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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Biomass Compositional Analysis Laboratory (Fact Sheet)

At the Biomass Compositional Analysis Laboratory, NREL scientists have more than 20 years of experience supporting the biomass conversion industry. They develop, refine, and validate analytical methods to determine the chemical composition of biomass samples before, during, and after conversion processing. These high-quality compositional analysis data are used to determine feedstock compositions as well as mass balances and product yields from conversion processes.

09 BIOMASS FUELS↗
DOE OSTI2014

Biomass Compositional Analysis: NIR Rapid Methods (Fact Sheet)

NREL biomass analysis scientists use near-infrared (NIR) spectroscopy correlated with compositional data, produced using traditional wet chemical techniques, to develop rapid calibration models. These models dramatically decrease the time required for (and the cost of) routine compositional analyses. NREL has models to predict the composition of a variety of biomass types.

09 BIOMASS FUELS↗
DOE OSTI2020

A Performance Comparison of Low-Cost Near-Infrared (NIR) Spectrometers to a Conventional Laboratory Spectrometer for Rapid Biomass Compositional Analysis

The performance of a conventional laboratory near-infrared (NIR) spectrometer and two NIR spectrometer prototypes (a Texas Instruments NIRSCAN Nano evaluation model (EVM) and an InnoSpectra NIR-M-R2 spectrometer) are compared by collecting reflectance spectra of 270 well-characterized herbaceous biomass samples, building calibration models using the partial least squares (PLS-2) algorithm to predict five constituents of the samples from the reflectance spectra, and comparing the resulting model statistics. The prediction models developed using spectra from the Foss XDS spectrometer were slightly better than the prediction models developed using spectra from either the TI NIRSCAN Nano EVM and the InnoSpectra NIR-M-R2 as measured by the root mean square error (RMSECV) and the correlation coefficient (R 2 _cv) for “leave-one-out” cross-validation (CV). The models built from the two prototype units were not statistically significantly different from each other (p = 0.05). The Foss spectrometer has a larger wavelength range (400–2500 nm) compared with the two prototypes (900–1700 nm). When the spectra from the Foss XDS spectrometer were truncated so their wavelength range matched the wavelength range of the two prototype units, the resulting model was not statistically significantly different from the models from either prototype.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Chapter 11 - Biomass Compositional Analysis for Conversion to Renewable Fuels and Chemicals. In the book Biomass Volume Estimation and Valorization for Energy.

As the world continues to deplete its non-renewable resources there has begun a shift towards using renewable materials for the production of fuels and chemicals. Terrestrial biomass, as well as municipal solid wastes, provide renewable feedstocks for fuel and chemical production. However, one of the major challenges to using biomass as a feedstock for fuels and chemicals production is the great amount of innate variability between different biomass types and within individual biomass species. This inconsistency arises from varied growth and harvesting conditions and presents challenges for conversion processes, which frequently require physically and chemically uniform materials. This chapter will examine intrinsic biomass compositional characteristics including: cellulose, hemicellulose, lignin, extractives/volatiles, and ash for a wide array of biomass types. Additionally, extrinsic properties, like moisture content and particle grind size, will be examined for their effect on biomass conversion to fuels using four major conversion processes: direct combustion, pyrolysis, hydrothermal liquefaction, and fermentation. A brief discussion on recent research for the production of building block chemicals from biomass will also be presented.

09 BIOMASS FUELS↗
DOE OSTI2018

Characterisation of sludge for pyrolysis conversion process based on biomass composition analysis and simulation of pyrolytic properties

Highlights: • Characterisation of different types sewage sludge for pyrolytic properties. • Developed analytical procedure to categorise and quantify composition of sludge. • Observed qualitative similarity and quantitative variation on properties of sludge. • Correlated organic components with thermochemical properties of sludge. • Coupled model compounds selection and composition analysis for simulation study. - Abstract: Pyrolytic behaviour of sludge is highly complex and obscure because of its heterogeneous and diverse composition. Therefore, an analytical procedure is proposed to categorise and quantify the main constituents in sludge. In addition, a simulation study of sludge characteristics is carried out to complement the composition analysis and to improve our understanding on the relationship between composition of sludge and its corresponding properties. Different types of sludge samples were collected at different treatment stages from four Water Reclamation Plants in Singapore in two separate batches. Model compounds are selected to represent components identified and are used in simulation of pyrolytic properties of sludge. Constituents of sludge are adequately categorised, quantified and characterised in this study. Qualitative similarities and quantitative variations on characteristics of different sludge samples were identified. Comparison among the samples collected with the simulation provided insights on how differences in organic composition of sludge affected its properties.

09 BIOMASS FUELS↗
DOE OSTI2015

Compositional analysis of biomass reference materials: Results from an interlaboratory study

Biomass compositional methods are used to compare different lignocellulosic feedstocks, to measure component balances around unit operations and to determine process yields and therefore the economic viability of biomass-to-biofuel processes. Four biomass reference materials (RMs NIST 8491–8494) were prepared and characterized, via an interlaboratory comparison exercise in the early 1990s to evaluate biomass summative compositional methods, analysts, and laboratories. Having common, uniform, and stable biomass reference materials gives the opportunity to assess compositional data compared to other analysts, to other labs, and to a known compositional value. The expiration date for the original characterization of these RMs was reached and an effort to assess their stability and recharacterize the reference values for the remaining material using more current methods of analysis was initiated. We sent samples of the four biomass RMs to 11 academic, industrial, and government laboratories, familiar with sulfuric acid compositional methods, for recharacterization of the component reference values. In this work, we have used an expanded suite of analytical methods that are more appropriate for herbaceous feedstocks, to recharacterize the RMs’ compositions. We report the median values and the expanded uncertainty values for the four RMs on a dry-mass, whole-biomass basis. The original characterization data has been recalculated using median statistics to facilitate comparisons with this data. We found improved total component closures for three out of the four RMs compared to the original characterization, and the total component closures were near 100 %, which suggests that most components were accurately measured and little double counting occurred. Here, the major components were not statistically different in the recharacterization which suggests that the biomass materials are stable during storage and that additional components, not seen in the original characterization, were quantified here.

09 BIOMASS FUELS↗
DOE OSTI2017

Harmonization of experimental approach and data collection to streamline analysis of biomass composition from algae in an inter-laboratory setting

In order to establish and design long-term algae cultivation experiments, inter-laboratory projects need to harmonize the requirements of techno-economic and life-cycle analysis models, with standardized data inputs. In order to provide a consistent foundation and allow for integration and analysis of the results in computational technical and resource analysis models, we implemented closely coordinated, harmonized and objective analytical protocols along with a common language for measuring growth and productivity for the major algal components. We describe here the process by which we developed a harmonization framework for analysis across five geographically diverse testbed sites. Our goal was to align analytical procedures to ensure consistent reporting on biomass and lipid content, quality and yields to eliminate measurement variability as a source of uncertainty in production data. Developing standards for analysis that streamline reporting on composition and expected fuel yields from biomass is one of the major outcomes of this work and this provides a starting place for further advanced characterization of algae to support the techno-economical process analyses and account for the mass balance accounting of algal biomass. In conclusion, initial analysis of data obtained from field studies shows trends in compositional shifts of lipid and protein content of the biomass that are in support of the physiological experiments demonstrated in the first geographically distributed unified outdoor cultivation trials.

09 BIOMASS FUELS↗
DOE OSTI2021

A rapid thioacidolysis method for biomass lignin composition and tricin analysis

Abstract Background Biomass composition varies from plant to plant and greatly affects biomass utilization. Lignin is a heterogeneous phenolic polymer derived mainly from p -coumaryl, coniferyl, and sinapyl alcohols and makes up to 10–25% of lignocellulosic biomass. Recently, tricin, an O -methylated flavone, was identified as a lignin monomer in many grass species. Tricin may function as a nucleation site for lignification and is advocated as a novel target for lignin engineering to reduce lignin content and improve biomass digestibility in grasses. Thioacidolysis is an analytical method that can be adapted to analyze both lignin monomeric composition and tricin content in the lignin polymer. However, the original thioacidolysis procedure is complex, laborious, and time consuming, making it difficult to be adopted for large-scale screening in biomass research. In this study, a modified, rapid higher throughput thioacidolysis method was developed. Results In combination with gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–mass spectrometry (LC–MS), the modified thioacidolysis method can be used to simultaneously characterize the lignin composition and tricin content using 2–5 mg of dry samples. The modified method eliminates the solvent extraction and drastically improves the throughput; 80 samples can be processed in one day per person. Our results indicate that there is no significant difference in the determination of lignin S/G ratio and tricin content between the original and modified methods. Conclusions A modified thioacidolysis protocol was established. The results demonstrate that the modified method can be used for rapid, high-throughput, and reliable lignin composition and tricin content analyses for screening transgenic plants for cell wall modifications or in large-scale genome-wide association studies (GWAS).

09 BIOMASS FUELS↗
DOE OSTI2019

A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630

Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branched-chain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, we performed 13 C-pathway tracing, 13 C-pulse-tracing, transcriptional profiling, biomass composition analysis, and metabolite profiling in conjunction with 13 C-metabolic flux analysis ( 13 C-MFA) of phenol metabolism. We found that 1) phenol is metabolized mainly through the ortho-cleavage pathway; 2) phenol utilization requires a highly active TCA cycle; 3) NADPH is generated mainly via NADPH-dependent isocitrate dehydrogenase; 4) active cataplerotic fluxes increase plasticity in the TCA cycle; and 5) gluconeogenesis occurs partially through the reversed Entner-Doudoroff pathway (EDP). We also found that phenol-fed R. opacus PD630 generally has lower sugar phosphate concentrations (e.g., fructose 1,6-bisphosphatase) compared to metabolite pools in 13 C-glucose-fed Escherichia coli (set as internal standards), while its TCA metabolites (e.g., malate, succinate, and α-ketoglutarate) accumulate intracellularly with measurable succinate secretion. In addition, we found that phenol utilization was inhibited by benzoate, while catabolite repressions by other tested carbon substrates (e.g., glucose and acetate) were absent in R. opacus PD630. In conclusion, three adaptively-evolved strains display very different growth rates when fed with phenol as a sole carbon source, but they maintain a conserved flux network. These findings improve our understanding of R. opacus' metabolism for future lignin valorization.

09 BIOMASS FUELS↗
DOE OSTI2022

Re-Programing Glucose Catabolism in the Microalga Chlorella sorokiniana under Light Condition

The microalga Chlorella sorokiniana has attracted much attention for lipid production and wastewater treatment. It can perform photosynthesis and organic carbon utilization concurrently. To understand its phototrophic metabolism, a biomass compositional analysis, a 13 C metabolic flux analysis, and metabolite pool size analyses were performed. Under dark condition, the oxidative pentose phosphate pathway (OPP) was the major route for glucose catabolism (88% carbon flux) and a cyclic OPP–glycolytic route for glucose catabolism was formed. Under light condition, fluxes in the glucose catabolism, tricarboxylic acid (TCA) cycle, and anaplerotic reaction (CO 2 fixation via phosphoenolpyruvate carboxylase) were all suppressed. Meanwhile, the RuBisCO reaction became active and the ratio of its carbon fixation to glucose carbon utilization was determined as 7:100. Moreover, light condition significantly reduced the pool sizes of sugar phosphate metabolites (such as E4P, F6P, and S7P) and promoted biomass synthesis (which reached 0.155 h –1 ). In addition, light condition increased glucose consumption rates, leading to higher ATP and NADPH production and a higher protein content (43% vs. 30%) in the biomass during the exponential growth phase.

13C-MFA↗
DOE OSTI2015

Rapid analysis of composition and reactivity in cellulosic biomass feedstocks with near-infrared spectroscopy

Obtaining accurate chemical composition and reactivity (measures of carbohydrate release and yield) information for biomass feedstocks in a timely manner is necessary for the commercialization of biofuels. Our objective was to use near-infrared (NIR) spectroscopy and partial least squares (PLS) multivariate analysis to develop calibration models to predict the feedstock composition and the release and yield of soluble carbohydrates generated by a bench-scale dilute acid pretreatment and enzymatic hydrolysis assay. Major feedstocks included in the calibration models are corn stover, sorghum, switchgrass, perennial cool season grasses, rice straw, and miscanthus. Here are the results: We present individual model statistics to demonstrate model performance and validation samples to more accurately measure predictive quality of the models. The PLS-2 model for composition predicts glucan, xylan, lignin, and ash (wt%) with uncertainties similar to primary measurement methods. A PLS-2 model was developed to predict glucose and xylose release following pretreatment and enzymatic hydrolysis. An additional PLS-2 model was developed to predict glucan and xylan yield. PLS-1 models were developed to predict the sum of glucose/glucan and xylose/xylan for release and yield (grams per gram). The release and yield models have higher uncertainties than the primary methods used to develop the models. In conclusion, it is possible to build effective multispecies feedstock models for composition, as well as carbohydrate release and yield. The model for composition is useful for predicting glucan, xylan, lignin, and ash with good uncertainties. The release and yield models have higher uncertainties; however, these models are useful for rapidly screening sample populations to identify unusual samples.

09 BIOMASS FUELS↗
DOE OSTI2025

3D printing of packaging inserts from biomass-fungi composites: Environmental sustainability analysis

In this study, a comprehensive Life cycle assessment (LCA) is conducted on molded packaging inserts from expanded polystyrene (EPS) foam, molded packaging inserts from biomass-fungi composite, and 3D-printed packaging inserts from biomass-fungi composite under the low mix / high volume (LMHV) scenario and molded and machined packaging inserts from EPS foam, molded and machined packaging inserts from biomass-fungi composite, and 3D-printed packaging inserts from biomass-fungi composite under the high mix / low volume (HMLV) scenario. Six environmental impact categories—climate change, acidification, eutrophication, fossil resource scarcity, land use, and water consumption—are analyzed to evaluate the environmental trade-offs associated with each type of packaging inserts. Under the LMHV scenario, molded packaging inserts from biomass-fungi composite emerge as the best option due to their lower impact on climate change, acidification and water consumption compared to other types of packaging inserts. Conversely, molded packaging inserts from biomass-fungi composite face challenges in land use and eutrophication, primarily due to raw material production. LCA also reveals that 3D-printed packaging inserts from biomass-fungi composite are the most environmentally favorable option under the HMLV scenario, due to significantly lower contributions to climate change, eutrophication, and water consumption compared to other types of packaging inserts. Conversely, 3D-printed packaging inserts from biomass-fungi composite face challenges in acidification and land use, primarily due to raw material production. As part of the LCA, sensitivity analyses show that sourcing energy from 100% renewable sources substantially lowers climate change impacts across all packaging types, while varying transportation distances results in only minor changes, indicating the dominant role of upstream material and manufacturing processes. Additional sensitivity analysis is conducted under the HMLV scenario to assess the impact of material removal during machining on the environment. The amount of material removal is varied from 10 to 70% for the sensitivity analysis and it highlights that the amount of material removed during machining has no significant impact on climate change for packaging inserts from EPS foam. However, molded and machined packaging inserts from biomass-fungi composite show an increasing trend in climate change with higher amount of material removal, while 3D-printed packaging inserts from biomass-fungi composite exhibit a decreasing trend, driven by reduced raw material usage and energy consumption.

09 BIOMASS FUELS↗
DOE OSTI2024

Biomass yields, reproductive fertility, compositional analysis, and genetic diversity of newly developed triploid giant miscanthus hybrids

Abstract Miscanthus × giganteus (giant miscanthus), first found as a naturally occurring hybrid, has shown promise as a bioenergy/biomass crop throughout much of the temperate world. This allotriploid (2 n = 3 x = 57) hybrid resulted from a cross between tetraploid Miscanthus sacchariflorus (2 n = 4 x = 76) and diploid Miscanthus sinensis (2 n = 2 x = 38) and is particularly desirable due to its low fertility that minimizes reseeding and potential invasiveness. However, there is limited genetic diversity in commonly grown cultivars of triploid M. × giganteus and breeding and development efforts to improve and domesticate this crop have been minimal. Here, we report on newly developed M. × giganteus hybrids compared with the industry standard M. × giganteus '1993‐1780'. Dry biomass yields of new hybrids ranged from 19.5 to 32.4 Mg/ha/year for the fourth growing season, compared with 21.0 Mg/ha/year for M. × giganteus '1993‐1780'. Plant reproductive fertility remained low for all accessions with overall fertility [(seed set × seed germination)/100] ranging from 0.3% to 4.5% for new hybrids compared to 0.4% for M. × giganteus '1993‐1780'. Culm density and height varied among accessions and were positively correlated with increased biomass. Based on compositional analyses, theoretical ethanol yields ranged from 9, 740 to 16,278 L/ha/year for new hybrids compared to 10,406 L/ha/year for M. × giganteus '1993‐1780'. Relative feed value indices were low overall and ranged between 66.0 and 72.8 for new hybrids compared to M. × giganteus '1993‐1780' with 71.3. The genetic diversity of new hybrids, compared with existing cultivars, was characterized using whole genome sequences. Based on pair‐wise distances, cluster analysis clearly showed increased diversity of new hybrids compared with earlier selections. These results document new triploid hybrids of M. × giganteus with enhanced biomass and theoretical ethanol yields in combination with broader genetic diversity and lowreproductive fertility.

Touchell, Darren H.↗
DOE OSTI2016

Evaluation of Relationships between Growth Rate, Tree Size, Lignocellulose Composition, and Enzymatic Saccharification in Interspecific Corymbia Hybrids and Parental Taxa

In order for a lignocellulosic bioenergy feedstock to be considered sustainable,it must possess a high rate of growth to supply biomass for conversion. Despite the desirability of a fast growth rate for industrial application,it is unclear what effect growth rate has on biomass composition or saccharification. We characterized Klason lignin,glucan,and xylan content with response to growth in Corymbia interspecific F1 hybrid families (HF) and parental species Corymbia torelliana and C. citriodora subspecies variegata and measured the effects on enzymatic hydrolysis from hydrothermally pretreated biomass. Analysis of biomass composition within Corymbia populations found similar amounts of Klason lignin content (19.7–21.3%) among parental and hybrid populations,whereas glucan content was clearly distinguished within C. citriodora subspecies variegata (52%) and HF148 (60%) as compared to other populations (28–38%). Multiple linear regression indicates that biomass composition is significantly impacted by tree size measured at the same age,with Klason lignin content increasing with diameter breast height (DBH) (+0.12% per cm DBH increase),and glucan and xylan typically decreasing per DBH cm increase (-0.7 and -0.3%,respectively). Polysaccharide content within C. citriodora subspecies variegata and HF-148 were not significantly affected by tree size. High-throughput enzymatic saccharification of hydrothermally pretreated biomass found significant differences among Corymbia populations for total glucose production from biomass,with parental Corymbia torelliana and hybrids HF-148 and HF-51 generating the highest amounts of glucose (~180 mg/g biomass,respectively),with HF-51 undergoing the most efficient glucan-to-glucose conversion (74%). Based on growth rate,biomass composition,and further optimization of enzymatic saccharification yield,high production Corymbia hybrid trees are potentially suitable for fast-rotation bioenergy or biomaterial production.

60 APPLIED LIFE SCIENCES↗
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↗