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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advanced mass balance characterization and fractionation of algal biomass composition

Abstract Opportunities associated with biomass production and bioproduct isolation from algae-derived feedstocks are plentiful and promising; however, there are challenges associated with realizing these applications. One of the most important, and often overlooked, challenges is the lack of availability of a strong foundation of compositional analysis methods validated on microalgal biomass. Currently, compositional analysis in algae is dominated by the use of interference-prone methods, a lack of full mass balance accounting, and the use of top-down approaches that bin all unaccounted-for mass into a single category, such as carbohydrates. We present here an approach based on a bottom-up algal biomass characterization aimed at moving towards, and highlighting the importance of, full and accurate mass closure to achieve the maximum economic potential from a sustainable and renewable feedstock. Algal biomass representing three genera, Nannochloropsis , Scenedesmus , and Monoraphidium , was subjected to a cell rupture and fractionation process, followed by detailed characterization of each fraction to determine the partitioning of measured and unknown components. The goal of this work is to identify where the missing components partition, and develop a strategy to close the mass balance or identify the unknowns, while utilizing a rigorous characterization approach for characterizing algal biomass. Although only 75–80% of the biomass was accounted for, the fractionation approach utilized here provides key insight into possible chemical components for future investigations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Evaluation of Brazilian Sugarcane Bagasse Characterization: An Interlaboratory Comparison Study

This paper describes a study of the variability of measured composition for a single bulk sugarcane bagasse conducted across eight laboratories using similar analytical methods, with the purpose of determining the expected variation for compositional analysis performed by different laboratories. The results show good agreement of measured composition within a single laboratory, but greater variability when results are compared among laboratories. These interlaboratory variabilities do not seem to be associated with a specific method or technique or any single piece of instrumentation. The summary censored statistics provide mean values and pooled standard deviations as follows: total extractives 6.7% (0.6%), whole ash 1.5% (0.2%), glucan 42.3% (1.2%), xylan 22.3% (0.5%), total lignin 21.3% (0.4%), and total mass closure 99.4% (2.9%).

09 BIOMASS FUELS↗
DOE OSTI2021

Machine Learning-Based Classification of Lignocellulosic Biomass from Pyrolysis-Molecular Beam Mass Spectrometry Data

High-throughput analysis of biomass is necessary to ensure consistent and uniform feedstocks for agricultural and bioenergy applications and is needed to inform genomics and systems biology models. Pyrolysis followed by mass spectrometry such as molecular beam mass spectrometry (py-MBMS) analyses are becoming increasingly popular for the rapid analysis of biomass cell wall composition and typically require the use of different data analysis tools depending on the need and application. Here, the authors report the py-MBMS analysis of several types of lignocellulosic biomass to gain an understanding of spectral patterns and variation with associated biomass composition and use machine learning approaches to classify, differentiate, and predict biomass types on the basis of py-MBMS spectra. Py-MBMS spectra were also corrected for instrumental variance using generalized linear modeling (GLM) based on the use of select ions relative abundances as spike-in controls. Machine learning classification algorithms e.g., random forest, k-nearest neighbor, decision tree, Gaussian Naïve Bayes, gradient boosting, and multilayer perceptron classifiers were used. The k-nearest neighbors (k-NN) classifier generally performed the best for classifications using raw spectral data, and the decision tree classifier performed the worst. After normalization of spectra to account for instrumental variance, all the classifiers had comparable and generally acceptable performance for predicting the biomass types, although the k-NN and decision tree classifiers were not as accurate for prediction of specific sample types. Gaussian Naïve Bayes (GNB) and extreme gradient boosting (XGB) classifiers performed better than the k-NN and the decision tree classifiers for the prediction of biomass mixtures. The data analysis workflow reported here could be applied and extended for comparison of biomass samples of varying types, species, phenotypes, and/or genotypes or subjected to different treatments, environments, etc. to further elucidate the sources of spectral variance, patterns, and to infer compositional information based on spectral analysis, particularly for analysis of data without a priori knowledge of the feedstock composition or identity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2013

Multivariate Calibration Models for Sorghum Composition using Near-Infrared Spectroscopy

NREL developed calibration models based on near-infrared (NIR) spectroscopy coupled with multivariate statistics to predict compositional properties relevant to cellulosic biofuels production for a variety of sorghum cultivars. A robust calibration population was developed in an iterative fashion. The quality of models developed using the same sample geometry on two different types of NIR spectrometers and two different sample geometries on the same spectrometer did not vary greatly.

09 BIOMASS FUELS↗
DOE OSTI2014

NREL National Bioenergy Center Overview

The demand for clean, sustainable, secure energy is growing... and the U.S. Department of Energy's National Renewable Energy Laboratory (NREL) is answering the call. NREL's National Bioenergy Center is pioneering biofuels research and development and accelerating the pace these technologies move into the marketplace.

09 BIOMASS FUELS↗
DOE OSTI2021

BETO 2021 Peer Review - Analytical Development & Support WBS 2.5.1.101

The objective of the Analytical Development and Support (ADS) Project is to produce and maintain the critical analytical methods and tools that enable evaluation of emerging biofuels R&D at NREL and in the broader biofuels research community. Our project is divided into two tasks: one task to develop novel analytical techniques and improve existing methods and one task to maintain existing analytical capabilities at NREL and provide outreach to the wider community. The ADS Project is world-recognized for our Laboratory Analytical Procedures (LAPS) which provide detailed procedures for compositional analysis of biomass and have been adopted as the de facto standards within the biofuels community largely due to the transparency of the methods and the high reputation of NREL's research. Our dialog with stakeholders allows us to provide robust, precise, accurate, and publicly available analytical procedures for better valuation of scientific tools such as our recent accomplishment in developing a cellulose assay to support the EPA and industry in calculation of converted cellulose during starch ethanol production. We continue to develop analytical capabilities to support BETO's directives to research cost advantaged fuels such as animal wastes and novel bioproducts like 2,3-Butane-diol.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Effects of Inoculation with Lactic Acid Bacteria on the Preservation of Nannochloropsis gaditana Biomass in Wet Anaerobic Storage and Its Impact on Biomass Quality

Wet anaerobic storage of algal biomass is a promising preservation approach that can ensure a continuous supply of these feedstocks to biorefineries year-round. An effective solution to preservation must ensure minimal dry matter loss and a change in biochemical composition during storage. Therefore, the objective of this study is to investigate the preservation of Nannochloropsis gaditana biomass through wet anaerobic storage and its impact on biomass quality. Prior to storage, the algae sample is inoculated with two different strains of lactic acid bacteria and thereafter stored for 30 and 180 days. Each inoculant limited the dry matter loss to <10% (dry basis) after the storage duration. Final pH values (4.3–4.8) indicate that the biomass samples are properly ensiled, achieving the acidic conditions necessary for preservation. Compositional analysis of the biomass after storage shows a reduction in carbohydrate content, a relative increase in lipid content, and no significant change in the protein fraction. Glucose and galactose were the most prevalent sugar monomers. The low dry matter loss and minimal compositional change indicate that wet anaerobic storage is an effective means of preserving algal biomass and ensuring a constant supply of algal biomass feedstock to a biorefinery.

Oginni, Oluwatosin↗
DOE OSTI2014

A comparative study of ethanol production using dilute acid, ionic liquid and AFEX™ pretreated corn stover

In a biorefinery producing cellulosic biofuels, biomass pretreatment will significantly influence the efficacy of enzymatic hydrolysis and microbial fermentation. Comparison of different biomass pretreatment techniques by studying the impact of pretreatment on downstream operations at industrially relevant conditions and performing comprehensive mass balances will help focus attention on necessary process improvements, and thereby help reduce the cost of biofuel production. An on-going collaboration between the three US Department of Energy (DOE) funded bioenergy research centers (Great Lakes Bioenergy Research Center (GLBRC), Joint BioEnergy Institute (JBEI) and BioEnergy Science Center (BESC)) has given us a unique opportunity to compare the performance of three pretreatment processes, notably dilute acid (DA), ionic liquid (IL) and ammonia fiber expansion (AFEX TM ), using the same source of corn stover. Separate hydrolysis and fermentation (SHF) was carried out using various combinations of commercially available enzymes and engineered yeast (Saccharomyces cerevisiae 424A) strain. The optimal commercial enzyme combination (Ctec2: Htec2: Multifect Pectinase, percentage total protein loading basis) was evaluated for each pretreatment with a microplate-based assay using milled pretreated solids at 0.2% glucan loading and 15 mg total protein loading/g of glucan. The best enzyme combinations were 67:33:0 for DA, 39:33:28 for IL and 67:17:17 for AFEX. The amounts of sugar (kg) (glucose: xylose: total gluco- and xylo-oligomers) per 100 kg of untreated corn stover produced after 72 hours of 6% glucan loading enzymatic hydrolysis were: DA (25:2:2), IL (31:15:2) and AFEX (26:13:7). Additionally, the amounts of ethanol (kg) produced per 100 kg of untreated corn stover and the respective ethanol metabolic yield (%) achieved with exogenous nutrient supplemented fermentations were: DA (14.0, 92.0%), IL (21.2, 93.0%) and AFEX (20.5, 95.0%), respectively. The reason for lower ethanol yield for DA is because most of the xylose produced during the pretreatment was removed and not converted to ethanol during fermentation. Compositional analysis of the pretreated biomass solids showed no significant change in composition for AFEX treated corn stover, while about 85% of hemicellulose was solubilized after DA pretreatment, and about 90% of lignin was removed after IL pretreatment. As expected, the optimal commercial enzyme combination was different for the solids prepared by different pretreatment technologies. Due to loss of nutrients during the pretreatment and washing steps, DA and IL pretreated hydrolysates required exogenous nutrient supplementation to ferment glucose and xylose efficiently, while AFEX pretreated hydrolysate did not require nutrient supplementation.

09 BIOMASS FUELS↗
DOE OSTI2020

Investigating biomass composition and size effects on fast pyrolysis using global sensitivity analysis and CFD simulations

It is notoriously difficult to build an accurate universal model for biomass pyrolysis due to its sensitivity to a wide number of critical material attributes such as chemical species and physical sizes. In this work, a biomass pyrolysis kinetics with 32 heterogeneous reactions and 59 species was implemented in an open-source multiphase computational fluid dynamics (CFD) software MFiX and validated against two different experimental pyrolysis data sets that provided detailed data describing chemical component yields. The reaction scheme was then used to build a surrogate model and assess the sensitivity of pyrolysis yields to feedstock compositions. The sensitivity analysis determined that the yield of bio-char showed a strong positive sensitivity to the carbon-rich lignin and tannin pseudo-species in the reaction scheme while the bio-oil and bio-gas were correlated to oxygen-rich lignin pseudo-species. The reaction scheme was then integrated into a coarse-grained discrete element model to simulate fast pyrolysis in a bubbling fluidized bed over a range of feedstock particle sizes. The reactor simulations showed further sensitivity to particle size and hydrodynamics. Notably, particles under 0.5 mm have small heat transfer limitations but left the reactor before completely converting and thus reduced the bio-oil yield. Results from this study can be used to guide future development of highly accurate models for fast pyrolysis reactors with a variety of feedstock properties and operating conditions.

09 BIOMASS FUELS↗
DOE OSTI2014

A systems biology, whole-genome association analysis of the molecular regulation of biomass growth and composition in Populus deltoides

Poplars trees are well suited for biofuel production due to their fast growing habit, favorable wood composition and adaptation to a broad range of environments. The availability of a reference genome sequence, ease of vegetative propagation and availability of transformation methods also make poplar an ideal model for the study of wood formation and biomass growth in woody, perennial plants. The objective of this project was to conduct a genome-wide association genetics study to identify genes that regulate bioenergy traits in Populus deltoides (eastern cottonwood). Populus deltoides is a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits and common and low-frequency single-nucleotide polymorphisms (SNPs) detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. These polymorphism are critical tools for the development of specialized plant feedstocks for bioenergy.

09 BIOMASS FUELS↗
DOE OSTI2015

A systems biology, whole-genome association analysis of the molecular regulation of biomass growth and composition in Populus deltoides

Poplars trees are well suited for biofuel production due to their fast growing habit, favorable wood composition and adaptation to a broad range of environments. The availability of a reference genome sequence, ease of vegetative propagation and availability of transformation methods also make poplar an ideal model for the study of wood formation and biomass growth in woody, perennial plants. The objective of this project was to conduct a genome-wide association genetics study to identify genes that regulate bioenergy traits in Populus deltoides (eastern cottonwood). Populus deltoides is a genetically diverse keystone forest species in North America and an important short rotation woody crop for the bioenergy industry. We searched for associations between eight growth and wood composition traits and common and low-frequency single-nucleotide polymorphisms (SNPs) detected by targeted resequencing of 18,153 genes in a population of 391 unrelated individuals. To increase power to detect associations with low-frequency variants, multiple-marker association tests were used in combination with single-marker association tests. Significant associations were discovered for all phenotypes and are indicative that low-frequency polymorphisms contribute to phenotypic variance of several bioenergy traits. These polymorphism are critical tools for the development of specialized plant feedstocks for bioenergy.

09 BIOMASS FUELS↗
NASA NTRS1999

Biomass Burning Influences on the Composition of the Remote South Pacific Troposphere: Analysis Based on Observations from PEM Tropics-A

Airborne, in-situ measurements from PEM-Tropics-A (September/October 1996) are analyzed to show the presence of distinct pollution plumes in the middle-tropical troposphere of the remote South Pacific (10-30degS). These elevated plumes cause a relative maximum at about 5-7km attitude in the vertical distribution of primary and secondary species characteristic of fuel combustion and biomass burning (CO, C2H2, C2H6, CH3Cl, PAN, O3). Similar plumes were also observed at mid-latitudes in the middle troposphere during three flights east of New Zealand (40-45degS). In all, pollution plumes with CO larger than 100 ppb were observed 24 times on 7 separate flight days south of the equator. The observed plumes were generally embedded in very dry air. Ten-day back trajectory analysis supports the view that these originated from the biomass burning regions of South Africa (and South America) and were transported to the South Pacific along long-distance subsiding trajectories. The chemical composition of the southern Pacific troposphere analyzed from the PEM-Tropics-A data is compared with data from the tropical regions of the northern Pacific (PEM-West-A) and southern Atlantic (TRACE-A) during the same Sept/Oct time period. Sizable perturbations in the abundance of ozone and its key precursors, resulting from the transport of pollution originating from biomass burning sources, are observed in much of the Southern Hemispheric troposphere.

Singh, H. B.↗
DOE OSTI2013

Global transcriptome analysis of Clostridium thermocellum ATCC 27405 during growth on dilute acid pretreated Populus and switchgrass

Background The thermophilic anaerobe Clostridium thermocellum is a candidate consolidated bioprocessing (CBP) biocatalyst for cellulosic ethanol production. The aim of this study was to investigate C. thermocellum genes required to ferment biomass substrates and to conduct a robust comparison of DNA microarray and RNA sequencing (RNA-seq) analytical platforms. Results C. thermocellum ATCC 27405 fermentations were conducted with a 5 g/L solid substrate loading of either pretreated switchgrass or Populus. Quantitative saccharification and inductively coupled plasma emission spectroscopy (ICP-ES) for elemental analysis revealed composition differences between biomass substrates, which may have influenced growth and transcriptomic profiles. High quality RNA was prepared for C. thermocellum grown on solid substrates and transcriptome profiles were obtained for two time points during active growth (12 hours and 37 hours postinoculation). A comparison of two transcriptomic analytical techniques, microarray and RNA-seq, was performed and the data analyzed for statistical significance. Large expression differences for cellulosomal genes were not observed. We updated gene predictions for the strain and a small novel gene, Cthe_3383, with a putative AgrD peptide quorum sensing function was among the most highly expressed genes. RNAseq data also supported different small regulatory RNA predictions over others. The DNA microarray gave a greater number (2,351) of significant genes relative to RNA-seq (280 genes when normalized by the kernel density mean of M component (KDMM) method) in an analysis of variance (ANOVA) testing method with a 5 % false discovery rate (FDR). When a 2-fold difference in expression threshold was applied, 73 genes were significantly differentially expressed in common between the two techniques. Sulfate and phosphate uptake/utilization genes, along with genes for a putative efflux pump system were some of the most differentially regulated transcripts when profiles for C. thermocellum grown on either pretreated switchgrass or Populus were compared. Conclusions Our results suggest that a high degree of agreement in differential gene expression measurements between transcriptomic platforms is possible, but choosing an appropriate normalization regime is essential.

BESC↗
DOE OSTI2023

Mechanisms of metabolic adaptation in the duckweed Lemna gibba: an integrated metabolic, transcriptomic and flux analysis

Abstract Background Duckweeds are small, rapidly growing aquatic flowering plants. Due to their ability for biomass production at high rates they represent promising candidates for biofuel feedstocks. Duckweeds are also excellent model organisms because they can be maintained in well-defined liquid media, usually reproduce asexually, and because genomic resources are becoming increasingly available. To demonstrate the utility of duckweed for integrated metabolic studies, we examined the metabolic adaptation of growing Lemna gibba cultures to different nutritional conditions. Results To establish a framework for quantitative metabolic research in duckweeds we derived a central carbon metabolism network model of Lemna gibba based on its draft genome. Lemna gibba fronds were grown with nitrate or glutamine as nitrogen source. The two conditions were compared by quantification of growth kinetics, metabolite levels, transcript abundance, as well as by 13 C-metabolic flux analysis. While growing with glutamine, the fronds grew 1.4 times faster and accumulated more protein and less cell wall components compared to plants grown on nitrate. Characterization of photomixotrophic growth by 13 C-metabolic flux analysis showed that, under both metabolic growth conditions, the Calvin-Benson-Bassham cycle and the oxidative pentose-phosphate pathway are highly active, creating a futile cycle with net ATP consumption. Depending on the nitrogen source, substantial reorganization of fluxes around the tricarboxylic acid cycle took place, leading to differential formation of the biosynthetic precursors of the Asp and Gln families of proteinogenic amino acids. Despite the substantial reorganization of fluxes around the tricarboxylic acid cycle, flux changes could largely not be associated with changes in transcripts. Conclusions Through integrated analysis of growth rate, biomass composition, metabolite levels, and metabolic flux, we show that Lemna gibba is an excellent system for quantitative metabolic studies in plants. Our study showed that Lemna gibba adjusts to different nitrogen sources by reorganizing central metabolism. The observed disconnect between gene expression regulation and metabolism underscores the importance of metabolic flux analysis as a tool in such studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa

Corresponding Standard Reference Material Data used in Partial Least Squares Regression Models for Sugar Composition Estimates in Biomass in: Economic Impact of Yield and Composition Variation in Bioenergy Crops: Populus trichocarpa (for corresponding manuscript: DOI: 10.1002/bbb.2148) PDF Files: Images of 1H NMR spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum). Suppression of the water peak was achieved using a NOESY-1D with presaturation, a recycle delay of 5 s, and a total of 64 scans. Spectra were acquired at 298 K and processed with automatic phase correction, baseline correction, and chemical shift referencing to TSP-d4. Images show all 1H data from 10 to 1ppm with inset spectra of region of interest (4.0 to 3.1 ppm). Text Files: Spectra for neutralized 2-stage acid hydrolysates of 4 NIST Standard Reference Material biomass samples (Monterey Pine 8493, Sugarcane Bagasse 8491, Wheat Straw 8494, and Eastern Cottonwood/Poplar 8492) and 2 Center for Bioenergy Innovation reference biomass samples (Poplar - Populus trichocarpa and Switchgrass - Panicum Virgatum) were converted into text files for plotting. Files contain 8192 points of raw spectral data from 12.23 to -2.78 ppm. The text file contains 4 columns of data and includes: Point number, Intensity, Hz, and ppm. Xcel Spreadsheet: HPLC measured monomeric sugar concentrations and bucketed 1H NMR data used to build monomeric sugar composition prediction models. Sugar composition in biomass determined from HPLC analyses are given in mg sugar/mg of biomass. Spectral bucketing was performed using Bruker’s AMIX software. Spectra were divided into 0.005 ppm buckets in the region of 3.10– 4.15 ppm for a total of 210 buckets. Headers for the bucketed data are the chemical shift in ppm of the center of the bucket. Bucketed data was used to build partial least squares models for subsequent predictions in The Unscrambler v. 10.5(CAMO A/S, Trondheim, Norway). The formation of methanol during hydrolysis interferes with the quantitative NMR analysis of sugars, so the methanol peak centered at 3.37 ppm and spanning four buckets (3.2925 – 3.2775 ppm) was set to zero for all spectra.

09 BIOMASS FUELS↗
DOE OSTI2026

Feedstock/Pretreatment Screening for Bioconversion of Sugars and Lignin Residues

This project will conduct biomass deconstruction (pretreatment and enzymatic saccharification) on two representative biomass feedstocks and four different pretreatment processes, including three high temperature steam/chemical pretreatments and one low temperature chemical/mechanical pretreatment. A third biomass feedstock will undergo biomass deconstruction with three different pretreatment processes, including two high temperature chemical pretreatments and one low temperature chemical/mechanical pretreatment. Several pretreatment conditions will be performed in a screening study using NLR pilot-scale pretreatment equipment to generate a range of pretreated biomass substrates. A selected number of these substrates will be chosen for enzymatic saccharification evaluation, based on standard compositional analysis of the pretreated substrates as a primary indicator of pretreatment efficacy. Resulting enzymatic hydrolysis slurries will be analyzed to determine overall biomass sugar yields. Additional compositional analysis will be performed to determine oligomeric sugar composition and structure, to analyze structural characteristics of solids fractions on native, pretreated, and enzymatically saccharified biomass residues for one of the biomass feedstocks, corn stover. The enzymatically saccharified materials will undergo 2,3-butanediol fermentation in a shaker flask as bench scale. Using relevant process performance data collected in these various conversion steps, technoeconomic analysis activities will be performed to compare the economic potential of the various biomass feedstock and pretreatment processes and to identify key economic drivers and sustainability metrics.

09 BIOMASS FUELS↗
DOE OSTI2025

The composition of gases from a diffusion flame above longleaf pine needle fuel beds

The gas and tar composition of a diffusion flame from longleaf pine needles is currently poorly understood and more data are needed to fill in the gap between pyrolysis data and smoke plume data, thus improving physical and chemical modeling of wildland smoke formation. A pilot experiment to measure light gas and tar composition of such a flame is described for three flame regions: persistent flame (flame base), intermittent flame, and smoke plume. Flame gases from 24 experimental fires were collected in canisters and analyzed using EPA method TO-14A for CO 2 , CO, H 2 , CH 4 , and C 2 to C 7 hydrocarbon gases. Condensed gas (tar) samples were collected and analyzed using GC/MS. Other light gases were measured using FTIR spectroscopy. Results from compositional data analysis suggest significant differences in (relative) concentration of compounds detected in the three regions of the flame. Statistical tests for differences in flame zones were performed using the canister data: Concentration of hydrocarbons relative to CO and CO 2 decreased from the persistent flame zone above the pyrolyzing needles through the intermittent flame region into the flame-free plume. This was likely due to both chemical reactions (oxidation) occurring in the flame as well as the introduction of air into the flame/plume by entrainment.

Biomass↗