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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Modification of Lignin by Protein Cross-linking to Facilitate Production of Biofuels From Poplar

The limited supply of fossil fuels and the associated environmental issues associated with their utilization has resulted in much effort put forth to promote renewable resources of energy. Switching to renewable fuels for energy will allow us to become carbon neutral by recycling carbon from plants and reduce carbon from dioxide emissions, which could potentially reduce global warming in future generations and generate new industries with exciting new technologies.

09 BIOMASS FUELS↗
DOE OSTI2012

Fast Pyrolysis of Poplar Using a Captive Sample Reactor: Effects of Inorganic Salts on Primary Pyrolysis Products

We have constructed a captive sample reactor (CSR) to study fast pyrolysis of biomass. The reactor uses a stainless steel wire mesh to surround biomass materials with an isothermal environment by independent controlling of heating rates and pyrolysis temperatures. The vapors produced during pyrolysis are immediately entrained and transported in He carrier gas to a molecular beam mass spectrometer (MBMS). Formation of secondary products is minimized by rapidly quenching the sample support with liquid nitrogen. A range of alkali and alkaline earth metal (AAEM) and transition metal salts were tested to study their effect on composition of primary pyrolysis products. Multivariate curve resolution (MCR) analysis of the MBMS data shows that transition metal salts enhance pyrolysis of carbohydrates and AAEM salts enhances pyrolysis of lignin. This was supported by performing similar separate studies on cellulose, hemicellulose and extracted lignin. The effect of salts on char formation is also discussed.

09 BIOMASS FUELS↗
DOE OSTI2025

Leveraging hyperspectral phenotyping for accurate, non-destructive prediction of metabolite profiles in poplar under drought stress

Accurately predicting drought tolerance in woody perennial bioenergy crops is critical for sustainable biomass production under fluctuating precipitation. Hyperspectral imaging (HSI) in the visible-near-infrared (VNIR) and shortwave-infrared (SWIR) ranges offers a promising approach for predicting plant biochemical traits, yet its application in metabolite profiling remains underexplored. We integrated VNIR+SWIR HSI with untargeted metabolomics to investigate drought-induced metabolic shifts in Populus leaves from eight Populus genotypes. Metabolite profiling identified 127 compounds, with 73 showing significant drought responses spanning amino acids (AA), carbohydrates (CHO), phenolic glycosides (PG), organic acids (OA), fatty acids and alcohols (FA), terpenes (T), phenolic metabolites (P), and unclassified metabolites. Spectral analysis revealed consistently higher reflectance across VNIR and SWIR wavelengths in drought-stressed plants, corresponding with increased accumulation of AA and reduced CHO and PG levels. Least absolute shrinkage and selection operator (LASSO) regression modeling identified robust spectral predictors of metabolite concentrations, associating VNIR wavelengths (500–700 nm) predominantly with AA and P, whereas SWIR wavelengths (1680–1700 nm) reliably predicted CHO, OA, and T. Several stable spectral-metabolite associations persisted across the two watering regimes (drought vs. well-watered), highlighting their potential as spectral biomarkers for non-destructive stress monitoring. Minimal genotype-specific variation suggests that observed spectral and metabolic responses were driven primarily by environmental factors, likely reflecting limited genetic diversity among the commercial Populus genotypes examined. This work establishes VNIR+SWIR hyperspectral imaging as a powerful, non-destructive phenotyping tool for precision monitoring and targeted improvement of drought resilience in bioenergy crops.

Biochemical trait prediction↗