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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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NASA NTRS2021

Simulation of Solar-Induced Chlorophyll Fluorescence from 3D Canopies with the Dart Model

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

radiative transfer↗
NASA NTRS2021

Simulation of Solar-Induced Chlorophyll Fluorescence from 3D Canopies with the Dart Model

The potential of solar-induced chlorophyll fluorescence (SIF) to monitor photosynthesis and plant stress has attracted considerable interest in SIF remote sensing (RS). However, canopy SIF and RS observations are impacted by topography, vegetation three dimension (3D) structure, leaf orientation, non foliar elements (e.g., tree woody skeleton), ... Physically based downscaling of canopy SIF RS data to leaf-level (i.e., to leaf photosynthesis) requires 3D radiative transfer (RT) models simulating canopy SIF and its observation. These models are necessary to better exploit the potential of SIF, by linking leaf SIF and SIF in RS observations as a function of canopy 3D architecture and experimental configurations (sun and viewing directions, etc.). The Discrete Anisotropic Radiative Transfer (DART) model is a comprehensive 3D radiative transfer (RT) model for urban and natural landscapes. This paper presents its SIF modeling for vegetation simulated with facets, its validation with the SCOPE/mSCOPE 1D models, and its recent extension to SIF modelling for landscapes simulated with 3D turbid medium.

Radiative transfer↗
NASA NTRSDate not supplied

Improving the Representation of Land Surface Processes using the Data Assimilation Research Testbed (DART)

The land surface is a critical part of the earth system as processes related to water, carbon, energy and nitrogen cycling have important implications for climate forcing, air quality, water availability and seasonal atmospheric forecasting. Despite advances in land surface modeling, land surface model performance is often limited because of errors related to initial and boundary conditions, model structure, and parameters. Data assimilation (DA) techniques combined with an expanding network of earth system observations present an opportunity to reduce these errors and improve simulations. Here we apply an Ensemble Kalman Filter DA system as part of the Data Assimilation Research Testbed to a variety of land surface simulations. First, we describe the use of remotely sensed biomass observations to provide improved simulations of plant phenology, carbon and water cycling for regions highly sensitive to climate change (Western US, China, and Arctic). We discuss approaches to account for systemic biases between models and observations, including the use of spatially-varying adaptive ensemble inflation as an alternative approach to re-scaling soil moisture observations. Finally, we discuss a strategy to incorporate complementary observations (snow water equivalent, solar-induced fluorescence) to better constrain the representation of carbon and water cycling across complex terrain.

DART↗
NASA NTRS2004

Plasma Propulsion Research at NASA Marshall Space Flight Center

The Propulsion Research Center at NASA Marshall Space Flight Center is pursuing a range of research efforts aimed at identifying and developing new technologies for primary spacecraft propulsion. Efficient high-power electric propulsion (Ep) thrusters are a particular area of emphasis; these would enable the relatively rapid transit of large payloads about the solar system for unmanned or manned science and exploration. Such a mission would make heavy demands on the propulsion system, which may be required to run reliably for several years at a specific impulse approaching 10,OOO s with an efficiency of turning electrical power into jet power of at least 70%. The transit time to a destination scales approximately inversely with the cube root of the specific power, which is the ratio of jet power to power-plant mass. Consequently, reducing a trip time by half requires roughly an eight-fold increase in specific power. Given a renewed NASA commitment to space nuclear power, developing efficient EP thrusters with high jet power (> 100 kW) would seem to provide the most direct means of significantly increasing the specific power and hence reducing trip times. In particular, electromagnetic devices, with their high inherent thrust densities, should be better suited to high power applications than thrusters which depend exclusively on electrostatic forces for propellant acceleration.

Sheehy, Jeffrey A.↗
NASA NTRS1981

Thermal mapping, geothermal source location, natural effluents and plant stress in the Mediterranean coast of Spain

Data obtained by HCMM satellite over a complex area in eastern Spain were evaluated and found to be most useful in studying macrostructures in geology and in analyzing marine currents, layers, and areas (although other satellites provide more data). The upper scale to work with HCMM data appears to be 1:2.000.000. Techniques used in preprocessing, processing, and analyzing imagery are discussed as well as methods for pattern recognition. Surface temperatures obtained for soils, farmlands, forests, geological structures, and coastal waters are discussed. Suggestions are included for improvements needed to achieve better results in geographic areas similar to the study area.

Delascuevas, R. N.↗
NASA NTRS1991

The Earth Observing System

The Earth Observing System (EOS), the centerpiece of NASA's Mission to Planet Earth, is to study the interactions of the atmosphere, land, oceans, and living organisms, using the perspective of space to observe the earth as a global environmental system. To better understand the role of clouds in global change, EOS will measure incoming and emitted radiation at the top of the atmosphere. Then, to study characteristics of the atmosphere that influence radiation transfer between the top of the atmosphere and the surface, EOS wil observe clouds, water vapor and cloud water, aerosols, temperature and humidity, and directional effects. To elucidate the role of anthropogenic greenhouse gas and terrestrial and marine plants as a source or sink for carbon, EOS will observe the biological productivity of lands and oceans. EOS will also study surface properties that affect biological productivity at high resolution spatially and spectrally.

Wilson, Stan↗
NASA NTRS1998

Study of a Simulation Tool to Determine Achievable Control Dynamics and Control Power Requirements with Perfect Tracking

This paper contains a study of two methods for use in a generic nonlinear simulation tool that could be used to determine achievable control dynamics and control power requirements while performing perfect tracking maneuvers over the entire flight envelope. The two methods are NDI (nonlinear dynamic inversion) and the SOFFT(Stochastic Optimal Feedforward and Feedback Technology) feedforward control structure. Equivalent discrete and continuous SOFFT feedforward controllers have been developed. These equivalent forms clearly show that the closed-loop plant model loop is a plant inversion and is the same as the NDI formulation. The main difference is that the NDI formulation has a closed-loop controller structure whereas SOFFT uses an open-loop command model. Continuous, discrete, and hybrid controller structures have been developed and integrated into the formulation. Linear simulation results show that seven different configurations all give essentially the same response, with the NDI hybrid being slightly different. The SOFFT controller gave better tracking performance compared to the NDI controller when a nonlinear saturation element was added. Future plans include evaluation using a nonlinear simulation.

Ostroff, Aaron J.↗
NASA NTRS1995

Expendable launch vehicle studies

Analytical support studies of expendable launch vehicles concentrate on the stability of the dynamics during launch especially during or near the region of maximum dynamic pressure. The in-plane dynamic equations of a generic launch vehicle with multiple flexible bending and fuel sloshing modes are developed and linearized. The information from LeRC about the grids, masses, and modes is incorporated into the model. The eigenvalues of the plant are analyzed for several modeling factors: utilizing diagonal mass matrix, uniform beam assumption, inclusion of aerodynamics, and the interaction between the aerodynamics and the flexible bending motion. Preliminary PID, LQR, and LQG control designs with sensor and actuator dynamics for this system and simulations are also conducted. The initial analysis for comparison of PD (proportional-derivative) and full state feedback LQR Linear quadratic regulator) shows that the split weighted LQR controller has better performance than that of the PD. In order to meet both the performance and robustness requirements, the H(sub infinity) robust controller for the expendable launch vehicle is developed. The simulation indicates that both the performance and robustness of the H(sub infinity) controller are better than that for the PID and LQG controllers. The modelling and analysis support studies team has continued development of methodology, using eigensensitivity analysis, to solve three classes of discrete eigenvalue equations. In the first class, the matrix elements are non-linear functions of the eigenvector. All non-linear periodic motion can be cast in this form. Here the eigenvector is comprised of the coefficients of complete basis functions spanning the response space and the eigenvalue is the frequency. The second class of eigenvalue problems studied is the quadratic eigenvalue problem. Solutions for linear viscously damped structures or viscoelastic structures can be reduced to this form. Particular attention is paid to Maxwell and Kelvin models. The third class of problems consists of linear eigenvalue problems in which the elements of the mass and stiffness matrices are stochastic. dynamic structural response for which the parameters are given by probabilistic distribution functions, rather than deterministic values, can be cast in this form. Solutions for several problems in each class will be presented.

Bainum, Peter M.↗
NASA NTRSDate not supplied

State of Wildfires

Fire is an essential component of ecosystems and acts as key driver of biogeochemical cycling with impacts on vegetation structure and composition, soil conditions, and climate feedbacks. Fire behavior and effects vary based on the types of fuel, fuel dryness, and frequency of ignition. Due to changing climate and land use patterns, fire danger is increasing in many regions globally, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Recurrent fires help to determine the distribution of trees and grasses, and overall fuel load, which inform the behavior of future fires. Process-based models can be used to capture multi-scale impacts at the forest stand-level up to the landscape level, and across minutes to centuries, but must capture variation in fire behavior and intensity as a function of the fuel and climate, from high intensity forest crown fires within boreal regions to low intensity rapid grass fires of the tropics. Fire model development demonstrates our ability to capture large scale fire influenced biogeography and vegetation distribution at the earth system scale through vegetation traits and fire feedbacks. At the individual stand scale the importance of interactions and feedbacks between above and below ground process is essential to capturing the vegetation dynamics across boreal forest systems. Improving the mechanics of including plant physiology and specifically live fuel moisture content is the next step to advancing the capability of process based models to inform fire research. Advances in the testing and creation of a mechanistic live fuel moisture model demonstrate the foundation for future live fuel dynamics research that can be informed by field and remote sensing information. Improved remote sensing, technological and modeling capabilities support a more comprehensive and cohesive fire response that will be better equipped to overcome current barriers and anticipate the new reality of fires in a warming world.

wildfires↗
NASA NTRS2023

Sewage Treatment Plant #1 Area, SWMU 117 Per- and Polyfluoroalkyl Substances (PFAS) Site Assessment Progress Report

This Per- and Polyfluoroalkyl Substances (PFAS) Site Assessment (SA) Progress Report (SAPR) discusses the investigation activities and findings for the Sewage Treatment Plant #1 (STP1) Area located at Kennedy Space Center (KSC), Florida (Figure 1-1). This site has been designated Solid Waste Management Unit (SWMU) 117 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action Program, as the sewage treatment plant and associated areas were identified as a potential source of PFAS to the environment. This PFAS SAPR was prepared by Tetra Tech, Inc., for the National Aeronautics and Space Administration (NASA) under Indefinite Delivery Indefinite Quantity Contract 80KSC019D0011-80KSC019F0070. This is the first progress report to document on-going SA activities; supplemental progress reports will be provided as additional data is collected. PFAS SA activities were conducted between April 2020 and March 2022 to collect additional data to supplement the existing datasets to better understand the extent of PFAS impacts to the environment in the STP1 Area. The SA for the STP1 Area covers an approximately 130-acre investigation area with multiple structures and buildings. The focus of the SA is the STP1 Complex and associated structures, including the former Polishing Pond, former Sludge Disposal8 Area, and former Spray Field. The STP1 Complex is located in the KSC Industrial Area, at the southwest corner of 4th Street SE and C Avenue SE. The STP1 Complex is located approximately ¼-mile south and downgradient of the Fire Station #1 site (SWMU 116), which is also currently undergoing a PFAS SA because of potential releases of PFAS-containing aqueous film-forming foam (AFFF). During the SA, a total of seven soil, 131 groundwater direct push technology (DPT), 24 groundwater monitoring well, and 11 surface water samples were collected between April 2020 and March 2022. Monitoring well samples were analyzed for 18 PFAS compounds, with all other samples analyzed for 28 PFAS compounds. The SA sample results were used along with historical results to evaluate the extent of PFAS impacts to the environment in the STP1 Area. Data generated to date and prior results were screened against the United States Environmental Protection Agency (USEPA) May 2022 Tap Water Regional Screening Levels (RSL) for groundwater and residential RSLs for soil (hazard quotient of 0.1). Surface water results were screened against the State of Florida Human Health Surface Water Screening Levels (SWSLs). Results from the SA showed exceedances of the applicable screening criteria for groundwater and surface water. Considering the current and historical dataset, PFOS is the prevalent PFAS compound. Based on these results, additional groundwater DPT and surface water sampling should be considered for PFAS analysis, focused on evaluating surface water bodies in the southeast portion of the Industrial Area, which discharge into the Banana River. Additionally, installation of monitoring wells should be considered to evaluate the interaction between the groundwater and surface water at the site. Collection of additional samples for TOC analysis should also be considered from representative groundwater (saturated soils) and surface water locations to further evaluate potential correlations between PFAS and TOC to provide a more comprehensive dataset to assist in fate and transport analyses.

Sewage Treatment Plant↗
NASA NTRS1985

Comparative study of Suits and SAIL canopy reflectance models

A detailed understanding of the relationships between the canopy reflectance and the characteristics of canopy elements is an important factor for the full exploitation of the potential of remote sensing from aircraft and spacecraft altitudes to map vegetation and estimate key agronomic parameters such as the leaf area index (LAI) and biomass (BM). Suits (1972) idealized the canopy geometry by replacing each plant component with three orthogonal projections of that component. Verhoeff and Bunnik (1981) extended the Suits model, henceforth called the SAIL (Scattering from Arbitrarily Inclined Leaves) model, by removing certain constraints. The present investigation is concerned with an evaluation of the performance of the Suits and SAIL models, taking into account two data sets on soybean and corn. It was found that the tested models have significant deficiencies. However, the performance of the SAIL model is better than that of the Suits model because it provides a more realistic description of the canopy architecture.

Badhwar, G. D.↗
NASA NTRSDate not supplied

Verification Testing and Veg-05 Tomato Crop Production on the International Space Station

VEG-05 is an experiment to better define best practices for crop production and handling in space. Light can impact the growth habit, yield, nutritional composition, microbial levels, and flavor within crops and VEG-05 used the Veggie facilities on the International Space Station to grow ‘Red Robin’ dwarf tomatoes under different spectral compositions to assess these characteristics. Prior to launch, both a science verification test (SVT), and an experiment verification test (EVT) were conducted. SVT, and a previous fertilizer test, grew plants in both plant pillows and PONDS units and tested different fertilizer treatments, with each test under one of the light conditions. EVT used only plant pillows with the highest fertilizer composition tested, and two Veggie units were utilized. One Veggie had light settings consisting of equal levels of red: blue light while the second Veggie had a 90:10 ratio of red: blue light, both with the addition of ~10% green. For SVT, under the equal red: blue light treatment, fruit ripening in plant pillows was delayed. For EVT we saw fruit ripening earlier, especially in the red-rich treatment. In SVT we had mostly daily watering, and this led to excess water in plant pillows which leaked and caused fungus to grow. We reduced this excess in EVT, however, wilting events occurred. Regardless, more than 10 fruit were produced from each plant on average, with the high red treatment producing slightly heavier fruit. Microbial testing from fruit indicated that fruit were safe for consumption. VEG-05 flight operations ran December 2022 and March 2023, with considerable challenges in watering and unexpectedly low humidity on ISS during critical periods. Only 12 ripe fruit formed, and all were returned. Fruit and other samples are currently being analyzed. This research was co-funded by NASA’s Human Research Program and Space Biology.

Gioia Donna Massa↗
NASA NTRSDate not supplied

Verification Testing and VEG-05 Tomato Crop Production on the International Space Station

VEG-05 is an experiment to better define best practices for crop production and handling in space. Light can impact the growth habit, yield, nutritional composition, microbial levels, and flavor within crops and VEG-05 used the Veggie facilities on the International Space Station to grow ‘Red Robin’ dwarf tomatoes under different spectral compositions to assess these characteristics. Prior to launch, both a science verification test (SVT), and an experiment verification test (EVT) were conducted. SVT, and a previous fertilizer test, grew plants in both plant pillows and PONDS units and tested different fertilizer treatments, with each test under one of the light conditions. EVT used only plant pillows with the highest fertilizer composition tested, and two Veggie units were utilized. One Veggie had light settings consisting of equal levels of red: blue light while the second Veggie had a 90:10 ratio of red: blue light, both with the addition of ~10% green. For SVT, under the equal red: blue light treatment, fruit ripening in plant pillows was delayed. For EVT we saw fruit ripening earlier, especially in the red-rich treatment. In SVT we had mostly daily watering, and this led to excess water in plant pillows which leaked and caused fungus to grow. We reduced this excess in EVT, however, wilting events occurred. Regardless, more than 10 fruit were produced from each plant on average, with the high red treatment producing slightly heavier fruit. Microbial testing from fruit indicated that fruit were safe for consumption. VEG-05 flight operations ran December 2022 and March 2023, with considerable challenges in watering and unexpectedly low humidity on ISS during critical periods. Only 12 ripe fruit formed, and all were returned. Fruit and other samples are currently being analyzed. This research was co-funded by NASA’s Human Research Program and Space Biology.

Gioia D. Massa↗
NASA NTRSDate not supplied

Observations of So2, O3, and Aerosols With the Langley Mobile Ozone Lidar

LMOL, the NASA Langley Mobile Ozone Lidar, is located near NASA's LaRC steam plant when not deployed in campaigns. The steam plant is an incinerator, and when SO2 was in the plume, it would affect the LMOL O3 measurements at the plume altitudes of 100m-200m. In 2022, we modified LMOL to observe four wavelengths simultaneously to distinguish between and measure both O3 and SO2. With an optimized selection of wavelengths, based on an analysis of the cross-sections, it is possible to retrieve both O3 and SO2 densities from three wavelengths. Adding a fourth wavelength enables better constrains the aerosol's backscatter and extinction. In this work, we present the dual observations of SO2 and O3 with the new channels of LMOL, and we present the advances in constraining the aerosol parameters from these multiple wavelengths. We highlight the SO2 and Ozone Water-Land Environmental Transition Study (SOWLETS) campaign in preparation for the validation of the new system.

Guillaume Gronoff↗
NASA NTRS2011

Climate Change, Extreme Weather Events, and Fungal Disease Emergence and Spread

Empirical evidence from multiple sources show the Earth has been warming since the late 19th century. More recently, evidence for this warming trend is strongly supported by satellite data since the late 1970s from the cryosphere, atmosphere, oceans, and land that confirms increasing temperature trends and their consequences (e.g., reduced Arctic sea ice, rising sea level, ice sheet mass loss, etc.). At the same time, satellite observations of the Sun show remarkably stable solar cycles since the late 1970s, when direct observations of the Sun's total solar irradiance began. Numerical simulation models, driven in part by assimilated satellite data, suggest that future-warming trends will lead to not only a warmer planet, but also a wetter and drier climate depending upon location in a fashion consistent with large-scale atmospheric processes. Continued global warming poses new opportunities for the emergence and spread of fungal disease, as climate systems change at regional and global scales, and as animal and plant species move into new niches. Our contribution to this proceedings is organized thus: First, we review empirical evidence for a warming Earth. Second, we show the Sun is not responsible for the observed warming. Third, we review numerical simulation modeling results that project these trends into the future, describing the projected abiotic environment of our planet in the next 40 to 50 years. Fourth, we illustrate how Rift Valley fever outbreaks have been linked to climate, enabling a better understanding of the dynamics of these diseases, and how this has led to the development of an operational predictive outbreak model for this disease in Africa. Fifth, We project how this experience may be applicable to predicting outbreaks of fungal pathogens in a warming world. Lastly, we describe an example of changing species ranges due to climate change, resulting from recent warming in the Andes and associated glacier melt that has enabled amphibians to colonize higher elevation lakes, only to be followed shortly by the emergence of fungal disease in the new habitats.

Tucker, Compton J.↗
NASA NTRSDate not supplied

Maximizing Spaceflight Biological Data with Omics Analytics: The NASA GeneLab Database

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

Sylvain Vincent Costes↗
NASA NTRSDate not supplied

GeneLab: The NASA System Biology Platform for Space Omics Repository, Analysis and Visualization

NASA’s GeneLab includes an open-access repository of some 250+ omics datasets generated by biological experiments relevant to spaceflight including simulated cosmic radiation and microgravity. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics background, GeneLab has become a knowledgebase platform converting raw genetic and proteomic signatures found in flight samples into biological and physiological meanings. A large community of more than 100 scientists has rallied behind GeneLab and organized into four Analysis Working Groups (AWGs: Animal, Plant, Microbe, and Multi-Omics). Together, the AWGs have gained scientific recognition worldwide by establishing a consortium in charge of adopting new complex standards for data analysis workflows and omics sample processing in a rapidly evolving field. We will demonstrate the usage of the repository with smart search capability, an online controlled-access toolshed "Galaxy" to process user data with vetted standard workflows, a workspace for data sharing and a data submission portal with ontology control for better metadata curation. The GeneLab visualization portal will also be demonstrated, showing how anyone without formal training in bioinformatics can now browse the space biology omics data to discover new biology and potential solutions to improve life in space.

GeneLab↗
NASA NTRS2019

Assessing precipitation, evapotranspiration, and NDVI as controls of US Great Plains plant production

Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation-related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi-year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite-derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April-to-July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April-to-July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May-to-September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county-level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county-level NRCS ANPP. Accounting for how different precipitation-related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision-making by land managers and use of grassland ANPP for biofuels.

Maosi Chen↗