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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 OSTI2026

Selective designer hydrolysates from Miscanthus bioenergy crop for sustainable utilization

Designer enzymatic hydrolysates deliver targeted, optimized fermentable sugars while reducing the formation of inhibitory by-products. Herein, five distinct enzymatic hydrolysates (EH) were generated from Miscanthus biomass using alkaline-ethanol fractionation and hydrothermal-mechanical refining (HMR) pretreatment approaches. Noticeably, alkali-ethanol fractionated hemicellulose A (HMA) and hemicellulose B (HMB) hydrolysis resulted in xylose-rich enzymatic hydrolysates with 104.8 ± 5.6 g L −1 and 173.4 ± 4.6 g L −1 xylose, respectively. All five hydrolysates encompassing varied carbon to nitrogen ratios were evaluated for lipid production using an engineered oleaginous yeast, R. toruloides 880-ADS. Microbial fermentation of xylose-rich hydrolysates, viz. HMR-EH2, HMA-EH, and HMB-EH, resulted in lipid yields ranging from 0.05 to 0.12 g g −1 sugar. Compositional and 2D NMR analyses revealed that HMA-EH residues are highly lignin-enriched while preserving the non-condensed structure conducive to valorization. Overall, the findings highlighted a novel, selective trade-off approach for maximizing lipid titers from designer hydrolysates, corroborating complete resource recovery for establishing integrated biorefineries.

Singh, Shuchi [University of Illinois at Urbana-Ch↗
DOE OSTI2020

Variability in structural carbohydrates, lipid composition, and cellulosic sugar production from industrial hemp varieties

The aim of this study was to determine carbohydrate recovery from hemp for ethanol production and quantify biodiesel from TAG (Triacylglycerol) present in hemp. The structural composition of five different hemp varieties (Seward County-SC, York County-YC, Loup County-LC, 19 m96136-19 m, and CBD Hemp-CBD) were analyzed. Concentration of glucan and xylan ranged between 32.63 to 44.52% and 10.62 to 15.48% respectively. The biomass was then pretreated with Liquid hot water followed by disk milling and then hydrolyzed enzymatically to yield monomeric sugars. High glucose (63-85%) and xylose (73-88%) recovery was achieved. Lipids were extracted from hemp using hexane and isopropanol and then transesterified to produce biodiesel. Approximately, 50% of total fatty acids in SC, LC, and CBD hemp were linoleic acid. Palmitic acid was present between 32 to 50% in varieties YC and 19 m. Highest TAG concentration at 25% of total lipids was observed in CBD hemp. In conclusion, the analysis on lipid composition and high sugar recovery demonstrates hemp as a potential bioenergy crop for ethanol and biodiesel coproduction.

09 BIOMASS FUELS↗
DOE OSTI2020

Impacts of climate and biophysical variability on global agriculture markets

Agricultural production is highly sensitive to changes in climate and weather patterns. The focus of the great majority of studies assessing climate impacts on agriculture has been on mean changes in agricultural responses. However, the manner in which global agricultural markets respond to the interannual variability of the climate and biophysical shocks is poorly understood. Here we show a strong transmission of interannual variations in climate-induced biophysical yield shocks to agriculture markets, which is magnified further by endogenous market fluctuations. We demonstrate the importance of imperfect versus perfect expectations of market and weather in generating the market fluctuations that play a key role in transferring the interannual variations to markets. We find that the volatility of market prices and consumption could be potentially reduced on average by 55% and 41%, respectively, with improved expectations, where agricultural producers make better decisions to adapt to climate and biophysical variability. We also find much heterogeneity in interannual variability across crops and regions, which is considerably mediated by trade as part of the economic response. Our study provides new insights on climate impacts on agricultural market variability and lays a foundation for further investigating the full range of climate impacts on biophysical and human systems.

Zhao, Xin↗
DOE OSTI2021

Soil carbon sequestration potential and the identification of hotspots in the Eastern Corn Belt of the United States

Soil carbon sequestration is a significant CO 2 mitigation strategy but precise assessments of sequestration require spatially explicit modeling of potential changes in soil organic carbon (SOC) in response to soil, climate, land condition and management interactions. We assessed the SOC sequestration potential of the Eastern Corn Belt (ECB) in the USA (Indiana, Kentucky, Maryland, Michigan, Ohio, Pennsylvania, and West Virginia) in response to the adoption of conservation farming practices and land use change (pasture and forestation) using the SOCRATES model. Input data was provided through an intersection of the STATSGO, National Land Cover Database and a PRISM climate surface. At the end of the 20th century, the 15.3 Mha of cropped soils in the ECB contained 632 Tg C, an estimated reduction of 52% since the introduction of agriculture in the mid-1800s. Complete adoption of no-tillage practices on Prime Cropland would potentially recover 147 Tg SOC over 20 years, whereas a continuation of conventional tillage would produce a loss of 35 Tg SOC. Sequestration hotspots (> 500 Gg increase in SOC) under no-tillage cover 2.3 Mha providing 28 Tg C over 20 years. The conversion of Marginal (non-Prime) agricultural lands to Forests would yield an additional 13 Tg C in SOC and 381 Tg C in aboveground biomass. The rehabilitation of Minelands to Forest would yield an additional 4 Tg C in SOC and 42 Tg C in biomass. Here, opportunities to sequester C in the ECB via tillage and reforestation are substantial and should be incorporated into regional and national climate change mitigation strategies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Whole-plant phenotypic engineering: moving beyond ratios for multi-objective optimization of nutrient use efficiency

Nutrient use efficiency (NUE) is typically measured as the ratio of yield to soil nutrient availability but ignores contributions of underlying plant traits. Relevant plant traits can be grouped as root acquisition efficiency, shoot radiation use efficiency, and plant metabolic efficiency. The intentional integration of these traits will lead to synergistic improvements of NUE. Recent progress in trait-focused research includes phenotyping root nutrient uptake rates and respiration, engineering reduced photorespiration, and identification of nutrient assimilation pathways. Traits need to be conceptualized in agricultural systems contexts to improve synchrony of plant demand and soil supply of nutrients, including consideration of crop mixtures. Use of simulation modeling and multi-objective optimization will allow accelerating NUE gains beyond selection for a single ratio.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Maize tissue culture, transformation, and genome editing

The importance of maize (Zea mays L.) to global agriculture, world economy, and food security is widely known and increasing. Current maize breeding programs are deeply integrated with recent and rapid technological advances in genome sequencing, computational biology, and new genotyping and phenotyping technologies. Transformation and genome editing capabilities are a central hub to an array of advanced molecular and breeding approaches to crop improvement. Tissue culture and somatic embryogenesis play essential and central roles in maize transformation biology. Synergistic applications of maize transformation, advanced genomics, and genome editing provide a potent interdependent triad for functional genomics research and advanced molecular breeding. Implementation of advanced capabilities to transform maize and conduct genome editing will profoundly influence the dynamics of global agriculture ushering in a new era of varietal development and molecular breeding. With over 60.9 Mha planted in 2019 alone, biotech maize accounts for 31% of the world’s maize production. Up to 10% higher yields are achieved using new varieties generated using genetic modification technologies compared to similar conventional varieties. By extension, the impact new varietal releases developed through genome editing will likely be more significant. Further, advances in transformation and genome editing technologies will facilitate an even wider applicability for the development of new varieties with increasingly complex traits. The introduction of biochemical pathways and the use of synthetic biology have become increasingly more attainable. The future is of genotype independent maize transformation and genome editing, as the working platform will impact world agriculture, global food security, and plant science well into the future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

In-line, High-Throughput Quality Monitoring for Fuel Cell and Electrolyzer Components Based on Transmission and Reflection Imaging

During the manufacturing of fuel cell and electrolyzer membranes and membrane electrode assemblies (MEAs), real-time, in-line, high-throughput optical-based quality monitoring methods are essential for detecting defects and monitoring thickness variations, thus improving the performance and increasing the durability of fuel cell and electrolyzer in the hydrogen industry. For the MEAs with very opaque coatings, optical transmission-based imaging has been developed and applied in the Roll-to-Roll system using a flashlight and a high-sensitivity CCD camera. We observed high signal-to-noise ratio images while the Roll-to-Roll system ran at 5 ft/min. The entire sample image could quickly be recovered from the discrete frames using customized Python codes for automatic frame cropping and stitching. We detected significant non-uniformities in our experimental MEAs specimen. For fuel cell and low-temperature electrolysis (LTE) transparent membranes, we used optical reflectance hyperspectral imaging with interference fringe-based thickness mapping. We set up a hyperspectral camera to measure various rolls of commercial membranes. The measurement results are analyzed to find the thickness distribution of each roll and to check for defects. Transmission and reflection imaging-based quality monitoring techniques demonstrated in this project can be widely used in the mass production environment to improve the production yield and performance of hydrogen devices.

DIRECT ENERGY CONVERSION,ENGINEERING↗
DOE OSTI2026

Linking Plant and Microbial Traits to Soil Carbon for Reliable and Resilient Bioenergy Systems

Bioenergy systems in the United States offer a dual opportunity to supply renewable feedstocks while enhancing ecosystem services such as hydrologic regulation, erosion control, and soil carbon (C) storage. National assessments highlight the potential to grow perennial energy crops to improve soil function and ecosystem resilience. Realizing this potential requires understanding the ecological mechanisms that govern how C is added, transformed, and stabilized in soils. Plant traits determine the quantity, depth, and chemistry of organic inputs, while microbial processes—including carbon use efficiency, necromass formation, and trophic interactions—mediate their transformation and partitioning among soil carbon pools. These biological pathways are shaped by soil physical and chemical properties, including aggregation, texture, and mineralogy, and by environmental drivers such as temperature, moisture, and disturbance, leading to context-dependent outcomes across landscapes. Management practices that diversify feedstocks, minimize disturbance, and maintain soil cover can promote both biomass production and C retention, while microbial amendments and rhizosphere engineering offer emerging, but often context-dependent, tools to optimize plant–microbe interactions. Trade-offs between biomass yield and soil carbon storage may arise when systems favor rapid aboveground productivity at the expense of belowground inputs and microbial processing, underscoring the importance of trait combinations that support both functions. Advances in monitoring, reporting, and verification—spanning precision agriculture, remote sensing, and biosensing—are improving predictive capacity through microbial-explicit process models and model–experiment (ModEx) frameworks. By connecting soil, plant, and microbial processes with advances in modeling and biosensing, this review outlines research priorities focused on trait-based parameterization and ModEx integration. These priorities will support the design of bioenergy systems that are both reliable and resilient, enhancing renewable energy production and ecosystem sustainability.

bioenergy systems↗
DOE OSTI2026

Development of male-sterile lines of Setaria viridis to accelerate C 4 model plant genetics

Setaria viridis is a diploid C 4 grass in the Poaceae family, notable for its rapid life cycle of 6–8 weeks from sowing to seed—much shorter than the 4–5 months required by crops such as Zea mays and Sorghum bicolor . This fast growth makes S. viridis a valuable model for C 4 crop research. Genetic crosses are essential for studying gene function, but manual crossing is labor-intensive and time-consuming. Here, to address this, we developed a male-sterile line by targeting the S. viridis ortholog of Setaria italica NO POLLEN 1 ( SiNP1 ), which encodes a glucose–methanol–choline oxidoreductase required for pollen exine formation. Using Cas9 and TREX2 -mediated genome editing, we generated SiNP1 knockouts in both the S. viridis ME034V and A10.1 backgrounds that were fully male-sterile. Backcrossing T 0 male-sterile plants to ME034V wild-type followed by selfing yielded a stable BC 1 F 2 line homozygous for a 59 bp deletion in the S. viridis NO POLLEN 1 gene, easily genotyped by PCR and maintained by heterozygous siblings. Using this line, we developed a simple and efficient crossing protocol that eliminates the need for emasculation. This method enables a single person to perform up to 100 crosses per day—compared to 15 using traditional methods—and yields 20–32 F 1 hybrid seeds per panicle with 100% genetic purity. We also quantified pollen flow and outcrossing frequencies under greenhouse conditions to develop optimal bagging strategies and prevent unintended pollination. This resource accelerates genetic research in S. viridis , enhancing its utility as a premier C 4 model for mapping and functional genomics.

C4 research↗
DOE OSTI2025

Experimental Study of a Novel Semi-Transparent Bi-Facial Agri-Voltaic System: Initial Results

Solar (photovoltaic)-agriculture practices have experienced notable progress across several countries over the past few decades, creating a field widely known as agrivoltaics. This co-location practice has proved effective for certain crops that prefer partial shade like tomatoes, lettuce, and peppers, although it is less effective for crops needing more sun. Availability of natural sunlight with a specific spectral signature is necessary for agricultural practices as many plant species require an optimum-level and quality of sunlight for photosynthesis. The goal of this work is to model and validate ground-level irradiance for a semi-transparent bifacial solar collector equipped with a custom spectral reflector. By introducing intentional gaps between opaque solar cells, we hope to allow 50% of visible light to pass through the module. Additionally, this technology could reduce the soil temperature by 2° C, improve moisture retention, and enhance yield by providing partial solar radiation. In our model, we can calculate the total solar irradiance for the plants as a function of the cell distribution, material absorption, height of the panel and time of year with a spatial resolution of 0.0625 m2. When comparing theoretical estimates to experimental measurements of photosynthetically active radiation (PAR), we find a discrepancy of 32-48%, and a discrepancy of 19-38% after correction with TMY data.

McGraw, Duncan (ORCID:0000000256388118)↗
DOE OSTI2024

Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Agriculture↗
DOE OSTI2024

Photosynthetic responses of switchgrass to light and CO 2 under different precipitation treatments

Switchgrass ( Panicum virgatum L .) is a prominent bioenergy crop with robust resilience to environmental stresses. However, our knowledge regarding how precipitation changes affect switchgrass photosynthesis and its responses to light and CO 2 remains limited. To address this knowledge gap, we conducted a field precipitation experiment with five different treatments, including −50%, −33%, 0%, +33%, and +50% of ambient precipitation. To determine the responses of leaf photosynthesis to CO 2 concentration and light, we measured leaf net photosynthesis of switchgrass under different CO 2 concentrations and light levels in 2020 and 2021 for each of the five precipitation treatments. We first evaluated four light and CO 2 response models (i.e., rectangular hyperbola model, nonrectangular hyperbola model, exponential model, and the modified rectangular hyperbola model) using the measurements in the ambient precipitation treatment. Based on the fitting criteria, we selected the nonrectangular hyperbola model as the optimal model and applied it to all precipitation treatments, and estimated model parameters. Overall, the model fit field measurements well for the light and CO 2 response curves. Precipitation change did not influence the maximum net photosynthetic rate ( P max ) but influenced other model parameters including quantum yield ( α ), convexity ( θ ), dark respiration ( Rd ), light compensation point ( LCP ), and saturated light point ( LSP ). Specifically, the mean P max of five precipitation treatments was 17.6 μmol CO 2 m −2 s −1 , and the ambient treatment tended to have a higher P max . The +33% treatment had the highest α , and the ambient treatment had lower θ and LCP , higher Rd , and relatively lower LSP . Furthermore, precipitation significantly influenced all model parameters of CO 2 response. The ambient treatment had the highest P max , largest α , and lowest θ , R d , and CO 2 compensation point LCP . Overall, this study improved our understanding of how switchgrass leaf photosynthesis responds to diverse environmental factors, providing valuable insights for accurately modeling switchgrass ecophysiology and productivity.

09 BIOMASS FUELS↗
DOE OSTI2021

Reanalysis of Water Withdrawal for Irrigation, Electric Power, and Public Supply Sectors in the Conterminous United States, 1950–2016

Accurately measuring water use by the economy is essential for developing reliable models of water resource availability. Indeed, these models rely on retrospective analyses that provide insights into shifting human population demands and adaptions to water shortages. However, accurate, methodologically consistent, empirically authentic, and spatiotemporally comprehensive historical datasets for water withdrawals are scarce. Herein, we present a reanalysis of annual resolution (1950–2016) historical data set on irrigation, electric power, and public supply water withdrawal within the conterminous United States (US) at the county-level, and, for power plants, at the site-level. To estimate electric power water use, we synthesized a historically comprehensive list of generators and historic patterns in generation across fuels, prime movers, and cooling technologies. Irrigation water use estimation required building a crop-demand model that utilized historical information on irrigated acreage for crops and golf courses, stage-specific crop water demand, and climate information. To estimate public water supply use, we developed a random forest model constructed from information on population, infrastructure, climate, and land cover. These estimates generally agree with total county and state water use information provided by the US Geological Survey (USGS) water use circular and estimates generated from independent studies for specific years. However, we also observed discrepancies between our estimates and USGS data that appear to be caused by inconsistencies in the methods used by the USGS's primary data sources at the state level over decades of data collection, highlighting the importance of reanalysis to yield spatiotemporally consistent and intercomparable estimates of water use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Quantifying nitrogen loss hotspots and mitigation potential for individual fields in the US Corn Belt with a metamodeling approach

The high productivity in the US Corn Belt is largely enabled by the consumption of millions of tons of manufactured fertilizer. Excessive application of nitrogen (N) fertilizer has been pervasive in this region, and the unrecovered N eventually escaped from croplands in forms of nitrous oxide (N 2 O) emission and N leaching. Mitigating these negative impacts is hindered by a lack of practical information on where to focus and how much mitigation potential to expect. At a large scale, process-based crop models are the primary tools for predicting variables required by decision making, but their applications are prohibited by expensive computational and data storage costs. To overcome these challenges, we built a series of metamodels to learn the key mechanisms regarding the carbon (C) and N cycle from a well-validated process-based biogeochemical model, ecosys. The trained metamodel captures over 98% of the variability of the ecosys simulated outputs for 99 randomly selected counties in Iowa, Illinois, and Indiana. To identify hotspots with high mitigation potential, we introduce net societal benefit (NSB) as an indicator for synthesizing the loss in yield and social benefits through emissions and pollutants avoided. Our results show that reducing N fertilizer by 10% leads to 9.8% less N 2 O emissions and 9.6% less N leaching at the cost of 4.9% more SOC depletion and 0.6% yield reduction over the study region. The estimated total annual NSB is $\$395$ M (uncertainty ranges from $\$114$ M to $\$1271$ M), including $\$334$ from social benefits (uncertainty ranges from $\$46$ M to $\$1076$ M), $\$100$ M from saving fertilizer (uncertainty ranges from $\$13$ M to $\$455$ M), and –$\$40$ M due to yield changes (uncertainty ranges from –$\$261$ M to $\$69$ M). For the median scenario, we noted that 20% of the study area accounts for nearly 50% of the NSB, and thus represent hotspot locations for targeted mitigation. Although the uncertainty range suggests that developing such a high-resolution framework is not yet settled and the scenario based estimations are not appropriate to inform the management practices for individual farmers, our efforts shed light on the new generation of analytical tools for life cycle assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Image-Based Methods to Score Fungal Pathogen Symptom Progression and Severity in Excised Arabidopsis Leaves

Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (Fv/Fm) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A regional inter-disciplinary partnership focusing on the development of a carinata-centered bioeconomy

Brassica carinata or Ethiopian mustard, a non-edible oilseed brassica, is a low carbon, purpose-grown, and none-to-low indirect land-use change bioenergy feedstock for the production of drop-in sustainable aviation fuel, biodiesel, renewable diesel, and a suite of value-added coproducts. Carinata oil converted to drop-in fuel using an American Society for Testing and Materials approved Catalytic Hydrothermolysis process has been successfully tested in commercial and military aviation. Carinata meal, the residue after oil extraction, is a high-protein feed supplement for livestock, poultry, and swine, and can also yield specialty products. The Southeast Partnership for Advanced Renewables from Carinata (SPARC) is a public–private partnership formed with a twofold mission: (1) Removing physical, environmental, social, and economic constraints that prevent regional intensification of carinata production as a low-carbon feedstock for renewable fuel and coproducts and (2) demonstrating enhanced value across the entire value chain by mitigating risk to farmers and other stakeholders. The partnership's goal is to energize the US bioeconomy through sustainable agriculture and thus contribute to energy security and economic diversification. SPARC relies on a combination of cutting-edge multidisciplinary research and active industry engagement to facilitate adoption of the crop. This involves informing stakeholders along the entire supply chain, from producers to end-users, policymakers, influencers, and the public, about the opportunities and best practices related to carinata. This article provides context and background concerning carinata commercialization as a winter cash crop in the Southeast US for renewable fuels and bioproducts. The advances made to date in the areas of feedstock development, fuel and coproduct development, meal valorization, supply chain logistics, and stakeholder engagement are outlined.

09 BIOMASS FUELS↗
DOE OSTI2020

Willow Biomass Crops Are a Carbon Negative or Low-Carbon Feedstock Depending on Prior Land Use and Transportation Distances to End Users

Few life cycle assessments (LCAs) on willow biomass production have investigated the effects of key geographically specific parameters. This study uses a spatial LCA model for willow biomass production to determine spatially explicit greenhouse gas (GHG) emissions and energy return on investment (EROI), including land use conversion from pasture and cropland or grassland. There were negative GHG emissions on 92% of the land identified as suitable for willow biomass production, indicating this system’s potential for climate change mitigation. For willow planted on cropland or pasture, life cycle GHG emissions ranged from −53.2 to −176.9 kg CO2eq Mg-1. When willow was grown on grassland the projected decrease in soil organic carbon resulted in a slightly positive GHG balance. Changes in soil organic carbon (SOC) associated with land use change, transportation distance, and willow yield had the greatest impacts on GHG emissions. Results from the uncertainty analysis exhibited large variations in GHG emissions between counties arising from differences in these parameters. The average EROI across the entire region was 19.2. Willow biomass can be a carbon negative or low-carbon energy source with a high EROI in regions with similar infrastructure, transportation distances, and growing conditions such as soil characteristics, land cover types, and climate.

09 BIOMASS FUELS↗