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DOE OSTI2020

Filtration Performance Results: Sierra Peaks Material No. 4

Sandia National Laboratories (SNL) assessed the filtration performance of materials from Sierra Peaks to identify alternatives which may perform similarly to materials used in FDA-approved N95 respirators. This work is meant to characterize the aerosol performance of materials to give Sierra Peaks information for them to determine if they elect to submit masks made using these materials for follow-on N95 certification testing at an accredited facility. The R&D testbed used is a large-scale filtration system designed to test commercial filter boxes. System modifications were performed to simulate, where possible, parameters defined by the National Institute for Occupational Safety and Health (NIOSH) for certification of filter materials for N95 respirators (NIOSH 2019). The system is a pull-through design. Air enters through a Laminar Flow Element (LFE) and the volumetric flow is measured based on the pressure drop across the LFE. Pressure is measured via a Pressure Transducer (PT). The air then passes through a High Efficiency Particulate Air (HEPA) filter to purge the air of ambient airborne particulates. Test aerosol is injected into the flow shortly after and mixing is induced via a coarse mesh. The airflow is allowed to fully develop prior to arriving at the test section. The aerosol then passes through the test material mounted in a box in the test section. Pressure drop across the test article is measured and aerosol sampling probes measure the aerosol concentrations upstream and downstream of the sample. The air passes through a second HEPA filter prior to being exhausted to ambient by a blower. A Topas aerosol generator is used to produce the test aerosol from Sodium Chloride (NaC1) dissolved in deionized (DI) water. Generated aerosol passes through a heated mixing chamber and a desiccant dryer to produce nanosized solid-state particulates. A dilution loop allows for the aerosol concentration to be regulated. The aerosol sampling probes upstream and downstream of the test section are aligned with the flow path. These are ducted directly to the aerosol sizing and counting instruments. A Laser Aerosol Spectrometer (LAS) was used for data collection in the original configuration of the system and was also used for initial testing in this project. Because the lower measurement range for the LAS is 90 nanometers (nm), the LAS was switched out for a more complicated Scanning Mobility Particle Sizer (SMPS) spectrometer system. The SMPS is comprised of an Electrostatic Classifier (EC), Differential Mobility Analyzer (DMA), and a Condensation Particle Counter (CPC). This enabled data collection at 75 nm, the particle size called out in the NIOSH guidelines.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential ‘fit for the purpose’ use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. In conclusion, the analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

NGEE Arctic Authorship Guidelines

Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.

Iversen, Colleen [ORNL] (ORCID:0000000182933450)↗

DOE OSTI2021

Accelerated Assessment of Critical Infrastructure in Aiding Recovery Efforts During Natural and Human-made Disaster

Relief and recovery from disasters (both natural and human-made) require a coordinated approach across several federal and state government agencies. In order to achieve optimal resource allocation and deployment of first responders, accurate and timely assessment of the impact and extent of destruction are the cornerstones to any recovery effort. Ideally, this knowledge should be gathered and shared within the first 0-24 hours (termed as "Acute Phase" by the U.S. CDC guideline) for informed decision-making. But achieving this poses significant challenges for the data collection and data harmonization processes, particularly when voluminous data are being generated from diverse and distributed sources during the disaster responses. To this end, this work developed a scalable and efficient workflow to dynamically collect and harmonize crowd-sourced geographic multi-modal data, and then assess critical infrastructure (CI) damaged during disaster events. We demonstrate the application of our framework with two real-world experiences in addressing post-disaster recovery efforts - for the Bahamas (Natural - due to Hurricane Dorian, 2019) and Beirut (Human-made - due to explosion caused by the ammonium nitrate stored in a warehouse, 2020). We have illustrated that a coordinated effort is needed for planning as well as for execution to achieve informed decision making.

Thakur, Gautam Malviya↗

DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗

DOE OSTI2025

Physics-informed machine learning for building performance simulation-A review of a nascent field

Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing the performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on accurate building energy models, which are primarily physics-based and heavily dependent on detailed building information, expert knowledge, and case-by-case model calibrations, significantly limiting their scalability. With the development of sensing technology and the increased availability of data, there is growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have begun integrating physics priors into data-driven models, a methodology known as physics-informed machine learning (PIML). PIML is an emerging field where its definitions, methodologies, evaluation criteria, application scenarios, and future directions remain open. To bridge those gaps, this study systematically reviews the state-of-the-art PIML for BPS, offering a comprehensive definition of PIML and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance, and computational cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages, and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.

Jiang, Zixin↗

DOE OSTI2022

ORNL Contributions to the ENDF/B-VIII.1 Library [Slides]

For uranium and plutonium isotopes, evaluation work is converging to a stable configuration with an overall satisfactory benchmark performance (more information on the validation tests after CSEWG). Strontium evaluation was included in the repository library and successfully tested by processing codes relative to an alternative quantification of the external function. Cerium covariance matrices were updated to address review comments on the magnitude of the uncertainty. For future uncertainty quantification, theoretical observables should be properly decoupled from current measured guidelines. Tests to correctly include LP (from resonance parameters) fitted to measured energy-averaged data are in progress. Current ENDF files contains experimental LP data.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

Quantifying and Optimizing the Energy Benefits of Mass Timber Construction

The International Mass Timber Alliance (IMTA) is a global organization of industry leaders, engineers, scientists, and associations dedicated to advancing mass timber construction. Its mission is to generate and disseminate scientific data supporting the development of standardized construction and energy efficient practices that promote the adoption of mass timber worldwide. IMTA collaborated with Oak Ridge National Laboratory (ORNL) to leverage ORNL’s expertise in building envelope modeling and testing to evaluate how mass timber construction can reduce peak heating and cooling demand, lower overall energy use, and improve resilience during power outages. A previous study of 80 mass timber buildings in Finland found measured energy use up to 50% lower than predicted by simulation. This project aimed to validate and extend those findings for U.S. buildings through analytical modeling, laboratory testing, and full-scale building evaluations. The research focused on the thermal performance of low-embodied-energy wall assemblies, such as cross-laminated timber (CLT) panels and log walls, with particular attention to the effects of thermal inertia on indoor comfort and energy performance. While mass timber’s structural and fire-resistance properties are well documented, its whole-building thermal behavior has received limited attention. Field data, simulation results, and resilience testing from this study will inform future modeling practices, design guidelines, and construction practices by quantifying the unique thermal and demand-flexibility benefits of mass timber construction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Methods of Securing Chemical and Pharmaceutical Knowledge and Recommendations for International Institutions to Enhance Research Integrity

Here, this paper examines strategies for securing chemical and pharmaceutical expertise in a globalized research environment, focusing on safeguarding intellectual property and preventing the misuse of sensitive and potentially dual-use information. The product of collective efforts between Pacific Northwest National Laboratory, Carol Davila University of Medicine and Pharmacy, and New Bulgarian University, highlights the challenges and opportunities posed by cross-border research collaborations, particularly in the context of differing regulatory frameworks and research cultures. It explores current mechanisms to prevent data loss and unauthorized access to sensitive information while assessing the effectiveness of existing security measures, frameworks, and international export control regimes. The approach examines the differing methodologies for promoting transparency, trust-building, and mutual accountability in joint research projects to cultivate secure data-sharing practices and intellectual property. It provides recommendations for international institutions to implement security guidelines in framing research priorities, encourages continual training and education programs, and the integration of processes for monitoring research compliance. This partnership aims to advance scientific innovation while maintaining global stability, ensuring compliance with international norms, and safeguarding valuable intellectual property as measures in chemical and pharmaceutical research security practices continue to expand due to international collaboration and knowledge exchange.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The Marine and Hydrokinetic ToolKit for Data Quality Control and Analysis: Preprint

The ability to handle data is critical at all stages of marine energy (ME) development. The marine hydrokinetic toolkit (MHKiT) is an open-source marine energy software, which includes modules for ingesting, applying quality control, processing, visualizing, and managing data. MHKiT-Python and MHKiT-MATLAB provide robust and verified functions that are needed by the ME community to standardize data processing. Calculations and visualizations adhere to International Electrotechnical Commission (IEC) technical specifications and other guidelines. A resource assessment of NDBC buoy 46050 near PACWAVE is performed using MHKiT and discusses comparisons to the resource assessment provided performed by Dunkel et al.

marine energy↗

DOE OSTI2021

The Marine and Hydrokinetic Toolkit (Mhkit) for Data Quality Control and Analysis

marine energy↗

DOE OSTI2020

Assessment of EPRI’s Tan Delta Approach to Manage Cables in Submerged Environments: Statistical Review of EPRI Data

Research conducted by the Electric Power Research Institute (EPRI) and other research institutions have concluded that water-trees are one of the leading degradation mechanisms that contribute to the loss of dielectric insulation strength in medium-voltage cable insulating materials in wet or submerged environments. The electrochemical reactions are caused by the combined effect of water presence and relatively high electrical stress. Records of cable failures provided by the licensees in response to Generic Letter (GL) 2007-01 (NRC 2007) have called into question the reliability of medium voltage cables in wetted or submerged environments. EPRI’s dissipation factor or Tan Delta testing guidelines and acceptance criteria have been adopted by most nuclear power plant operators as the primary tool for condition monitoring of medium voltage cables in wet or submerged environments. EPRI has been collecting member data since late 2009 to analyze and provide feedback to members, validate the EPRI-developed acceptance criteria guidelines, support analysis of test results, recommend appropriate actions for the "action required" category, and gather candidate cables for EPRI-sponsored forensic research on causes for insulation degradation. EPRI has collected data from 37 nuclear sites, which represent 44 operating units. The test results have been organized by insulation type, such as cross-linked polyethylene (XLPE); butyl rubber; black, pink, and brown ethylene-propylene rubber (EPR); and compact insulation (black and pink EPR)1. The data have been analyzed, and follow-up information was obtained from members for “action required” test results. EPRI has also performed correlations between Tan Delta tests and the information gathered under the EPRI forensic research on medium-voltage cables. In addition, EPRI has developed guidance by cable insulation type on how to systematically analyze Tan Delta test results. The analysis described here reviewed the two primary EPRI reports (EPRI 3002000557 and EPRI 3002005321) as well as two precedent EPRI reports (EPRI 1028262 and EPRI 1021070) that were cited in the primary reports. The principal results and conclusions from the project analyses are provided.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

RC-SFA Data Management Templates and Guidance for Standardized, Reusable AI-Ready Data Packages

This data package provides templates and supporting documentation developed by the River Corridor Science Focus Area (RC-SFA; https://www.pnnl.gov/projects/river-corridor) to communicate its approach to managing and publishing AI-ready data. The package is intended to help data users and data producers understand the structures, metadata practices, and quality-control approaches that support consistent, reusable, and machine-actionable data products across RC-SFA studies. Rather than focusing on a single experimental dataset, this package documents the data management framework used to make RC-SFA data easier to find, ingest, navigate, and interpret. The materials in this package reflect RC-SFA practices for standardized data package organization, including the use of a human- and machine-readable README, file-level metadata, data dictionaries, descriptive file naming, method identifiers, and automated and review-based quality assurance procedures. Together, these components illustrate how RC-SFA extends FAIR data principles toward AI-readiness by prioritizing deep metadata, consistency across data packages, and support for informed downstream reuse by both humans and computational tools. This dataset is comprised of (1) readme; (2) presentation slides with an overview of RC-SFA approach and guidance; (3) document of RC-SFA best practices; (4) data dictionary (dd); (5) file level metadata (flmd); and a subfolder containing templates for dd and flmd. All files are .csv and .pdf. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About.

AI-readiness↗

DOE OSTI2023

NGEE Arctic Rainfall Simulator Validation Data from Los Alamos National Laboratory, New Mexico, Summer 2022

Experiments evaluating the uniformity and intensity of rainfall produced by the NGEE Arctic Rainfall Simulator (NARS) were conducted at Los Alamos National Laboratory, New Mexico, over summer 2022. Petri dishes were placed in a grid within the NARS plot. Simulated rainfall was collected in each petri dish and the intensity and uniformity of the simulator was subsequently calculated. This data package contains two .csv files, one that summarizes the rainfall intensity and uniformity for each experiment, the other that contains individual petri dish water volume and intensity for each plot location and experiment. The Python scripts to control NARS are also included. The NGEE Arctic Rainfall Simulator (NARS) is a variable intensity rainfall simulator (RFS) with a frame design based on the Humphry et al. (2002) RFS and a water delivery system based on the Walnut Gulch (Paige et al., 2004) RFS. The NARS uses an aluminum frame that is fully deconstructable for transportation to field locations and a water system that enables variable rain intensity. Rain intensity control and data collection are automated using a Raspberry Pi microcomputer. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Corrosion Testing of Refractory inContact with Molten Glasses Designed for Waste Vitrification - VSL Touchpoint Matrix Glasses

It is known that the predictive life of the refractory ceramic liner of nuclear waste glass melters is conservative, as demonstrated by performance of these materials such as in the Defense Waste Processing Facility (DWPF). The motivation for this task is to maximize the useful life of the melters that will be operated at the Waste Treatment and Immobilization Plant (WTP), which will in turn minimize procurement and disposal costs and melter outage times, as well as to identify maximum loadings in the waste glass of those species that corrode melter components. This task was initiated jointly with Pacific Northwest National Laboratory (PNNL) with the objective to develop a methodology and model to enable more accurate prediction of refractory service life under prototypic conditions from laboratory-scale material corrosion tests. Refractory corrosion is generally reported as physical material loss, measured in units of distance (e.g., inch) or as physical material loss rate, measured in units of distance per time (e.g., inch/day). In post-operational melters, the refractory corrosion is measured directly, sometimes reported as corrosion depth. Crucible tests are used in the laboratory to accelerate the refractory corrosion to facilitate a meaningful measurement in a commensurate amount of time. Crucible tests are particularly useful in understanding refractory corrosion across a large glass composition space, where operational testing would be prohibitive. Some of the critical parameters that are known to influence refractory corrosion by molten glass in a crucible test are temperature, system redox, molten salt phases, glass chemistry, and test duration. The majority of data collected for Monofrax® K-3 (hereafter referred to as K-3) corrosion is from crucible tests, but a small amount comes directly from scaled and production melters. Crucible test data has been collected under varying conditions, whereas data collected from operational melters is relatively fewer and represents conditions specific to the melter campaign. The result is that the published data can be grouped and analyzed in multiple ways, not all of which are readily comparable. The Standard Test Method for Isothermal Corrosion Resistance of Refractories to Molten Glass (ASTM C621) outlines the general guidelines used across industry. That method describes a sealed, static test in which the surface area of the refractory coupon and the volume of glass are fixed. A significant portion of the crucible data pertaining to nuclear waste glasses has been collected in a modified configuration; the most notable differences being the surface area of the refractory coupon to volume of the glass and use of a method for bubbling the melt. To our knowledge, the influence of those parameters on the refractory corrosion has not been quantified. In this work, it was determined that static tests and bubbled tests would be performed. Savannah River National Laboratory (SRNL) was tasked with setting up and performing static testing while PNNL was tasked with setting up and performing bubbled testing. Initial activities were performed to establish laboratory methods that reproduce data comparable to existing data sets of K-3 refractory corrosion by low activity waste (LAW) and high-level waste (HLW) glass compositions. Later activities were focused on refining the test parameters to establish a standard test practice to be used between Laboratories and collecting additional data to be used in the enhanced waste glass model development. This document serves primarily to convey the refractory loss measurement results from corrosion testing of K-3 refractory with waste glass compositions developed for use in the WTP melters. The data will be used in the enhanced property/composition models being developed for waste glass vitrification and melter operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗

DOE OSTI2022

An expert-driven literature review of “negative” chemicals for developmental neurotoxicity (DNT) in vitro assay evaluation

To date, approximately 200 chemicals have been tested in US Environmental Protection Agency (EPA) or Organization for Economic Co-operation and Development (OECD) developmental neurotoxicity (DNT) guideline studies, leaving thousands of chemicals without traditional animal information on DNT hazard potential. To address this data gap, a battery of in vitro DNT new approach methodologies (NAMs) has been proposed. Evaluation of the performance of this battery will increase the confidence in its use to determine DNT chemical hazards. One approach to evaluate DNT NAM performance is to use a set of chemicals to evaluate sensitivity and specificity. Since a list of chemicals with potential evidence of in vivo DNT has been established, this study aims to develop a curated list of “negative” chemicals for inclusion in a “DNT NAM evaluation set”. A workflow, including a literature search followed by an expert-driven literature review, was used to systematically screen 39 chemicals for lack of DNT effect. Expert panel members evaluated the scientific robustness of relevant studies to inform chemical categorizations. Following review, the panel discussed each chemical and made categorical determinations of “Favorable”, “Not Favorable”, or “Indeterminate” reflecting acceptance, lack of suitability, or uncertainty given specific limitations and considerations, respectively. Further, the panel determined that 10, 22, and 7 chemicals met the criteria for “Favorable”, “Not Favorable”, and “Indeterminate”, for use as negatives in a DNT NAM evaluation set. Ultimately, this approach not only supports DNT NAM performance evaluation but also highlights challenges in identifying large numbers of negative DNT chemicals.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Measurement and Reporting Guidelines for Solar Mirror Aging Tests Using Xenon Arc Lamp Exposure (XALE)

This technical report provides a guideline for accelerated aging tests on solar reflectors. Solar reflectors must have a high durability, with goal lifespans reaching 30 years. To test and develop state-of-the-art solar reflectors, relevant durability tests must be conducted. Existing guidelines for materials testing include isothermal testing, humidity testing, corrosion, etc., but many tests do not specify to include solar irradiance or address its relevance to solar reflector durability. The guideline focuses on test design and reporting guidelines for using Xenon Arc Lamp Exposure (XALE). The XALE environmental chambers can subject samples to a combination of relevant environmental factors including concentrated solar irradiance, heat, humidity, and weathering cycles. The guideline is meant to provide information on developing relevant test conditions, exposure times, reflectance characterization procedures, and data reporting.

14 SOLAR ENERGY↗