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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

SCOMAP-XD : atomistic deuterium contrast matching for small-angle neutron scattering in biology

The contrast-variation method in small-angle neutron scattering (SANS) is a uniquely powerful technique for determining the structure of individual components in biomolecular systems containing regions of different neutron scattering length density ρ . By altering the ρ of the target solute and the solvent through judicious incorporation of deuterium, the scattering of desired solute features can be highlighted. Most contrast-variation methods focus on highlighting specific bulk solute elements, but not on how the scattering at specific scattering vectors q , which are associated with specific structural distances, changes with contrast. Indeed, many systems exhibit q -dependent contrast effects. Here, a method is presented for calculating both bulk contrast-match points and q -dependent contrast using 3D models with explicit solute and solvent atoms and SASSENA , an explicit-atom SANS calculator. The method calculates the bulk contrast-match points within 2.4% solvent D 2 O accuracy for test protein–nucleic acid and lipid nanodisc systems. The method incorporates a general model for the incorporation of deuterium at non-exchangeable sites that was derived by performing mass spectrometry on green fluorescent protein. The method also decomposes the scattering profile into its component parts and identifies structural features that change with contrast. The method is readily applicable to a variety of systems, will expand the understanding of q -dependent contrast matching and will aid in the optimization of next-generation neutron scattering experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Survey on the Design, Detection, and Prevention of Pre-Silicon Hardware Trojans

The complexity of the semiconductor design lifecycle and globalized manufacturing process creates concern over the threat of deliberate malicious alterations, or hardware Trojans, being inserted into microelectronic designs. This has resulted in a significant corpus of hardware Trojan research including Trojan design and benchmarking efforts and development of corresponding metrics and detection and prevention techniques, over the last two decades. In this survey, we first highlight efforts in Trojan design and benchmarking, followed by a cataloging of seminal and recent works in Trojan detection and prevention and their accompanied metrics. Given the volume of literature in this field, this survey considers only pre-silicon techniques. We make this distinction between pre- and post-silicon to properly scope and provide appropriate context into the capabilities of existing hardware Trojan literature. Each major section (design, prevention, and detection) is accompanied by insights, and common pitfalls, which we highlight can be addressed by future research.

42 ENGINEERING↗
DOE OSTI2026

Power Electronic Development Challenges for High Power Density Integrated Electric Drive Applications

The ever-increasing demand for compact, efficient, and high-performance traction drive systems for transportation applications has accelerated the development of integrated electric drives. In these systems, the electric machine and inverter are integrated within a single housing, offering significant advantages in electrical performance, volume, weight, cost, and overall system efficiency. Despite these benefits, such high levels of integration introduce a new set of challenges, particularly in power electronic design and component selection. This article presents an in-depth investigation of a highly integrated electric drive architecture with an internal stator-mounted inverter, highlighting key design considerations and trade-offs aimed exclusively at maximizing system power density.Based on a comprehensive review of the literature, the power density of a voltage-source-inverter-driven electric drive is primarily governed by the volumetric contributions of the power modules, heat sinks, and DC-link capacitors. Accordingly, this work focuses on the optimization of these three critical components to enhance the inverter's overall power density. The proposed design achieves a power density of 100 kW/L, demonstrating the effectiveness of the presented approach for next-generation integrated electric drive systems.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Exploring the Landscape of Distributed Graph Clustering on Leadership Supercomputers

The rapid growth of large-scale datasets in fields like biology and social networks has driven the need for advanced graph analytics techniques. Community detection, a fundamental task in graph analytics, identifies closely connected groups of nodes within a network, providing valuable insights across various disciplines. This study focuses on two classic community detection methods, the Louvain algorithm and Markov Clustering (MCL), and evaluates the performance of two prominent distributed community detection algorithms: HiPDPL-GPU, our prior implementation, and HipMCL. We conduct experiments on GPU-accelerated heterogeneous HPC systems, Summit and Frontier, to assess their performance under varying conditions. Our objective is to identify the strengths and weaknesses of these algorithms in terms of scalability, and quality of solutions. We evaluate these algorithms on a diverse set of 70+ networks spanning 13 domains, with sizes ranging up to 4.2 billion edges. Our results demonstrate that HiPDPL-GPU consistently outperforms HipMCL, especially for large-scale networks. HiPDPL-GPU achieves significantly faster runtimes (47x to 1439x), higher modularity scores, and improved scalability. These findings highlight HiPDPL-GPU as a promising solution for efficient and effective large-scale graph analytics in diverse application domains, and provide insights into the feasibility of using MCL-based approaches for certain application domains.

Community detection, graph algorithms↗
DOE OSTI2024

Frequency Stability Enhancement in a Fully Solar Power Grid: A Case Study on the Saudi Model

Recently, renewable energy sources (RES) such as solar photovoltaic (PV) have been recognized for their significant potential. Several studies have explored the issue of frequency stability with high RES penetration. A significant observation related to this issue is the decline in the weighted short circuit ratio (WSCR), which has the potential to adversely affect system stability. This research uniquely focuses on the gradual transition of the Saudi Electricity Company (SEC) system to a fully integrated solar PV grid, offering insight and methodology that can be adapted for other power systems. While a variety of mitigation methods are available, synchronous condensers (SC) were used in this study to stabilize grid frequency, and their implementation has been shown to improve the overall performance of the system. In response to the observed decline in WSCR, a comprehensive short circuit analysis was applied across all regions in the Saudi power grid model to evaluate the reduction in short circuit power and highlight areas requiring enhancement. Finally, scenarios illustrating frequency response with a gradual increase in SCR were analyzed, with the objective of establishing a stable, fully solar PV power system that is resilient to sudden power grid disturbances.

14 SOLAR ENERGY↗
DOE OSTI2023

Towards Smart Grids Enhanced Situation Awareness: A Bi-Level Quasi-Static State Estimation Model

Smart Grid situational awareness is provided by Energy Management Systems. A core process of these systems is State Estimation. The great majority of state estimators model the Smart Grid through a set of nonlinear algebraic equations, named the measurement model. Problem formulation considers the Gauss solution. Several model improvements have been presented regarding the Gauss solution, aiming between others to provide measurement noise robustness to the state estimation process. While considerable effort has been focused on such developments, state estimation is still constrained by the implicit modelling error, and thus inevitably vulnerable to cyber-threats. In this work, a state estimation bi-level formal model is presented towards Smart Grids enhanced situational awareness considering the concepts of synthetic measurements and innovation. Comparative test results with the state-of-the-art on the IEEE 14-bus system are presented highlighting improved situational awareness to bad data. Easy-to-implement model, without hard-to-derive parameters, built-on the classic weighted least squares solution, highlight potential aspects for real-life implementation.

cyber security↗
DOE OSTI2021

Applying Utility's Advanced Grid Technologies to Improve Resiliency of a Critical Load

The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

BESS↗
DOE OSTI2024

Increasing Multilayer Ceramic Capacitor Lifetime With Bipolar Voltage Cycling

Enhancing the lifetime of multilayer ceramic capacitors (MLCCs) is critical in many aerospace, naval, or electrical grid applications, where device failure could lead to catastrophic consequences. The migration of oxygen vacancies from the ceramic to the electrode interface under constant bias is known to reduce the lifetime of oxide-based MLCCs. Bias cycling presents an opportunity to enhance MLCC lifetime by reducing oxygen vacancy migration. The ideal frequency range is expected to lie between frequencies low enough to avoid self-heating but high enough to avoid interfacial defect formation. However, the impact of low-frequency bipolar voltage cycling (BVC) on MLCC degradation mechanisms has not been well studied. This work investigates the impact of periodic BVC on the degradation of MLCCs through highly accelerated lifetime testing (HALT) on X7R capacitors. HALT tests were conducted at 255 °C and 60 V using different switching frequencies: 0 (dc), 0.1, 2.5, and 10 Hz. BVC was found to improve the lifetime of MLCCs compared to dc test conditions. MLCCs tested at 10-Hz BVC showed a 311% increase in average time to failure compared to the dc case. Impedance spectroscopy shows that BVC decreases the rate of resistance degradation within MLCCs, indicating that oxygen vacancy migration to the electrodes is mitigated. The impedance spectra taken on BVC samples highlight how grain boundaries play a vital role in trapping oxygen vacancies. Periodic cycling causes oxygen vacancies to become trapped at grain boundaries, resulting in oxygen vacancies taking longer to reach the electrode interface and thus increasing MLCC lifetime. This work highlights not only how BVC can be used to increase MLCC lifetime but also how periodically cycling MLCCs could increase lifetime in extreme environments, such as at elevated temperatures and electric fields.

Chuong, Kayla Y.↗
DOE OSTI2026

Scalable Computation of Topological Abstractions for Scalar Data

Topological data analysis has become an important tool for large scale scalar data analysis and visualization, efficiently extracting the inherent structure and features of interest of the data. However, with growing dataset sizes and complexity, it is increasingly becoming infeasible to compute topological abstractions of interest in serial and on single machines. This paper presents the state of the art in the scalable computation of topological abstractions on scalar data, in shared memory parallel on single machines, and in distributed memory parallel on multiple machines. We highlight results for set‐based, graph‐based and complex‐based abstractions and organize the state of the art based on this taxonomy. The paper identifies parallelization and distribution techniques common in topological algorithms and highlights further areas of interest with underdeveloped efforts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Soil Carbon Saturation: What Do We Really Know?

Managing soils to increase organic carbon storage presents a potential opportunity to mitigate and adapt to global change challenges, while providing numerous co-benefits and ecosystem services. However, soils differ widely in their potential for carbon sequestration, and knowledge of biophysical limits to carbon accumulation may aid in informing priority regions. Consequently, there is great interest in assessing whether soils exhibit a maximum capacity for storing organic carbon, particularly within organo–mineral associations given the finite nature of reactive minerals in a soil. While the concept of soil carbon saturation has existed for over 25 years, recent studies have argued for and against its importance. Here, we summarize the conceptual understanding of soil carbon saturation at both micro- and macro-scales, define key terminology, and address common concerns and misconceptions. We review methods used to quantify soil carbon saturation, highlighting the theory and potential caveats of each approach. Critically, we explore the utility of the principles of soil carbon saturation for informing carbon accumulation, vulnerability to loss, and representations in process-based models. We highlight key knowledge gaps and propose next steps for furthering our mechanistic understanding of soil carbon saturation and its implications for soil management.

Environmental sciences↗
DOE OSTI2026

Multi‐Model Ensembles in Ecosystem Modeling: Challenges and Best Practices for Decision‐Making

Ecosystem models are increasingly central to the decision-making for environmental policy, conservation planning, and climate-related investments. Yet, the growing reliance on Multi-Model Ensembles (MMEs) of ecosystem models by practitioners and policymakers, sometimes under tight timelines and imperfect information, has frequently outpaced the scientific rigor required to ensure ensemble reliability. Here, MMEs refer to approaches that combine targeted predictions from multiple models with the expectation of improving robustness and quantifying predictive uncertainty. Poorly designed MMEs may create a false sense of confidence and lead to suboptimal policy and market decisions. This perspective argues that robust decision-making-relevant MMEs must be grounded on two pillars: (1) rigorous Model Intercomparison Projects (MIPs), which identify inter-model agreement and disagreement, characterize model uncertainties, and evaluate robustness with observationally based benchmarks—MIPs' diagnostic evaluation is so critical that it must be needed to drive MME's decision in model selection and weighting, especially when only a limited number of models available; and (2) co-design by both stakeholders and scientists to ensure that scenarios, metrics and uncertainty requirements provide decision-relevant information. Building upon the past success and lessons from the existing MIPs-MMEs efforts (e.g., climate/Earth system/crop), we derived the theoretical basis for MMEs, addressed their specific challenges in ecosystem modeling, and highlighted proper consideration of model numbers and diversity, risk of model inter-dependence, effective calibration of model parameters, possible overdue of some ecosystem model development, critical roles of open benchmark data across a wide range of conditions, and suggested use of Artificial Intelligence to support MIPs-MMEs. We highlighted the under-recognized opportunity for MIPs and MMEs to drive scientific progress and innovation through identifying better performing models, systematic benchmarking, feedback loops, and targeted model improvement. By following actionable best practice guidelines, MMEs can evolve from ad hoc aggregation of models into a trusted backbone of environmental policy and decision-making.

ecosystem modeling↗
DOE OSTI2021

Predicting nepheline precipitation in waste glasses using ternary submixture model and machine learning

Nepheline precipitation in nuclear waste glasses during vitrification can be detrimental due to its negative effect on chemical durability. Developing models to accurately predict nepheline precipitation from compositions is important to increase waste loading since existing models can be overly conservative. In this study, an expanded dataset containing 955 glasses was compiled from literature data, where 355 glasses are for high-level waste (HLW). Previously developed submixture models were refitted using the new dataset, where a misclassification rate of 7.8% was achieved. Nine machine learning (ML) algorithms (e.g., k-nearest neighbor, Gaussian process regression, artificial neural network, support vector machine, decision tree, etc.) were applied to evaluate their ability of predicting nepheline precipitation from compositions. Model accuracy, precision, recall/sensitivity, and F1 score were systemically compared between different ML algorithms and modeling protocols. Good model prediction with an accuracy ~0.9 (misclassification rate of ~10%) was observed with different algorithms under certain protocol. This study evaluated various ML models to predict nepheline precipitations in waste glasses, highlighting the importance of data preparation, modeling protocol, and their effect on model stability and reproducibility. The results provide insights into applying ML to predict glass properties and suggest areas for future research on modeling nepheline precipitations.

Lu, Xiaonan↗
DOE OSTI2024

Unveiling and Mapping Polymorphs in Fluorite Y2TiO5 Using 4D-STEM and Unsupervised Machine Learning

Y2TiO5 belongs to the Ln2TiO5 (Ln = lanthanide or Y) family of ceramic materials and exhibits a range of desirable material properties such as radiation tolerance, frustrated magnetism, and large dielectric constant. However, understanding the complex crystal structure of Y2TiO5 remains elusive, given that Y2TiO5 can adopt multiple polymorphs such as cubic, orthorhombic, and hexagonal phases within the lattice. In this work, we report a detailed structural analysis of Y2TiO5 using four-dimensional scanning transmission electron microscopy coupled with unsupervised machine learning. The pyrochlore nanodomains, characterized by the ordered arrangement of yttrium cations on the A site of their A2BO5 structure, are present within the matrix of a predominantly fluorite-structured Y2TiO5 along with a third polymorph, the hexagonal phase. The pyrochlore phase is found to form 2 nm boundary regions around hexagonal phase stacking faults, highlighting the potential influence of the hexagonal phase on the occurrence and distribution of the pyrochlore phase. Lastly, we identify a unique pyrochlore phase with asymmetric arrangement of cation ordering along a single planar direction. Our findings provide invaluable insights into the possible mechanisms stabilizing pyrochlore nanodomains within the fluorite lattice of Y2TiO5.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Characterizing manufacturing wastewater in the United States for the purpose of analyzing energy requirements for reuse

This paper seeks to inform an improved understanding of the energy tradeoff associated with on-site manufacturing water reuse in the United States from a lifecycle perspective, in part by developing an analytical framework for understanding when this tradeoff for reuse is beneficial. We survey the literature to assess the current state of reuse and its motives and barriers in the United States, before synthesizing information from publicly available EPA data on contaminants in US manufacturing wastewaters and technologies for treating them. Using the available data, we derive a set of “ubiquitous contaminants” among the top ten in terms of mass discharged in more than half of US manufacturing subsectors (NAICS 31–33) according to EPA permit data. We also present information on proven treatment trains and their energy requirements. We then compare water quality requirements for specific contaminants in reclaimed water to those characteristic of wastewater streams currently being discharged from manufacturing plants into surface waters to highlight sectors with reuse opportunities that could require little cost to realize, such as primary metals and, to a lesser extent, petroleum and coal products. We conclude by highlighting data limitations that need to be rectified before applying the framework more broadly and discussing how these data gaps could be filled. Better understanding the relationship between energy and water in the context of on-site manufacturing water reuse would allow manufacturers to improve resiliency by reducing regulatory, physical, and reputational risks while lessening their footprint on local watersheds.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

PagMYB128 regulates secondary cell wall formation by direct activation of cell wall biosynthetic genes during wood formation in poplar

The biosynthesis of cellulose, lignin, and hemicelluloses in plant secondary cell walls (SCWs) is regulated by a hierarchical transcriptional regulatory network. Here, this network features orthologous transcription factors shared between poplar and Arabidopsis, highlighting a foundational similarity in their genetic regulation. However, knowledge on the discrepant behavior of the transcriptional-level molecular regulatory mechanisms between poplar and Arabidopsis remains limited. In this study, we investigated the function of PagMYB128 during wood formation and found it had broader impacts on SCW formation compared to its Arabidopsis ortholog, AtMYB103. Transgenic poplar trees overexpressing PagMYB128 exhibited significantly enhanced xylem development, with fiber cells and vessels displaying thicker walls, and an increase in the levels of cellulose, lignin, and hemicelluloses in the wood. In contrast, plants with dominant repression of PagMYB128 demonstrated the opposite phenotypes. RNA sequencing and reverse transcription – quantitative polymerase chain reaction showed that PagMYB128 could activate SCW biosynthetic gene expression, and chromatin immunoprecipitation along with yeast one-hybrid, and effector–reporter assays showed this regulation was direct. Further analysis revealed that PagSND1 (SECONDARY WALL-ASSOCIATED NAC-DOMAIN PROTEIN1) directly regulates PagMYB128 but not cell wall metabolic genes, highlighting the pivotal role of PagMYB128 in the SND1-driven regulatory network for wood development, thereby creating a feedforward loop in SCW biosynthesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Functional Adaptation of LPS-affected Dentoalveolar Fibrous Joints in Rats

We report the functional interplay between cementum of the root and alveolar bone of the socket is tuned by a uniquely positioned 70–80 µm wide fibrous and lubricious ligament in a dentoalveolar joint (DAJ). In this study, structural and biomechanical properties of the DAJ, periodontal ligament space (PDL-space also known as the joint space), alveolar bone of the socket, and cementum of the tooth root that govern the biomechanics of a lipopolysaccharide (LPS)-affected DAJ were mapped both in space and time. The hemi-maxillae from 20 rats (4 control at 6 weeks of age, 4 control and 4 LPS-affected at 12 weeks of age, 4 control and 4 LPS-affected at 16 weeks of age) were investigated using a hybrid technique; micro-X-ray computed tomography (5 µm resolution) in combination with biomechanical testing in situ. Temporal variations in bone and cementum volume fractions were evaluated. Trends in mineral apposition rates (MAR) in additional six Sprague Dawley rats (3 controls, 3 LPS-affected) were revealed by transforming spatial fluorochrome signals to functional growth rates (linearity factor - RW) of bone, dentin, and cementum using a fast Fourier transform on fluorochrome signals from 100-µm hemi-maxillae sections. An overall change in LPS-affected DAJ biomechanics (a 2.5–4.5X increase in tooth displacement and 2X tooth rotation at 6 weeks, no increase in displacement and a 7X increase in rotation at 12 weeks; 27% increase in bone effective strain at 6 weeks and 11% at 12 weeks relative to control) was associated with structural changes in the coronal regions of the DAJ (15% increase in PDL-space from 0 to 6 weeks but only 5% from 6 to 12 weeks compared to control). A significant increase (p < 0.05) in PDL-space between ligated and age-matched control was observed. The bone fraction of ligated at 12 weeks was significantly lower than its age-matched control, and no significant differences (p > 0.05) between groups were observed at 6 weeks. Cementum in the apical regions grew faster but nonlinearly (11% and 20% increase in cementum fraction (CF) at 6 and 12 weeks) compared to control. Alveolar bone revealed site-specific nonlinear growth with an overall increase in MAR (108.5 µm/week to 126.7 µm/week after LPS treatment) compared to dentin (28.3 µm/week in control vs. 26.1 µm/week in LPS-affected) and cementum (126.5 µm/week in control vs. 119.9 µm/week in LPS-affected). A significant increase in CF (p < 0.05) in ligated specimens was observed at 6 weeks of age. Anatomy-specific responses of cementum and bone to the mechano-chemo stimuli, and their collective temporal contribution to observed changes in PDL-space were perpetuated by altered tooth movement. Data highlight the “resilience” of DAJ function through the predominance of nonlinear growth response of cementum, changes in PDL-space, and bone architecture. Despite the significant differences in bone and cementum architectures, data provided insights into the reactionary effects of cementum as a built-in compensatory mechanism to reestablish functional competence of the DAJ. The spatial shifts in architectures of alveolar bone and cementum, and consequently ligament space, highlight adaptations farther away from the site of insult, which also is another novel insight from this study. These adaptations when correlated within the context of joint function (biomechanics) illustrate that they are indeed necessary to sustain DAJ function albeit being pathological.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Fermentation‐mediated growth, signaling, and defense in plants

Summary While traditionally considered important mainly in hypoxic roots during flooding, upregulation of fermentation pathways in plants has recently been described as an evolutionarily conserved drought survival strategy, with acetate signaling mediating reprograming of transcription and cellular carbon and energy metabolism from roots to leaves. The amount of acetate produced directly correlates with survival through potential mechanisms including defense gene activation, biosynthesis of primary and secondary metabolites, and aerobic respiration. Here, we review root ethanolic fermentation responses to hypoxia during saturated soil conditions and summarize studies highlighting acetate fermentation under aerobic conditions coupled with respiration during growth and drought responses. Recent work is discussed demonstrating long‐distance transport of acetate via the transpiration stream as a respiratory substrate. While maintenance and growth respiration are often modeled separately in terrestrial models, here we propose the concept of ‘Defense Respiration’ fueled by acetate fermentation in which upregulation of acetate fermentation contributes acetate substrate for alternative energy production via aerobic respiration, biosynthesis of primary and secondary metabolites, and the acetylation of proteins involved in defense gene regulation. Finally, we highlight new frontiers in leaf‐atmosphere emission measurements as a potential way to study acetate fermentation responses of individual leaves, branches, ecosystems, and regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Structure and sequence evolution in the pennycress ( Thlaspi arvense ) pangenome

Eukaryotic genomes harbor many forms of variation, including nucleotide diversity and structural polymorphisms, which experience natural selection and contribute to genome evolution and biodiversity. Harnessing this variation for agriculture hinges on our ability to detect, quantify, catalog, and deploy genetic diversity. Here, we explore seven complete genomes of the emerging biofuel crop pennycress ( Thlaspi arvense ) drawn from across the species' current genetic diversity to catalog variation in genome structure and content. Across this new pangenome resource, we find contrasting evolutionary modes in different genomic zones. Gene-poor, repeat-rich pericentromeric regions experience frequent rearrangements, including repeated centromere repositioning. By contrast, conserved gene-dense chromosome arms maintain large-scale synteny across accessions even in fast-evolving NOD-like receptor immune genes, where microsynteny breaks down across species, but gene cluster positioning macrosynteny is maintained. Our findings highlight that multiple elements of the genome experience dynamic evolution that conserves functional content on the chromosome scale but allows repositioning and presence–absence variation on a local scale. This diversity is invisible to classical reference-based strategies and highlights the strength and utility of pangenomic resources. These results provide a valuable case study of rapid genomic structural evolution within a species and powerful resources for crop development in an emerging biofuel crop.

Thlaspi arvense↗