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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 OSTI2023

Using Multiple Geophysical Methods to Refine a Stratigraphic Conceptual Site Model at a Nuclear Waste Site

At contaminated sites, there is a critical reliance on conceptual site models of which a key component is the geologic framework model (GFM), which is used to build predictive fate and transport modeling in support of remediation goals. Here, we detail an ongoing study at a nuclear waste site (Hanford Site, Washington, USA) where surface geophysical methods were used to complement existing borehole information and to help site new boreholes to update and refine the GFM. Ground-based electrical resistivity tomography (ERT), time-domain electromagnetics (TEM), and seismic data was acquired on the Hanford Site’s Central Plateau, where the presence of a hydraulically transmissive subsurface feature, or paleochannel, was suspected but uncertain based on contaminant concentrations at sparsely located boreholes. To assess and compare ERT, TEM, and seismic geophysical datasets, co-located data was acquired in an area where the existing GFM was more certain. Surrounding the suspected paleochannel, ERT and seismic refraction tomography showed similar subsurface structures consistent with a channelized feature. While TEM interpretation was limited in the suspected paleochannel area due to a thick resistive top layer and high electromagnetic noise, TEM showed greater utility in a different area where these factors were less prevalent. Based on the results of quasi-3D ERT inversions, we propose updates to the GFM, including identifying a paleochannel. Further, we evaluate the utility of geophysical methods and examine lessons learned that will be used as a basis for continued characterization efforts to support site management decisions and implementation efforts.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Rare earth element potential in coal and coal ash in the U.S. Gulf Coast

United States heavy reliance on imports of critical minerals (CMs), including rare earth elements (REEs), underscores the importance of development of domestic sources. The study objective was to quantify CM and REE concentrations in coal and coal ash in the US Gulf Coast region. CM and REE concentrations were measured for 118 samples from outcrops and 14 mines in the Gulf Coast. Results show that total REE + Yttrium (REY) concentrations (dry coal basis) are comparable to those of the upper continental crust (UCC) with localized hot spots, such as the Texas Gibbons Creek mine (REY ≤ ~ 2860 ppm). When normalized to UCC REY concentration (169 ppm, dry coal basis), REY to UCC ratios for Gulf Coast coal samples range from 0.1 to 17 (median ratio 0.6). REE extractability from lignites is high (median: 63%–93%) using environmentally benign weak acid. In addition to raw coal, coal ash from power plants could also serve as an REE source with a median ratio of REY in ash relative to coal of 4; however, extractability from coal ash is generally much lower (≤ 5% using the same weak acid as in coal). The median basket price for extracted REY as oxides from coal, assuming 70% extractability, is $\$3.2$ per tonne of coal and $\$186$ billion based on 58 billion metric tonnes of dry coal in the Gulf Coast. REEs important for magnets (Pr + Nd + Tb + Dy) account for ~ 80% of the total value. The corresponding median basket price for extracted REY as oxides from coal ash, assuming ~ 30% extractability, is ~$\$4.4$ per tonne of ash and $\$1.2$ billion based on 258 million tonnes of ash. REE production from coal would likely require co-products, such as activated carbon or humic acids, to attain economic viability. Production of REEs from coal ash could offset remediation costs related to potential water contamination. This reconnaissance study shows the potential for REE production from coal and coal ash in the Gulf Coast; however, carbon coproducts and/or societal benefits would likely be required for socioeconomic viability.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Cultivating clarity: understanding the impact of land cost assumptions on biofuel viability

The transition to sustainable energy has increased interest in biofuel production to reduce greenhouse gas emissions, decrease reliance on imported oil, and ensure energy resilience. Here, this study examines the often-overlooked impact of land cost assumptions on the economic viability of biofuel production. Using a discounted cash flow techno-economic framework, we evaluated three land cost scenarios—no land costs, land rental costs, and land purchase costs—across six bioenergy feedstocks (corn, soybeans, switchgrass, miscanthus, poplar, and microalgae) and three biofuel products (corn ethanol, soybean biodiesel, and sustainable aviation fuel) at the county level for the contiguous United States. The analysis reveals substantial variation in minimum fuel selling prices due to these scenarios. High-yield crops like algae showed low sensitivity to land costs, while low-yield crops such as soybeans were highly sensitive. Geographical differences were significant, with minimum fuel selling price increases most pronounced in high-value land regions like the Corn Belt. Case studies further illustrate the influence of local productivity and land costs on economic outcomes across the United States. These findings emphasize the importance of maintaining consistent land cost assumptions in biofuel economic assessments. By quantifying the interplay between land value, crop productivity, and economic feasibility, this study provides essential insights for policymakers and stakeholders to advance sustainable energy solutions.

09 BIOMASS FUELS↗
DOE OSTI2023

Athermal annealing of pre-existing defects in crystalline silicon

Systematic investigations of electronic energy loss (S e ) effects on pre-existing defects in crystalline silicon (Si) are crucial to provide reliance on the use of ionizing irradiation to anneal pre-existing defects, leading to successful implementation of this technology in the fabrication of Si-based devices. In this regard, the S e effects on nonequilibrium defect evolution in pre-damaged Si single crystals at 300 K has been investigated using intermediate-energy ions (12 MeV O and Si ions) that interact with the pre-damaged surface layers of Si mainly by ionization, except at the end of their range where the nuclear energy loss (S n ) is no longer negligible. Furthermore, under these irradiation conditions, experimental results and molecular dynamics simulations have revealed that pre-existing disorder in Si can be almost fully annealed by subsequent irradiation with intermediate-energy incident ions with S e values as low as 1.5–3.0 keV/nm. Selective annealing of pre-existing defect levels in Si at room temperature can be considered as an effective strategy to mediate the transient enhanced diffusion of dopants in Si. This approach is more desirable than the regular thermal annealing, which is not compatible with the processing requirements that fall below the typical thermal budget.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Resilience and economics of microgrids with PV, battery storage, and networked diesel generators

Current designs and assessments of microgrids have ignored component reliability, leading to significant errors in predicting a microgrid’s performance while islanded. Existing life cycle cost studies on hybrid microgrids—which combine photovoltaics (PV), battery storage and networked emergency diesel generators—also have not identified all the potential economic opportunities. Reducing the number of emergency diesel generators through reliance on PV and battery, retail bill savings, and demand response and wholesale market revenue streams are all important. This paper provides a new statistical methodology that calculates the impact of distributed energy reliability and variability on a microgrid’s performance and a novel use of the optimization platform REopt to explore multiple cost savings and revenue streams. We examine the impacts for microgrids in California, Maryland, and New Mexico and show that a hybrid microgrid is a more resilient and cost-effective solution than a diesel-only system. Under realistic conditions, a hybrid microgrid can provide higher system reliability when islanded and have a lower life cycle cost under multiple market conditions than a traditional diesel generator-based system. The improved performance of the hybrid system is resilient to conditions experienced over the last 20 years in solar irradiance and sees little degradation in performance immediately after a hurricane. The cost savings to provide this more resilient backup power system as compared to a diesel-only microgrid are significant. The net present cost for a hybrid microgrid is 19% lower in New Mexico and 35% lower in Maryland than a diesel-only microgrid. In California, the net present cost of the hybrid microgrid is negative because, unlike a diesel-only microgrid, a hybrid microgrid has lower life cycle costs than the power costs without a microgrid.

25 ENERGY STORAGE↗
DOE OSTI2022

Optimal Gas-Electric Energy System Decarbonization Planning

As energy utilities implement climate change mitigation policies, system planners require strategies for achieving affordable emissions reductions. Coordinated planning of electric power and natural gas systems will allow synergistic investments to address cross-sector operational constraints, competing uses for net-zero emissions fuels, and shifts in energy demands across energy carriers. In this study, we develop a novel optimization program that finds the cost-minimizing mix of infrastructure expansion or reduction across gas and electric systems to satisfy sector-specific emissions constraints. Alongside energy supply resources, our framework allows for central-planning of end-use equipment stocks to allow switching between gas and electric appliances upon failure or premature replacement. The proposed model is used to simulate case study scenarios for a benchmark 24-pipe gas network coupled to a 24-node power system test network. We find that electrification of greater than 80% of core gas demands is a component of the least-cost solution for modeled energy systems. Despite this substitution, the gas system is maintained to service difficult-to-electrify customers and to deliver net-zero emissions gas to electricity generators in times of peak electricity demand. Restricting electrification of gas appliances increases reliance on power- to-gas technologies and increases annual costs by 15% in 2040. Neglecting constraints on pipeline blending of hydrogen can produce a misleading result that relies on hydrogen blend fractions of greater than 50%. In all cases, we find the average costs of delivered gas increase nearly 5-fold across the decarbonization transition, highlighting the importance of future work investigating cost-allocation strategies for ensuring an equitable energy transition.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Evaluating grid stress and reliability in future electricity grids across a range of demand, generation mix, and weather trends

The reliability of power grids in the future will depend on how system planners account for the integration of new technologies, extreme weather events, and uncertainties in demand growth from increased electrification and data centers. This study introduces an open-source, multisectoral, multiscale modeling framework that projects grid stress and reliability trends between 2020 and 2055 in the Western Interconnection of the United States. The framework integrates global to national energy-water-land dynamics with power plant siting and hourly grid operations modeling. We analyze future wholesale electricity price shocks and unserved energy events across eight scenarios spanning a range of population growth and economic change, generation mixes, and weather conditions. Our results show future grids with high percentage of non-renewable generation and strong economic growth are characterized by higher reliability and lower wholesale electricity prices than lower growth scenarios because of larger reliance on dispatchable generators and lower fossil fuel extraction costs. Scenarios with high percentage of renewable resources have lower median but more volatile wholesale electricity prices as well as more frequent and severe unserved energy events compared to scenarios relying more on dispatchable generators. These events occur because higher proportion of solar and wind energy causes net demand curves to deepen during midday (duck curves get progressively severe), exacerbating the challenge of meeting demand during summer evening peaks. This study suggests that robust and co-optimized transmission and energy storage planning could help maintain low wholesale electricity prices and high reliability levels in future electricity grids across uncertainties in generation mixes.

Electric grid reliability↗
DOE OSTI2024

Temperature-dependent mechanical properties and crystal plasticity parameters for additively manufactured Haynes-214 alloy: Experiments and numerical modeling

Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Stochastic fracture generation and thermo-hydro-mechanical modeling in an equivalent continuum framework for enhanced geothermal systems

Enhanced geothermal systems (EGS) involve fracturing low permeability material to establish well connectivity and then injecting and circulating fluid into the fractured subsurface for geothermal power production. Changes in fracture aperture from contraction of the cooling matrix rock may alter network connectivity and risk thermal short-circuiting. Thermo-hydro-mechanical (THM) models are a useful tool to study these processes. However, as fracture networks are complex, and data may be limited, fracture networks in THM models are often stochastically generated. Given reliance on stochastic fracture networks and THM modeling to represent the subsurface and assess productivity of EGS, increased understanding of the influence of such statistically derived fracture networks on flow and heat transport in THM models is needed. Here, a new fracture process model is developed in the reactive transport code PFLOTRAN to stochastically generate fracture families and simulate changes in fracture aperture over time due to temperature changes of the rock matrix. Sixty-four different fracture networks ranging from well to poorly-connected, are modeled in PFLOTRAN with and without mechanical processes (THM vs TH). Results indicate that for well-connected fracture networks, thermal short-circuiting is less of a concern due to the abundance of available alternative flowpaths. For poorly-connected fracture networks, inclusion of mechanical processes showed steep thermal drawdown coincident with increase in fracture aperture along developing colder flowpaths, demonstrating the risk of thermal short-circuiting. Simulations with additional, larger fractures engineered to establish connectivity in a poorly-fractured subsurface, indicate that while stochastic variation of fracture orientation of the background network had limited influence, such variation in the engineered fractures significantly affected flow and heat transport.

Discrete fracture networks (DFN)↗
DOE OSTI2025

A mathematical approach to using the forgetting curve to evaluate experience and training factors in human reliability analysis

Traditional human reliability analysis (HRA) methods have difficulty dealing with the dynamic nature of factors such as time and rely on static and expert-judgment-based assessments of performance-shaping factors (PSFs) across limited levels. In this study, we introduce a mathematical approach for dynamically evaluating the experience and training PSF. Our proposed method integrates the psychological concept of the “forgetting curve” to evaluate how PSFs are impacted by the number of trainings and the time elapsed since training. To confirm the validity of the model, we provide experimental data fitted by identifying the quantitative relationship between training and human performance. This research enables dynamic and objective assessments, thus reducing reliance on subjective expert judgment and improving the accuracy of HRA.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

A method for assessing economic, environmental, and reliability tradeoffs of interregional transmission connecting ERCOT (the Texas grid) to the eastern and western grids

Reliable development of the power grid is an evolving concern for humanity due to extreme weather that frequently threatens power sector infrastructure. The state of Texas is a uniquely structured testbed for grid planners to study when looking for solutions to development, innovation, and overcoming such challenges. Because of its size and islanded structure, Texas is small enough to model, but big enough to matter. Texas is a global leader in energy production, energy consumption, and maintains an unusually diverse fuel mix. In addition, the state has experienced winter freezes, heat waves, wind storms, droughts and floods that have threatened power sector infrastructure or caused recent blackouts and calls for demand side conservation. One of the most devastating of these events was the North American winter storm, dubbed “Winter Storm Uri” by the Weather Channel, that froze the region in February 2021 and led to an extended power outage event that put the majority of Texan residents in darkness for days. While preparing to avoid such outage events in the future, various tools have been proposed to improve grid reliability, including energy efficiency, demand response, and distributed energy resources. An additional option would be to develop interregional transmission that connects the Texas grid to other national grids. To assess the merits of this idea, we developed a novel, universally-applicable and internationally-relevant framework to study how the Texas grid would evolve alongside access to various interregional ties. This method allows us to stress the synthetic grid structure and analyze how it would respond to the shock of a simulated winter storm event. Our method leverages open-source modeling tools, such as PowerGenome, pyGRETA, and GenX to synthesize unique zonal grid data, construct a consolidated network of model regions, and simulate different developmental pathways of capacity expansion and operational dispatch. We demonstrate our method with an analysis connecting the Electric Reliability Council of Texas (ERCOT), the grid that serves most of Texas, the Western Electricity Coordinating Council (WECC), the grid that serves the western half of the contiguous U.S., and the Eastern Interconnect, the grid that serves the eastern half of the contiguous U.S. Our results indicate that the cost-optimal capacity of interregional transmission connecting the ERCOT grid to other grids lies between 9–13 GW assuming baseline conditions. Building this amount of connecting capacity in one or multiple directions lowers the costs and emissions of development and operation by up to $16 billion and 257 million metric tonnes (MMT) respectively. Additionally, our results show that the interregional connections between ERCOT and other national grids reduce the amount of total load shed required through mild winter storm events. However, our results also show that there is a threshold of very extreme winter storm conditions, spanning multiple service areas, above which the connections exacerbate resource adequacy problems. Therefore, the results indicate that the connections need to be carefully planned alongside the rest of the grid infrastructure to avoid over-reliance on specific resources or technology options.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Reinforcement Learning for feedback-enabled cyber resilience

The rapid growth in the number of devices and their connectivity has enlarged the attack surface and made cyber systems more vulnerable. As attackers become increasingly sophisticated and resourceful, mere reliance on traditional cyber protection, such as intrusion detection, firewalls, and encryption, is insufficient to secure the cyber systems. Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism (CRM) adapts to the known or zero-day threats and uncertainties in real-time and strategically responds to them to maintain the critical functions of the cyber systems in the event of successful attacks. Feedback architectures play a pivotal role in enabling the online sensing, reasoning, and actuation process of the CRM. Reinforcement Learning (RL) is an important gathering of algorithms that epitomize the feedback architectures for cyber resilience. It allows the CRM to provide dynamic and sequential responses to attacks with limited or without prior knowledge of the environment and the attacker. In this work, we review the literature on RL for cyber resilience and discuss the cyber-resilient defenses against three major types of vulnerabilities, i.e., posture-related, information-related, and human-related vulnerabilities. Here we introduce moving target defense, defensive cyber deception, and assistive human security technologies as three application domains of CRMs to elaborate on their designs. The RL algorithms also have vulnerabilities themselves. We explain the major vulnerabilities of RL and present develop several attack models where the attacker target the information exchanged between the environment and the agent: the rewards, the state observations, and the action commands. We show that the attacker can trick the RL agent into learning a nefarious policy with minimum attacking effort. The paper introduces several defense methods to secure the RL-enabled systems from these attacks. However, there is still a lack of works that focuses on the defensive mechanisms for RL-enabled systems. Last but not least, we discuss the future challenges of RL for cyber security and resilience and emerging applications of RL-based CRMs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Modeling hydrodynamic and biomass pyrolysis effects of recycled product gases in a bubbling fluidized bed reactor

Fast pyrolysis of biomass in a fluidized bed reactor is typically conducted in a nitrogen gas environment. Recycling product gas can improve the economics of operating such a system by reducing reliance on pure process streams, but much less is known about how recycling pyrolysis product gas may affect fluidization behavior and pyrolysis kinetics. Therefore, gas effects in a fluidized bed biomass pyrolysis reactor were investigated using engineering correlations, low-order models, and CFD simulations for N 2 , H 2 , CO, CO 2 , and CH 4 carrier gas mixtures. Here, our findings reveal viscosity of a gas mixture can be significantly underestimated depending on the model and correlation. Furthermore, fluidization characteristics such as U mf and gas-solid convective heat transfer can be greatly affected by the gas properties. By utilizing H 2 as the fluidizing gas (instead of N 2 ), while maintaining a constant fluidization ratio (U s /U mf ), the bio-oil yields can be increased ~5%. This is due to the lower density H 2 producing similar hydrodynamics as N 2 at higher gas flow rates. These higher flow rates result in shorter gas residence times, and as a result, less secondary reactions that convert bio-oil to light gases and char. Model results also suggest that bio-oil yield is not significantly affected by the type of carrier gas used, with bio-oil yield varying by ~2% across different gas mixtures while maintaining constant flow rate. In conclusion, this indicates that recycled pyrolytic gas can be used as the carrier gas for biomass pyrolysis.

09 BIOMASS FUELS↗
DOE OSTI2022

From fault-detection to automated fault correction: A field study

A fault detection and diagnostics (FDD) tool, as addressed by this study, is a tool that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Although FDD tools can inform operators of building operational faults, currently an action is always required to correct the faults to generate energy savings. Fault auto-correction integrating with commercial FDD technology offerings can close the loop between the passive diagnostics and active control, increase the savings generated by FDD tools, and reduce the reliance on human intervention. This paper presents the field study of seven fault auto-correction algorithms implemented in commercial FDD platforms. Implementation includes software changes in the FDD tools and additional controls hardware or software changes in the BAS that were required to enable the execution of different types of auto-correction algorithms in real buildings. The routines successfully and automatically correct faults and improve the operation of large built-up Heating, Ventilation, and Air Conditioning (HVAC) systems, common in most commercial buildings. The auto-correction algorithms are tested across four buildings and three different building automation systems, following a rigorous procedure to make sure they work properly and do not negatively impact the system and building occupants. Finally, technology benefits, market drivers, and scalability changes are drawn from the implementation effort and test results, to drive future research and industry engagement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Internal calibration of transient kinetic data via machine learning

The temporal analysis of products (TAP) reactor provides a vast amount of transient kinetic information that may be used to describe a variety of chemical features including residence time distributions, kinetic coefficients, number of active sites, reaction mechanism, etc. However, as with any measurement device, the TAP reactor signal is convoluted with noise and drift is common. In order to reduce the uncertainty of the kinetic measurement and any derived parameters or mechanisms, proper preprocessing must be performed prior to any advanced type of analysis. This preprocessing includes baseline correction, i.e., a shift in the voltage response, and calibration, i.e., a scaling of the flux response based on prior experiments. The traditional methodology of preprocessing requires significant user discretion and reliance on separate calibration experiments that may drift over time. Herein we use machine learning techniques combined with physical constraints to understand the noise and drift that is being generated within and between experiments for enhancement of the chemical kinetic signal. As such, the proposed methodology demonstrates clear benefits over the traditional preprocessing approach by eliminating the need for separate calibration experiments or heuristic input from the user.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Selectively extracting lithium from single and mixed cathode materials

With the burgeoning reliance on lithium-ion batteries for sustainable energy solutions and electric transportation, the environmental and resource management associated with battery disposal are increasingly critical. Addressing these challenges necessitates innovative recycling techniques that recover valuable battery components, particularly lithium. This research introduces a universal, eco-friendly approach tailored for the efficient selective extraction of lithium from both single and mixed cathode materials, achieving impressive selective leaching efficiencies of lithium (99.51 % for LFP, 90 % for NMC, and 97.24 % for mixed cathode). Surprisingly, leaching efficiency of lithium from NMC can be significantly improved by introducing LFP since LFP can remove the dense transition-metal salts on the surface of NMC. The extracted lithium is recovered as lithium carbonate with battery-grade purity. This study also highlights the reuse of formic acid and the adoption of oxygen as an oxidizing agent to prevent wastewater production. Therefore, this method provides a robust foundation for sustainable lithium battery recycling.

economically and environmentally feasible↗
DOE OSTI2026

Green pretreatment strategies for enhanced microbial lipid fermentation and synergistic high-quality lignin recovery for next-generation integrated biorefineries

Miscanthus × giganteus (Mxg) is a warm-season perennial grass being commercialized as a biomass feedstock for temperate farms. Three process strategies were compared for converting Mxg into single-cell oil and extracted lignin intermediates, as hydrothermal (HT) processing and two natural deep eutectic solvents (NADES), ChCl:LA (choline chloride:lactic acid) and ChCl:Gly (choline chloride:glycerol). Pretreatments were performed at 10% and 50% solids loading at 140 °C, 2 h; HT: 190 °C, 10 min. Enzymatic hydrolysates, generated at 10% solids using washed and unwashed biomass, were evaluated for microbial lipid production. Maximum glucose conversions from washed biomass reached 83.5% (ChCl:LA), 52.7% (ChCl:Gly), and 74.0% (HT). Remarkably, NADES-derived hydrolysates effectively replaced refined sugars for the cultivation of the oleaginous yeast Rhodotorula toruloides, achieving ∼51% higher biomass (OD 90.7) and lipid titers of 19.36 g/L within 45 h using a two-stage fermentation strategy. Lipid contents ranged from 34.5–44.5% dry weight, demonstrating reduced reliance on purified sugars. Beyond carbohydrate valorization, ChCl:LA pretreatment enabled high-purity lignin recovery (>89%) in a lignin-first strategy. Structural analyses (2D-HSQC and ³¹P NMR) showed syringyl (78.15%), guaiacyl (15.15%), and p-hydroxyphenyl (6.41%) units, with higher phenolic hydroxyl content (0.91 mmol/g) and a lower S/G ratio (0.19) than HT lignin (0.87 mmol/g, S/G 0.22). These attributes favor downstream lignin depolymerization into low-molecular-weight aromatics. Overall, NADES pretreatment simultaneously enhances microbial lipid yields and recovers high-quality lignin, advancing the economic feasibility of renewable diesel and sustainable aviation fuel (SAF) production from bioenergy crops within an integrated biorefinery framework.

2D HSQC↗