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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Traveler: Navigating Task Parallel Traces for Performance Analysis

Understanding the behavior of software in execution is a key step in identifying and fixing performance issues. This is especially important in high performance computing contexts where even minor performance tweaks can translate into large savings in terms of computational resource use. To aid performance analysis, developers may collect an execution trace —a chronological log of program activity during execution. As traces represent the full history, developers can discover a wide array of possibly previously unknown performance issues, making them an important artifact for exploratory performance analysis. However, interactive trace visualization is difficult due to issues of data size and complexity of meaning. Traces represent nanosecond-level events across many parallel processes, meaning the collected data is often large and difficult to explore. The rise of asynchronous task parallel programming paradigms complicates the relation between events and their probable cause. Here, to address these challenges, we conduct a continuing design study in collaboration with high performance computing researchers. We develop diverse and hierarchical ways to navigate and represent execution trace data in support of their trace analysis tasks. Through an iterative design process, we developed Traveler , an integrated visualization platform for task parallel traces. Traveler provides multiple linked interfaces to help navigate trace data from multiple contexts. We evaluate the utility of Traveler through feedback from users and a case study, finding that integrating multiple modes of navigation in our design supported performance analysis tasks and led to the discovery of previously unknown behavior in a distributed array library.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Herbaceous Feedstock 2021 State of Technology Report

The U.S. Department of Energy (DOE) promotes the production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the State of Technology (SOT). As part of its involvement with this mission, Idaho National Laboratory (INL) completes an annual SOT report for biomass feedstock logistics. This report summarizes supply system impacts of Bioenergy Technologies Office (BETO)-funded research and development efforts at INL and INL collaboration with external partners (e.g. Forest Concepts, Purdue University ) that lead to improvements in feedstock supply systems. These include improvements to and observed performance of innovative harvest and collection methods, storage technologies, transportation and handling approaches, and advanced preprocessing technologies. Biomass quality and variability, and the interface between feedstock quality and conversion performance are key drivers in addition to delivered feedstock cost. In this report, we estimate the benefits of R&D technology improvements to individual supply system unit operations and present the status of feedstock logistics technology development for converting herbaceous biomass into biofuels. These analyses are supported by experimental data where possible and help to align the SOT relative to the cost goals defined in the Multi-Year Plan.

09 BIOMASS FUELS↗
DOE OSTI2016

The Liquid Argon Software Toolkit (LArSoft): Goals, Status and Plan

LArSoft is a toolkit that provides a software infrastructure and algorithms for the simulation, reconstruction and analysis of events in Liquid Argon Time Projection Chambers (LArTPCs). It is used by the ArgoNeuT, LArIAT, MicroBooNE, DUNE (including 35ton prototype and ProtoDUNE) and SBND experiments. The LArSoft collaboration provides an environment for the development, use, and sharing of code across experiments. The ultimate goal is to develop fully automatic processes for reconstruction and analysis of LArTPC events. The toolkit is based on the art framework and has a well-defined architecture to interface to other packages, including to GEANT4 and GENIE simulation software and the Pandora software development kit for pattern recognition. It is designed to facilitate and support the evolution of algorithms including their transition to new computing platforms. The development of the toolkit is driven by the scientific stakeholders involved. The core infrastructure includes standard definitions of types and constants, means to input experiment geometries as well as meta and event- data in several formats, and relevant general utilities. Examples of algorithms experiments have contributed to date are: photon-propagation; particle identification; hit finding, track finding and fitting; electromagnetic shower identification and reconstruction. We report on the status of the toolkit and plans for future work.

Pordes, Rush↗
DOE OSTI2022

Printable Fiber Reinforced Cement Composites – Feasibility Study

Additive manufacturing is enabling the manufacturability of structures with previously unattainable complexity or functionality, and there is growing interest in additive manufacturing of “printed” concrete structures. The focus of this Phase 1 Technical Collaboration (TC) project was to evaluate feasibility of printing hybrid cement composite structures reinforced with textile carbon fibers (tCF). This project leverages other (non-IACMI) projects on cement formulation and printing process development, as well as on the production process for tCF. This project’s primary focus was to explore cement composite mix design with textile carbon fibers to be manufactured by MonteFibre (TC partner) and evaluate suitable fiber-matrix interface or sizing for cement composites working with Michelman (TC Partner). This project supports IACMI’s goal of reducing the cost and embodied energy of carbon fiber composites. Cost is one of the fundamental challenges to carbon fiber reinforced cement composites. Cement is an extremely inexpensive material (approximately $\$$0.05/lb). Adding 1 wt% of conventional carbon fiber to cement quadruples its cost. Therefore, the need to use low-cost carbon fiber and ensure that the additional cost of the carbon fiber has a greater cost benefit to the final product. This was the first preliminary evaluation to integrate tCF reinforcement in cement composites, and such potential tCF utilization should significantly reduce materials cost. Cement composite production is energy and emissions intensive, thus by strengthening it less material will be required. Hence, the embodied energy and production time of the resulting structures will be reduced. Additionally, integrating these new materials into additive processes can enable selective use of the material in high load or stress areas. It is noteworthy that past work in this field of fiber reinforced cement composites did not consider the optimization of fiber-matrix interface using suitable sizing. Carbon fiber reinforcement offers potential added benefits of thermal conductivity (which affects cure rate) and flow behavior that could provide opportunities for site specific utilization of carbon fiber on hybrid cement structures (e.g. use the fiber reinforcement on outer surfaces to enhance strength and modulus and then infiltrating the internal structures with conventional concrete). MonteFibre was the industry lead and planned on supplying the tCF for this project. However, during the short Phase-1 duration of this project, MonteFibre was unable to produce tCF for this project due to manufacturing plant being off-line throughout the course of the project. The project team decided to pursue an alternate option which involved demonstrating printable concrete with steel fibers by the ORNL lead, Dr. Brian Post. The University of Tennessee collaboration team focused on evaluating the suitable chemical sizing for carbon fibers working with Michelman and also developed methods for material characterization of cement-based composites to evaluate the material response for compression, shear, flexure, and tension. The two milestones for University of Tennessee, Knoxville were realized related to identification of one sizing suitable for carbon fiber reinforced cement composite and developing data associated with mechanical behavior of unreinforced (neat) and carbon fiber reinforced cement composites. ORNL could not complete the task of carbon fiber reinforced printed cement composites due to the reasons mentioned earlier, but was able to replace tCF with steel fibers to demonstrate the feasibility of printing with fiber reinforced cement composites. The Project team reviewed possible sizing chemistry available in collaboration with Michelman for use on carbon fiber reinforcement in cement composites and concrete applications. Our initial goal was to identify a sizing most promising for formulation with textile carbon fibers (tCF) to deliver excellent mechanical properties in composite material state. Since tCF was not available for this project as originally envisioned, the team continued this task to identify a suitable sizing for carbon fiber applications by applying such sizing to lower cost carbon fibers currently available commercially from Zoltek called Panex fibers. At a future time this can be optimized for textile carbon fibers from Montefibre. The bulk of previous work on carbon fiber reinforced cement has neglected the importance of fiber-matrix adhesion on mechanical properties of the cement composite and identifying this missing link was an important accomplishment for future research. Tensile behavior of fiber reinforced concrete is important to evaluate in order to realize the dream of concrete products that do not need reinforcing steel. Important sample preparation and testing procedures were addressed in this study and it was concluded that substantial improvements in tensile behavior, without compromising compressive strength, and improved ductility can result from the use of carbon fiber reinforcement.

36 MATERIALS SCIENCE↗
DOE OSTI2017

An approach to and web-based tool for infectious disease outbreak intervention analysis

Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Process Image Analysis using Big Data, Machine Learning, and Computer Vision

The development of algorithms for machine learning and data analysis for the 3013 MIS corrosion surveillance program is a collaborative effort by SRNL, USC and GT. For corrosion detection, LCM image data is extracted from large binary files, with software written to convert the data to physical attributes (i.e. height, color and grayscale values; all as functions of a location in a plane projection). The user interface for the software permits selective downloading of binary data and interrogation of attributes. User input thresholds are used to flag attributes of interest. Machine learning algorithms, developed for this application, are used to determine whether the features are the result of corrosion. To address the fundamental mechanisms of corrosion, machine learning algorithms are being developed to derive interatomic potential force-fields from ab-initio DFT calculations. The goal is to apply molecular modeling on a large enough scale to guide the design of resistant materials.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Surrogate Model Integration with MOOSE XFEM for Creep Crack Growth

Ferritic-martensitic steels are key structural materials for advanced reactors but experience time-dependent deformation and damage under prolonged high temperature and irradiation, leading to creep-driven crack initiation and growth. High-fidelity models—crystal plasticity with irradiation mechanisms, phase-field for microstructural evolution, and continuum-damage viscoplasticity—capture the underlying physics but are too computationally intensive for broad design-space exploration and uncertainty quantification. This milestone advances a scalable alternative by integrating a microstructure-sensitive surrogate creep model into the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework and extending it to fracture via the extended finite element method (XFEM). The surrogate model, developed with collaborators at Sandia and Los Alamos National Laboratories, maps relevant microstructural descriptors to the viscoplastic response of HT9. We embed this surrogate within a coupled deformation-damage workflow in MOOSE/XFEM to simulate creep-driven crack initiation and propagation. Implementation enhancements include updates to the material interface, a plastic correction phase involving microstructure evolution, and fracture criteria to ensure numerical robustness and compatibility with the surrogate structure. Demonstrations on canonical creep benchmarks spanning uniaxial and multiaxial states show that the surrogate reproduces key trends of high-fidelity models while substantially reducing computational cost. The resulting capability bridges physics fidelity and performance, providing a practical path to a predictive, microstructure-aware assessment of creep and fracture in reactor materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Systems Innovation: Modernization & Efficiencies for ESH&Q Reviews

Environmental compliance reviews at INL have traditionally been managed through fragmented systems, relying on multiple spreadsheets and manual processes. This inefficiency led to time-consuming status updates and redundant tasks, such as manually sending reminder emails and transferring data from Excel to the Environmental Review Process (ERP). Initial attempts to streamline these processes using Power Automate and Excel revealed significant limitations, necessitating a more comprehensive solution. To address these immediate inefficiencies, automated workflows were developed using Power Automate. These workflows were designed to send scheduled status update reminders and capture responses through standardized forms, with submitted data flowing directly into centralized Excel trackers. This automation reduced the administrative burden, improved data accuracy, and enabled faster, more consistent reporting. Specifically, email automation achieved a 65% efficiency gain, while data integration saw a 48% improvement, resulting in 91% of project statuses being updated within two months. Despite the improvements brought by Power Automate, the fragmented nature of the review processes persisted. To further enhance efficiency and accuracy, the Integrated Review Tool (IRT) was developed. The IRT aims to centralize review initiation and connect team systems, creating an interconnected data infrastructure that preserves team autonomy while enhancing overall efficiency. This tool automates email reminders, centralizes reviews, and streamlines data integration, significantly improving the accuracy and efficiency of environmental compliance reviews. The design and development of the IRT involved advanced systems methodology, process mapping, project management, and collaboration with subject matter experts. The minimum viable product design is 100% complete, and system development is currently underway, with expected outcomes including a centralized entry point for all ESH&Q reviews, automated routing, real-time tracking and analytics, AI integration, and a user-friendly interface. This project demonstrates the potential of leveraging automation and integrated systems to enhance efficiency, accuracy, and decision-making in environmental reporting and compliance processes at INL.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

PROVIDING PLANT DATA ANALYTICS THROUGH A SEAMLESS DIGITAL ENVIRONMENT

As technology continues to evolve and become more integrated into a worker’s daily routine in the Nuclear Power industry the need for easy access to data becomes a priority. Not only does the need for data increase but the amount of data collected increases. In most cases the data is collected and stored in various software applications, many of which are legacy systems, which do not offer any other option to access the data except through the application’s user interface. Furthermore the data gets grouped in “silos” according to work function and not necessarily by subject. Hence, in order to access all the information needed for a particular task or analysis one may have to access multiple applications to gather all the data needed. The industry and the research community have identified the need for a digital architecture and more importantly the need for a Seamless Digital Environment. An SDE provides a means to access multiple applications, gather the data points needed, conduct the analysis requested, and present the result to the user with minimal or no effort by the user. In addition, the nuclear utilities have identified the need for research focused on data analytics. The effort should develop and evaluate use cases for data mining and analytics for employing information from plant sensors and database for use in developing improved business analytics. Idaho National Laboratory is leading such effort, which is conducted in close collaboration with vendors, nuclear utilities, Institute of Nuclear Power Operations, and Electric Power Research Institute. The goal of the study is to research potential approaches to building an analytics solution for equipment reliability, on a small scale, focusing on either a single piece of equipment or a single system. The analytics solution will likely consist of a data integration layer, predictive and machine learning layer and the user interface layer that will display the output of the analysis in a straight forward, easy to consume manner. This paper will describe the study and the initial results.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Empowering Scientific Discovery Through Computing at the Advanced Photon Source

This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS Data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership Computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication.

Parraga, Hannah↗
DOE OSTI2021

UPF and Y-12 Criticality Accident Alarm System (CAAS) Radiation Testing Report

This report documents the experimental conditions and results for the testing of the Mirion Technologies CAAS-3S Criticality Accident Alarm System (CAAS) for the Y-12 National Security Complex (Y-12) and Uranium Processing Facility (UPF). The testing was conducted at the Godiva IV Burst Reactor at the National Criticality Experiments Research Center (NCERC) at the Nevada National Security Site (NNSS) during the week of January 11, 2021. The testing was conducted in accordance with 25774-QL-POA-JR00-00002-VDE-27.0, CAAS-3S Radiation Testing of Cabinet Components Test Plan. The tests subjected the CAAS-3S system to intense and short duration mixed neutron and gamma radiation fields and to high integrated radiation doses. The purpose of the test was to environmentally qualify various CAAS-3S components in a radiation environment. Successful completion of the tests was predicated on the collaborative efforts of several organizations. Sandia National Laboratory (SNL) and Lawrence Livermore National Laboratory (LLNL) provided dosimetry to estimate photon and neutron dose. Los Alamos National Laboratory (LANL) manages the NCERC facility, operated the Godiva IV reactor, and provided field support for CAAS-3S placement and communications interface for data acquisition systems supplied by Mirion Technologies. Throughout the test campaign, Y-12 and UPF personnel provided technical support in determining changes to the test schedule, as needed, interpretation of the results, and comparison between the measured dosimetry data and predicted results. Throughout the test campaign, Mirion personnel provided technical support in the operation of the CAAS-3S system itself.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Management and International Sorption Model Collaboration (M4SF-23LL010302062-NEA-TDB)

This progress report (Level 4 Milestone Number M4SF-23LL010302062) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline International Collaborations Activity Number SF-23LL01030206. The activity is focused on our long-term commitment of engaging our partners in international nuclear waste repository research. This includes participation in the Nuclear Energy Agency Thermochemical Database (NEA-TDB) Project and development of methodologies for integrating US and international thermodynamic databases for use in SFWST Generic Disposal System Assessment (GDSA) efforts. A continuing focus for FY23 efforts has been to support the US participation in the NEA-TDB effort (Mavrik Zavarin replaced Cindy Atkins-Duffin on the NEA-TDB Management Board (MB) and Executive Group (EG)) and developing mechanisms for integration of NEA-TDB thermochemical data with LLNL’s SUPCRTNE thermodynamic database that supports the SFWST GDSA activities. This effort is coordinated with the Argillite work package SUPCRTNE database development efforts. The goal is to provide a downloadable database that will be hosted on a LLNL website which integrates NEA-TDB data into the LLNL SUPCRTNE database where appropriate. As part of our international activities, we continue our effort to integrate international sorption databases into L-SCIE (Zavarin et al., 2022b). We presented opportunities to include sorption in the next phase of NEA-TDB efforts at the April 2023 EG meeting in Paris. FY23 efforts focused on ensuring interoperable database development across multiple international database development activities. The overall goal is to produce an open source database that can be shared and integrated with multiple nuclear waste programs internationally and harness modern data science workflows and algorithms to incorporate these new approaches into reactive transport and performance assessment models. In collaboration with our Helmholtz Zentrum Dresden Rossendorf partners, we recently demonstrated the power of FAIR open source databases by fitting iron oxide (hydrous ferric oxide, goethite, hematite, and magnetite) protolysis constants to all available L-SCIE data. The results were submitted as a manuscript to J. Colloid Interface Science. This work will inform future metal sorption studies on a variety of iron oxides in order to discern the most appropriate acidity constants and surface complexation modeling constructs to account for pH-dependent mineral surface charge behavior. This work also explored automated surface complexation model development workflows in order to generate higher throughput model input files for a more facile incorporation into GDSA activities.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

Degradation Characterization of Thermal Interface Greases: Preprint

Thermal interface materials (TIMs) are used in power electronics packaging to minimize thermal resistance between the heat generating component and the heat sink. Thermal greases are one such class. The conformability and thin bond line thickness (BLT) of these TIMs can potentially provide low thermal resistance throughout the operation lifetime of a component. However, their performance degrades over time due to pump-out and dry-out during thermal and power cycling. The reliability performance of greases through operational cycling needs to be quantified to develop new materials with superior properties. NREL, in collaboration with DuPont, has performed thermal and reliability characterization of several commercially available thermal greases. Initial bulk and contact thermal resistance of grease samples were measured, and then the thermal degradation that occurred due to pump-out and dry-out during temperature cycling was monitored. The thermal resistances of five different grease materials were evaluated using NREL's steady-state thermal resistance tester based on the ASTM test method D5470. Greases were then applied, utilizing a 2.5 cm x 2.5 cm stencil, between invar and aluminum plates to compare the thermomechanical performance of the materials in a representative test fixture. Scanning Acoustic microscopy, thermal, and compositional analyses were performed periodically during thermal cycling from -40 degrees Celcius to 125 degrees Celcius. Completion of this characterization has allowed for a comprehensive evaluation of thermal greases both for their initial bulk and contact thermal performance, as well as their degradation mechanisms under accelerated thermal cycling conditions.

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

Using probability distribution function as a scaling approach to incorporate soil heterogeneity into biogeochemical models for greenhouse gas predictions (Final Technical Report)

The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Metaproteomics Initiative: a coordinated approach for propelling the functional characterization of microbiomes

Through connecting genomic and metabolic information, metaproteomics is an essential approach for understanding how microbiomes function in space and time. The international metaproteomics community is delighted to announce the launch of the Metaproteomics Initiative (www.metaproteomics.org), the goal of which is to promote dissemination of metaproteomics fundamentals, advancements, and applications through collaborative networking in microbiome research. The Initiative aims to be the central information hub and open meeting place where newcomers and experts interact to communicate, standardize, and accelerate experimental and bioinformatic methodologies in this field. We invite the entire microbiome community to join and discuss potential synergies at the interfaces with other disciplines, and to collectively promote innovative approaches to gain deeper insights into microbiome functions and dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Large Load Integration - Task List and Overview

Large Load Integration Tasks: Task 1 – Workshops Support stakeholder engagement across industry to promote collaboration and identify solutions to challenges that will guide other work Task 2 – Ancillary Services Characterize different types of large loads to assess under what conditions they may be utilized to provide grid stability services Task 3 – Communications Explore the cybersecurity and communications infrastructure required to enable large loads to interface with grid operations to provide ancillary services Task 4 – Nuclear Integration Explore risks and methods for supporting large load energy needs with SMRs and incorporating them into the wider power system Task 5 – Decision Support and TA Provide support to stakeholders through the creation of planning tools and direct technical assistance.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Multimode Characterization Approach for Understanding Cell-Level PV Performance and Degradation

Cell-level degradation processes impact the economic viability and large-scale deployment prospects for both established and emerging photovoltaic (PV) technologies. This project addresses the need to develop experimental and device-modeling approaches for studying cell-level degradation processes in photovoltaic (PV) devices using a variety of characterization techniques that provide access to complementary material and device properties. Our results demonstrate that by coupling characterization results with device modeling it is possible to develop comprehensive understanding of processes leading to performance limitations and degradation. This project developed a suite of novel measurement techniques including pulsed-light-bias operando X-ray and photoelectron spectroscopy (popXPS), light-biased scanning microwave impedance microscopy (sMIM), and near-field transport imaging (TI). In addition, operando characterization methodologies and in situ stressing capabilities have been developed and applied for techniques including electron-beam-induced current (EBIC), cathodoluminescence (CL), and Kelvin probe force microscopy (KPFM). Device-physics models were developed and applied to simulate correlative, multi-mode measurements to extract material and device parameters that control performance degradation. These characterization and modeling techniques were applied in a multi-mode approach to probe cell-level degradation mechanisms in Cd(Se,Te) and hybrid perovskite PV devices. Together these efforts contribute to foundational PV degradation science by establishing a framework for understanding PV performance degradation at the cell level and benefit the U.S. PV industry by providing resources in the form of novel experimental capabilities, knowledge gained, and available expertise that can accelerate research and development of improved PV device materials and architectures. The project provided a comprehensive understanding of degradation in baseline Cd(Se,Te) solar cells provided by our collaborators at Colorado State University (CSU). EBIC and CL-based measurements and revealed unusual collection and recombination profiles in these devices, which underwent significant changes with during stressing. KPFM and operando XPS measurements showed that device stressing permanently alters energy-band alignments at the (Mg,Zn)O/Cd(Se,Te) interface, which in turn account for an observed loss in fill factor. Studies on hybrid perovskite devices were hampered to a significant extent by delays related to the pandemic. Nevertheless, a set of hybrid perovskite devices (supplied through an NREL-industry partnership) were stress tested and characterized with techniques including EBIC, sMIM, popXPS/popUPS and optically excited TI. Available results from these measurements informed the device modeling effort and suggest that defects and related band offsets at the C60/LiF/hybrid perovskite interface are the primary source of degradation in these devices.

14 SOLAR ENERGY↗
DOE OSTI2024

Shaping the Future of Self-Driving Autonomous Laboratories Workshop

The "Shaping the Future of Self-Driving Autonomous Laboratories" workshop, held in Denver on November 7-8, 2024, brought together leading experts from materials science and computing to address the growing need to revolutionize scientific research through AI-driven autonomous laboratories. The workshop identified critical challenges, including the integration of heterogeneous data, development of AI systems that understand fundamental physical principles, and comprehensive safety protocols. Key recommendations emerged around developing universal laboratory equipment interfaces, implementing automated metadata collection systems, and creating hybrid AI approaches that combine data-driven learning with scientific principles. The workshop emphasized maintaining human oversight while leveraging automation, transforming scientific education to prepare the next generation of researchers, and establishing a national consortium leveraging DOE facilities as anchors for broader collaboration with academia and industry. Participants stressed the urgency of addressing the growing disconnect between human decision-making timescales and modern instrumentation capabilities, highlighting the need for strategic automation while preserving essential human insight and oversight in the research process.

36 MATERIALS SCIENCE↗