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NASA NTRS2020

Credible practice of modeling and simulation in healthcare: ten rules from a multidisciplinary perspective

The complexities of modern biomedicine are rapidly increasing. Thus, modeling and simulation have become increasingly important as a strategy to understand and predict the trajectory of pathophysiology, disease genesis, and disease spread in support of clinical and policy decisions. In such cases, inappropriate or ill-placed trust in the model and simulation outcomes may result in negative outcomes, and hence illustrate the need to formalize the execution and communication of modeling and simulation practices. Although verification and validation have been generally accepted as significant components of a model’s credibility, they cannot be assumed to equate to a holistic credible practice, which includes activities that can impact comprehension and in-depth examination inherent in the devel-opment and reuse of the models. For the past several years, the Committee on Credible Practice of Modeling and Simulation in Healthcare, an interdisciplinary group seeded from a U.S. interagency initiative, has worked to codify best practices. Here, we provide Ten Rules for credible practice of modeling and simulation in healthcare developed from a comparative analysis by the Committee’s multidisciplinary membership, followed by a large stakeholder com-munity survey. These rules establish a unified conceptual framework for modeling and simulation design, implementation, evaluation, dissemination and usage across the modeling and simulation life-cycle. While biomedical science and clinical care domains have somewhat different requirements and expectations for credible practice, our study converged on rules that would be useful across a broad swath of model types. In brief, the rules are: (1) Define context clearly. (2) Use contextually appropriate data. (3) Evaluate within context. (4) List limitations explicitly. (5) Use version control. (6) Document appropriately. (7) Disseminate broadly. (8) Get independent reviews. (9) Test competing imple-mentations. (10) Conform to standards. Although some of these are common sense guidelines, we have found that many are often missed or misconstrued, even by seasoned practitioners. Computational models are already widely used in basic science to generate new biomedical knowledge. As they penetrate clinical care and healthcare policy, contributing to personalized and precision medicine, clinical safety will require established guidelines for the credible practice of modeling and simulation in healthcare.

Credibility↗

NASA NTRSDate not supplied

Medical System Requirements Development for Lunar Operations

The major health hazards of spaceflight include higher levels of damaging radiation, altered gravity, extended periods of isolation and confinement, a closed and potentially hostile living environment, and the stress associated with being a long distance from Earth. As we increase the duration of lunar stays with foreseeable communication latencies and disruptions, there will be a progressive need for crew to maintain their own health and independently respond to critical medical events. The Exploration Medical Capability element of the NASA Human Research Program is developing a set of Medical System requirements for lunar transit and surface operations. These requirements specify the capabilities, processes and procedures of a habitat Medical System needed for a range of conditions known to occur during spaceflight. Requirement text is written so as not to constrain innovative design solutions necessary for a resilient system. The requirement set includes attributes and functions the Medical System imposes on eight additional habitat systems. A key property of the Medical System is the provision of medical knowledge that will be stored, updated, analyzed, and secured within a Habitat Data System. A Task Performance Support System will aid in medical data acquisition and interpretation, crew training, medical condition prevention, diagnosis and treatment, provide interactive procedures, and track medical inventory. A Wellness System will focus on the provision of countermeasures to prevent, mitigate or treat adverse physical and behavioral health effects while the Medical System recommends adjustments to these countermeasures to maintain crew health. An Environmental Monitoring System will share out-of-bounds readings of air and water quality, acoustics, and radiation exposure levels with the Medical System to help identify issues before they affect crew health and performance. A Communications System will provide secured and private consultations between crew and the ground medical team and their loved ones on Earth. The Medical System also imposes requirements on a Research & Testbed System, fostering advanced medical science such as human research. A Waste Management System provides biohazard waste containment and waste disposal options (recycle and reuse). An Extravehicular Activity System supports crew health during lunar surface activities. And finally, a Maintenance Support System ensures that medical equipment is performing as expected. These requirements are being specifically developed for lunar surface operations but could help to identify Medical System requirements for any space habitat (e.g., I-Hab, commercial endeavors, etc.).

technology↗

DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process – 23280

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from spent fuel for recycling. The extraction of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. Beartooth will allow researchers to refine extraction processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry that uses centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on sensor data to determine signal features with the goal of providing a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Use of Sensors and Machine Learning for Signal Discovery in a Solvent Extraction Process

Reprocessing is an important step in the nuclear fuel cycle where usable nuclear materials are extracted from used fuel for recycling. The separation of materials for reuse simultaneously reduces not only the volume of nuclear waste, but its decay time to radioactivity levels similar to that of the originating uranium ore. As part of an initiative to steward research, development, and innovation into the nuclear fuel cycle, Idaho National Laboratory is designing and constructing a solvent extraction testbed named Beartooth. This testbed will allow researchers to refine separation processes, test innovative extraction processes, and give early career scientists opportunities to gain skills in performing separations chemistry utilizing centrifugal contactors. In addition, the Beartooth testbed is being uniquely designed to enable novel technologies including machine learning capabilities for the characterization of chemical process operations in near real-time. To aid in the design of Beartooth, a team of researchers are installing a variety of atypical sensors into a system of contactors for signal discovery. The team will implement machine learning methods on acquired sensor data to extract signal features. The goal is to provide a process operator with a deeper understanding of the chemical process and equipment usage. This work will summarize sensors utilized and preliminary results from an infrared camera.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRS1994

Software agents for the dissemination of remote terrestrial sensing data

Remote terrestrial sensing (RTS) data is constantly being collected from a variety of space-based and earth-based sensors. The collected data, and especially 'value-added' analyses of the data, are finding growing application for commercial, government, and scientific purposes. The scale of this data collection and analysis is truly enormous; e.g., by 1995, the amount of data available in just one sector, NASA space science, will reach 5 petabytes. Moreover, the amount of data, and the value of analyzing the data, are expected to increase dramatically as new satellites and sensors become available (e.g., NASA's Earth Observing System satellites). Lockheed and other companies are beginning to provide data and analysis commercially. A critical issue for the exploitation of collected data is the dissemination of data and value-added analyses to a diverse and widely distributed customer base. Customers must be able to use their computational environment (eventually the National Information Infrastructure) to obtain timely and complete information, without having to know the details of where the relevant data resides and how it is accessed. Customers must be able to routinely use standard, widely available (and, therefore, low cost) analyses, while also being able to readily create on demand highly customized analyses to make crucial decisions. The diversity of user needs creates a difficult software problem: how can users easily state their needs, while the computational environment assumes the responsibility of finding (or creating) relevant information, and then delivering the results in a form that users understand? A software agent is a self-contained, active software module that contains an explicit representation of its operational knowledge. This explicit representation allows agents to examine their own capabilities in order to modify their goals to meet changing needs and to take advantage of dynamic opportunities. In addition, the explicit representation allows agents to advertize their capabilities and results to other agents, thereby allowing the collection of agents to reuse each others work.

Toomey, Christopher N.↗

NASA NTRS2012

Getting a Cohesive Answer from a Common Start: Scalable Multidisciplinary Analysis through Transformation of a System Model

One of the challenges of systems engineering is in working multidisciplinary problems in a cohesive manner. When planning analysis of these problems, system engineers must tradeoff time and cost for analysis quality and quantity. The quality is associated with the fidelity of the multidisciplinary models and the quantity is associated with the design space that can be analyzed. The tradeoff is due to the resource intensive process of creating a cohesive multidisciplinary system model and analysis. Furthermore, reuse or extension of the models used in one stage of a product life cycle for another is a major challenge. Recent developments have enabled a much less resource-intensive and more rigorous approach than handwritten translation scripts or codes of multidisciplinary models and their analyses. The key is to work from a core system model defined in a MOF-based language such as SysML and in leveraging the emerging tool ecosystem, such as Query-View- Transform (QVT), from the OMG community. SysML was designed to model multidisciplinary systems and analyses. The QVT standard was designed to transform SysML models. The Europa Hability Mission (EHM) team has begun to exploit these capabilities. In one case, a Matlab/Simulink model is generated on the fly from a system description for power analysis written in SysML. In a more general case, a symbolic mathematical framework (supported by Wolfram Mathematica) is coordinated by data objects transformed from the system model, enabling extremely flexible and powerful tradespace exploration and analytical investigations of expected system performance.

Cole, Bjorn↗

DOE OSTI2020

Supercritical CO2 sterilization of N95 Masks

A preliminary investigation of the use of supercritical carbon dioxide for treating of 3M 1860 N95 masks was undertaken to evaluate a potential route to low-cost, scalable, sterilization of personal protective equipment for multiple reuse in hospital settings. Upon entering the supercritical regime, the normally distinct liquid and gaseous phases of CO 2 merge into a single homogeneous phase that has density, short-range order, and solvation capacity of a liquid, but the volume-filling and permeation properties that of a gas. This enables supercritical CO2 to function as a vehicle for delivery of biocidal agents such peracetic acid into microporous structures. The potentially adverse effect of a liquid-to-gas phase transition on mask filter media is avoided by conducting cleaning operations above 31 C, the critical temperature for carbon dioxide. A sample of fifteen 3M 1860 N95 masks was subjected to ten consecutive cycles of supercritical CO 2 cleaning to determine its effect on mask performance. These 15 masks, along with 5 control samples then underwent a battery of standardized tests at the CDC NIOSH NPPTL research facility in Pittsburgh, PA. The data from these tests strongly suggest (but do not prove) that supercritical carbon dioxide do not damage 3M 1860 N95 masks. Additional tests conducted during this project confirmed the compatibility of supercritical CO 2 with ventilator tubing that, like N95 masks, has been in short supply during portions of the COVID-19 pandemic and cannot be sterilized by conventional means. Finally, a control experiment was also conducted to examine the effect of supercritical CO 2 on a BSL-2 surrogate virus, vesicular stomatitis virus (VSV), Indiana serotype strain. In the absence of biocidal additives, supercritical CO 2 exhibited no measurable lethality against VSV. This surrogate virus experiment suggests that a biocidal additive such as peracetic acid will be necessary to achieve required sterilization metrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Data & Code from Phoenix CPPP Phase 2 Analysis

This data and code package supports the analysis presented in “Beyond Surface Cooling: Comprehensive Field Assessment of Reflective Pavement Thermal Performance in Phoenix, Arizona” and provides fully reproducible workflows for evaluating the thermal performance of cool pavement treatments in a hot urban environment. The dataset integrates multi-modal field measurements collected across residential and nonresidential settings, including mobile air temperature traverses, stationary air temperature monitoring, residential mean radiant temperature (MRT) measurements, subsurface temperature profiles, and controlled testbed observations. The data package contains raw and processed datasets in comma-separated value (CSV) format, accompanying metadata files describing site characteristics and measurement protocols, and R scripts (.R files) used for data cleaning, time synchronization, spatial and temporal matching, quality control filtering, statistical comparison, and figure generation. All analyses were conducted using R (version ≥ 4.2.0) with commonly available packages (e.g., tidyverse, lubridate, data.table, ggplot2). No proprietary software is required to reproduce results. Field campaigns were designed to quantify the effects of high-reflectance pavement coatings on surface temperature, near-surface air temperature, subsurface heat propagation, and radiative heat exposure. Temporal alignment procedures include standardized timestamp conversion and nearest-neighbor matching of high-frequency sensor measurements to stop-based metadata within defined tolerance windows to ensure comparability across instruments. The workflows generate summary statistics, treatment–control contrasts, depth-dependent thermal gradients, and time-series visualizations used in the associated publication. By integrating mobile, stationary, radiative, and subsurface measurements within a unified and transparent processing framework, this package enables comprehensive evaluation of cool pavement performance across multiple thermal exposure pathways and supports reuse in future urban heat mitigation and climate resilience studies.

AIR TEMPERATURE↗

NASA NTRS2007

ISHM Anomaly Lexicon for Rocket Test

Integrated Systems Health Management (ISHM) is a comprehensive capability. An ISHM system must detect anomalies, identify causes of such anomalies, predict future anomalies, help identify consequences of anomalies for example, suggested mitigation steps. The system should also provide users with appropriate navigation tools to facilitate the flow of information into and out of the ISHM system. Central to the ability of the ISHM to detect anomalies is a clearly defined catalog of anomalies. Further, this lexicon of anomalies must be organized in ways that make it accessible to a suite of tools used to manage the data, information and knowledge (DIaK) associated with a system. In particular, it is critical to ensure that there is optimal mapping between target anomalies and the algorithms associated with their detection. During the early development of our ISHM architecture and approach, it became clear that a lexicon of anomalies would be important to the development of critical anomaly detection algorithms. In our work in the rocket engine test environment at John C. Stennis Space Center, we have access to a repository of discrepancy reports (DRs) that are generated in response to squawks identified during post-test data analysis. The DR is the tool used to document anomalies and the methods used to resolve the issue. These DRs have been generated for many different tests and for all test stands. The result is that they represent a comprehensive summary of the anomalies associated with rocket engine testing. Fig. 1 illustrates some of the data that can be extracted from a DR. Such information includes affected transducer channels, narrative description of the observed anomaly, and the steps used to correct the problem. The primary goal of the anomaly lexicon development efforts we have undertaken is to create a lexicon that could be used in support of an associated health assessment database system (HADS) co-development effort. There are a number of significant byproducts of the anomaly lexicon compilation effort. For example, (1) Allows determination of the frequency distribution of anomalies to help identify those with the potential for high return on investment if included in automated detection as part of an ISHM system, (2) Availability of a regular lexicon could provide the base anomaly name choices to help maintain consistency in the DR collection process, and (3) Although developed for the rocket engine test environment, most of the anomalies are not specific to rocket testing, and thus can be reused in other applications.

Schmalzel, John L.↗

NASA NTRS2012

Getting a Cohesive Answer from a Common Start: Scalable Multidisciplinary Analysis through Transformation of a Systems Model

One of the challenges of systems engineering is in working multidisciplinary problems in a cohesive manner. When planning analysis of these problems, system engineers must trade between time and cost for analysis quality and quantity. The quality often correlates with greater run time in multidisciplinary models and the quantity is associated with the number of alternatives that can be analyzed. The trade-off is due to the resource intensive process of creating a cohesive multidisciplinary systems model and analysis. Furthermore, reuse or extension of the models used in one stage of a product life cycle for another is a major challenge. Recent developments have enabled a much less resource-intensive and more rigorous approach than hand-written translation scripts between multi-disciplinary models and their analyses. The key is to work from a core systems model defined in a MOF-based language such as SysML and in leveraging the emerging tool ecosystem, such as Query/View/Transformation (QVT), from the OMG community. SysML was designed to model multidisciplinary systems. The QVT standard was designed to transform SysML models into other models, including those leveraged by engineering analyses. The Europa Habitability Mission (EHM) team has begun to exploit these capabilities. In one case, a Matlab/Simulink model is generated on the fly from a system description for power analysis written in SysML. In a more general case, symbolic analysis (supported by Wolfram Mathematica) is coordinated by data objects transformed from the systems model, enabling extremely flexible and powerful design exploration and analytical investigations of expected system performance.

Operational QVT↗

NASA NTRS2026

PROTECT: Production and Reuse of Thermally Efficient Ceramic Thermal Protection Systems

PROTECT (Production and Reuse Of Thermally Efficient Ceramic TPS) is a NASA Early Career Initiative focused on developing the next generation of reusable ceramic thermal protection systems (TPS). This project addresses key challenges in TPS design, including temperature capability, thermal stability, and refurbishment time, by integrating novel material development with predictive modeling. Leveraging enhanced capabilities in NASA’s Porous Microstructure Analysis (PuMA) software, PROTECT introduces new modeling tools to predict the thermal and mechanical behavior of fibrous ceramic materials. These tools enable accurate prediction of performance metrics such as thermal conductivity and structural integrity, reducing reliance on costly physical testing. Preliminary advances in these areas will be presented. To support model validation, PROTECT is building a comprehensive database of raw material properties using advanced characterization techniques, including micro computed tomography (CT) scanning in collaboration with the University of Illinois Urbana-Champaign (UIUC). The presentation will detail the sampling workflows and analysis methods used to generate this detailed microstructural data and how it is used to develop improved models in PuMA. This multi-center collaboration, spanning NASA (JSC, ARC, KSC, GRC), Oak Ridge National Laboratory, UIUC, and SpaceX, is developing tailored TPS solutions for LEO, lunar, and Martian missions. By bridging heritage systems with the demands of modern spaceflight, PROTECT contributes to the advancement of reusable TPS technologies for future exploration missions.

Propulsion, Refractory, and Coating Materials↗

NASA NTRS2026

PROTECT: Production and Reuse of Thermally Efficient Ceramic Thermal Protection Systems

Propulsion, Refractory, and Coating Materials↗

NASA NTRS2008

Product Engineering Class in the Software Safety Risk Taxonomy for Building Safety-Critical Systems

When software safety requirements are imposed on legacy safety-critical systems, retrospective safety cases need to be formulated as part of recertifying the systems for further use and risks must be documented and managed to give confidence for reusing the systems. The SEJ Software Development Risk Taxonomy [4] focuses on general software development issues. It does not, however, cover all the safety risks. The Software Safety Risk Taxonomy [8] was developed which provides a construct for eliciting and categorizing software safety risks in a straightforward manner. In this paper, we present extended work on the taxonomy for safety that incorporates the additional issues inherent in the development and maintenance of safety-critical systems with software. An instrument called a Software Safety Risk Taxonomy Based Questionnaire (TBQ) is generated containing questions addressing each safety attribute in the Software Safety Risk Taxonomy. Software safety risks are surfaced using the new TBQ and then analyzed. In this paper we give the definitions for the specialized Product Engineering Class within the Software Safety Risk Taxonomy. At the end of the paper, we present the tool known as the 'Legacy Systems Risk Database Tool' that is used to collect and analyze the data required to show traceability to a particular safety standard

Hill, Janice↗

NASA NTRS2009

Space Toxicology: Environmental Health Considerations during Spaceflight Operations and Potential Paths for Research

Space Toxicology is a specialized discipline for spaceflight, space habitation and occupation of celestial bodies including planets, moons and asteroids [1]. Astronaut explorers face unique challenges to their health while working and living with limited resources for rescue and medical care during space operation. At its core the practice of space toxicology to identify, assess and predict potential chemical contaminants and limit the astronaut s exposure to these environmental factors in order to protect crew health. Space toxicologists are also charged with setting safe exposure limits that will protect the astronaut against a multitude of chemical exposures, in a physiologically altered state. In order to maintain sustained occupation in space, toxicological risks are gauged and managed within the context of isolation, continual exposures, reuse of air and water, limited rescue options, and the necessary use of highly toxic compounds required for propulsion. As the space program move towards human presence and exploration other celestial bodies in situ toxicological risks, such as inhalation of unusual and/or reactive mineral dusts must also be analyzed and controlled. Placing humans for long-term presence in space creates several problems and challenges to the long-term health of the crew, such as bone-loss and immunological challenges and has spurred research into acute, chronic and episodic exposure of the pulmonary system to mineral dusts [2]. NASA has demonstrated that lunar soil contains several types of reactive dusts, including an extremely fine respirable component. In order to protect astronaut health, NASA is now investigating the toxicity of this unique class of dusts. Understanding how these reactive components behave "biochemically" in a moisture-rich pulmonary environment will aid in determining how toxic these particles are to humans. The data obtained from toxicological examination of lunar dusts will determine the human risk criteria for lunar dust exposure and produce a lunar health standard.

Khan-Mayberry, Noreen N.↗

DOE OSTI2020

A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species

Modern microbial biodesign relies on the principle that well-characterized genetic parts can be reused and reconfigured for different functions. However, this paradigm has only been successful in a limited set of hosts, mostly comprised from common lab strains of Escherichia coli . It is clear that new applications such as chemical sensing and event logging in complex environments will benefit from new host chassis. This study quantitatively compared how the same chemical event logger performed across four strains and three different microbial species. An integrase-based sensor and memory device was operated by two representative soil Pseudomonads— Pseudomonas fluorescens SBW25 and Pseudomonas putida DSM 291. Quantitative comparisons were made between these two non-traditional hosts and two benchmark E. coli chassis including the probiotic Nissle 1917 and common cloning strain DH5α. The performance of sensor and memory components changed according to each host, such that a clear chassis effect was observed and quantified. These results were obtained via fluorescence from reporter proteins that were transcriptionally fused to the integrase and downstream recombinant region and via data-driven kinetic models. The Pseudomonads proved to be acceptable chassis for the operation of this event logger, which outperformed the common E. coli DH5α in many ways. This study advances an emerging frontier in synthetic biology that aims to build broad-host-range devices and understand the context by which different species can execute programmable genetic operations.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Nondestructive In Operando Imaging of Thin Film Composite Membrane Compaction Enhanced by AI-Based Segmentation

Reverse osmosis (RO) membranes are essential for desalination and water reuse, yet their permeability declines in high-pressure applications due to membrane compaction. This study investigates the structural and functional responses of commercial brackish, seawater, and high-pressure RO membranes at applied pressures up to 120 bar using a multiscale, nondestructive in operando scanning electron microscopy (iSEM) imaging platform. The iSEM technique reveals progressive densification across the composite membrane structure, which correlates with observed declines in water and solute permeance. To quantify these structural changes with greater fidelity, we combined X-ray computed tomography with AI-based segmentation enabling precise analysis of pore size distribution and thickness of the polysulfone support layer. Compared to traditional thresholding, AI segmentation accurately delineates material phases and void spaces, enhancing the reproducibility and resolution of morphological assessments. The results demonstrate that compaction-induced reductions in porosity and thickness strongly impact membrane transport properties. These findings provide mechanistic insights into the compaction behavior of RO membranes and underscore the potential for advanced imaging and AI-driven data analysis to guide the design of next-generation membranes with improved mechanical resilience and operational longevity.

13 HYDRO ENERGY↗

DOE OSTI2020

RAMSeS: Rapid Analysis of Mission Software Systems

Over the past few decades, software has become ubiquitous as it has been integrated into nearly every aspect of society, including household appliances, consumer electronics, industrial control systems, public utilities, government operations, and military systems. Consequently, many critical national security questions can no longer be answered convincingly without understanding software, including its purpose, its capabilities, its flaws, its communication, or how it processes and stores data. As software continues to become larger, more complex, and more widespread, our ability to answer important mission questions and reason about software in a timely way is falling behind. Today, to achieve such understanding of third-party software, we rely predominantly on the ability of reverse engineering experts to manually answer each particular mission question for every software system of interest. This approach often requires heroic human effort that nevertheless fails to meet current mission needs and will never scale to meet future needs. The result is an emerging crisis: a massive and expanding gap between the national security need to answer mission questions about software and our ability to do so. Sandia National Laboratories has established the Rapid Analysis of Mission Software Systems (RAMSeS) effort, a collaborative long-term effort aimed at dramatically improving our nation’s ability to answer mission questions about third-party software by growing an ecosystem of tools that augment the human reverse engineer through automation, interoperability, and reuse. Focusing on static analysis of binary programs, we are attempting to identify reusable software analysis components that advance our ability to reason about software, to automate useful aspects of the software analysis process, and to integrate new methodologies and capabilities into a working ecosystem of tools and experts. We aim to integrate existing tools where possible, adapt tools when modest modifications will enable them to interoperate, and implement missing capability when necessary. Although we do hope to automate a growing set of analysis tasks, we will approach this goal incrementally by assisting the human in an ever-widening range of tasks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Energy, greenhouse gas, and water life cycle analysis of synthetic graphite anode production in the United States

This study presents a comprehensive life cycle analysis of potential synthetic graphite battery anode material (BAM) production in the U.S. based on industrial-scale data. The analysis focuses on three impacts: greenhouse gas (GHG) emissions, total energy use, and water consumption. We also conducted sensitivity analyses to evaluate the effect of variation in process parameters and energy sources used for synthetic graphite BAM production on its life cycle GHG emissions. A detailed supply chain analysis of graphite BAM in the U.S. was also undertaken, along with a study of its associated GHG emissions. The results show GHG emissions of 29.7 kg CO 2 -eq. per kg BAM, total energy use of 580 MJ kg −1 BAM, and water consumption of 121 L kg −1 BAM for the baseline condition. The graphitization step is a major process hotspot, contributing to over 74% of all impacts. This is attributed to the energy and material input requirements for this step, particularly through the use of crucibles. Across the entire synthetic graphite production process, electricity is the primary contributor, followed by crucibles used in graphite block production, and then calcined petroleum coke. Sensitivity analyses indicate that improvement in micronization yield, reuse of crucibles, and use of low-carbon nuclear energy can significantly reduce GHG emissions of potential domestic graphite production (by ∼70%). Supply chain analysis identified major graphite BAM sources in the U.S. and showed that the U.S. has a competitive advantage in domestic production of synthetic graphite BAM in terms of reduced life cycle GHG emissions compared to present-day imported sources (by ∼20%).

Battery anode↗