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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

ADAM Program Execution Plan LANL Inputs (FY2022)

The National Security Research Center (NSRC) is Los Alamos National Laboratory’s classified library. There are two groups associated with the NSRC, both of who work for LANL’s Weapons Research Services (WRS) division (WRS-SIS and WRS-WMT). These groups are funded in part by the NNSA Archives Program. The NSRC’s collections include tens of millions of documents from the Manhattan Project era through today. It is staffed with an expert, highly trained staff of librarians, archivists, digitizers, historians, and communications specialists. The NSRC traces its lineage to the wartime Technical Library created by J. Robert Oppenheimer during in 1943. Today, it supports a broad range of researchers within the LANL Weapons Program and beyond. The NSRC also has customers across other National Nuclear Security Administration labs and sites, and partners in the Department of Defense. This report highlights LANL accomplishments through NSRC.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2021

Performance Results for Sensor Assignment Problem as Solved on a Multi-Node Cluster

An earlier report described a procedure for optimal sensor set selection and its implementation on a computational cluster. This new and innovative capability was developed to facilitate a reduction in operations staffing levels to improve plant economics. By automating surveillance and maintenance tasks through early detection of degrading sensors and equipment, staff can be more efficiently deployed. The method uses automated reasoning and domain knowledge in the form of the conservation equations to infer from plant measurements the state of equipment health. Inclusion of domain knowledge addresses the problem that exists with pure data-driven methods that there are no rigorous guidelines for determining what constitutes an adequate sensor set. Formalizing the procedure for sensor set selection as we have done results in a more reliable and explainable diagnosis of plant equipment health. Importantly, from the standpoint of the plant owner, personnel are provided with an early and explicit diagnosis of an equipment problem. That in principle automates the process and eliminates having to send personnel into the plant to find the cause as typically occurs when a data-driven method detects an anomaly. In this report we describe first results obtained using a computational cluster to solve the sensor set selection problem as framed above. The case described addresses the problem of equipment health monitoring in the high-pressure (HP) feedwater system of a pressurized light water reactor as seen through the eyes of our collaborating utility partner. Maintenance of this system can amount to millions of dollars per year if equipment health issues go undiagnosed and lead to loss of function. On examining the potential that is inherent in the installed sensor set for diagnosing equipment health degradation, it was found that greater fault resolution capability can be achieved using a sensor set that is 20 percent fewer in number. The take-away is that compared to the installed sensor set there exists a more strategic assignment of sensors that will furnish better health monitoring capability and with fewer sensors. Where the problem defies solution by manual inspection, as is the case here, one can be found by an algorithm. The solution was obtained in four hours using 30 computational cores. The HP feedwater problem as posed above illustrates the added value of approaching the sensor selection problem as one amenable to algorithmic solution. This problem is of interest to advanced reactor designers and to utilities that are setting up remote monitoring and diagnostic centers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Using the CYBER-Champ Model to Determine Cyber Competencies and Role Alignment

Cybersecurity roles, tasks, and skills are seen by many organizations as nonessential, abstract, or complicated. Although standards are in place, such as the National Institute of Standards and Technology (NIST) 800-181 document titled “Workforce Framework for Cybersecurity (NICE Framework)” available since September 2012, companies are still showing security vulnerabilities in their cyber networks (Hatzes, 2020). Barriers towards creating a cyber-ready workforce are not always due to a lack of resources, but often caused by organizational structure. The need for cyber-cognizant job postings, improved communication between Operational Technology (OT) and Information Technology (IT) employees, increases in staffing and funding for cyber teams, and better communication of standards and training can all contribute to increasing an organization’s cyber resilience. Cybersecurity Competency Health and Maturity Progression model (CYBER-CHAMP) is a model aimed at evaluating an organization’s structure and individual employee’s cyber knowledge and helps develop a plan to reach competency. This model was used to engage with organizations to promote proper cyber protocols and policies. The aim of this paper is to answer if it is possible for an organization to build a cyber-ready workforce by providing education and training options for current employees and prepare the future workforce to be ready on day one of employment. Both open source and firsthand interviews with organizations were used to conduct the research contained in this document. Further research may include additional development to the CYBER-CHAMP model and its delivery platform.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

United States Nuclear Data Program Annual Report for FY2021

The US Nuclear Data Program (USNDP) Annual Report for Fiscal Year 2021 (FY21) summarizes the work of USNDP for the period of October 1, 2020 through September 30, 2021, with respect to the Work Plan for FY21 that was prepared in 2019. The Work Plan and Final Report for USNDP are prepared for the DOE Office of Science, Office of Nuclear Physics. The support for the nuclear data activity from sources outside the nuclear data program is described in the staffing table and in Appendix A. This leverage amounts to about 3.24 FTE scientific, to be compared with 25.095 FTEs at USNDP units funded by the DOE Office of Science, Office of Nuclear Physics. Since it is often difficult to separate accomplishments funded by various sources, some of the work reported in the present report was accomplished with nuclear data program support leveraged by other funding.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

HR Recruitment banners Round 2 [Poster]

Series of banners designed for HR Office of Diversity and Strategic Staffing at LANL to be printed and used at recruitment events.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Status Update on the Development of Transducers and Bonding Techniques for Enabling Acoustic Measurements of Damage in Microreactor Components

This report provides an overview of potential sensors and sensor-bonding techniques to enable the online acoustic interrogation of microreactor components and enhance structural health monitoring capabilities. The report focuses primarily on optical fiber–based acoustic sensors and describes initial experimental progress toward the deployment of these sensors for microreactor applications. The general approach is to monitor the resonant frequencies of microreactor components and search for evidence of structural defects that could indicate imminent failure. If properly identified, then the components could be repaired during the next reactor outage to prevent costly unplanned shutdowns. The ability to monitor the structural health of components could also reduce the need for time-consuming visual inspections and reduce staffing to improve microreactor economic viability. Increased sensor density is also one of the first steps to moving toward eventual semiautonomous operation. The expected microreactor conditions in which acoustic sensors must survive are characterized, including temperatures, neutron fluences, thermomechanical strains, and vibrational frequencies. Optical fiber–based acoustic sensors are identified as an attractive candidate for acoustic monitoring because of their high accuracy, immunity to electromagnetic interference, and resiliency in high-temperature, high-radiation environments. Optical fiber–based intrinsic sensors, such as type-II fiber-Bragg gratings and Fabry-Pérot Cavities (FPCs), are particularly attractive for a microreactor environment because of their high temperature stability, and FPCs also enable higher frequency interrogation with a lower sensitivity to radiation-induced drift. This report describes multiple interrogation systems, but the best interrogation system for a given situation will depend on the specific microreactor application, including the desired acoustic vibrational amplitudes, modes, and resonant frequencies. Initial experiments included fabricating three FPCs, tack-welding these FPCs to stainless-steel pipes or rods and performing room-temperature acoustic sensing tests to capture the vibrational frequency content. Peaks were identified in the measured frequency spectra and compared with the theoretical fundamental frequencies obtained from Euler-Bernoulli beam theory. Two of the three FPCs measured vibrational frequencies that generally matched those obtained from theory. Future work will include similar testing on pipes or other microreactor components with intentional flaws to evaluate the ability to determine changes in resonant frequencies. Finally, these tests will be repeated at high temperatures, potentially with an applied thermomechanical stress, to include environmental conditions similar to those for a microreactor application.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Generating and Sustaining Microplasma with Microwaves (Final Report)

Plasma can be generated in spaces ranging in size from astronomical down to the microscopic. The physics that describes the gas breakdown is different at each size scale. The dominant physical phenomenon also depends on the type of electromagnetic field that drives the breakdown – static, AC, RF, microwave, visible. This project, staffed by undergraduate researchers and their mentor, focused on plasma generated in the size range of 10 to 500 micrometers using microwave energy. This project was motivated by the prior observation that plasma can only be generated in microgaps when the gas is above a certain gap-dependent pressure, here called the transition pressure. A microwave-driven microgap was designed and constructed in this project and then used to measure the electric field required to initiate plasma over a wide range of pressures from 0.1 Torr to 760 Torr (1 atmosphere).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

ARM FY2023 Radar Plan

The fundamental objective of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s radar capability is to provide high-quality radar observations to the scientific user community with an overarching goal of improving the treatment of clouds and precipitation in climate models. ARM has a complement of 32 radars, including scanning and zenith-pointing varieties (Table 1). The effort to maintain and operate these radars is significant and requires a large, distributed staffing commitment. It has not been feasible to ensure the continuous, high-level operations expected by the ARM user community for all radars at all times, so priorities are established to routinely manage select radar operations every fiscal year. These priorities for FY23 are listed herein.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

How We Do Our Work Is as Important as What We Do: A History of Los Alamos National Laboratory Operations

Los Alamos National Laboratory is known for its world-renowned research and Nobel Prize–winning scientists. Few know about the support required to keep the Laboratory running. This book takes you behind the scenes for a closer look at Laboratory operations and the individuals, programs, and facilities of yesteryear and today. From infrastructure, security, staffing, and many other facets, Laboratory operations is what makes the science happen.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2022

LANL ASC FY23 L2 Milestone Description and Completion Criteria [Slides]

In early 2021, the ASC Program decided that Ristra should provide a production code (Moya) for LANL’s mission in the Low Energy-Density Physics application space. At the same time, Ristra should design an environment for rapid development of new codes that can answer new questions on new hardware. We started FY22 by expanding upon our R&D efforts of high-order DG methods for hydrodynamics; Technical & staffing challenges caused us to abandon this for a traditional FV SGH approach, but leveraging modern techniques. Significant changes in FleCSI (v1.4→2.x) have us writing code anew, in a co-design effort with the FleCSI and FleCSI specialization projects.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Development and Demonstration of a Fuel-Efficient, Class 8 Tractor & Trailer Engine System (SuperTruck II)

Navistar presents the SuperTruck II (ST II) Final Report to the Unites States Department of Energy (US DOE), which covers the five Budget Periods (BPs) from 10-1-2016 through 6-30-2022. For ST II, Navistar built on the achievements of the SuperTruck I (ST I) Program as a catalyst to continue critical research, design and development, testing, and operations to reach the ambitious goals of the ST II project. This approach allowed Navistar to continue contributing to the essential needs of our nation for safe, efficient, and cost-effective delivery of goods and services, as we reduced negative environmental effects and improved operational productivity. This document contains information specified in DOE F 4600.2, Final Scientific/Technical Report DOE F 241.3, B. SCIENTIFIC/TECHNICAL REPORTS, explaining how we met and exceeded program requirements. Throughout this Final Report, Navistar extracted information from documents prepared during the project that represent our management, design and development, building, and testing efforts to meet and exceed SuperTruck II project goals. Navistar followed Plan requirements to achieve / exceed Project Objectives: a) >100% improvement in vehicle freight efficiency (FE) (on ton-MPG basis) relative to 2009 baseline with stretch goal of 140% improvement [actual: 170%); b) >55% engine brake thermal efficiency (BTE) demonstrated in operational engine at a 65-mph cruise point on a dynamometer – ≥31% increase from 2009 baseline [actual: 55.20% of combined BTE) ; and c) development and implementation of commercially cost effective technologies (in terms of a simple payback). Technology selection / development path focused on developing technologies applicable for production within 3-year approach, while ensuring technology readiness and cost of ownership for end users. The Program was organized into five budget periods: Requirements / Technology Assessment and Initial Hardware Testing; Technology Development and Concept Readiness Demonstration; Technology Finalization and Validation Tractor / Trailer Fabrication, Integration and Commissioning Demonstration; and Fuel Economy (FE) and Brake Thermal Efficiency (BTE) and Program Completion. Leadership was provided by DOE, with tasks performed by laboratories (Argonne National Laboratory, Lawrence Livermore National Laboratory); partners at Bosch, TPI, Dana, and J.B. Hunt; , and support from University of Michigan and Clemson University. Navistar lead this team with Principal Investigator / Contracting Officer; Project Manager (PM); Vehicle, Engine, and Aftertreatment Engineers; Finance Manager, Technical Program Leads, and Legal/IP; and other key personnel. Work also included personnel in risk management; funding / budget / finance. Work involved analysis, development, testing, and down selection of individual/system engine, aftertreatment, and vehicle technologies, with integration of selected technologies into a prototype vehicle for demonstration of fuel-efficiency gain. Work also included component/integrated system level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. As ST II progressed, Navistar performed computer-based modeling / simulations of technologies focused on the primary operational areas: Engine, Aftertreatment, and Vehicle. During the ST II Program, the COVID Virus outbreak unexpectedly challenged by the effects of, which affected staffing, scheduling, design, supplies, availability of materials, production procedures, and testing. The DOE responded by extending the program by three quarters to ensure that project tasks were completed for this vital project. Focus continued on analyzing, developing, testing, and down selecting individual-/system-level engine and vehicle technologies for integration of the final selected technologies into a prototype vehicle that would demonstrate fuel-efficiency gains made possible through these technologies. This included component/integrated system-level development of truck and trailer aerodynamics, base engine efficiency, advanced aftertreatment, combustion efficiency, waste heat recovery, solar power, distributed and intelligent vehicle power, hybrid powertrain, reduced rolling resistance, weight reduction, idle reduction, and driver feedback. Throughout the program, function, reliability, and performance at all levels were ensured through testing. Proof of this approach was demonstrated in multiple, on-road demonstrations: Scenario A (Flatland) Fuel Economy, Scenario B (Hilly) Fuel Economy, and City Cycle Tests. Other benefits derived from ST II included new/improved products, publications, patents, and next-step capabilities related to electric/hydrogen vehicles and autonomous driving.

Zukouski, Russ↗
DOE OSTI2023

High Explosives and Energetics Asset Analysis Tool

The High Explosives and Energetics (HE&E) Asset Analysis Tool is a database and reference for LANL facilities, equipment, staffing and financial information pertinent to the HE&E program. The tool is used for the communication of the current state and investment planning for the program assets. Additional analyses can offer more insights into the dependencies and impacts of the assets within the HE&E portfolio. This tool is developed under collaboration with the HE&E Program Office.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2023

Nuclear Criticality Experiments Research Center Futures: A Report of a Workshop held September 6-9, 2022, Los Alamos, NM, November, 2022

Experiments and training with critical assemblies and fissionable material (at or near the critical state) that explore reactivity phenomena are central to a number of national security challenges. From fission energy to nuclear weapons to a broad suite of scientific challenges, it is clear that additional capacity and capability are needed. The National Criticality Experiments Research Center (NCERC) marked 10 years of operations in 2021. This anniversary was an opportune time to celebrate our successes and progress, and to evaluate the remaining and emergent challenges. Against this backdrop, a workshop of approximately 140 national and international leaders in nuclear research was convened in Los Alamos, New Mexico to explore “NCERC Futures.” Therefore, the present workshop focused specifically on needed capabilities and tools to meet the research challenges in eight topical mission areas served by NCERC. As each Topical Group summarized their discussions in the out brief, it was recognized that key challenges could be met through enabling infrastructure investments and new critical assemblies. Enabling infrastructure includes staffing, additional space/buildings, developing an agile bounding safety basis, the ability to keep pace with technological advances in detectors and data acquisition systems (allowing use of those with Bluetooth™ and similar technologies), an expanded set of materials options (especially plutonium), a “Plug and Play” design and implementation mindset, and a facility that enabled free-field measurements. The new critical assemblies that were identified as having the most impact were a bare plutonium (Pu) Critical Assembly, a Horizontal Split Table (HST), a Super Comet, and a Uranium Solution Burst Assembly. NECRC is a unique, one-of-a-kind facility in the United States. If all the improvements were to be made, NCERC would enable the United States and its partners to address many important research questions related to criticality. These include but are not limited to: (1) Covering the entire neutron energy spectrum for both highly enriched uranium (HEU) and Pu in configurations for virtually all conceivable applications; (2) Performing multi-physics solution experiments and irradiations with a Uranium Solution Burst Assembly, which more closely resembles actual criticality accidents; (3) Conducting free-field experiments to make basic fission physics measurements and much cleaner benchmarks with various experimental observables; and (4) Providing more training classes and more experiments annually at greater cost efficiency enabled by additional buildings and machines and an agile, bounding, risk-balanced Safety Basis. In the end, workshop attendees enthusiastically concluded that NCERC Futures are bright and the workshop helped to identify a roadmap of capability gaps that need to be addressed. This report documents the results of those efforts.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

An Updated Review of the Solar PV Installation Workforce Literature

In order to develop a well-trained, equitable, and inclusive workforce with high quality jobs, the DOE Solar Energy Technologies Office (SETO) identified a need for analytical context around different PV project characteristics and labor aspects, and how they might impact workforce wellbeing and PV industry growth. To determine the most valuable novel analytical contributions, this work reviews existing literature on solar workforce topics to identify what areas have been studied previously and where gaps remain. Focus areas included the following topics: (1) metrics for solar workforce, deployment, costs, and associated studies capturing aspects such as demographics and regional distribution, (2) solar workforce wellbeing, including employee contracting mechanisms, compensation, occupational safety and health, and community impacts, (3) national and state policies most relevant to the U.S. solar workforce, and (4) ongoing efforts to expand solar workforce participation, including local staffing dynamics, challenges, and relevant strategies.

14 SOLAR ENERGY↗
DOE OSTI2023

LANL/UTEP Metallurgical Science Center of Excellence Planning

LANL seeks research assistance and skills development in three distinct subject areas over the next three years. Individualized scope and detail implementation schedules will be the first deliverable for these subject areas and will represent a joint determination between LANL SMEs and UTEP faculty based on LANL priority (schedule) and UTEP staffing (faculty and student- graduate and undergraduate) and capability of immediate availability per schedule. LANL will endeavor to make summer internships available to UTEP students within these subject areas: Chemical Sciences, Additive Manufacturing, and Metallurgy: casting and solidification, characterization, and metal purification. Unarticulated but generalized scoping tasks follow within each topical area to indicate that students and technicians familiar with these skill sets are of value and interest in LANL workforce pipeline and the tasks that seek to develop these skill sets are intentional on LANL's part. Consequently, LANL will sponsor 2-3 senior (capstone) projects within these subject areas per year over the three year time period within these task areas and within separations technologies, wet chemical analysis, and analytical characterization and quantification as required by all tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML)

Although the interest in the use of artificial intelligence (AI) and machine learning (ML) in nuclear energy is increasing rapidly, at present their implementation is limited. This rapid increase in interest is not surprising considering that implementing AI and ML technology would allow for continuous monitoring, facilitate the implementation of predictive maintenance with optimized staffing plans, enable automation and autonomy opportunities that could drastically reduce fixed operation and maintenance costs, and provide training for operations and maintenance. Other industries are using AI for construction, and in the nuclear arena AI could provide great benefit in decommissioning activities. The ability of AI and ML to operate in real time vastly increases their potential impact. Before AI can be used in design, operations, or as a regulatory tool, the specifics on the regulations applicable to the use of AI for nuclear power applications need to be established. The difficulty is that the specific use cases will dictate the applicability of regulations. For example, even within the application domain associated with operations, the regulations might vary if the AI is used to create a virtual reference for plant operations or is used for training, optimization of maintenance intervals, prioritization of maintenance activities, etc. Different still is if the AI is to be used for design or setting technical specifications, which will introduce additional requirements. US Nuclear Regulatory Commission (NRC) licensing reviews are based on an applicant’s design meeting its performance assessment based on (1) safety goals and objectives, (2) deterministic and/or probabilistic analysis of accident scenarios, and (3) quantitative assessment of design alternatives against the safety goals and objectives using accepted engineering tools, methodologies, and performance criteria. The current regulatory framework does not explicitly address AI or autonomous control. However, as implementing AI technology will require the use of a digital platform, it must meet the requirements of an instrumentation and control (I&C) system. The regulatory requirements for AI, which will be incorporated into the I&C system, will be very dependent on how it is used (i.e., its functionality, safety classification, etc.). The licensing process is primarily risk-based with the identification of components and systems as nonsafety, important to safety, or safety related. A risk-informed approach allows further gradation of components and systems based on risk metrics such as core damage frequency or large early release fractions. Thus, the use cases and the risk categorization of impacted systems and components will determine the regulatory requirements. Regardless of how AI is used it presents new opportunities for risk-informing operating, maintenance, and regulatory decisions. Trustworthiness, transparency, and the ability to validate and verify the results will be paramount in showing that the systems and plant still meet their performance requirements. This report describes the results of research to identify regulatory implications of AI technologies and their uses. Specifically, this report reviews current regulatory guidance relevant to the application of AI for design (including design changes or new designs including advanced reactors), construction, operations, training, maintenance, research, testing, and as a regulatory tool. AI can be automated at different levels from purely informative purposes to autonomous controls. The focus of this review included determination of constraints on the application of AI technology, identification of any regulatory gaps or uncertainties, and clarification of anticipated technical basis information likely to be important for regulatory acceptance of these technologies. Currently, any use of AI at nuclear power plants is focused on nonsafety-related applications. The NRC and other regulatory bodies are evaluating providing guidance to address gaps rather than create new regulations to address the use of AI and ML. This approach seems to be the best to encourage AI development without adding regulatory uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Powder Characterization Inter-Comparison

We performed qualitative and quantitative image analysis on SEM images for 5 uranium samples. Qualitative assessment was completed using the lexicon of Tamasi et al. 2017 on a subset of images from each sample to provide an overall morphological profile of each material. Quantitative analysis of the particles was done using the Morphological Analysis for Materials Attribution, or MAMA, software. We performed particle analysis primarily on samples labeled U Mo, U Si, and UO 2 . Samples labeled ADU and DU Ox were not prioritized for quantitative analysis due to staffing and time it took to segment these images. Two lab analysts worked on this effort, one focusing on the qualitative assessment and the other focusing on the quantitative assessment.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗