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Results for “Biomass fractionation”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Cellular and extracellular C contributions to respiration after wetting dry soil

Wetting of dry soil triggers a pulse of microbial respiration that has been attributed to two broad mechanisms: (1) recycling of microbial cellular carbon (C), and (2) consumption of extracellular organic C made available to microbes by wetting. We evaluated these two mechanisms by measuring cumulative CO 2 release, changes in the size and chemical composition of microbial biomass, and water-extractable organic carbon (WEOC) concentrations following artificial wetting of soil sampled from two depths at each of seven sites across California spanning a range of geologic parent materials. In samples collected from surface soil (0–10 cm depth), we found that cumulative CO 2 release after wetting in the laboratory was most strongly correlated with microbial biomass. In these samples, the relative abundance of trehalose—a putative microbial osmolyte—decreased from 25% (SD = 12) to 16% (SD = 7) of the chloroform-labile fraction of the microbial biomass after wetting. This suggested a role for osmolyte consumption in generating the respiration pulse. In subsoil (40–50 cm depth, or sampled at contact with rock), however, the cumulative CO2 release after wetting was unrelated to microbial biomass and more strongly related to WEOC. The concentrations of selected microbial biomass constituents (e.g. trehalose and amino acids) in WEOC were negligible (< 1%), suggesting that cell lysis was not important in generating WEOC in this study. The amount of WEOC relative to total organic C was greatest in subsoil, and negatively related to ammonium oxalate-extractable Fe (Pearson’s R = 0.42, p < 0.01), suggesting a role for soil mineralogical properties in controlling WEOC release. Together, these findings suggest that microbial cellular C and extracellular C jointly contribute to the respiration pulse, and that their relative contribution depends on depth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Recovering Native-Like Lignin from Poplar Biomass Using Flow-Through Solvolysis

Lignin valorization, frequently involving a depolymerization step, is necessary for economic production of lignocellulosic biofuels. However, isolation of lignin frequently degrades the structure such that depolymerization yields from the isolated lignin are decreased relative to the "native" lignin present in the plant. In this work we show that by using flow-through extraction with methanol at 225 degrees C, combined with rapid quenching, we can isolate a "native-like" lignin that produces monomer yields comparable the lignin in the parent biomass under reductive catalytic fractionation (RCF) conditions. This isolated, native-like lignin is shelf-stable at room temperature on a time scale of months, and can be concentrated to a lignin oil and reconstituted in methanol without losing activity. These features will facilitate the study of intrinsic lignin properties and steady-state depolymerization processes.

BIOMASS FUELS↗
DOE OSTI2023

A high-solid DES pretreatment using never-dried biomass as the starting material: towards high-quality lignin fractionation

This study investigated a high-solid diol deep eutectic solvent (DES) pretreatment using a wet substrate as the starting material. This pretreatment led to a remarkable glucan saccharification of 94.8% with efficient lignin and xylan removal (as high as 63.1% and 73.0%, respectively). Here the chemical structures of the substrates were analyzed comprehensively to reveal the impact of the pretreatment. In addition, over 90% of the removed lignin was recovered from the pretreated liquid, which exhibited a well-preserved β-O-4 structure (46–56/100Ar). The protection mechanism of our DES was investigated by 2D HSQC NMR, GPC, and 31 P NMR analysis. This study emphasized that diol-based DES pretreatment of undried lignocellulosic biomass at a high-solid loading can significantly utilize both carbohydrates and lignin fractions with high saccharification yields and high-quality lignin as a co-product.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Switchgrass ( Panicum virgatum L.) cultivars have similar impacts on soil carbon and nitrogen stocks and microbial function

Abstract Switchgrass ( Panicum virgatum L.) production for biofuel has the potential to produce reasonable yields on lands not suited for conventional agriculture. We assessed nine switchgrass cultivars representing lowland and upland ecotypes grown for 11 years at a site in the upper Midwest USA for belowground differences in soil carbon and nitrogen stocks, soil organic matter fractions, and standing root biomass to 1 m depth. We also compared potential nitrogen mineralization and carbon substrate use through community‐level physiological profiling in surface soils (0–10 cm depth). Average yields and standing root biomass differed among cultivars and between ecotypes, but we found no significant cultivar‐related impacts on soil carbon and nitrogen stocks, on the distribution of particulate and mineral‐associated soil organic matter fractions, nor on potential nitrogen mineralization or microbial community‐level physiological profiles. That these traits did not differ among cultivars suggests that soil carbon and nitrogen gains under switchgrass are likely to be robust with respect to cultivar differences, and to this point not much affected by breeding efforts.

Agriculture↗
DOE OSTI2023

Yield, chemistry, and TEA/LCA data for 84 switchgrass (Panicum virgatum L.) variants

Switchgrass yield, chemistry, technoeconomic assessment (TEA), and life cycle assessment (LCA) data referenced in the manuscript "Economics and Sustainability impacts of yield and composition variation in bioenergy crops: Panicum virgatum L.", Happs, et. al. (manuscript in revision at ACS Sustainable Chemistry & Engineering). These results are based on analysis of yield data generated from a set of switchgrass common garden experiments established by the Center for Bioenergy Innovation (CBI; https://cbi.ornl.gov/). These data include estimates of commercial-scale yields and characterization of biomass chemistry (fermentable carbohydrate fraction) for 84 different switchgrass variants from a genome-wide association study population, as well as estimates of minimum fuel selling price and various life-cycle footprint metrics for cellulosic ethanol produced from that switchgrass biomass.

switchgrass, biofuels↗
DOE OSTI2020

Partitioning sources of CO 2 emission after soil wetting using high-resolution observations and minimal models

Wetting dry soil triggers a pulse of microbial respiration. Respiration pulses after wetting can be complex, evolving through several stages before subsiding—possibly reflecting contributions from multiple carbon (C) sources. In this work we hypothesized that respiration after wetting combines C from cellular sources (notably trehalose, a cellular osmo-protectant) and soluble extracellular C made available by the physical effects of wetting, with cellular sources contributing early during the pulse and extracellular C dominating later. We tested this hypothesis by making measurements of soil respiration, microbial biomass, trehalose, and water extractable organic C after wetting dry soil in the laboratory. We then devised several alternative minimal models to evaluate the potential contributions of trehalose and extracellular C to the respiration pulse. We found that the chloroform-labile fraction of the microbial biomass was comprised of 50% trehalose in dry soil. Both trehalose and water extractable organic C pools declined rapidly after wetting; trehalose disappeared within 3 h of water addition. Chloroform-labile glucose increased 3 h after water addition and then declined, suggesting rapid hydrolysis of trehalose and consumption of the resulting glucose. Respiration dynamics after wetting could be best explained by models that included two C sources, combining rapid metabolism of trehalose with slower consumption of extracellular soluble C. These results suggest that pulses of respiration after wetting reflect both osmolyte consumption within the microbial biomass and enhanced extracellular C availability; hence, models of microbial drought response must account for both processes to fully resolve soil C dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Effect of particle size and moisture on flow performance of loblolly pine anatomical fractions: Experimental findings and model predictions

The rising energy demand has highlighted biomass as a promising next-generation energy source. However, commercializing biomass-derived energy faces challenges, particularly in handling biomass feedstock. Factors like particle size, shape, moisture content, and surface roughness significantly impact biomass flowability. This study addresses a crucial knowledge gap by examining the effects of particle size and moisture content on the flow behavior and shear properties of different anatomical fractions of loblolly pine (Pinus taeda). The bulk shear behavior was examined using a Schulze ring shear tester, while flow performance was tested through gravity-driven flow experiments in a variable wedge-shape hopper. Results were incorporated into empirical and machine learning-based flow prediction models to evaluate their accuracy and limitations. The study found that samples with higher moisture content show higher unconfined yield strength. The critical arching distance increased with particle size, e.g., from approximately 13 and 33 mm for 2- and 6-mm whole chips, respectively at a 32-degree inclination angle. Conversely, the flow rate decreased for a given hopper opening as particle size increased. For instance, at a 60-mm hopper opening and a 32-degree inclination angle, the mass flow rates for 2- and 6-mm whole chips were 7.83 and 6.42 tonne/h, respectively. The empirical model consistently overpredicted the mass flow rate for all anatomical fractions, while the machine learning model more accurately predicted the central tendency of flow rate but was insensitive to varying tissue proportions. These novel findings provide comprehensive characterization of anatomical fractions, reveal significant combined effects of particle size and moisture content on biomass flow behavior, and demonstrate a better predictive accuracy of a machine learning model, all of which are useful for optimizing material handling strategies and biomass utilization technologies in the industry.

09 - BIOMASS FUELS↗
NASA NTRS2019

Importance of Tree- and Species-Level Interactions With Wildfire, Climate, and Soils in Interior Alaska: Implications for Forest Change Under A Warming Climate

The boreal zone of Alaska is dominated by interactions between disturbances, vegetation, and soils. These interactions are likely to change in the future through increasing permafrost thaw, more frequent and intense wildfires, and vegetation change from drought and competition. We utilize an individual tree-based vegetation model, the University of Virginia Forest Model Enhanced (UVAFME), to estimate current and future forest conditions across sites within interior Alaska. We updated UVAFME for application within interior Alaska, including improved simulation of permafrost dynamics, litter decay, nutrient dynamics, fire mortality, and post-fire regrowth. Following these updates, UVAFME output on species-specific biomass and stem density was comparable to inventory measurements at various forest types within interior Alaska. We then simulated forest response to climate change at specific inventory locations and across the Tanana Valley River Basin on a 2 × 2 km2 grid. We derived projected temperature and precipitation from a five-model average taken from the CMIP5 archive under the RCP 4.5 and 8.5 scenarios. Results suggest that climate change and the concomitant impacts on wildfire and permafrost dynamics will result in overall decreases in biomass (particularly for spruce (Picea spp.)) within the interior Tanana Valley, despite increases in quaking aspen (Populus tremuloides) biomass, and a resulting shift towards higher deciduous fraction. Simulation results also predict increases in biomass at cold, wet locations and at high elevations, and decreases in biomass in dry locations, under both moderate (RCP 4.5) and extreme (RCP 8.5) climate change scenarios. These simulations demonstrate that a highly detailed, species interactive model can be used across a large region within Alaska to investigate interactions between vegetation, climate, wildfire, and permafrost. The vegetation changes predicted here have the capacity to feed back to broader scale climate-forest interactions in the North American boreal forest, a region which contributes significantly to the global carbon and energy budgets.

forest modeling↗
DOE OSTI2025

Molecular simulation and artificial intelligence for the circular economy of bioenergy and bioproducts

The concept of the circular bioeconomy is a carbon neutral, sustainable system with zero waste. One vision for such an economy is based upon lignocellulosic biomass. This lignocellulosic circular bioeconomy requires CO 2 absorption from biomass growth and the efficient deconstruction of recalcitrant biomass into solubilized and fractionated biopolymers, which are then used as precursors for the sustainable production of high-quality liquid fuels, chemical bioproducts, and bio-based materials. Here, in this study, we summarize the roles that molecular dynamics (MD) simulations and machine learning (ML) are playing in overcoming several fundamental challenges hindering the adoption of a circular bioeconomy. Specifically, we discuss the role of MD and ML/AI in overcoming lignocellulose recalcitrance by designing biomass pretreatment methods to efficiently produce solubilized cellulose/lignin/hemicellulose and of that in improving energy-intensive manufacturing of biomass-based materials and their structural and mechanical properties. Quantum mechanical methods and MD simulations, in addition to offering a mechanistic understanding of biomass deconstruction and biomaterials design, can provide meaningful structural, energetics, and physiochemical properties as inputs to train AI/ML models. The ML models can guide the experimental prioritization of materials/solvents and process parameters that significantly accelerate the development of biofuel and biomaterial components of the circular bioeconomy.

Smith, Jeremy C. [Oak Ridge National Laboratory (O↗
NASA NTRS1994

Microbial community structure at the U.S.-Joint Global Ocean Flux Study Station ALOHA: Inverse methods for estimating biochemical indicator ratios

Modeling biogeochemical fluxes in the marine plankton requires the application of factors for extrapolation of biomass indicators measured in the field (chlorophyll a, adenosine triphosphate, bacterial counts) to biomass carbon or nitrogen. These are often inferred from culture studies and are poorly constrained for natural populations. At least squares inverse method with a simple linear model constrains the values of several common indicator ratios, giving self-consistent solutions that provide useful information about the structure of the microbial community at our North Pacific Ocean study site (Station ALOHA (A Long-term Oligotrophic Habitat Assessment)). These results indicate that the fraction of the microbial biomass that is autotrophic (pigmented) is greater in the mixed layer than at the deep chlorophyll maximum layer and that heterotrophic bacteria are a significant but not necessarily predominant component of the microbial community in the euphotic zone.

Christian, James R.↗
DOE OSTI2020

The critical role of lignin in lignocellulosic biomass conversion and recent pretreatment strategies: A comprehensive review

Heterogeneity and rigidity of lignocellulose causing resistance to its deconstruction have provided technical and economic challenges in the current biomass conversion processes. Lignin has been considered as a crucial recalcitrance component in biomass utilization. An in-depth understanding of lignin properties and their influences on biomass conversion can provide clues to improve biomass utilization. Also, utilization of lignin can significantly increase the economic viability of biorefinery. Recent lignin-targeting pretreatments have aimed not only to overcome recalcitrance for biomass conversion but also to selectively fractionate lignin for lignin valorization. Numerous studies have been conducted in biomass characteristics and conversion technologies, and the role of lignin is critical for lignin valorization and biomass pretreatment development. In this review, we provide a comprehensive review of lignin-related biomass characteristics, the impact of lignin on the biological conversion of biomass, and recent lignin-targeting pretreatment strategies. The desired lignin properties in biorefinery and future pretreatment directions are also discussed.

09 BIOMASS FUELS↗
DOE OSTI2025

Data for Identifying the best high-biomass sorghum hybrids based on biomass yield potential and feedstock quality affected by nitrogen fertility management under various environments

Data were collected from agronomy fields in Urbana and Ewing, IL, during the 2022 and 2023 growing seasons. The dataset includes dry biomass yield, nitrogen, phosphorus, and potassium concentrations and removals, and chemical composition elements (cellulose, hemicellulose, lignin, and soluble fractions) for 13 high-biomass sorghum hybrids. data_sharing.xlsx contains 20 columns and 104 rows. Below is the explanation of all variables in the file: Year: 2022; 2023 Location: Urbana, IL; Ewing, IL N rate (kg-N/ha): 0; 112 Hybrid #: H1-H13 Pedigree: Pedigree for 13 hybrids Dry biomass yield (Mg/ha): Aboveground dry biomass yield N (g/kg): Nitrogen concentration in plant tissue P (g/kg): Phosphorus concentration in plant tissue K (g/kg): Potassium concentration in plant tissue N (kg/ha): Nitrogen removal by aboveground biomass P (kg/ha): Phosphorus removal by aboveground biomass K (kg/ha): Potassium removal by aboveground biomass Cellulose (g/kg): Cellulose concentration in plant tissue Hemicellulose (g/kg): Hemicellulose concentration in plant tissue Lignin (g/kg): Lignin concentration in plant tissue Soluble (g/kg): Soluble concentration in plant tissue Cellulose (Mg/ha): Cellulose content in aboveground biomass Hemicellulose (Mg/ha): Hemicellulose content in aboveground biomass Lignin (Mg/ha): Lignin content in aboveground biomass Soluble (Mg/ha): Soluble content in aboveground biomass

environmental adaptability↗
DOE OSTI2022

Impact of anatomical fractionation of corn stover on hammer mill throughput and energy consumption

The goal of this Case Study was to quantify the impacts of variable moisture and ash on hammer mill throughput and energy consumption and on loss of very wet stover that causes failures in the first stage grinder and that are not able to be fed to conversion, as compared to a status quo Base Case system. Also considered was convertible carbohydrate content (minimum total carbohydrate specification) and maximum ash content and the delivered feedstock cost impacts of not being able to feed stover not meeting the total carbohydrate specification to the conversion reactor. Laboratory data on the impacts of moisture content and tissue fraction on throughput and energy consumption in a stage 2 hammer mill were received from FCIC Subtask 5.1. Additional air classifier throughput, energy consumption and separation efficiency data were obtained from FCIC Subtask 5.1 for the new air classifier, which has three exit streams (lights, middle and heavies). These data were utilized to develop the necessary response surface equations to perform throughput analysis using discrete event simulation. Because the ash contents and particle sizes had not been analyzed in the laboratory at the time of the model runs, we assumed that the ash distributed proportionally with total mass into the lights and heavies in an air classifier having two exit streams (lights and heavies) and that the lights fraction from the air classifier was not removed. Key takeaways from this Case Study are that due to lower energy consumption, it is more cost effective to hammer mill fractionated corn stover tissues than whole stover. Reduction of grinding energy was significant and may possibly be connected to particle-particle interactions in the grinder that lead to increased residence time of leaves and husks, resulting in decreased throughput and higher generation of fines when milling whole stover. While we did not see significant impacts to throughput, this was due to moisture failures of the first stage grinder in each system dominating failures and downtime. The operating cost savings of reduced grinding energy savings in the second stage hammer mills alone was high enough to offset the added capital cost of the air classifier and extra grinding line.

09 BIOMASS FUELS↗
DOE OSTI2022

Isolation, Characterization, and Depolymerization of L -Cysteine Substituted Eucalyptus Lignin

Lignin condensation reactions are hard to avoid or control during separation, which is a deterrent to lignin isolation and post-conversation, especially for the full utilization of lignocelluloses. Selective protection of β-aryl ether linkages in the isolation process is crucial to lignin valorization. Herein, a two-step acid/alkali separation method assisted with l-cysteine for eucalyptus lignin separation is developed, and the isolated L -cysteine lignins (LCLs) are comprehensively characterized by 2D NMR, 31 P NMR, thioacidolysis, etc. Compared to the two-step control treatment, a much higher β-O-4 content is preserved without reducing the separation efficiency assisted by L -cysteine, which is also significantly higher than alkali lignin and kraft lignin. The results of hydrogenolysis show that LCLs generate a much higher monomer yield than that of control sample. Structural analysis of LCLs suggests that lignin condensation reaction, to some extent, is suppressed by adding L -cysteine during the two-step acid/alkali separation. Further, mechanistic studies using dimeric model compound reveals that L -cysteine may be the α-carbon protective agent in the two-step separation. The role of L -cysteine in the two-step lignin isolation method provides novel insights to the selective fractionation of lignin from biomass, especially for the full valorization of lignocellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2023

Scalable aqueous-phase fabrication of reduced graphene oxide nanofiltration membranes by an integrated roll-to-roll (R2R) process

Economic fractionation of multicomponent biomass-derived streams is a key challenge in biobased fuels and chemicals production. Kraft black liquor (BL) is generated at ~1 billion tons/yr globally from biomass pulping and contains ~15wt% total solids including lignin, hemicellulose fragments, and inorganics. Membrane-based BL concentration is attractive but challenged by low solute rejections and poor stability in BL, which combines alkaline pH (~13), high dissolved solids content (15+ wt%) and high temperature (70–85°C). Here we discuss the scaled fabrication and characterization of reduced graphene oxide (rGO) nanofiltration membranes by slot die coating on a roll-to-roll (R2R) with integrated vacuum filtration. Here, we obtain high-quality 90×30 cm rGO membranes (referred to as “R2R-rGO membranes” in this work) with ~100 nm average thickness supported on porous poly(ethersulfone) (PES) sheets, and an effective area of 2700cm 2 . We present characterizations of their microstructure and uniformity as mapped over large areas by scanning electron microscopy (SEM), ellipsometry, adhesion test and X-ray photoelectron spectroscopy (XPS). The constructed ellipsometry model for the rGO membrane/PES substrate composite was successfully applied to 48 different locations of the R2R-rGO membranes. The R2R-rGO membranes showed >98% lignin rejection with a steady state flux of ~12 LMH at 50bar in a 15.7wt% total solids (TS) kraft BL stream at 72°C and 2–3 gal/min crossflow rates. Slot die coating on a R2R platform with integrated vacuum filtration can enable rapid fabrication of high-quality GO membranes at scale without organic solvents or volatile organic compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1992

Coupling plant growth and waste recycling systems in a controlled life support system (CELSS)

The development of bioregenerative systems as part of the Controlled Ecological Life Support System (CELSS) program depends, in large part, on the ability to recycle inorganic nutrients, contained in waste material, into plant growth systems. One significant waste (resource) stream is inedible plant material. This research compared wheat growth in hydroponic solutions based on inorganic salts (modified Hoagland's) with solutions based on the soluble fraction of inedible wheat biomass (leachate). Recycled nutrients in leachate solutions provided the majority of mineral nutrients for plant growth, although additions of inorganic nutrients to leachate solutions were necessary. Results indicate that plant growth and waste recyling systems can be effectively coupled within CELSS based on equivalent wheat yield in leachate and Hoagland solutions, and the rapid mineralization of waste organic material in the hydroponic systems. Selective enrichment for microbial communities able to mineralize organic material within the leachate was necessary to prevent accumulation of dissolved organic matter in leachate-based solutions. Extensive analysis of microbial abundance, growth, and activity in the hydroponic systems indicated that addition of soluble organic material from plants does not cause excessive microbial growth or 'biofouling', and helped define the microbially-mediated flux of carbon in hydroponic solutions.

Garland, Jay L.↗
NASA NTRS1998

Predicting Ecologically Important Vegetation Variables from Remotely Sensed Optical/Radar Data Using Neural Networks

A number of satellite sensor systems will collect large data sets of the Earth's surface during NASA's Earth Observing System (EOS) era. Efforts are being made to develop efficient algorithms that can incorporate a wide variety of spectral data and ancillary data in order to extract vegetation variables required for global and regional studies of ecosystem processes, biosphere-atmosphere interactions, and carbon dynamics. These variables are, for the most part, continuous (e.g. biomass, leaf area index, fraction of vegetation cover, vegetation height, vegetation age, spectral albedo, absorbed photosynthetic active radiation, photosynthetic efficiency, etc.) and estimates may be made using remotely sensed data (e.g. nadir and directional optical wavelengths, multifrequency radar backscatter) and any other readily available ancillary data (e.g., topography, sun angle, ground data, etc.). Using these types of data, neural networks can: 1) provide accurate initial models for extracting vegetation variables when an adequate amount of data is available; 2) provide a performance standard for evaluating existing physically-based models; 3) invert multivariate, physically based models; 4) in a variable selection process, identify those independent variables which best infer the vegetation variable(s) of interest; and 5) incorporate new data sources that would be difficult or impossible to use with conventional techniques. In addition, neural networks employ a more powerful and adaptive nonlinear equation form as compared to traditional linear, index transformations, and simple nonlinear analyses. These neural networks attributes are discussed in the context of the authors' investigations of extracting vegetation variables of ecological interest.

Kimes, Daniel S.↗