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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Engineering Synthetic Anaerobic Consortia Inspired by the Rumen for Biomass Breakdown and Conversion

Lignocellulosic plant biomass is a widely-abundant renewable resource that can be harnessed for value-added production of fuels & chemicals. While microbes have been engineered to breakdown lignocellulose and turn released sugars into products, this remains an energy-intensive process that requires expensive pre-treatment and separation steps. Furthermore, it is difficult to engineer all desirable traits for breakdown and conversion into one organism. This project developed a new strategy that relies on microbial partnerships formed in the herbivore rumen to liberate sugars from crude plant biomass and convert that sugar to value-added chemicals. Microbial consortia consisting of fungi, bacteria, and archaea form tight associations in the herbivore rumen, which divide-and-conquer the difficult tasks of biomass breakdown. This project leveraged a “synthetic rumen” consortium composed of anaerobic fungi and chain-elongating bacteria to study which metabolites are shared and exchanged between microbes and identify strategies to bolster lignocellulose conversion to value-added products. Our approach developed high-throughput systems and synthetic biology approaches to realize stable synthetic consortia that route lignocellulosic carbon into short and medium chain fatty acids (SCFAs/MCFAs) rather than methane. Key research objectives were to (1) design and predict anaerobic fungal and bacterial consortia that efficiently convert lignocellulosic biomass into medium-chain fatty acids (MCFAs), (2) understand how fermentation parameters and microbe-microbe interactions regulate and drive microbiome metabolic fluxes, and (3) use genomic editing to alter the fermentation byproducts of anaerobic fungi and bolster MCFA titers and yields.

09 BIOMASS FUELS↗
DOE OSTI2024

Protein Structure Inspired Discovery of a Novel Inducer of Anoikis in Human Melanoma

Drug discovery historically starts with an established function, either that of compounds or proteins. This can hamper discovery of novel therapeutics. As structure determines function, we hypothesized that unique 3D protein structures constitute primary data that can inform novel discovery. Using a computationally intensive physics-based analytical platform operating at supercomputing speeds, we probed a high-resolution protein X-ray crystallographic library developed by us. For each of the eight identified novel 3D structures, we analyzed binding of sixty million compounds. Top-ranking compounds were acquired and screened for efficacy against breast, prostate, colon, or lung cancer, and for toxicity on normal human bone marrow stem cells, both using eight-day colony formation assays. Effective and non-toxic compounds segregated to two pockets. One compound, Dxr2-017, exhibited selective anti-melanoma activity in the NCI-60 cell line screen. In eight-day assays, Dxr2-017 had an IC50 of 12 nM against melanoma cells, while concentrations over 2100-fold higher had minimal stem cell toxicity. Dxr2-017 induced anoikis, a unique form of programmed cell death in need of targeted therapeutics. Our findings demonstrate proof-of-concept that protein structures represent high-value primary data to support the discovery of novel acting therapeutics. This approach is widely applicable.

Oncology↗
DOE OSTI2022

A P-61 Black Widow Inspired Palladium Biladiene Complex for Efficient Sensitization of Singlet Oxygen Using Visible Light

Photodynamic therapy (PDT) is a promising treatment option that ablates cancerous cells and tumors via photoinduced sensitization of singlet oxygen. Over the last few decades, much work has been devoted to the development of new photochemotherapeutic agents for PDT. A wide variety of macrocyclic tetrapyrrole based photosensitizers have been designed, synthesized and characterized as PDT agents. Many of these complexes have a variety of issues that pose a barrier to their use in humans, including biocompatibility, inherent toxicity, and synthetic hurdles. We have developed a non-traditional, non-cyclic, and non-aromatic tetrapyrrole ligand scaffold, called the biladiene (DMBil1), as an alternative to these traditional photosensitizer complexes. Upon insertion of a heavy atom such as Pd 2+ center, Pd[DMBil1] generates singlet oxygen in substantial yields (Φ Δ = 0.54, λ exc = 500 nm) when irradiated with visible light. To extend the absorption profile for Pd[DMBil1] deeper into the phototherapeutic window, the tetrapyrrole was conjugated with alkynyl phenyl groups at the 2- and 18-positions (Pd[DMBil2-PE]) resulting in a significant redshift while also increasing singlet oxygen generation (Φ Δ = 0.59, 600 nm). To further modify the dialkynyl-biladiene scaffold, we conjugated a 1,8-diethynylanthracene with to the Pd[DMBil1] tetrapyrrole in order to further extend the compound’s π-conjugation in a cyclic loop that spans the entire tetrapyrrole unit. This new compound (Pd[DMBil2-P61]) is structurally reminiscent of the P61 Black Widow aircraft and absorbs light into the phototherapeutic window (600–900 nm). In addition to detailing the solid-state structure and steady-state spectroscopic properties for this new biladiene, photochemical sensitization studies demonstrated that Pd[DMBil2-P61] can sensitize the formation of 1 O 2 with quantum yields of Φ Δ = 0.84 upon irradiation with light λ = 600 nm. These results distinguish the Pd[DMBil2-P61] platform as the most efficient biladiene-based singlet oxygen photosensitizer developed to date. When taken together, the improved absorption in the phototherapeutic window and high singlet oxygen sensitization efficiency of Pd[DMBil2-P61] mark this compound as a promising candidate for future study as an agent of photodynamic cancer therapy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Novel Relaxation Time Approximation: A Consistent Calculation of Transport Coefficients with QCD-inspired Relaxation Times

We use a novel formulation of the relaxation time approximation to consistently calculate the bulk and shear viscosity coefficients using QCDinspired energy-dependent relaxation times and phenomenological thermal masses obtained from fits to lattice QCD thermodynamics. The matching conditions are conveniently chosen to simplify the computations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Retinal-inspired method and system for improved detection

The present disclosure provides a method and device for filtering sensor data. Signals from an array of sensor pixels are received and checked for changes in pixel values. Motion is detected based on the changes in pixel values, and motion output signals are transmitted to a processing station. If the sum of correlated changes in pixel values across a predetermined field of view exceeds a predetermined value, indicating sensor jitter, the motion output signals are suppressed. If a sum of motion values within a defined subsection of the field of view exceeds a predetermined threshold, indicating the presence of a large object of no interest, the motion output signals are suppressed for that subsection.

42 ENGINEERING↗
DOE OSTI2022

A Boundary Topology Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing

Minimizing the mass in electric machines while maintaining superior performance has become a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization for light-weighting electric machines using traditional approaches typically explore a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods like cell-based density approaches suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the IEA-15MW radial flux direct-drive wind turbine generator. The free-from boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with upto 20-ton reduction in electrical steel mass.

additive manufacturing↗
DOE OSTI2022

A New Shape Optimization Approach for Lightweighting Electric Machines Inspired by Additive Manufacturing: Preprint

Minimizing the mass in electric machines while maintaining superior performance is a new requirement for the advancement of drivetrains used in wind energy and electric mobility. Topology optimization (TO) for lightweighting electric machines using traditional approaches typically explores a restricted design space allowed by standard parametrizable geometry and manufacturing, while advanced methods, such as cell-based density approaches, suffer from a lack of robust manufacturability constraints during the optimization process. To overcome these drawbacks, we explore a grid-independent, boundary TO where the outer shape of the magnet is parameterized using Bezier curves. We conduct a design of experiments (DOE) to study the effect of different magnet shapes on machine performance by varying the control points on the Bezier curves. A machine-learning-based surrogate model is constructed using the data from the DOE to quantify the relationship between the control points, air-gap torque, and mass. The control points are then optimized to maximize the torque density. The approach is used for minimizing electrical steel mass in the International Energy Agency (IEA) 15-MW radial flux direct-drive wind turbine generator. The free-form boundary TO resulted in smooth and concise shapes that can be easily additively manufactured with up to a 20-ton reduction in electrical steel mass.

additive manufacturing↗
DOE OSTI2021

Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning

Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limitation, are designed to generalize well to new tasks with limited data. Typically, models are evaluated on unseen classes and datasets that are defined by the same fundamental task as they are trained for (e.g. category membership). One can also ask how well a model can generalize to fundamentally different tasks within a fixed dataset (for example: moving from category membership to tasks that involve detecting object orientation or quantity). To formalize this kind of shift we define a notion of “independence of tasks” and identify three new sets of labels for established computer vision datasets that test a model's ability to generalize to tasks which draw on orthogonal attributes in the data. We use these datasets to investigate the failure modes of metric-based few-shot models. Based on our findings, we introduce a new few-shot model called Fuzzy Simplicial Networks (FSN) which leverages a construction from topology to more flexibly represent each class from limited data. In particular, FSN models can not only form multiple representations for a given class but can also begin to capture the low-dimensional structure which characterizes class manifolds in the encoded space of deep networks. We show that FSN outperforms state-of-the-art models on the challenging tasks we introduce in this paper while remaining competitive on standard few-shot benchmarks.

deep learning↗