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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fabrication of engineered dopant profiles in Er/Lu:YAG transparent laser ceramics via additive manufacturing

Transparent ceramic Er:YAG laser rods were fabricated via the direct ink write (DIW) method with engineered doping profiles featuring an Er-doped core with endcaps and core-clad structures. Laser rods up to 11 cm in length were produced which required development of a scalable process. To achieve this, multiple improvements were implemented, including printing the rods horizontally on a substrate, rather than vertically, eliminating the need for an external support structure and using a sacrificial drying layer to mitigate warping and defects. Highly transparent rods were achieved with optical scatter levels as low as 0.5%/cm (at 543 nm). A small refractive index difference of 5.7 ppm was measured at the interface between the Er-doped core and the Lu-doped endcaps and cladding. These results demonstrate DIW as a straightforward method for making good optical quality laser rods with engineered doping profiles to improve laser performance.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Consequence-driven cyber-informed engineering and related systems and methods

Embodiments of the disclosure relate to a computer-implemented consequence-driven cyber-informed engineering tool for performing and reporting consequence-based prioritization, system-of-systems breakdown, consequence-based targeting, and mitigations and protections. Embodiments of a CCE tool may perform one or more steps of defining a target industrial control system (ICS), wherein the target ICS includes operational goals, critical functions, and critical services; determining one or more scored high consequence events (HCE) associated with the defined target ICS; prioritizing the scored HCEs according to an HCE severity index; and updating a dashboard with one or more representations of the prioritized HCEs, wherein the updated dashboard is associated with the CCE tool and presented at a display.

Assante, Michael↗
DOE OSTI2021

Microbial production of advanced biofuels

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Further, advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Isolated oleaginous yeast

Some aspects provide engineered microbes for oil production. Methods for microbe engineering and for use of engineered microbes are also provided herein. Such engineered microbes exhibit greatly enhanced conversion yields and TAG synthesis and storage properties.

Stephanopoulos, Gregory↗
DOE OSTI2025

Using phage display for rational engineering of a higher-affinity humanized 3’ phosphohistidine-specific antibody

Abstract Histidine phosphorylation is a non-canonical post-translational modification (PTM), with 1-phosphohistidine (1-pHis) and 3-phosphohistidine (3-pHis) isoforms, that is understudied due to a lack of robust reagents, including high-affinity pHis-specific antibodies. Engineering pHis antibodies is challenging due to the labile nature of its phosphoramidate (P-N) bond. We developed a strategy for in vitro engineering of antibodies for the detection of native 3-pHis targets, in which the rabbit SC44-8 anti-3-pTza mAb is humanized into a scaffold (hSC44) that is suitable for phage display. Six unique Fab phage-displayed hSC44 scaffold libraries were screened for antibodies that bound 3-pHis with higher affinity and had specificity for 3-pHis versus 3-pTza. hSC44.20N32F L , the best engineered antibody, has ~10-fold higher affinity for 3-pHis than parental hSC44. Eleven new Fab structures, including the first antibody-pHis peptide structures, together with structural and quantum mechanical calculations, provided molecular insights into 3-pHis and 3-pTza discrimination by hSC44.20N32F L and the increased affinity obtained through engineering. We demonstrated the utility of these high-affinity 3-pHis-specific antibodies for the recognition of pHis proteins in mammalian cells by immunoblotting and immunofluorescence staining. Our work describes a general method for engineering labile PTM-specific antibodies and provides novel antibodies for investigating the role of 3-pHis in cell biology.

Martyn, Gregory D.↗
DOE OSTI2024

Cyber-Informed Engineering (CIE) Benefits Quantification: Recommendations for Consideration

Cyber-Informed Engineering (CIE) integrates engineering principles into the design, development, and operation of cyber-physical systems (CPS) to mitigate or eliminate the impact of cyber-enabled attacks. In July 2024, Idaho National Laboratory (INL) engaged MITRE researchers to investigate methods for systematically measuring the benefits of CIE implementation. This included evaluating the success and outcomes of CIE, identifying and quantifying the value of early adoption, and determining the business justification for its implementation, especially in existing infrastructure. MITRE reviewed existing methods in engineering and cybersecurity to understand how organizations prioritize security investments, considering their strengths, weaknesses, and relevance to CIE stakeholders. Based on this analysis, MITRE proposed potential approaches for quantifying CIE benefits and provided recommendations for INL's consideration.

42 ENGINEERING↗
DOE OSTI2022

Chapter 20: Advances and Application of CRISPR-Cas Systems

A new gene-editing technique called clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated (Cas) proteins have revolutionized genome engineering because of its high efficiency, relatively low cost, and ease of use compared with other techniques such as zinc-finger nucleases and transcription activator-like effector nucleases. CRISPR-Cas systems have transformed biological research, quickly becoming the preferred method for engineering specific genome sequences in industrially relevant microbes, important food crops, and human cells. This powerful new tool has the potential for limitless applications ranging from the treatment of human diseases to improving food security to the generation of renewable and sustainable bioproducts and biofuels. In this chapter, we will discuss advances and applications for these CRISPR-Cas systems in biological engineering.

bacterial↗
DOE OSTI2021

Power System Modeling for the Study of High Penetration of Distributed Photovoltaic Energy

Many conventional power systems are evolving due to the growth of renewable energy and distributed energy resources (DERs). Modeling the interplay of transmission and distribution systems is critical to analyze how DERs impact a system’s conventional operation and which electric infrastructure improvements are needed to achieve a balance between centralized generation and DERs. This article describes the process, tools, and resources used to model electric power systems with a centralized infrastructure in an isolated context and limited access to actual utility data. Photovoltaic systems installed on residential rooftops were the main design option. This work broadened the typical power system modeling to include planning and social considerations. This integrative engineering-social method allows for interdisciplinary teams to work in the development of a model as part of broader design goals for a renewable-dominant energy system. The Puerto Rico electric power system was used as a case study to demonstrate the process. The integrative engineering-social perspective in developing the model and the actions to manage data limitations are aspects that could be followed in other locations with aggressive renewable energy goals and where utility data are not readily available.

Cuello-Polo, Gustavo↗
DOE OSTI2022

Cas9-Based Metabolic Engineering of Issatchenkia orientalis for Enhanced Utilization of Cellulosic Hydrolysates

Issatchenkia orientalis, exhibiting high tolerance against harsh environmental conditions, is a promising metabolic engineering host for producing fuels and chemicals from cellulosic hydrolysates containing fermentation inhibitors under acidic conditions. Although genetic tools for I. orientalis exist, they require auxotrophic mutants so that the selection of a host strain is limited. We developed a drug resistance gene (cloNAT)-based genome-editing method for engineering any I. orientalis strains and engineered I. orientalis strains isolated from various sources for xylose fermentation. Specifically, xylose reductase, xylitol dehydrogenase, and xylulokinase from Scheffersomyces stipitis were integrated into an intended chromosomal locus in four I. orientalis strains (SD108, IO21, IO45, and IO46) through Cas9-based genome editing. Furthermore, the resulting strains (SD108X, IO21X, IO45X, and IO46X) efficiently produced ethanol from cellulosic and hemicellulosic hydrolysates even though the pH adjustment and nitrogen source were not provided. As they presented different fermenting capacities, selection of a host I. orientalis strain was crucial for producing fuels and chemicals using cellulosic hydrolysates.

09 BIOMASS FUELS↗
DOE OSTI2026

Vapor-phase pillarization of MXenes for engineering hierarchical interlayer porosity

MXenes, a family of two-dimensional (2D) multilamellar materials, possess excellent thermal and electronic properties for a range of applications. Their use in heterogeneous catalysis, however, is limited by the low surface area resulting from stacked layers. Pillarization with inorganic oxides can create more open, mesoporous MXene structures, improving accessibility for guest species to diffuse, reside or react in the space between 2D layers. A previous liquid-phase pillarization method, however, involves excessive use of solvent-based precursors and multiple processing steps. Here, we report a vapor-phase pillarization (VPP) strategy to introduce pillars, exemplified by silica pillars, with high pillar precursor usage efficiency and a simplified processing workflow. The resulting silica-pillared mesoporous MXene exhibits significantly increased surface area and porosity. These textural properties can be easily tuned by the VPP synthesis conditions. When applied as a ruthenium (Ru) catalyst support for the hydrogenolysis of low-density polyethylene (LDPE), the silica-pillared MXene enabled high Ru dispersion and catalytic activity. This study highlights the potential of the VPP method for engineering mesoporous, 2D MXene materials and demonstrates the effectiveness of mesoporous MXene as a catalyst support in overcoming mass transport and active-site accessibility challenges in heterogeneous catalysis involving bulky substances, such as plastics upcycling.

Luo, Song [University of Delaware, Newark, DE (Uni↗
DOE OSTI2023

Neural Network-Enhanced Reproducing Kernel Particle Method for Image-Based Multiphysics Damage Modeling of Energy Storage Materials

Energy storage materials undergo significant stresses during charge/discharge cycling, which makes understanding their reliability and durability fundamental in predicting performance and service life. Strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking, largely along material interfaces and grain boundaries. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), image-based modeling techniques are used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electrochemical-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in capturing crack propagation due to mesh dependency. Additionally, commonly used damage models, such as the continuous damage model and the cohesive zone model, often have steep tradeoffs between discontinuous field accuracy and computational expense. In this work, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1] is leveraged to accurately capture damage and crack propagation throughout the material by learning the location, orientation, and sharpness of discontinuity while allowing for a coarser nodal distribution than that necessary for capturing sharp solution transitions using traditional mesh-based methods. NN-RKPM is used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022.

damage modeling↗
DOE OSTI2023

Methods for generating barcoded combinatorial libraries

Provided herein are methods and composition for trackable genetic variant libraries. Further provided herein are methods and compositions for recursive engineering. Further provided herein are methods and compositions for multiplex engineering. Further provided herein are methods and compositions for enriching for editing and trackable engineered sequences and cells using nucleic acid-guided nucleases.

Gill, Ryan T.↗
DOE OSTI2024

The phycosphere and its role in algal biofuel production

Oleaginous microalgae have become a focus for large-scale biofuel production due to their ability to accumulate large quantities of lipids. However, production is currently limited by cost and predation. At present, algal biofuel cultivation is optimized through starvation, supplementing media with nutrients, or genetic engineering; these methods can often be costly with little to no increase in lipid production or the culture’s defense. Investigating the phycosphere of algal-bacterial interactions may overcome these current barriers to large-scale production. The phycosphere of algal-bacterial interactions have formed over millions of years through mutualistic and symbiotic relationships and can provide a more direct source of nutrients compared to adding the nutrients in bulk. The most promising of these interactions include the production of phytohormones and quorum signaling compounds that alter the behaviors of the consortia. Phytohormones can improve algal growth rates, lipid production, and stress resistance. Quorum signaling could create consortia capable of warding off invaders—such as rotifers—while self-regulating and altering behavior based on population density. Mechanisms within the algal phycosphere present many opportunities for the development of novel engineering strategies to further improve algal lipid production and operational costs. This review outlines previous preliminary phycosphere research as well as posing possible opportunities to be pursued in future biofuel production.

09 BIOMASS FUELS↗
DOE OSTI2023

Physics-guided neural networks with engineering domain knowledge for hybrid process modeling

As neural networks are more frequently used to solve problems in science and engineering, the methods used to incorporate scientific knowledge into these networks are becoming increasingly complex. Here, this work breaks down these complicated techniques into a set of basic strategies which can easily be applied to diverse situations. Several novel neural networks are built using the categories laid out in this work. These networks are tested on simulated data from a continuous stirred tank reactor (CSTR) model to evaluate the advantages provided by each network. The three points demonstrated in this work are: (1) architectural hybrid models can speed up convergence and reduce the amount of data necessary to train a model; (2) adding a physics-guided loss function can improve model generalization and make models more physically consistent; (3) using physics-guided initialization and transfer learning improves accuracy and speeds up convergence, but can harm generalizability if used incorrectly.

42 ENGINEERING↗
DOE OSTI2025

Toward engineering lattice structures with the material point method (MPM)

This study examines the potential of two variants of the material point method—the generalized interpolation material point (GIMP) and dual domain material point (DDMP) methods—in developing a robust computational framework for engineering lattice structures under different loading conditions. The study begins with assessing the ability of the two methods in predicting elastic buckling phenomena using column geometries with and without initial geometric imperfections. The results indicate that both methods effectively capture buckling phenomena when initial geometric imperfections are introduced. After this verification step, we create several models of tetrahedral lattice structures with varying strut diameter and orientation and subject them to quasi-static loading. We then validate the numerical results using laboratory test results. The results show that, while both methods accurately predict load–displacement curves in the pre-buckling regime, their predictive capabilities diminish in the post-buckling regime. Through visual comparison between the numerical and experimental deformed shapes, it appears that the discrepancies between model and experimental results are attributed to initial geometric imperfections in the lattices that occurred during 3D printing. We then establish a second set of lattice models where different types of initial geometric imperfections are considered. The results from these models show that imperfections have a negligible influence in the pre-buckling regime but affect the behavior considerably in the post-buckling regime. As a final step in this work, we subject the lattice models to impact loading and employ hypothetical soft and stiff materials. These results show that the lattice stiffness, which depends on material stiffness, strut diameter, and orientation, significantly influences the ability of a lattice structure to resist impact. In particular, we find that a stiffer lattice (i.e., one made with a stiff material and thicker struts) is capable of absorbing more energy than a softer one during impact. Although material nonlinearities, inelasticity, and detailed contact formulations are not considered in this study, the findings obtained herein lay the groundwork for engineering lattice structures under extreme loading conditions through a simulation-driven framework based on particle-based methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Demonstration and Evaluation of the Human-Technology Integration Function Allocation Methodology

There is an imminent need for the existing nuclear power plants to reduce their operating and maintenance (O&M) costs to remain economically viable. Digital technology, including automation, provides a significant opportunity for the existing nuclear power plant fleet to transform the way in which work is accomplished, reducing O&M costs, and allowing the fleet to remain economically competitive. One notable opportunity to significantly reduce O&M costs pertains to modifications to the plant equipment and main control room (MCR). Existing instrumentation and control (I&C) technologies in the MCR are highly analog, costly to operate and maintain, and demand a high cognitive and physical workload from plant staff (i.e., operators). Digitalizing the MCR has a range of broad economic benefits, including improved plant performance and reduced manual work. Further, digital I&C systems can fundamentally change the way in which plant staff operate the plant; this is the concept of operation. Human-technology integration is important to ensure that impacts to the concept of operation are done in a way that account for capabilities of people and technology. Human-technology integration employs human factors engineering (HFE) methods and principles to maximize the benefits of digital technology, reducing human error, improving overall decision-making and usability. The U.S. Department of Energy Light Water Reactor Sustainability Program is applying human-technology integration research to ensure digital technologies are safe, reliable, and efficient. This paper documents the demonstration of the human-technology guidance developed by the Light Water Reactor Sustainability Program from a first-of-a-kind digital I&C upgrade, specifically addressing function analysis and allocation for a new digital I&C system that included changes in automation levels. The program’s specific approach is included in this work, following lessons learned. This document serves as a resource for industry to follow in applying human-technology integration and HFE to digital modifications, specific to function analysis and allocation. The lessons learned should be considered in the planning and execution of HFE activities that support such digital modifications.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Low-Cost Magnesium Alloy Sheet Component Development and Demonstration Project

The overarching objective of this USAMP project was to develop and demonstrate door panels made from magnesium (Mg) sheet with a cost penalty over conventional steel stampings of no more than $5.50/kg saved. The technical approach integrated experiments with advanced computational tools based on Integrated Computational Materials Engineering (ICME) methods to develop new alloy chemistries and their thermomechanical processing that promise improved formability and lower forming temperatures. A penultimate task before finally forming the stampings was to incorporate actual microstructure into models that would enable formability simulations. This approach would, for the first time, account for individual magnesium grains moving in an anisotropic fashion unlike that for aluminum or steel that have isotropic properties upon which the current simulation tools are based. In separate activities, new coatings and lubricants to facilitate forming and improved corrosion protection and joining strategies, were developed to ensure that the door could be produced with stated product requirements. A technical cost model, which included parts production, assembly, and paint for a door specifically designed for Mg sheet, showed the cost penalty to be between 4.26 USD to 6.60 USD/kg saved, which enveloped the project’s cost targets. The cost of the coated Mg sheet was identified as the key driver for the cost penalty. The mass of the Mg-intensive door was 7.9 kg, which was 54% less than the baseline steel door.

36 MATERIALS SCIENCE↗