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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Properties and performance of lignin-based polyurethane foams from lignin and castor oil as synergistic bio-polyols

Lignin and castor oil with intrinsic hydroxyl groups are attractive green resources for polyurethane (PU) applications. However, lignin's heterogeneous and highly crosslinked structure, poor processability, as well as the feedstock variability, lead to inconsistent and poor performance of the final foam products. Castor oil-based polyurethane foam (PUF) has a relatively high price and density, which are big hurdles to its practical applications. Here, in this study, we hypothesized that synergistic bio-polyol mixtures composed of lignin and castor oil could balance the drawbacks from individual component. Castor oil could improve the dispersity of lignin, while lignin simultaneously addressed the issues caused by PUF prepared with castor oil, such as its high density and low thermal stability. For a comprehensive understanding of the effects of structural properties of lignin on its PUF processing and applications, various lignin fractions were isolated by co-solvent enhanced lignocellulosic fractionation (CELF) from different species, including hardwood, softwood, and herbaceous plants, and the processed lignins with different molecular weights were applied with castor oil. The lignin fractions with lower molecular weight showed good dispersity in castor oil with a high lignin content (up to 50 wt%) and completely replaced petroleum-based polyols. The produced foam with 50 wt% low-molecular-weight lignin fractions from woody biomass showed comparable/higher compressive strength (up to 20 psi) and thermal insulation performance (up to 5.69 R-value in −1 for 50 % L-Pine foam). In addition, this study revealed the relationship between lignin's structural properties and foam performance, providing insights for practical applications of lignin-based PUF.

Jeong, Soyeon [State Univ. of New York (SUNY), Syr↗
DOE OSTI2021

Omics analyses and biochemical study of Phlebiopsis gigantea elucidate its degradation strategy of wood extractives

Wood extractives, solvent-soluble fractions of woody biomass, are considered to be a factor impeding or excluding fungal colonization on the freshly harvested conifers. Among wood decay fungi, the basidiomycete Phlebiopsis gigantea has evolved a unique enzyme system to efficiently transform or degrade conifer extractives but little is known about the mechanism(s). In this study, to clarify the mechanism(s) of softwood degradation, we examined the transcriptome, proteome, and metabolome of P. gigantea when grown on defined media containing microcrystalline cellulose and pine sapwood extractives. Beyond the conventional enzymes often associated with cellulose, hemicellulose and lignin degradation, an array of enzymes implicated in the metabolism of softwood lipophilic extractives such as fatty and resin acids, steroids and glycerides was significantly up-regulated. Among these, a highly expressed and inducible lipase is likely responsible for lipophilic extractive degradation, based on its extracellular location and our characterization of the recombinant enzyme. Our results provide insight into physiological roles of extractives in the interaction between wood and fungi.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Prospects for Engineering Biophysical CO 2 Concentrating Mechanisms into Land Plants to Enhance Yields

Although cyanobacteria and algae represent a small fraction of the biomass of all primary producers, their photosynthetic activity accounts for roughly half of the daily CO 2 fixation that occurs on Earth. These microorganisms are able to accomplish this feat by enhancing the activity of the CO 2 -fixing enzyme Rubisco using biophysical CO 2 concentrating mechanisms (CCMs). Biophysical CCMs operate by concentrating bicarbonate and converting it into CO 2 in a compartment that houses Rubisco (in contrast with other CCMs that concentrate CO 2 via an organic intermediate, such as malate in the case of C 4 CCMs). This activity provides Rubisco with a high concentration of its substrate, thereby increasing its reaction rate. The genetic engineering of a biophysical CCM into land plants is being pursued as a strategy to increase crop yields. This review focuses on the progress toward understanding the molecular components of cyanobacterial and algal CCMs, as well as recent advances toward engineering these components into land plants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Discrete element modeling of irregular-shaped soft pine particle flow in an FT4 powder rheometer

Pine residues are a commonly used biomass feedstock that consists of different anatomical fractions, each with distinct particle characteristics. Here, in this work, an experiment-informed discrete element model is developed to investigate the flowability of pine residues in an FT4 rheometer. Multi-sphere particles with distinct particle attributes are created to model each anatomical fraction type. A systematic analysis of specimens with varying particle characteristics (e.g., anatomical fraction type, particle shape, and size) is conducted to elucidate the relationship between particle attributes and flowability. The results show that stems recorded the highest axial force and torque and, correspondingly, the highest flow energy, which is attributed to their high stiffness and interlocking effect. Increasing their percentage in the mixture increases the flow energy while increasing needles tends to decrease flow energy. Knowledge gained in this study on the flow of anatomical fractions is important for the efficient and robust processing of pine residues.

09 BIOMASS FUELS↗
DOE OSTI2023

FUN-BioCROP model with litter decomposition parameters derived from the LIDET dataset

This repository contains the code and data necessary to run the FUN-BioCROP (Fixation and Uptake of Nitrogen-Bioenergy Carbon, Rhizosphere, Organisms, and Protection) model with litter decomposition parameters derived from a modified Monte Carlo simulation that used the Long-term Intersite Decomposition Experiment Team dataset. Related publication:Juice, S.M., Ridgeway, J.R., Hartman, M.D., Parton, W.J., Berardi, D.M., Sulman, B.N., Allen, K.E., & Brzostek, E.R. Reparameterizing litter decomposition using a simplified Monte Carlo method improves litter decay simulated by a microbial model and alters bioenergy soil carbon estimates. Description of Files: FUNBioCROP_LIDET Study.Rmd R code with FUN-BioCROP model that can be run with 10 different sets of parameters for litter decomposition (Baseline, LIDET, or eight other best parameter sets identified in the modified Monte Carlo simulation. CORPSE Functions_Bioenergy_V2.R Code with CORPSE model functions, called by FUNBioCROP_LIDET Study.Rmd Model Input Data: bulk.csv, bulk_till.csv, rhizo.csv, rhizo_till.csv, litter.csv Initial C and N (kg C or N/m2) pool values for each soil compartment, final values from spin up. All five files have the same columns: (Column - Description - Units) uFastC - Unprotected fast decomposing carbon - kg carbon/m2 uSlowC - Unprotected slow decomposing carbon - kg carbon/m2 uNecroC - Unprotected necromass carbon - kg carbon/m2 pFastC - Protected fast decomposing carbon - kg carbon/m2 pSlowC - Protected slow decomposing carbon - kg carbon/m2 pNecroC - Protected necromass carbon - kg carbon/m2 livingMicrobeC - Carbon in living microbial biomass - kg carbon/m2 uFastN - Unprotected fast decomposing nitrogen - kg nitrogen/m2 uSlowN - Unprotected slow decomposing nitrogen - kg nitrogen/m2 uNecroN - Unprotected necromass nitrogen - kg nitrogen/m2 pFastN - Protected fast decomposing nitrogen - kg nitrogen/m2 pSlowN - Protected slow decomposing nitrogen - kg nitrogen/m2 pNecroN - Protected necromass nitrogen - kg nitrogen/m2 inorganicN - Inorganic nitrogen - kg nitrogen/m2 CO2 - Carbon in carbon dioxide - kg carbon/m2 livingMicrobeN - Nitrogen in living microbial biomass - kg nitrogen/m2 Model Input Data: FluxTower_AvgSoilT.csv: Average daily soil temperature (oC) at 10 cm depth at University of Illinois Urbana-Champaign (UIUC) Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: FluxTower_AvgSoilVWC.csv: Average daily soil volumetric water content (VWC) at 10 cm depth at UIUC Energy Farm flux tower from 7/2008-3/2016. (One year of averaged data) Model Input Data: input_CCS_LIDET Study.csv: This file has daily data to run FUN-BioCROP (Column - Description - Units): yr - calendar year - year doy - day of year (1 to 365) (no leap year) - day anpp - aboveground NPP (DayCent) - kg C/m2/day bnpp - belowground NPP (DayCent) - kg C/m2/day aglivc - live aboveground biomass carbon (DayCent) - kg C/m2 bglivcj - live juvenile fine root biomass carbon (DayCent) - kg C/m2 bglivcm - live mature fine root biomass carbon (DayCent) - kg C/m2 aglivn - live aboveground biomass nitrogen (DayCent) - kg N/m2 bglivnj - live juvenile fine root biomass nitrogen (DayCent) - kg N/m2 bglivnm - live mature fine root biomass nitrogen (DayCent) - kg N/m2 nyr - simulation year - year cult - indicates a cultivation event (0 or 1) crop - indicates a new crop (0 or 1) fert - indicates a fertilizer event (0 or 1) frst - indicates the first day of the growing season (0 or 1) harv - indicates a harvest event (0 or 1) last - indicates the end of the growing season (0 or 1) croptype - crop type (0=none; 1=alfalfa; 2=corn; 3=grass clover pasture; 4=soybean; 5=wheat) cropsrl - crop specific root length - mm/g root cultrhizmix - fraction of rhizosphere mixed with bulk soil during cultivation (0.0-1.0) - fraction cultlitmix - fraction of litter mixed with bulk soil during cultivation (0.0-1.0) - fraction harvremov - fraction of above ground biomass removed during harvest (0.0-1.0) - fraction fertamt - fertilization amount - g N/m2 lifehist - plant life history (0 = annual, 1 = perennial) froot_turnover_c - amount of C in fine root turnover - kg C/m2 froot_turnover_n - amount of N in fine root turnover - kg N/m2 agrd_turnover_c - amount of C in aboveground biomass turnover - kg C/m2 agrd_turnover_n - amount of N in aboveground biomass turnover - kg N/m2 leaf_litter_fastfrac - Fast decomposing fraction of leaf litter (0.0-1.0) - fraction root_litter_fastfrac - Fast decomposing fraction of root litter (0.0-1.0) - fraction root_diameter - root diameter - mm root_length - root length - mm root/m2 rhizo_frac - fraction of total soil volume that is rhizosphere (0.0 - 1.0) - fraction date - date in format YYYY-MM-DD Instructions: Save the model code ("FUN-BioCROP_LIDET Study.Rmd") and accompanything files (data streams and CORPSE function code) in the same folder. In model code "Chunk 3: Load CORPSE Data Streams" set the working directory (setwd) to the folder with the files saved in step #1. In "Chunk 5: Define LIDET parameter sets" select the litter decomposition parameter set to be used in the run, and comment out all other sets. If changing any parameter values, edit them in "Chunk 6: Load parameters." Run all chunks up to and including "Chunk 10: Prepare Data for Export." In "Chunk 11: Export Output Data" edit data frames for export and filenames, as necessary. "Chunk 12: Graph Total Soil C" makes a figure of C remaining over the model run period. Description of each model chunk (in file FUN-BioCROP_LIDET Study.Rmd): Chunk 1: Remove all functions, clear memory. Removes all functions from R environment, clears the memory. Chunk 2: Load Packages. Loads packages necessary to run the code. Chunk 3: Load CORPSE Data Streams. Sets the working directory and loads the data files necessary to run CORPSE. Chunk 4: Load CORPSE Functions. Loads the R script with CORPSE functions from the working directory, "CORPSE Functions_Bioenergy_V2.R". Chunk 5: Define LIDET parameter sets. Has ten different parameter sets for litter decomposition tested in this study: Baseline parameters, LIDET parameters, and the other 8 best performing parameter sets identified in the modified Monte Carlo. To run the model, all but one parameter set must be commented out. Chunk 6: Load Parameters. Loads all fixed parameters to run the model. Data frame with definitions of parameters is in the CORPSE function script "CORPSE Functions_Bioenergy_V2.R" Chunk 7: Prepare Data Streams. Takes data streams loaded in Chunk 3 and puts them in the format necessary to run the model. The model is coded to run at least two sites at a time, so if only one site is being run it must be run in duplicate. Individual data tables of daily values are created in this chunk from the input data file. Chunk 8: Set Initial Conditions. Creates data tables of soil C and N pools for each soil compartment (rhizo_till, rhizo, bulk_till, bulk, litter) and loads initial values into the data tables. Creates lists for each soil compartment to hold model output. Chunk 9: Load FUN Data and Set Up Matrices. Uses DayCent data to calculate FUN input data: root and leaf N demand, total N demand, plant CN, leaf N available for retranslocation, and litter production. Creates matrices for FUN model outputs. Chunk 10: Run Model. Runs the model. Chunk 11: Prepare Data for Export. Combines data from each day saved as lists into data frames for each soil compartment. Adds values from all soil compartments together to calculate total soil values, creates separate data frames for each soil C and N pool (e.g., protected slow C) for the total soil value. Adds different C and N pools together to calculate total soil C and N for all layers. Creates data frame of ratio of protected to unprotected SOC. Organizes FUN data for export. Chunk 12: Export Results. Exports CSV files of model results to the working directory. Chunk 13: Graph Total Soil C. Makes figure of C remaining over time. Related Links: Original FUN-BioCROP model: https://github.com/BrzostekEcologyLab/FUN-BioCROP LIDET dataset: https://andlter.forestry.oregonstate.edu/data/abstract.aspx?dbcode=TD023

Juice, Stephanie↗
DOE OSTI2021

Distribution of Bound and Free Water in Anatomical Fractions of Pine Residues and Corn Stover as a Function of Biological Degradation

Biomass quality is influenced by water’s abundance, distribution, and status in relation to other chemical species within the polymer matrix. Water interacts with polymers that make up the cell walls, and these interactions govern the physical and chemical changes that occur during the storage and preprocessing of biomass feedstocks. Time-domain nuclear magnetic resonance (TD-NMR) was employed to explore variations in the physical constraints of water within the lignocellulosic microstructure in distinct anatomical fractions of biomass and as a function of biological degradation. The Carr–Purcell–Meiboom–Gill sequence, when combined with knowledge of the chemical composition and physical structure of pine residues and corn stover anatomical fractions, gives an accurate measurement of the bound and free water. In this work, the impacts of storage and biological degradation were investigated to elucidate changes in the status and distribution of water within distinct plant tissues. We also investigate how degradation during storage affects water interactions in different pine residues (e.g., bark, branch, and needle) and corn stover (e.g., cob, leaf, and stalk) anatomical fractions using transverse relaxation times (T2). As demonstrated herein, TD-NMR provides quantitative data on lignocellulosic biomass–water interactions within anatomical fractions, which can further aid in the investigation of preprocessing effects on feedstock quality. Our findings suggest that biological heating enhances biomass–water interactions at the cellular and macromolecular scale. In addition, analysis of three-dimensional scanning electron microscopy reconstructions indicates that surface roughness wavelengths align with microscale roughness, suggesting that pine forestry residue and corn stover particles have primarily hydrophobic exterior surfaces. Furthermore, this study offers multiscale insights into understanding the microstructure, wettability, and chemical environment that dictate diffusion, enzyme access, and recalcitrance of lignocellulosic biomass.

09 BIOMASS FUELS↗
DOE OSTI2024

Air Classification of Forestry Residues for Fast Pyrolysis

Understanding critical biomass attributes through efficient fractionation is crucial for advancing sustainable pyrolysis for renewable energy and chemical production. This study investigates the intricate relationship between biomass preprocessing and pyrolysis product yields, employing the air classification technique for the treatment of loblolly pine residues with varying moisture content. A comprehensive exploration of the physicochemical properties of air-classified loblolly pine informs a sophisticated pyrolysis simulation model. Given the complex and multifaceted nature of biomass pyrolysis, operating across diverse temporal and spatial scales, a pyrolysis kinetics-based CFD–DEM simulation method is employed to predict product yields. Results showed that the elevated moisture content amplifies particle adhesiveness, necessitating augmented air velocities for effective separation, thereby influencing the efficiency of the separation process. While carbon and hydrogen contents exhibit relative stability across diverse moisture contents and blower frequencies, the oxygen content undergoes noticeable changes. For example, the oxygen contents were measured as 29.2 and 38.6 wt% in the light fraction of 30% moisture content sample at blower frequencies of 10 and 20 Hz, respectively. An intriguing finding emerges from pyrolysis simulation, indicating that a lower blower frequency in air classification moderately enhances bio-oil yield and significantly improves its quality, particularly in terms of water content. For instance, the water content in the bio-oil was about 1.5% and 10% in the heavy and light fractions, respectively from 10% moisture sample under 15 Hz blower frequency.

09 - BIOMASS FUELS↗
DOE OSTI2025

Understanding the impacts of inorganic species in woody biomass for preprocessing and pyrolysis–A review

Woody biomass represents an abundant resource for sustainable biofuels, biochemicals, and bioproducts. Technologies for converting woody biomass have been established for decades, and research consistently highlights the critical role of inorganic species and ash plays in feedstock handling and conversion processes, including equipment plugging, corrosion, and catalyst deactivation. A thorough understanding of the variability, transport behavior, and downstream impact of inorganic species in woody biomass is essential for defining feedstock quality specifications and developing effective management strategies for conversion processes. This review compiles critical information in five main sections: 1) inorganic species concentration in woody biomass, based on anatomical fractions and their sources of variability; 2) technique features for quantifying inorganic elemental chemical analysis; 3) impacts of inorganic species on biomass preprocessing; 4) impacts of inorganic species on pyrolysis, and 5) mitigation strategies. Additionally, this review explores future challenges and opportunities in addressing the impacts of inorganic species on biomass quality. These insights aim to support the sustainable development of the biomass-to-bioenergy pipeline and ensure high-quality lignocellulosic feedstocks for efficient downstream conversions. The findings offer valuable guidance to policy makers, industry stakeholders, and researchers in developing effective strategies for managing inorganic species in woody biomass and fostering the sustainable processes for lignocellulosic biorefineries.

09 BIOMASS FUELS↗
DOE OSTI2021

FCIC DFO--Moisture Management and Optimization in Municipal Solid Waste Feedstock through Mechanical Processing

The project goal is to produce densified product, focusing on characteristics of bulk density, durability, and moisture content, to reduce the cost of preprocessing. The objective of the project is to efficiently manage and handle the properties of MSW feedstock through INL-developed fractional milling, high moisture pelleting, and low temperature drying preprocessing technologies .

09 BIOMASS FUELS↗
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↗