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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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27 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Air pollution control strategies directly limiting national health damages in the US

Exposure to fine particulate matter (PM 2.5 ) from fuel combustion significantly contributes to global and US mortality. Traditional air pollution control strategies typically focus on emission reductions for specific air pollutants or sectors to maintain air pollutant concentrations within acceptable levels. Here we directly set national PM 2.5 mortality cost reduction targets within a global human-earth system model with US state-level energy systems, identifying endogenously the control actions, sectors, and locations that most cost-effectively reduce PM 2.5 mortality. Our results show that substantial health benefits can be cost-effectively achieved by using electricity to replace sources with high primary PM 2.5 emission intensities, including industrial coal, building biomass, and industrial liquids. Increasing the stringency of PM 2.5 reduction targets expedites the phaseout of high emission intensity sources, leading to larger declines in major air pollutant emissions, but very limited co-benefits in reducing CO 2 emissions. Control strategies achieve the greatest mortality cost reductions in the East North Central and Middle Atlantic states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Discovery of multi-functional polyimides through high-throughput screening using explainable machine learning

Polyimides have been widely used in modern industries because of their excellent mechanical and thermal properties, e.g., high-temperature fuel cells, displays, and aerospace composites. However, it usually takes decades of experimental efforts to develop a successful product. Aiming to expedite the discovery of high-performance polyimides, we utilize computational methods of machine learning (ML) and molecular dynamics (MD) simulations. Our study provides compelling evidence for the effectiveness of a data-driven approach in discovering novel polyimides. We first build a comprehensive library of more than 8 million hypothetical polyimides based on the polycondensation of existing dianhydride and diamine/diisocyanate molecules. Then we establish multiple ML models for the thermal and mechanical properties of polyimides based on their experimentally reported values, including glass transition temperature, Young’s modulus, and tensile yield strength. The obtained ML models demonstrate excellent predictive performance in identifying the key chemical substructures influencing the thermal and mechanical properties of polyimides. The use of explainable machine learning describes the effect of chemical substructures on individual properties, from which human experts can understand the cause of the ML model decision. Applying the well-trained ML models, we obtain property predictions of the 8 million hypothetical polyimides. Then, we screen the whole hypothetical dataset and identify three (3) best-performing novel polyimides that have better-combined properties than existing ones through Pareto frontier analysis. For an easy query of the discovered high-performing polyimides, we also create an online platform https://polyimide-explorer.herokuapp.com/ that embeds the developed ML model with interactive visualization. Furthermore, we validate the ML predictions through all-atom MD simulations and examine their synthesizability. The MD simulations are in good agreement with the ML predictions and the three novel polyimides are predicted to be easy to synthesize via Schuffenhauer’s synthetic accessibility score. Following the proposed ML guidance, we successfully synthesized a novel polyimide and the experimentally obtained high glass transition/thermal decomposition temperature demonstrated its excellent thermal stability. Here our study demonstrates an efficient way to expedite the discovery of novel polymers using ML prediction and MD validation. The high-throughput screening of a large computational dataset can serve as a general approach for new material discovery in other polymeric material exploration problems, such as organic photovoltaics, polymer membranes, and dielectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Scaffolded and annotated nuclear and organelle genomes of the North American brown alga Saccharina latissima

Increasing the genomic resources of emerging aquaculture crop targets can expedite breeding processes as seen in molecular breeding advances in agriculture. High quality annotated reference genomes are essential to implement this relatively new molecular breeding scheme and benefit research areas such as population genetics, gene discovery, and gene mechanics by providing a tool for standard comparison. The brown macroalga Saccharina latissima (sugar kelp) is an ecologically and economically important kelp that is found in both the northern Pacific and Atlantic Oceans. Cultivation of Saccharina latissima for human consumption has increased significantly this century in both North America and Europe, and its single blade morphology allows for dense seeding practices used in the cultivation of its Asian sister species, Saccharina japonica. While Saccharina latissima has potential as a human food crop, insufficient information from genetic resources has limited molecular breeding in sugar kelp aquaculture. We present scaffolded and annotated Saccharina latissima nuclear and organelle genomes from a female gametophyte collected from Black Ledge, Groton, Connecticut. This Saccharina latissima genome compares well with other published kelp genomes and contains 218 scaffolds with a scaffold N50 of 1.35 Mb, a GC content of 49.84%, and 25,012 predicted genes. We also validated this genome by comparing the synteny and completeness of this Saccharina latissima genome to other kelp genomes. Our team has successfully performed initial genomic selection trials with sugar kelp using a draft version of this genome. This Saccharina latissima genome expands the genetic toolkit for the economically and ecologically important sugar kelp and will be a fundamental resource for future foundational science, breeding, and conservation efforts.

DeWeese, Kelly↗
DOE OSTI2023

Adaptive learning-driven high-throughput synthesis of oxygen reduction reaction Fe–N–C electrocatalysts

Reducing human reliance on inefficient energy systems and fossil fuels has become more urgent due to the consequences of global climate change. However, traditional trial-and-error approaches have hampered our ability to accelerate the discovery and implementation of functional materials for efficient energy conversion devices, such as polymer electrolyte fuel cells (PEFCs). To address this, we develop an adaptive learning framework that integrates machine learning and state-of-the-art capabilities in high-throughput synthesis to achieve expedited optimization of iron-nitrogen-carbon PEFC oxygen reduction reaction (ORR) electrocatalysts. We use statistical inference, uncertainty quantification, and global optimization to build a computational design-of-experiment tool that identifies the optimum compositions to be investigated next to reduce the demands placed on experimental materials discovery. We benchmark the ability of the proposed strategy to discover optimum catalyst synthesis conditions in a six-dimensional search space when starting with a thirty-six-sample database. By following the adaptive learning strategy, we synthesize fourteen new catalysts from approximately ten billion unique compositions and discover four catalysts that outperform all original samples. The best machine learning-optimized catalyst is 33% more active than the highest-performing one in the initial database, showing an ORR activity seven times larger than those typically reported for the same class of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Flat and Level Analysis Tool (FLAT) for real-time automated segmentation and analysis of concrete slab point clouds

In the United States, the flatness and levelness of concrete floors during construction is traditionally specified by a maximum allowable gap under a 3 meter straightedge. However, the straightedge method is inexact and rarely representative of the entire floor since the technician is free to choose any location on the floor to perform the measurement. In cases requiring a higher degree of precision and repeatability, concrete floor flatness and levelness can be measured using the standard test method ASTM E1155. With the recent introduction of advanced surveying instruments such as robotic theodolites and terrestrial laser scanners (TLS), the means now exist to modernize and expedite the measurement of floor flatness and levelness. This paper details the development and demonstration of a digital tool, named the Flat and Level Analysis Tool (FLAT), to automate and expedite the segmentation and analysis of flatness and levelness from dense point cloud data of concrete floor slabs. Segmentation algorithms were developed using unsupervised machine learning to extract the set of points belonging to the concrete floor slab from a full 360 scan of a construction site. After segmentation, automated analysis algorithms report the results according to the standard method. The developed algorithms were demonstrated on a dense point cloud captured from a concrete slab-on-grade at a construction site. Results show that the digital tool can quickly provide estimates for floor flatness and levelness with minimal human involvement with comparable accuracy to manual methods.

Hayes, Nolan↗
DOE OSTI2025

Evaluating algorithmic bias on biomarker classification of breast cancer pathology reports

Objectives: This work evaluated algorithmic bias in biomarkers classification using electronic pathology reports from female breast cancer cases. Bias was assessed across 5 subgroups: cancer registry, race, Hispanic ethnicity, age at diagnosis, and socioeconomic status. Materials and Methods: We utilized 594 875 electronic pathology reports from 178 121 tumors diagnosed in Kentucky, Louisiana, New Jersey, New Mexico, Seattle, and Utah to train 2 deep-learning algorithms to classify breast cancer patients using their biomarkers test results. We used balanced error rate (BER), demographic parity (DP), equalized odds (EOD), and equal opportunity (EOP) to assess bias. Results: We found differences in predictive accuracy between registries, with the highest accuracy in the registry that contributed the most data (Seattle Registry, BER ratios for all registries >1.25). BER showed no significant algorithmic bias in extracting biomarkers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2) for race, Hispanic ethnicity, age at diagnosis, or socioeconomic subgroups (BER ratio <1.25). DP, EOD, and EOP all showed insignificant results. Discussion: We observed significant differences in BER by registry, but no significant bias using the DP, EOD, and EOP metrics for socio-demographic or racial categories. This highlights the importance of employing a diverse set of metrics for a comprehensive evaluation of model fairness. Conclusion: A thorough evaluation of algorithmic biases that may affect equality in clinical care is a critical step before deploying algorithms in the real world. We found little evidence of algorithmic bias in our biomarker classification tool. Artificial intelligence tools to expedite information extraction from clinical records could accelerate clinical trial matching and improve care.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Impact of Molecular Design on Degradation Lifetimes of Degradable Imine-Based Semiconducting Polymers

Transient electronics are a rapidly emerging field due to their potential applications in the environment and human health. Recently, a few studies have incorporated acid-labile imine bonds into polymer semiconductors to impart transience; however, understanding of the structure–degradation property relationships of these polymers is limited. In this work, we systematically design and characterize a series of fully degradable diketopyrrolopyrrole-based polymers with engineered sidechains to investigate the impact of several molecular design parameters on the degradation lifetimes of these polymers. By monitoring degradation kinetics via ultraviolet–visible spectroscopy, we reveal that polymer degradation in solution is aggregation-dependent based on the branching point and M n , with accelerated degradation rates facilitated by decreasing aggregation. Additionally, increasing the hydrophilicity of the polymers promotes water diffusion and therefore acid hydrolysis of the imine bonds along the polymer backbone. The aggregation properties and degradation lifetimes of these polymers rely heavily on solvent, with polymers in chlorobenzene taking six times as long to degrade as in chloroform. We develop a new method for quantifying the degradation of polymers in the thin film and observe that similar factors and considerations (e.g., interchain order, crystallite size, and hydrophilicity) used for designing high-performance semiconductors impact the degradation of imine-based polymer semiconductors. We found that terpolymerization serves as an attractive approach for achieving degradable semiconductors with both good charge transport and tuned degradation properties. This study provides crucial principles for the molecular design of degradable semiconducting polymers, and we anticipate that these findings will expedite progress toward transient electronics with controlled lifetimes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Capability Building Progression of an Insider Threat Mitigation Program at an International Research Reactor

The nuclear industry recognizes the difficulties involved in developing effective managerial and leadership skills in a highly technical and proficient workforce such as that found in nuclear facilities. Implementing an insider threat mitigation program (ITMP) within the nuclear industry is a complex and ongoing process that demands a comprehensive understanding of human behavior, an organization’s security culture, and rigorous regulatory requirements yet also accounts for facility characteristics, physical security, material flow, and activities involving nuclear material. Given the high-consequence nature of research reactor operations, even minor lapses can lead to safety, security, and reputational risks. An effective ITMP requires a defense-in-depth approach that incorporates behavioral analysis, robust vetting procedures, continuous monitoring, and cross-disciplinary coordination. It must also promote a culture of vigilance and accountability at all levels up to and including executive leadership but be flexible enough to adapt to evolving global threats and technological advances. Insider threat mitigation is not a one-time effort but rather a sustained commitment to excellence in safety and security. Establishing a culture in which personnel proactively report incidents and issues that could affect nuclear safety and security is vital to maintaining a safe and secure operational environment. This document was developed to guide senior management and research reactor organizations in creating comprehensive programs to effectively manage and mitigate insider threat behaviors and actions. It focuses on the key pillars of an effective ITMP, including the national legal framework, security culture, preventive and protective measures, cyber security, and performance evaluation. By using a systematic approach during implementation, facilities can foster environments conducive to insider threat detection and support long-term program sustainability. The document also provides strategies for improving communication across all levels of an organization, helping to eliminate barriers that hinder the development of robust ITMPs and enhance overall security culture. In today’s organizations, the concept of leveraging safety and security culture lessons to facilitate knowledge transfer is rapidly evolving to expedite insider threat management and security culture improvements. This document outlines the rationale for evaluating an ITMP based on national customs, culture, and stakeholders. The elements are all germane to reliability and trustworthiness and relate to security concerns that states may encounter. The document focuses not only on individual perceptions regarding security issues and capability building but also on team building and how to resolve concerns. The implementers of a facility’s ITMP may zero in on indicators of insider threats within their enterprise. This material will benefit organizations when it is applied using a systematic and structured approach as demonstrated throughout the document.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Middlesex South FUSRAP Site: Collaboration Towards Beneficial Reuse - 20338

The US Department of Energy (DOE) Office of Legacy Management (LM) manages DoE's post-closure responsibilities and ensures the future protection of human health and the environment with respect to sites that have no continuing DOE mission after undergoing remediation. LM's beneficial reuse program promotes the LM strategic goal to sustainably manage and optimize use of public lands. The beneficial reuse program aims to repurpose former contaminated sites to restore the environment, protect the public health, revitalize communities, and spur economic growth. Benefits from the reuse of a site may increase the local tax base, facilitate job growth, utilize existing infrastructure, and enhance or protect natural resources. Beneficial reuse also promotes protectiveness by ensuring activities are compatible with long-term maintenance and protection of public health and the environment, as well as by retaining good stewardship of natural resources. LM actively participates in promoting the economic development vision of the surrounding communities by collaborating with local communities to promote regional or municipal initiatives. The Borough of Middlesex, New Jersey, has prepared the Lincoln Boulevard Redevelopment Plan, which will reinvent a once-viable downtown and enhance the quality of life for the community. The Middlesex South, New Jersey, Formerly Utilized Sites Remedial Action Program (FUSRAP) site lies within the boundaries of this redevelopment area. In support of this development plan, the Borough is interested in acquiring the site for its Department of Public Works and to increase street access in the future commercial district to be built near adjacent properties. The opportunity to put the FUSRAP site into productive use aligns with LM's mission and strategic goals. In support of beneficial reuse activities, DOE can dispose of excess real property using several mechanisms. The two disposal options applicable to the Middlesex south site include (1) utilizing the US General Services Administration (GSA) or (2) using the Title 10 Code of Federal Regulations Section 770, 'Transfer of Real Property at Defense Nuclear Facilities for Economic Development' (known as a '770 transfer') process. Using the 770 transfer requires economic development as the primary driver, and a specific redevelopment proposal must be submitted. If there is no interest in specific economic development, the GSA option becomes the default mechanism for disposal of the property. In the case of the Middlesex south site, the 770 transfer process is being utilized and the Borough has submitted its economic development proposal to LM. Although LM is encouraged by the opportunity to put the site into productive use, the Middlesex south site is currently on the US Environmental Protection Agency (EPA) National Priorities List and remediation is being performed by the US Army Corp of Engineers (USACE) under the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). Consequently, any property transfer must meet the requirements under CERCLA Section 120(h), 'Property Transferred by Federal Agencies.' As the groundwater remedy will not be in place for several years, approval would be required from the US EPA administrator and the governor of the state of New Jersey to perform an early transfer of the site (before all response actions have been completed). The time frame for these approvals to occur under the normal process conflicts with the proposed redevelopment schedule; therefore, various options for expedited sale, transfer, or lease of portions of the site to the Borough were evaluated. The subsequent decision on a mutually beneficial path forward was the result of a collaborative effort between LM, USACE, the Borough and its redevelopment team, EPA, and the New Jersey Department of Environmental Protection. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗