Waste biomass valorization through strategic partnerships
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36 MATERIALS SCIENCE↗
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Results for “Bioproduct”
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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Accelerating Materials Discovery: Artificial Intelligence for Sustainable, High-Performance Polymers
PolyID enables the discovery of polymers with advanced performance and greater sustainability while reducing material development timelines. The material design space is immense and cannot be reasonably probed using an Edisionian approach. High-throughput property prediction, enabled by artificial intelligence provides a hypothesis driven approach for down selection of candidate polymers to pursue experimentally. To aid experimentalists in the down selection of material targets this high-throughput, machine learning-based tool is capable of predicting polymer properties simply from molecular structures. Currently, transport, thermal, and mechanical properties across 7 polymer class (polyamides, polyesters, polycarbonates, polyimides, polyolefins, polyacrylates, and polyurethanes) can be predicted, and the PolyID platform has been flexibly designed so new materials and properties can be added.
artificial intelligence↗
Bioenergy: A Pathway to Decarbonization
An overview of BETO’s efforts to help engineer carbon reduction and decarbonize the atmosphere with improved carbon conversion technologies, along with bio-based alternatives to fossil fuels and petrochemical-related products.
biofuels, bioproducts, greenhouse gas, carbon foot↗
Biofuel Innovation: Clean Energy Solutions, Ready Today
An overview of BETO's innovations in biofuels research to enhance transportation technologies and infrastructure. With over 300 active R&D projects, BETO initiatives encourage cost-competitive, efficient, and sustainably produced biofuels.
biofuels, bioproducts, aviation, maritime↗
..beta..-Ketoadipic Acid Production in P. putida for Performance-Advantaged Nylons
Biomass derived chemicals can possess extended functionality, often in the form of heteroatoms like oxygen and nitrogen, that can enable performance advantaged properties in emergent materials. One illustrative example of this concept is ..beta..-ketoadipic acid (..beta..KA), a C6 dicarboxylic acid with a ketone in its backbone. ..beta..KA can be obtained via biological cultivation from aromatic compounds, sugars, and even waste plastics. When ..beta..KA is implemented into polymers, namely nylons, in place of adipic acid the overall material properties are increased. Specifically, the glass transition temperature (which is the measurement at which temperature a plastic softens) is increased by 69 degrees C and the water permeability is reduced by 20%. Molecular dynamic (MD) simulations reveal that the ..beta..-ketone in ..beta..KA leads to restricted movement across multiple backbone carbons when compared to adipic acid. Furthermore, MD simulations reveal that the ..beta..-ketone, when compared to no-ketone or an a-ketone, can lead to an increase in hydrogen bonding between neighboring chains. Both phenomena may help explain the molecular origin of the increases in polymer performance. Aside from performance advantages in thermomechanical performance, the production of ..beta..KA also is advantaged in its manufacturing. Although estimates for the minimum selling price (MSP) of ..beta..KA exceed adipic acid the manufacture of ..beta..KA would reduce supply chain energy and GHG emissions by >50% and >30% respectively. Further TEA analysis reveals that the MSP of ..beta..KA is primarily driven by the feedstock cost while the capital expenditures are driven largely by fermentation costs. Overall, the work presented within will be illustrative of how biomass derived molecules, namely ..beta..KA, can manifest performance advantages across multiple benchmarks and may enable a path to market for biomass-derived materials.
beta-ketoadipic acid↗
Biomass Feedstock Flow Modeling at Exascale
Variable biomass feedstock handling in hoppers and screw conveyors can cause upstream failures and disrupt downstream conversion operations. Understanding how particle size, shape, and moisture content affect feedstock flowability is important for efficient biorefinery operation. We present the use of our open-source discrete element method (DEM) solver for predicting flows in large scale hoppers and assess its performance on state-of-the-art high performance computing platforms that use heterogenous computing architectures.
bioenergy↗