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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A critical review of existing and emerging technologies and systems to optimize solid waste management for feedstocks and energy conversion

Solid waste generation and its accumulation is increasing at an alarming pace due to population growth and urbanization posing severe risks to health, safety, and natural ecosystems. Herein this review strategically addresses the challenges and solutions to increasing the sustainability footprint of solid waste management (SWM) systems by revealing multipronged approaches that reduce solid waste and handling costs while generating revenue and reducing greenhouse gas and related emissions. For example, the United States sends ~150 million tons of waste to landfills, which is composed of over 75% organic and recyclable materials having a potential to be diverted to alternative scenarios. The emergence of an automated upstream and downstream sorting process for solid waste to increase material diversion from landfills is a promising approach for creating sustainable SWM. The utilization of artificial-intelligence-enabled smart and automated systems at the home and industrial scales, comprehensive public re-education including awareness of the adverse effects of landfilled waste on the ecosystem, and more eco-friendly product development are required to significantly reduce landfills and their negative footprint.

09 BIOMASS FUELS↗
DOE OSTI2026

A synergistic approach to improve the cellulose nanofibril dispersion in poly(lactic acid) composites as feedstocks for extrusion-based additive manufacturing

Hybrid fiber-reinforced polymer composites (HFRPs) are multifunctional materials that bear more than one characteristic benefit. In this study, hybrid cellulose fibers, e.g., pulp fiber (PF) and cellulose nanofibril (CNF), were used for reinforcing poly(lactic acid) (PLA). A synergistic approach was applied to disperse CNF. The low-surface-tension solvent (ethanol), together with PF templating, assisted the dispersion of CNF in PLA. The HFRP achieved a higher tensile strength (∼70 MPa, 29% increase from PLA) and modulus (∼7 GPa, 46% increase from PLA), as well as a higher heat deflection temperature (∼60℃, 6℃ increase compared to neat PLA), compared to single-reinforcement FRPs. Additionally, the formulation was demonstrated to be potentially suitable for large-scale additive manufacturing.

Bista, Bivek [University of Maine]↗
DOE OSTI2016

Plant-Derived Terpenes: A Feedstock for Specialty Biofuels

Research toward renewable and sustainable energy has identified candidate terpenes capable of blending/replacing petroleum-derived jet, diesel and tactical fuels. Additionally, despite being naturally produced and stored by many plants, there are few examples of commercial recovery of terpenes from plants due to low yields. Plant terpene biosynthesis is regulated at multiple levels leading to wide variability in terpene content and chemistry. Advances in the plant molecular toolkit including annotated genomes, high-throughput omics profiling and genome-editing provides an ideal platform for high-resolution analysis and in-depth understanding of plant terpene metabolism. Concomitantly, such information is useful for bioengineering strategies of metabolic pathways for candidate terpenes. Within this paper, we review the status of terpenes as an advanced biofuel and discuss the potential of plants as a viable agronomic solution for future advanced terpene-derived biofuels.

09 BIOMASS FUELS↗
DOE OSTI2025

Rapid characterization of MSW and RDF feedstocks for waste-to-energy process using LIBS and ML techniques

The heterogeneity in the composition of municipal solid wastes (MSW) poses significant challenges in the production of biofuel and bioproducts. This research aims to enhance the accuracy and efficiency of waste analysis and characterization by introducing a fast characterization approach for MSW-derived refuse-derived fuels (RDF) by combining Laser-Induced Breakdown Spectroscopy (LIBS) with advanced machine learning (ML) techniques. The approach combines data pre-processing of LIBS spectra of RDF, and the development of ML models trained on domain and theory-based spectral features for predicting process parameters. These models are adept at predicting key process parameters like High Heating Value (HHV), carbon content, and volatile matter. This approach can achieve an average RRMSE of 2.13% and R 2 of 0.98 or higher for all considered parameters on testing data. This work demonstrates significant potential for improving waste sorting, processing efficiency, and environmental compliance over traditional labor- and time-intensive laboratory waste analysis and characterization.

09 BIOMASS FUELS↗