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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Temperature Quenching and Fluorescence Depolarization of Carbon Nanodots Obtained via Paraffin Pyrolysis

A temperature effect on fluorescence intensity and polarization of a colloidal system of carbon nanodots in glycerol under linearly polarized pumping conditions is studied. Nanodots are obtained via pyrolysis of paraffin in nanopores of a mesoporous silica. An increase in temperature leads to quenching of nanodots fluorescence, and activation energy of the quenching process is assessed. An experimental relationship between the linear fluorescence polarization and temperature is described by the Levshin–Perrin equation, which takes into account the rotational diffusion of luminescent particles (fluorophores) in a liquid matrix. The size of fluorophores is noticeably smaller than that of carbon nanodots according to the Levshin–Perrin model. A difference between the dimensions of the fluorophore and the nanodot indicates that the small atomic groups responsible for luminescence of the nanodot possess high segmental mobility.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Properties of MgFe{sub 2}O{sub 4} Nanoparticles Synthesized by Ultrasonic Aerosol Pyrolysis for Biomedical Applications

We present the data of studies on the structure, phase states, and magnetic properties of magnetic nanoparticles (MNPs) of magnesium ferrite spinel (MgFe{sub 2}O{sub 4}), synthesized by ultrasonic aerosols pyrolysis. Primary single-phase MNPs with an average size of 9.6, 11.5, and 14.0 nm, synthesized from precursors at concentrations of 0.06, 0.12, and 0.24 M, respectively, agglomerate into tightly aggregated spherical particles (secondary particles) with sizes of 206, 300, and 340 nm, respectively. Primary particles inside the spheres do not interact with each other and are in a superparamagnetic state. There is a layer on the surface of the particles, the magnetic structure of which differs from the structure of the inner part of the MNP; this is explained by the formation of a canted spin structure or a spin glass state in the surface layer of the MNPs. MgFe{sub 2}O{sub 4} nanospheres obtained from a precursor at a concentration of 0.06 M are most promising as valid sources of heat in magnetic hyperthermia therapy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Accurate determination of genotypic variance of cell wall characteristics of a Populus trichocarpa pedigree using high-throughput pyrolysis-molecular beam mass spectrometry

Abstract Background Pyrolysis-molecular beam mass spectrometry (py-MBMS) analysis of a pedigree of Populus trichocarpa was performed to study the phenotypic plasticity and heritability of lignin content and lignin monomer composition. Instrumental and microspatial environmental variability were observed in the spectral features and corrected to reveal underlying genetic variance of biomass composition. Results Lignin-derived ions (including m/z 124, 154, 168, 194, 210 and others) were highly impacted by microspatial environmental variation which demonstrates phenotypic plasticity of lignin composition in Populus trichocarpa biomass. Broad-sense heritability of lignin composition after correcting for microspatial and instrumental variation was determined to be H 2 = 0.56 based on py-MBMS ions known to derive from lignin. Heritability of lignin monomeric syringyl/guaiacyl ratio ( S / G ) was H 2 = 0.81. Broad-sense heritability was also high (up to H 2 = 0.79) for ions derived from other components of the biomass including phenolics (e.g., salicylates) and C5 sugars (e.g., xylose). Lignin and phenolic ion abundances were primarily driven by maternal effects, and paternal effects were either similar or stronger for the most heritable carbohydrate-derived ions. Conclusions We have shown that many biopolymer-derived ions from py-MBMS show substantial phenotypic plasticity in response to microenvironmental variation in plantations. Nevertheless, broad-sense heritability for biomass composition can be quite high after correcting for spatial environmental variation. This work outlines the importance in accounting for instrumental and microspatial environmental variation in biomass composition data for applications in heritability measurements and genomic selection for breeding poplar for renewable fuels and materials.

09 BIOMASS FUELS↗
DOE OSTI2020

In-situ measurement of pyrolysis and combustion gases from biomass burning using swept wavelength external cavity quantum cascade lasers

Broadband high-speed absorption spectroscopy using swept-wavelength external cavity quantum cascade lasers (ECQCLs) is applied to measure multiple pyrolysis and combustion gases in biomass burning experiments. Two broadly-tunable swept-ECQCL systems were used, with the first tuned over a range of 2089-2262 cm -1 (4.42 – 4.79 µm) to measure spectra of CO 2 , H 2 O, and CO. The second was tuned over a range of 920-1150 cm -1 (8.70 – 10.9 µm) to measure spectra of ammonia (NH 3 ), ethene (C 2 H 4 ), and methanol (MeOH). Absorption spectra were measured continuously at a 100 Hz rate throughout the burn process, including inhomogeneous flame regions, and analyzed to determine time-resolved gas concentrations and temperature. The results provide in-situ, dynamic information regarding gas-phase species as they are generated, close to the biomass fuel source.

09 BIOMASS FUELS↗
DOE OSTI2018

Ex Situ Catalytic Fast Pyrolysis of Lignocellulosic Biomass to Hydrocarbon Fuels: 2018 State of Technology and Future Research

This report will update modeled costs for a fixed bed ex situ catalytic fast pyrolysis process for the conversion of biomass to liquid hydrocarbon fuels based on recent bench-scale experimental results. It will also update previously published information for this conversion pathway in the DOE Bioenergy Technologies Office 2016 Multi-Year Program Plan with current understanding of research goals achievable by 2022 and associated modeled cost reductions. All costs will be updated to a consistent set of assumptions in 2016 dollars.

09 BIOMASS FUELS↗
DOE OSTI2018

Supply Chain Sustainability Analysis of Renewable Hydrocarbon Fuels via Indirect Liquefaction, Ex Situ Catalytic Fast Pyrolysis, Hydrothermal Liquefaction, Combined Algal Processing, and Biochemical Conversion: Update of the 2018 State-of-Technology Cases and Design Cases

This technical report describes the SCSAs for the production of renewable hydrocarbon transportation fuels via a range of conversion technologies: (1) renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of woody lignocellulosic biomass (note that the IDL pathway in this SCSA represents the syngas conversion design in the 2018 SOT and 2022 design cases [Tan et al., 2018]); (2) renewable gasoline (RG) and diesel (RD) blendstocks via ex situ catalytic fast pyrolysis of woody lignocellulosic biomass; (3) RD via hydrothermal liquefaction (HTL) of wet sludge from a wastewater treatment plant; (4) renewable hydrocarbon fuels via biochemical conversion of herbaceous lignocellulosic biomass; (5) renewable diesel via HTL of a blend of algae and woody biomass; and (6) renewable diesel via combined algae processing (CAP). This technical report focuses on the environmental performance of these six biofuel production pathways in their 2018 SOT cases, as well as in their design cases (future target projections). The results of these renewable hydrocarbon fuel pathways in these SCSA analyses update those for the respective 2015 and 2016 SOT cases (Edward Frank et al. 2016; Hao Cai et al. 2016, 2017; Cai et al. 2018) in the case of IDL, algae CAP, and biochemical conversion pathways. They also provide an opportunity to examine the impact of technology improvements in both biomass feedstock production and biofuel production that have been achieved in 2018 SOTs on the sustainability performance of these renewable transportation fuels, and they reflect updates to Argonne National Laboratory's Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET[R]) model, which was released in October 2018 (Wang et al. 2018). These GREET updates include production of natural gas, electricity, and petroleum-based fuels that can influence biofuels' supply chain greenhouse gas (GHG) (CO 2 , CH 4 , and N 2 O) emissions, water consumption and air pollutant emissions. GHG emissions, water consumption, and 2 nitrogen oxides (NO x ) emissions are the main sustainability metrics assessed in this analysis. In this analysis, we define water consumption as the amount of water withdrawn from a freshwater source that is not returned (or returnable) to a freshwater source at the same level of quality. Life-cycle fossil energy consumption and net energy balance, which is the life-cycle fossil energy consumption deducted from the renewable biofuel energy produced, are also assessed.

09 BIOMASS FUELS↗
DOE OSTI2021

Catalytic Fast Pyrolysis for Chemicals: Cooperative Research and Development, CRADA Number CRD-16-00637 (Final Report)

The purpose of this partnership between the ExxonMobil Chemical Company and NREL is to advance the state of the art for converting biomass to liquid hydrocarbons using catalytic fast pyrolysis (CFP), with particular emphasis on directing and improving yields of such liquid hydrocarbons for uses in commodity chemical markets. CFP of biomass is a promising route for the production of renewable fuels and chemicals, and the key driver for economic viability is carbon yield to valuable products. The majority of prior CFP work has been targeted at fuel production, but this CRADA seeks to pursue routes to chemical co-products, thereby enhancing the overall economic viability of CFP. This project is beneficial to the public because it advances a technology that could one day provide renewable fuels and chemicals to our economy, thus improving the sustainability of our transportation and chemical manufacturing sectors.

09 BIOMASS FUELS↗
DOE OSTI2023

Customization for Current Market Applications of the Carbon Nanotubes Produced by PNNL’s Regenerable Catalytic Pyrolysis (ReCaP) for Scaled Hydrogen Production

Here, we demonstrated the Regenerative Catalytic Pyrolysis (ReCaP) process that generates carbon dioxide-free hydrogen (CO 2 -free H 2 ) from inexpensive and domestically abundant natural gas (NG), while simultaneously producing H 2 at net production cost of $1.0/kg through the sale of valuable carbon nanotube (CNT) co-product. The specific goal of this project is to 1) scale up the production of CNT, 2) correlate the properties of the CNT with the reaction conditions, 3) perform a techno-economic analysis to obtain minimum selling price of H 2 and CNT, and 4) identify industrial partners interested in the CNT co-product.

03 NATURAL GAS↗
DOE OSTI2025

Climate-focused Life Cycle Assessments of Biochar Production by an ARTi Pyrolysis Reactor and an Air Burners CharBoss® Air Curtain Incinerator

This report presents a limited, dynamic, consequential life cycle assessment (LCA) to compare the climate impacts of two biochar production methods using wood as a feedstock. The two methods are a pyrolysis reactor supplied by ARTi (Des Moines, IA, https://www.arti.com/) and a T26 CharBoss® air curtain incinerator supplied by Air Burners, Inc. (Palm City, FL, https://airburners.com/). The underlying LCA methodology is described in a chapter by Singh et al. (2024) and implemented in the form of a workbook freely available as online Supplementary Material for the chapter. For the convenience of the reader, a pre-print version of the relevant portions of Singh et al. (2024) is attached as Appendix A to this report. Specific assumptions and calculations to obtain the input parameters used in this LCA for each production method are described in the Methodology section below. This implementation of the LCA considers emissions associated with biomass loading, comminution and conversion, biochar decay in soil, and the production and use of bioenergy generated during the conversion process. The LCA is “limited” in that upstream emissions associated with biomass production, harvest, transportation, and land-use change, as well as embodied emissions in equipment and facilities are not considered. Similarly, downstream emissions from biochar transport and incorporation into soil (i.e., tillage), and the impact of biochar soil amendments on soil greenhouse gas (GHG) emissions (other than CO 2 from biochar decay in soil), soil organic carbon stocks, crop response, and surface albedo are not considered. As the intent is to compare different biochar production methods in a simple unbiased manner, the primary alternative biomass pathway for the LCA is immaculate combustion, which is the hypothetical instantaneous and complete conversion of carbon in the biomass to CO 2 at time zero without generation of any other greenhouse gases or aerosols (GHGAs) or any useful bioenergy. Use of this pathway provides relative values for the production methods and, when the embodied emissions are similar and the same feedstock is used, these relative values are reasonable approximations for those attained with a full LCA.

09 BIOMASS FUELS↗
DOE OSTI2026

Opportunities and Challenges for Hydrotreating of Catalytic Fast Pyrolysis Oil to Fuels

Catalytic fast pyrolysis (CFP) provides a versatile platform for producing fuels to combat climate change and meet decarbonizing targets. In this contribution, we will discuss the hydroprocessing of CFP oils to a variety of fuels, including sustainable aviation fuel (SAF), diesel, and marine fuel. Both standalone and co-hydroprocessing with petroleum streams will be covered. Hydrotreating CFP oil at temperatures around 400 Degrees Celsius can produce a highly deoxygenated product with oxygen contents below the detection limit. However, the quality of fuel fractions produced has been a problem, manifesting as low octane numbers for the gasoline-range fraction and low cetane numbers for the diesel-range fraction. Incorporating an initial transition zone for hydrogenation during hydrotreating was shown to dramatically increase the diesel fraction cetane number from 24 to 45. A similar approach enabled the production of a cycloalkane-rich SAF fraction meeting key ASTM 4054 guidelines with respect to density, viscosity, heating value, volatility, freeze and flash point. Over 400 hours of hydrotreating for SAF was demonstrated with no signs of catalyst bed fouling, measured by pressure drop over the catalyst bed. Compared to other fuels, marine fuel is unique in that it does not require complete deoxygenation. We investigated the minimum hydrotreating requirements to produce fuel compatible with very low sulfur fuel oil, and the results suggested 30% reduction in hydrotreating costs for this approach. Co-hydroprocessing offers an opportunity to take advantage of refinery infrastructure and economies of scale although the operation is less flexible with respect to operating conditions. Co-hydroprocessing CFP oil with petroleum streams gave efficient deoxygenation at milder conditions (e.g. at temperatures of 320 Degrees Celsius) than required for standalone hydrotreating. Over 90% incorporation of biogenic carbon in the CFP oil was confirmed by carbon-14 analysis. Hydrotreating of CFP oil, whether by standalone or co-processing, can produce a variety of fuel cuts, whose quality can be tailored by changing process conditions. Challenges remain, including long-term catalyst performance and determining CFP oil quality requirements.

09 BIOMASS FUELS↗
DOE OSTI2026

Pyrolysis Molecular Beam Mass Spectrometry_Analysis_of_Natural_Variants_of_Poplulus_Trichocarpa_Leaves

Select leaves from natural variants of Poplar (Populus Trichocarpa) grown in a greenhouse at Oak Ridge National Laboratory were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Leaves were harvested, cryomilled and kept frozen until analysis. Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectrometry_Analysis_of_Specific_Switchgrass_Genotypes

Select natural variant switchgrass genotypes grown in Tifton, GA were analyzed by Pyrolysis-Molecular Beam Mass Spectrometry (Py-MBMS). Biomass was harvested, milled, several genotypes were analyzed with and without being destarched and extracted with ethanol prior to analysis (indicated with -DE if destarched and extracted). Py-MBMS analysis was conducted using approximately 4 mg of biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectrometry_Analysis_of_hybrid_cross_of_Populus_tremula_x_P_alba_717-1B4_and_overexpression_of_a_lectin_receptor-like_kinase_(PtLecRLK1)

Stem tissues from the hybrid poplarPopulus tremula × P. albaclone 717-1B4 and from lectin receptor-like kinase overexpression lines PP7 and PP19 were individually colonized with the ectomycorrhizal fungiLaccaria bicolorstrain S238N,Hyaloscypha finlandicastrain PMI746, orUmbelopsis vinaceastrain PMI3018, as well as with a mixed fungal inoculum; non-inoculated plants served as controls. Plants were grown in a greenhouse at Oak Ridge National Laboratory and harvested in January 2025. Stem samples were analyzed using Pyrolysis–Molecular Beam Mass Spectrometry (Py-MBMS). Stems were harvested, debarked, dried, milled, destarched and ethanol extracted prior to analysis. Py-MBMS analysis was conducted using approximately 4 mg of wood from biomass and each sample was analyzed in duplicate. A Frontier PY2020 unit pyrolyzed samples at 500°C for 30 s in 80 µL deactivated stainless steel cups. An Extrel Super-Sonic MBMS Model Max 1000 was used to collect mass spectral data fromm/z30 to 450 at 17 eV and processed using Merlin Automation software (V3). Spectral ion intensities were normalized to the total ion chromatogram signal for each sample for analysis of spectral variance. Lignin content (wt %) was estimated based on relative responses from standards of known Klason lignin content using mean-normalized ion intensities ofm/z120, 124 (G), 137 (G), 138 (G), 150 (G), 152, 154 (S), 164 (G), 167 (S), 168 (S), 178 (G), 180, 181, 182 (S), 194 (S), 208 (S) and 210 (S) where G indicates guaiacyl-derived ions, S indicates syringyl-derived ions, and other ions either derive from other lignin monomers or multiple sources. Ratios of S and G lignin monomer units (S/G) were obtained by dividing the sum of S-based ions by the sum of G-based ions using mean-normalized ion intensities.

CBI↗
DOE OSTI2026

Pyrolysis_Molecular_Beam_Mass_Spectra_for_Standard_Biomass_Samples_and_Natural_Variants_of_Switchgrass_Grown_Under_Different_Conditions

This data consists of total ion chromatogram (TIC) normalized Pyrolysis-Molecular Beam Mass Spectrometry (py-MBMS) spectra collected to predict lignin content and lignin monomeric ratios for “Variation in Biomass Yield and Cell Wall Composition in Switchgrass Natural Variants Under Two Nitrogen Regimes” (https://doi.org/10.1007/s12155-025-10838-8).

09 BIOMASS FUELS↗
DOE OSTI2025

Estimation of extreme temperatures in direct solar methane pyrolysis within a porous medium

Porous media have wide application in renewable energy conversion processes, such as solar-thermal fuels production and decarbonization. Heat transport mechanisms within porous media can be highly complex, particularly under extreme conditions encountered in concentrated solar thermal reactors in which direct measurement of temperature is challenging. Here, we implement and report an inverse heat conduction model to estimate the temperature distribution throughout a porous substrate domain in a direct solar methane pyrolysis process. By solving a two-dimensional heat transfer problem and applying an inverse optimization algorithm, we estimate the quasi-steady state spatial temperature distribution in a fibrous porous carbon substrate. The results are validated indirectly by experimentally measured graphite deposition and a simplified reaction kinetic model.

finite difference method↗
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↗