collection contains nearly 1000 carbonaceous chondrites of various types including many in well-established groups as well as ungrouped, unusual or anomalous groups [1]. Some of the latter have been included in, or are possibly related to, recently proposed new carbonaceous chondrite classifications – CA and CY chondrites [2,3]. In addition to these, there are numerous ungrouped samples that have properties intermediate between established groups (like CM and CO; [4]), distinct from any other groups [5], or have anomalous properties that might be attributable to parent body processes such as heating, fluid interaction, or impacts [6]. Some CM anomalous or ungrouped chondrites share spectral features with Ryugu, which has a small hydration peak arguably due to hydrated minerals left after either impact heating or shock in carbonaceous chondrites [7]. PCA 91008, PCA 02012, GRO 95566, and LEW 85311 are all CMs that have experienced heating or metamorphism that may be due to impacts, solar radiation, or radiogenic decay [6]. These samples all have low H contents, C/H (bulk), and low 17O [6,8] and may hold clues to understanding the mineralogy of Ryugu, or interpretations of its spectral properties. WIS 91600, on the other hand, appears to be related to several other highly altered CM [6], and shares properties with the newly proposed CY chondrites [6]. An understanding of this grouplet will also aid in the interpretation of Bennu samples which have strong hydration features. In addition, ungrouped carbonaceous chondrites may provide valuable insights into the aqueous alteration potentially recorded in Bennu and Ryugu samples; such as the relatively moderate aqueous alteration recognized and dated at 4-5 myr after CAIs in MAC 88107 (C2-ungrouped) by [9] to the extensive aqueous alteration apparent in MIL 090292 (C1-ungrouped) [10]. Finally, LON 94101/94102 is a brecciated CM chondrite with numerous lithologies. Its appearance is similar to some of the brecciated lithologies visible at the surface of Bennu and Ryugu [11]. Although CM chondrites with multiple lithologies are common (e.g., [12]), the lithologies are often difficult to resolve at the hand specimen scale and only after some detailed e-beam characterization are the subtle lithologic differences evident (e.g., [13]). LON 94102 contains visibly distinct clasts at the hand specimen scale, relatively rare for CM breccias. The largest CM chondrites by mass from the U.S. Antarctic meteorite collection may provide insights into the extent of brecciation and heterogeneity within more typical CM chondrite-like source asteroids. For example, recent curation CT scans [14] show possible clasts within in a subsplit of ALH 83100 (CM1/2) which is the largest CM chondrite or CM chondrite pairing group in the U.S. Antarctic collection with an original mass of 3.019 kg. Similar work on additional subsplits or meteorites may aid our understanding of heterogeneity within CM chondrites from Antarctica. Initial classification of CM chondrites in U.S. Antarctic meteorite collection includes preliminary pairing when petrographically similar CM chondrites that have been previously found in the same field area. The two largest CM chondrite preliminary pairing groups by mass are the ALH 83102 (CM2) pairing group with an original mass of 2.554 kg and the EET 96005 (CM2) pairing group with an original mass of 1.125 kg. However, preliminary pairing groups assigned at classification—intrinsically— do not include stones that are petrographically distinct. Rigorous pairing group studies of CM chondrites from specific field sites are needed to investigate if there is unrecognized heterogeneity in CM chondrites from Antarctica that may represent common asteroid impactors (pre-atmospheric entry asteroids/meteoroids). Detailed pairing studies have the potential to recognize initial stones with multiple lithologies and investigate if there are stones that only sample one of those respective lithologies in the collection. These groups of heated CMs, extensively hydrated CMs, intermediate between CM and CO chondrites, and brecciated samples are all potentially relevant to Bennu and Ryugu samples, where heating, hydration, and brecciation have all come into play. These bodies might also be comprised of material intermediate to CM and CO chondrites, or at least distinct from the well-established CC groups. These small and unusual groups of carbonaceous chondrites may help to unlock new information about early solar system processes and aid in the understanding of the evolution of these carbonaceous asteroids.

meteorites↗
DOE OSTI2023

Chemical Blast Standard (1 kg)

Chemical explosions create blast waves with large overpressure disturbances. It is important to develop a standard blast model based on data to accurately predict acoustic blast-wave amplitudes near detonations and invert for explosion energy from distant observations of blast-wave signals. However, open data from large, controlled chemical explosions with reliable ground truth can be challenging to find. The lack of access to such data could limit the number of contributions to related research and potentially stifle the rate of discoveries or validation of existing models. Here, to address these data scarcity problem, we have curated and compiled a standardized set of 817 blast-wave waveforms from 19 distinct high-explosive events. The blast-wave waveforms are standardized to a 1 kg trinitrotoluene explosion using scaling laws and corrections for location effects. A brief overview of the dataset is presented along with explosion feature models as well as recommendations for extracting explosion features. The resulting dataset is distributed to an open repository in both Seismic Analysis Code and pandas DataFrame formats containing the waveforms, the scaled distances, and the sample rates.

58 GEOSCIENCES↗
DOE OSTI2024

Active learning for the design of polycrystalline textures using conditional normalizing flows

Generative modeling has opened new avenues for solving previously intractable materials design problems. However, these new opportunities are accompanied by a drastic increase in the required amount of training data. This is in stark juxtaposition to the high expense and difficulty in curating such large materials datasets. In this work, we propose a novel framework for integrating generative models within an active learning loop. Further, this enables the training of generative models with datasets significantly smaller than what has previously been demonstrated, providing a direct route for their application in data constrained environments. The functionality of this framework is then demonstrated by addressing the challenge of designing polycrystalline textures associated with target anisotropic mechanical properties. The developed protocol exhibited a cost reduction between 14 to 18 times over a randomly sampled experimental design.

36 MATERIALS SCIENCE↗
NASA NTRS2016

Biospecimen Culling: Temporal RNA Integrity Analysis Across Spaceflight Missions Dating from 1985 to 2011

The Ames Life Science Data Archive (ALSDA) at NASA Ames Research Center is managed by the Space Biosciences Division and has been operational since 1993. The ALSDA is responsible for archiving information and biospecimens collected from life science spaceflight experiments and matching ground control experiments. They are stored in the Ames biobank, which is located in the Biospecimen Storage Facility (BSF). The ALSDA also manages a Biospecimen Sharing Program, performs curation and long-term storage operations, and makes biospecimens available to the scientific community for research purposes via the Life Science Data Archive public website (https:lsda.jsc.nasa.gov). The BSF maintains both fixed and frozen spaceflight and ground tissues, collected from recent and past spaceflight missions. Due to the ever increasing demand for space to preserve current and future flight biospecimens, the ALSDA has initiated the development of a culling plan for biospecimens currently stored in the BSF. Culling enables the ALSDA to assess the quality of archived samples, and supports the development of standardized culling procedures that improve the operational efficiency of the BSF. The culling plan focuses on generating disposition recommendations for samples in the BSF, and currently is based on measuring ribonucleic acid (RNA) integrity number (RIN). The culling process includes (1) sorting and identification of candidate samples for RIN analysis, (2) completion of RIN analysis on select samples, and (3) development of disposition recommendations for specimens based on the RIN values. Furthermore, our approach allows for unique scientific opportunities, including development of a RIN-based methodology for culling, and temporal assessment of the quality of the tissues that have been stored in BSF since the 1980s. Results of this work will also support NASA open science initiatives.

biospecimen↗
DOE OSTI2014

A metabonomic evaluation of the monocrotaline-induced sinusoidal obstruction syndrome (SOS) in rats

The main curative treatment of colorectal cancer remains the surgery. However, when metastases are suspected, surgery is followed by a preventive chemotherapy using oxaliplatin which, unfortunately, may cause liver sinusoidal obstruction syndrome (SOS). Such hepatic damage is barely detected during or after chemotherapy due to a lack of effective diagnostic procedures, but liver biopsy. The primary objective of the present study was to identify potential early diagnosis biomarkers of SOS using a metabonomic approach. SOS was induced in rats by monocrotaline, a prototypical toxic substance. {sup 1}H NMR spectroscopy analysis of urine samples collected from rats treated with monocrotaline showed significant metabolic changes as compared to controls. During a first phase, cellular protective mechanisms such as an increased synthesis of GSH (reduced taurine) and the recruitment of cell osmolytes in the liver (betaine) were seen. In the second phase, the disturbance of the urea cycle (increased ornithine and urea reduction) leading to the depletion of NO, the alteration in the GSH synthesis (increased creatine and GSH precursors (glutamate, dimethylglycine and sarcosine)), and the liver necrosis (decrease taurine and increase creatine) all indicate the development of SOS. - Highlights: • Urine metabonomic profiles of SOS have been identified. • Urine osmoprotectants and anti-oxidants indicated an initial liver protection. • Liver necrosis was demonstrated by increased urine levels of taurine and creatine. • NO depletion was suggested by changes in ornithine and urea.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Electricity and natural gas tariffs at United States wastewater treatment plants

Abstract Wastewater treatment plants (WWTPs) are large electricity and natural gas consumers with untapped potential to recover carbon-neutral biogas and provide energy services for the grid. Techno-economic analysis of emerging energy recovery and management technologies is critical to understanding their commercial viability, but quantifying their energy cost savings potential is stymied by a lack of well curated, nationally representative electricity and natural gas tariff data. We present a dataset of electricity tariffs for the 100 largest WWTPs in the Clean Watershed Needs Survey (CWNS) and natural gas tariffs for the 54 of 100 WWTPs with on-site cogeneration. We manually collected tariffs from each utility’s website and implemented data checks to ensure their validity. The dataset includes facility metadata, electricity tariffs, and natural gas tariffs (where cogeneration is present). Tariffs are current as of November 2021. We provide code for technical validation along with a sample simulation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Prognostic Utility of Cell Cycle Progression Score in Men With Prostate Cancer After Primary External Beam Radiation Therapy

Purpose: To evaluate the prognostic utility of the cell cycle progression (CCP) score, a RNA signature based on the average expression level of 31 CCP genes, for predicting biochemical recurrence (BCR) in men with prostate cancer treated with external beam radiation therapy (EBRT) as their primary curative therapy. Methods and Materials: The CCP score was derived retrospectively from diagnostic biopsy specimens of men diagnosed with prostate cancer from 1991 to 2006 (n=141). All patients were treated with definitive EBRT; approximately half of the cohort was African American. Outcome was time from EBRT to BCR using the Phoenix definition. Median follow-up for patients without BCR was 4.8 years. Association with outcome was evaluated by Cox proportional hazards survival analysis and likelihood ratio tests. Results: Of 141 patients, 19 (13%) had BCR. The median CCP score for patient samples was 0.12. In univariable analysis, CCP score significantly predicted BCR (P=.0017). The hazard ratio for BCR was 2.55 for 1-unit increase in CCP score (equivalent to a doubling of gene expression). In a multivariable analysis that included Gleason score, prostate-specific antigen, percent positive cores, and androgen deprivation therapy, the hazard ratio for CCP changed only marginally and remained significant (P=.034), indicating that CCP provides prognostic information that is not provided by standard clinical parameters. With 10-year censoring, the CCP score was associated with prostate cancer-specific mortality (P=.013). There was no evidence for interaction between CCP and any clinical variable, including ethnicity. Conclusions: Among men treated with EBRT, the CCP score significantly predicted outcome and provided greater prognostic information than was available with clinical parameters. If validated in a larger cohort, CCP score could identify high-risk men undergoing EBRT who may need more aggressive therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Toward accelerating rare-earth metal extraction using equivariant neural networks

The separation of rare-earth metals, vital for numerous advanced technologies, is hampered by their similar chemical properties, making ligand discovery a significant challenge. Traditional experimental and quantum chemistry approaches for identifying effective ligands are often resource-intensive. We introduce a machine learning protocol based on an equivariant neural network, Allegro, for the rapid and accurate prediction of binding energies in rare-earth complexes. Key to this work is our newly curated dataset of rare-earth metal complexes—made publicly available to foster further research—systematically generated using the Architector program. This dataset distinctively features functionalized derivatives of proven rare-earth-chelating scaffolds, hydroxypyridinone (HOPO), catecholamide (CAM), and their thio-analogues, selected for their established efficacy in binding these elements. Trained on this valuable resource, our Allegro models demonstrate excellent performance, particularly when trained to directly predict DFT-level binding energies, yielding highly accurate results that closely correlate with theoretical calculations on a diverse test set. Furthermore, this strategy exhibited strong out-of-sample generalization, accurately predicting binding energies for an isomeric HOPO-derivative ligand not seen during training. By substantially reducing computational demands, this machine learning framework, alongside the provided dataset, represent powerful tools to accelerate the high-throughput screening and rational design of novel ligands for efficient rare-earth metal separation.

Gupta, Ankur K. [Lawrence Berkeley National Labora↗
DOE OSTI2026

Project 2.8: Biological Specimen Repository for the Mayak Project

Curation of the existing specimens, including Standard Operating Procedures: The Southern Urals Biophysics Institute (SUBI) will continue to manage the day-to-day operations of the biorepository, but will have to assume the financial costs of maintaining and purchasing equipment such as freezers; Standard Operating Procedures are already in place for all aspects of the operations, and the Georgetown University team will be available on a voluntary and ad hoc basis to answer any technical questions in the future. Acquisition and tracking of new specimens: SUBI will decide on the collection of new tissues and blood samples in the future, and on what scale, depending on available resources and research needs. The Georgetown University team is willing to provide advice on a voluntary and ad hoc basis. Procedures for receiving and approving specimen requests from users: SUBI can take over this function; Chris Loffredo and other qualified research scientists at Georgetown University would be willing to serve as Tissue Review Committee members on a voluntary and ad hoc basis. Biospecimens remaining at Georgetown University: pursuant to the original terms of the Biospecimen Transfer Agreement between SUBI and Georgetown University, all unused portions of specimens (FFPE blocks, slides, and frozen tissues) will be returned to SUBI at the conclusion of the approved scientific research for which they were transferred. It was anticipated that the return shipment of biospecimens would occur in the fall of 2023. However, at this time, due to international developments, there are no shipping companies who can provide such deliveries to Russia. For now, the biospecimens can remain at Georgetown University where they are stored at the Genomics and Epigenomics Shared Resource. There have not been any storage costs to date, but this could change in the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Accelerating Low-Income Financing and Transactions (LIFT) for Solar Access Everywhere (Final Technical Report)

The Accelerating Low-Income Financing and Transactions (LIFT) for Solar Access Everywhere project’s goal was to expand Low-to-Moderate Income (LMI) solar access for homeowners and renters. The LIFT project researched and gathered data on 453 LMI community solar project across the country. Following three years of research, the project delivered three groundbreaking research papers in June 2022, focused on 1) customer experience, 2) the growth of community solar programs, and 3) project-level financial best practices for serving LMI communities. These were followed by a user-friendly web-based Toolkit allowing users to interact with project data and key findings in November 2022. The customer experience research examined community solar subscribers’ primary motivations to join and remain satisfied with projects. Our research identified 453 projects across the country that dedicated some portion of the system capacity to LMI households. Seventeen of these projects participated in the LIFT customer experience research, allowing the project team to survey their customers and gain insight into how LMI subscribers feel about community solar and the programs that serve them. Subscribers in our sample indicated that the most critical issue that motivated them to participate in their program, however, was not savings but helping the environment. This was true for both LMI and non-LMI subscribers. Helping the environment was also the most important issue for LMI subscribers to measure how well their program was working for them. LIFT also explored how rapidly community solar has grown since its inception in 2006, publishing results in the Growth of U.S. Community Solar Serving LMI Households report. The results showed that community solar projects serving LMI households are one of the fastest growing segments of the solar industry. The report identifies and recommends ways developers should overcome real or perceived risks to LMI customer acquisition and subscriber management. Through the analysis of community solar project finance research, LIFT showed that most community solar projects serving LMI households are financed in the same ways mainstream community solar projects are financed. The value stacks and financial returns are no different, although LMI inclusion and participation rate varied across programs in our sample, ranging from between 10% and 100%. Based on the findings from the LIFT research, the team built a web-based user-friendly Toolkit, consisting of case studies, project finance best practices, and several tools built around the national dataset of 453 community solar projects that serve LMI households. These allow users to engage with the dataset in multiple ways; to explore the landscape of LMI community solar in the U.S., and to design community solar projects to optimize LMI inclusion, equity, and savings levels. The Toolkit also includes a library of LIFT-generated and LIFT-curated resources for users to learn more about how to best serve LMI communities through community solar. LIFT officially published the Toolkit on October 31, 2022, followed by a launch event (public webinar) on November 17, 2022. The core LIFT partners continue to engage in outreach and dissemination efforts to promote the LIFT Toolkit and research publications. Our driving motivation is to continue enabling solar developers to leverage the findings of this three-year research effort. By implication, the LIFT Toolkit is designed for use by utilities, energy service providers, and financiers or investors as a learning and decision-making tool to rapidly scale project models that optimize LMI inclusion and maximize real household savings.

14 SOLAR ENERGY↗
NASA NTRS2003

Earth's earliest biosphere-a proposal to develop a collection of curated archean geologic reference materials

The discovery of evidence indicative of life in a Martian meteorite has led to an increase in interest in astrobiology. As a result of this discovery, and the ensuing controversy, it has become apparent that our knowledge of the early development of life on Earth is limited. Archean stratigraphic successions containing evidence of Earth's early biosphere are well preserved in the Pilbara Craton of Western Australia. The craton includes part of a protocontinent consisting of granitoid complexes that were emplaced into, and overlain by, a 3.51-2.94 Ga volcanigenic carapace - the Pilbara Supergroup. The craton is overlain by younger supracrustal basins that form a time series recording Earth history from approximately 2.8 Ga to approximately 1.9 Ga. It is proposed that a well-documented suite of these ancient rocks be collected as reference material for Archean and astrobiological research. All samples would be collected in a well-defined geological context in order to build a framework to test models for the early evolution of life on Earth and to develop protocols for the search for life on other planets.

Archaea↗
DOE OSTI2026

Identifying Anomalous DESI Galaxy Spectra with a Variational Autoencoder

The tens of millions of spectra being captured by the Dark Energy Spectroscopic Instrument (DESI) provide tremendous discovery potential. In this work we show how Machine Learning, in particular Variational Autoencoders (VAE), can detect anomalies in a sample of approximately 200,000 DESI spectra comprising galaxies, quasars and stars. We demonstrate that the VAE can compress the dimensionality of a spectrum by a factor of 100, while still retaining enough information to accurately reconstruct spectral features. We then detect anomalous spectra as those with high reconstruction error and those which are isolated in the VAE latent representation. The anomalies identified fall into two categories: spectra with artefacts and spectra with unique physical features. Awareness of the former can help to improve the DESI spectroscopic pipeline; whilst the latter can lead to the identification of new and unusual objects. To further curate the list of outliers, we use the Astronomaly package which employs Active Learning to provide personalised outlier recommendations for visual inspection. In this work we also explore the VAE latent space, finding that different object classes and subclasses are separated despite being unlabelled. We demonstrate the interpretability of this latent space by identifying tracks within it that correspond to various spectral characteristics. For example, we find tracks that correspond to increasing star formation and increase in broad emission lines along the Balmer series. In upcoming work we hope to apply the methods presented here to search for both systematics and astrophysically interesting objects in much larger datasets of DESI spectra.

Nicolaou, C. [University Coll. London] (ORCID:0000↗
DOE OSTI2026

A curated benchmark for cofolding models on kinase conformational states

Abstract Protein kinases are critical drug targets, requiring therapeutics that can modulate their active and inactive conformational states. While cofolding models can generate global folds directly from kinase sequences and ligand SMILES strings, these models have not yet been tested on their ability to recover ligand-induced-fit conformational states of the kinase proteins. Here, we introduce KinConfBench, a curated benchmark of 2225 high-quality human kinase chains to evaluate the ability of four state-of-the-art cofolding models—Boltz-2, Chai-1, Protenix, and RoseTTAFold-All-Atom—to recover both canonical and rare conformational states. We show that geometric success metrics of a ligand pose in the active site do not correlate strongly with the correct kinase conformational state, motivating a new set of dynamical benchmarks for assessing cofolding models. While all four cofolding models achieve ~60–80% prediction accuracy for kinase conformational classification, they exhibit severe mode collapse when performing multiple inferences, show negligible structural diversity in sampling induced-fit motions, and display a prevalent “apo-drift” in which most cofolding models predominantly predict the kinase to be in its ligand-free state. Our results highlight that capturing ligand-induced protein conformational diversity, not just geometric fit, is critical for next-generation structure-based drug discovery.

Sun, Kunyang↗
NASA NTRSDate not supplied

In-Lab Rapid Analytical Detection of Lunar Volatiles By Universal Gas Analyzer With Comparison to GC-MS System

Introduction: The curation of permanently shadowed regions (PSRs) [1] on the lunar surface is centered around studies based upon the observed volatiles from the LCROSS mission [2]. The rapid detection of important volatile gases and vapors present in planetary bodies and Astromaterials by a standalone analytical device is an area of intense research interest in our group and Planetary Exploration & Astromaterials Research Laboratory (PEARL) facility and this work is relevant to the future preparation of viable lunar simulants for testing curation efforts down the road. The groundbreaking results obtained from the LCROSS Mission [2] open the requirements for the direct detection of volatiles present in regolith materials collected from the lunar surface. The mass spectrometry of volatile chemicals is a general technique that utilizes a set of instruments that creates charged ions from a gaseous chemical species and measures the intensities vs. mass-to-charge ratio (m/z) [3]. In this context, we discuss in-lab experimental results and procedures for rapid qualitative analysis of main LCROSS volatiles (water, H2S, NH3, CO2, and CH3OH) by a Universal Gas Analyzer (UGA) instrument. Additionally, the instrument performance was evaluated by measuring the isotopic abundance ratio of atmospheric Ar-40 to Ar-36 present in room air since, argon is a relevant gas in planetary studies as it can provide an insight and reference point to isotope studies [4]. Additional, cross comparisons were attempted and made between the two instruments to develop a robust analytical technique by comparing mass spectral data for H2S headspace samples with a Trace-1310/ ISQ 7000 (ThermoFisher Scientific.) GC-MS system. Background: The benchtop UGA System is equipped with an SRS UGA 300 quadrupole mass spectrometer designed and built by Stanford Research Systems [5]. This system can be configured for several types of gaseous chemical analysis. The inlet line continuously samples gases at low flow rates (several milliliters per minute) through a capillary limiting the intake pressure making the instrument ideal for online analysis of select gases and/or room atmosphere. Moreover, in our current UGA system, a change in composition at the inlet can be detected in about 200 milliseconds and a complete spectrum is acquired (for a range of 1-100 amu) in under 45 sec with masses measured at rates up to 25 msec per point [5]. This system provides a quick upstream analytical data that we can then compare to results obtained by our GC-MS system. Sample Preparation: Small volume (2-4 mL) of analyte sample was taken in a 10 mL glass vial and sealed with a crimped cap and purged with pure Ar or N2 gas to displace air from the top. The headspace sample was scanned by the UGA instrument at analog, histogram, and pressure vs. time modes. The isotopic abundance ratio for 40Ar-to-36Ar was estimated by measuring partial pressure vs. time scans and setting the mass at 40 and 36 respectively. Results and Discussions: In this work, we have investigated the applicability of the UGA system by qualitative analysis of a series of LCROSS volatiles measured individually. Fig. 1 demonstrates a set of vertically offset spectra for the partial pressures measured as a function of mass-to-charge (m/z) ratios. The average acquisition time for each spectrum was less than a minute suggesting that the UGA system is ideal for quick analysis of geochemical volatiles. For the cross-comparison, we analyzed an H2S headspace sample by a Trace-1310/ISQ-7000 system and compared mass spectral data with previously measured UGA histogram scan data (Fig. 2). In both cases, major peak positions are the same, however, the intensities of fragment ions ([1H132S]+ and [32S]+) are higher for UGA suggesting that the fragment ionization process is stronger in UGA compared to that of GC-MS. To investigate how the integrated area under each chromatogram varies with the headspace sample volume, a set of five H2S headspace samples with increasing volumes was analyzed by the GC-MS system (Fig. 3, inset). A small volume (e.g., 200 to 1000 µL) of H2S/H2O vapor was withdrawn from a 20 mL stock sample vial containing ~5 mL of 0.4% H2S in water by a gas-tight syringe and added to another 20 mL vial filled with argon and analyzed by the GC-MS system. Finally, the UGA detector sensitivity was evaluated by calculating the atmospheric 40Ar-to-36Ar isotopic abundance ratio in room air by running a partial pressure vs. time scan with setting the atomic mass at 40, and 36. Fig. 4(a) shows a ~25 min duration “P vs. time” scan for 40Ar (plot for 36Ar is not shown). The partial pressure values (~100 points) were corrected by subtracting the corresponding background pressure value for 37Ar and utilized to calculate 40Ar-to-36Ar isotopic abundance ratios as shown by Fig. 4b. The average isotopic abundance ratio is ~306 with a 2*STDEV ~13. This abundance ratio is significantly close to the previously reported value of 298.56 [6] and the ratio obtained by our GC-MS system (303 for a UHP grade Ar sample). Conclusions: Our study strongly evidenced that the benchtop UGA system is a valuable analytical tool for the detection of major LCROSS volatiles. The rapid scanning capability, the inexpensiveness of the whole system, and impressive detection sensitivity prove its worthiness as an essential device for advanced geochemical applications. Moreover, cross comparisons with the GC-MS provide important bridges into advanced curatorial efforts into the future. References: [1] Bickel, V.T., et al. (2021) Nat Commun 12, 5607. [2] Colaprete, A., et al. (2010) Science, 330, 463-468. [3] Glavin, D. P. et al. (2012) 2012 IEEE Aerospace Conference, 1-11. [4] Willett, C. D., et al. (2022) Geochimica et Cosmochimica Acta 329, 119-134. [5] Operation Manual and Programming Reference. (2018) Universal gas Analyzers, Stanford Research Systems. [6] Lee, J. Y., et al. (2006) Geochimica et Cosmochimica Acta 70, 4507–4512. Notes: (4 figures are attached with text as shown by the attached file)

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