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All sourcesNASA NTRSDOE OSTI
NASA NTRS1996

Fact Sheet: National Space Policy

For over three decades, the United States has led the world in the exploration and use of outer space. Our achievements in space have inspired a generation of Americans and people throughout the world. We will maintain this leadership role by supporting a strong, stable, and balanced national space program that serves our goals in national security, foreign policy, economic growth, environmental stewardship, and scientific and technical excellence. Access to and use of space are central for preserving peace and protecting US national security as well as civil and commercial interests. The United States will pursue greater levels of partnership and cooperation in national and international space activities and work with other nations to ensure the continued exploration and use of outer space for peaceful purposes. The goals of the US space program are to: (a) Enhance knowledge of the Earth, the solar system, and the universe through human and robotic exploration; (b) Strengthen and maintain the national security of the United States; (c) Enhance the economic competitiveness and scientific and technical capabilities of the United States; (d) Encourage State, local, and private sector investment in, and use of, space technologies; (e) Promote international cooperation to further US domestic, national security, and foreign policies. The United States is committed to the exploration and use of outer space by all nations for peaceful purposes and for the benefit of all humanity. "Peaceful purposes" allow defense and intelligence-related activities in pursuit of national security and other goals. The United States rejects any claims to sovereignty by any nation over outer space or celestial bodies, or any portion thereof, and rejects any limitations on the fundamental right of sovereign nations to acquire data from space. The United States considers the space systems of any nation to be national property with the right of passage through and operations in space without interference. Purposeful interference with space systems shall be viewed as an infringement on sovereign rights. The US Government will maintain and coordinate separate national security and civil space systems where differing needs dictate. All actions undertaken by agencies and departments in implementing the national space policy shall be consistent with US law, regulations, national security requirements, foreign policy, international obligations, and nonproliferation policy. The National Science and Technology Council (NSTC) is the principal forum for resolving issues related to national space policy. As appropriate, the NSTC and NSC will co-chair policy process. This policy will be implemented within the overall resource and policy guidance provided by the President.

Source record↗
NASA NTRS2011

Capabilities of the Impact Testing Facility at Marshall Space Flight Center

The test and analysis capabilities of the Impact Testing Facility at NASA's Marshall Space Flight Center are described. Nine different gun systems accommodate a wide range of projectile and target sizes and shapes at velocities from subsonic through hypersonic, to accomplish a broad range of ballistic and hypervelocity impact tests. These gun systems include ballistic and microballistic gas and powder guns, a two-stage light gas gun, and specialty guns for weather encounter studies. The ITF "rain gun" is the only hydrometeor impact gun known to be in existence in the United States that can provide single impact performance data with known raindrop sizes. Simulation of high velocity impact is available using the Smooth Particle Hydrodynamic Code. The Impact Testing Facility provides testing, custom test configuration design and fabrication, and analytical services for NASA, the Department of Defense, academic institutions, international space agencies, and private industry in a secure facility located at Marshall Space Flight Center, on the US Army's Redstone Arsenal in Huntsville, Alabama. This facility performs tests that are subject to International Traffic in Arms Regulations (ITAR) and DoD secret classified restrictions as well as proprietary and unrestricted tests for civil space agencies, academic institutions, and commercial aerospace and defense companies and their suppliers.

Finchum, Andy↗
DOE OSTI2021

Artificial Diversity and Defense Security (ADDSec)

Artificial Diversity and Defense Security (ADDSec) machine learning algorithms are used to classify and cluster threats so that an appropriate response can be initiated as a mitigation strategy. The package includes an ensemble of machine learning algorithms such as Support Vector Machines, naïve bayes, logistic regression, and random forest that evolve with the data to recognize anomalous behavior at the host and network levels. Inputs into the machine learning algorithms include end host system calls, system utilization, packet captures, and syslog messages. The machine learning algorithms can be retrained based on user defined intervals or on the number of packets received. ADDSEC's threat responses include Internet Protocol (IP) Address randomization, application port number randomization, and application library randomization. The IP randomization implementation is built on top of a Software Defined Networking (SDN) framework. The SDN controller installs flows on each of the SDN switches with randomized source and destination IP addresses. The application port numbers are randomized using iptables. The application library randomization is created with a LLVM compiler. All randomization schemes are transparent to the endpoints on the network. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-3379 O

Cox, RebeccaE.↗
DOE OSTI2019

Acoustic Source Parameters and Yield Estimation for SPE Phase I and Forensics Surface Events

Acoustic signals recorded for explosive events are sensitive to pressure changes in the source region and can provide valuable information about source characteristics. However, acoustic wave propagation in the atmosphere is significantly affected by weather conditions (e.g., temperature, pressure, and wind), and the atmospheric propagation effects should be considered properly to obtain accurate source information. Acoustic waveform inversion method has been increasing useful to determine quantitative source parameters by incorporating accurate Green’s function into the inversion and by effectively removing propagation path effects. This paper reviews recent progress in the acoustic waveform inversion and its application to explosion yield/emplacement (e.g., height-of-burst or depth-of-burial) estimation. The inversion method is applied to acoustic signals recorded during Source Physics Experiment Phase I and Forensics Surface Event conducted at the Nevada National Security Site (NNSS). Inverted acoustic source strengths are related to the explosion yield and emplacement, and their estimation uncertainties are explored by the comparison with the ground-truth data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

The surprisingly large neutron capture cross-section of 88 Zr

The probability that a nucleus will absorb a neutron, described in units of area as the neutron-capture cross section, is important to many areas of nuclear science including stellar nucleosynthesis, reactor performance, nuclear medicine, and defense applications. Although neutron-capture cross-sections have been directly measured for most stable nuclei, fewer results exist for radioactive isotopes and predictions typically have large uncertainties. There are almost no nuclear data for neutron-induced reactions on the radioactive nucleus 88 Zr, despite its significance for nuclear-security missions. By exposing 88 Zr to the intense neutron flux of a nuclear reactor, it was determined that 88 Zr has a thermal neutron capture cross section of (861,000 ± 69,000) b, which is the second-largest ever measured, despite being predicted to be only 10 b. The only other nuclei known to have thermal neutron capture cross sections greater than 1x10 5 b are 135 Xe and 157 Gd (2.6x10 6 b and 2.5x10 5 b, respectively) and no other cross section of comparable size has been discovered in the past 70 years (Fig. 1). The measurement of the 135 Xe cross section followed the determination that a fission product was poisoning the early reactors.7 The applications for 157 Gd were not immediately appreciated, but now the isotope has been developed into a detector material, burnable reactor poison, and potential medical neutron capture therapy (NCT) agent. In the case of 88 Zr, a unique aspect of the neutron-capture reaction is that both the target and the product 89 Zr nuclei are radioactive and give intense γ-ray emissions upon decay. This result suggests that as additional measurements with radioactive isotopes become feasible in the near future via access to new facilities for sample production and measurement, other cross-section surprises may be uncovered.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2023

The Cost and Benefit of Enhancing Cybersecurity for Hybrid AC/DC Grids

As critical interfaces of AC grids and DC grids inside a hybrid AC/DC grid, the voltage-sourced-converter (VSC) has been demonstrated to be vulnerable to false data injection (FDI) cyber-attacks. As a result, the cyber-attack-induced AC grid frequency deviations and DC grid voltage deviations threaten the secure operation. Here, to enhance cybersecurity in a not only feasible but also cost-effective manner, this paper proposes a cost-benefit-based cyber-defense strategy for a hybrid AC/DC grid. First, this paper establishes a spatial-temporal dual cyber-attack evaluation model, in which the cyber-attack-induced frequency and voltage deviations are modelled in both a spatially and temporally dual manner. Then, the proposed cost-benefit-based cyber-defense strategy is modelled as a VSC commitment problem to achieve the trade-off between maximizing the cyber-defense benefits and minimizing the cyber-defense costs. The VSC commitment problem is then mathematically convexified into a mixed-integer second-order cone programming (MISOCP) problem, which could be efficiently solved in an event-triggered manner against unfolding cyber-attack events. Simulation results on a test hybrid AC/DC grid verified the feasibility and the cost-effectiveness of the proposed cyber-defense strategy.

cost-benefit analysis↗
DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Design and Implementation of Full-Scale Industrial Control System Test Bed for Assessing Cyber-Security Defenses

In response to the increasing awareness of the Ethernet-based threat surface of industrial control systems (ICS), both the research and commercial communities are responding with ICS-specific security solutions. Unfortunately, many of the properties of ICS environments that contribute to the extent of this threat surface (e.g. age of devices, inability or unwillingness to patch, criticality of the system) similarly prevent the proper testing and evaluation of these security solutions. Production environments are often too fragile to introduce unvetted technology and most organizations lack test environments that are sufficiently consistent with production to yield actionable results. Cost and space requirements prevent the creation of mirrored physical environments leading many to look towards simulation or virtualization. Examples in literature provide various approaches to building ICS test beds, though most of these suffer from a lack of realism due to contrived scenarios, synthetic data and other compromises. In this paper, we provide a design methodology for building highly realistic ICS test beds for validating cybersecurity defenses. We then apply that methodology to the design and building of a specific test bed and describe the results and experimental use cases.

Gillen, Rob↗
DOE OSTI2018

Securing Your ICS Software with the AttackSurface Host Analyzer (AHA)

Implementing a secure development lifecycle (SDL) presents increasing challenges to software developers as they must ensure software correctly integrates both underlying operating system security features while also managing dependencies on third-party libraries or executables. There are a growing number of security functions that require a close integration between the OS security features and software builds to ensure strong protection. Furthermore, as software platforms grow in complexity, they present many opportunities for misconfigurations and inadequate defenses. This challenge is especially prevalent for industrial control systems (ICS), which oten depend on both legacy sotware platforms, or out of date operating systems. This paper presents the AttackSurface Host Analyzer (AHA) tool, which is used to assess the security of a software platform through its integration with a host operating system. The tool collects data from the various platforms running on an OS, evaluates an array of security properties, and then introduces metrics and visualizations to provide feedback on the system's attack surface based on the external interconnections and the completeness of the available security protections. The paper then explores the attack surface of a variety of industry-standard ICS platforms to provide insight into the current degree of protection enabled by them.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Zero Trust Strategies for Chemical, Biological, Radiological, and Nuclear Detection Systems: D.1 Cyber Scenarios

The evolving landscape of cybersecurity necessitates a paradigm shift to a Zero Trust (ZT) model, which assumes breaches and continuously verifies trust. This approach reshapes how trust boundaries are established, focusing on identities, devices, networks, applications, and data, rather than solely relying on perimeter defenses such as firewalls. Central to this transformation is the National Institute of Standards and Technology's (NIST) Special Publication 800-207, outlining the Zero Trust Architecture (ZTA), along with Executive Order 14028, which mandates federal agencies to adopt ZT principles. Complementary to these efforts, the Cybersecurity and Infrastructure Security Agency (CISA) developed the Zero Trust Maturity Model (ZTMM), providing a framework with five pillars and three cross-cutting capabilities to guide agencies toward enhanced cybersecurity maturity. In support of these initiatives, the DHS Countering Weapons of Mass Destruction Office (CWMD) is applying ZT principles to secure Chemical, Biological, Radiological, and Nuclear (CBRN) detection systems. Recognizing the diverse deployment models and network connectivity of these systems—from stationary, non-networked units to mobile, cloud-connected devices—the Pacific Northwest National Laboratory (PNNL) is developing cybersecurity scenarios specifically for CBRN environments. These scenarios examine various configurations and technological capabilities, offering insights into the application of ZTMM pillars in enhancing the security postures of CBRN devices. The cybersecurity scenarios presented by PNNL are hypothetical, crafted to explore theoretical situations and stimulate discussion on the potential use or compromise of CBRN detection systems in varied contexts. These narratives are illustrative and do not reference any real events or actual networks. Instead, they employ generalized reference models to highlight concepts and potential issues within CBRN security, focusing on how Zero Trust strategies can be adapted to address these challenges effectively.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2018

Nondestructive Testing Research and Development Efforts at Argonne National Laboratory—An Overview

The Nuclear Engineering (NE) Division of Argonne National Laboratory (ANL) has been involved for many years with research and development efforts associated with the nondestructive testing/evaluation (NDT/NDE) of critical components and structures in power generation (nuclear and fossil plants), aerospace, defense, homeland security, medical, and scientific applications. A wide range of NDT methods including electromagnetic, thermal, radiographic, acoustic and ultrasonic methods have been used for a wide range of material evaluation applications. Argonne’s expertise in analytical and numerical modeling of thermal, acoustic and electromagnetic interaction with complex media as well as advanced signal processing and data analysis have been utilized extensively for NDT applications. Three active areas of research, all in connection with NDT of materials and systems utilized in nuclear energy applications, are discussed in detail in this paper.

Eddy current testing↗
DOE OSTI2020

Vulnerabilities in Artificial Intelligence and Machine Learning Applications and Data

Artificial intelligence (AI) applications driven by machine learning (ML) are transformational technologies within the international nuclear security regime. Advancements realized by AI—faster and improved data insights, more efficient and automated processes, reductions in human error—enable nuclear security applications such as behavior analysis for insider threat mitigation, source tracking of stolen nuclear material, and facial recognition software for physical protection. In addition to the advantages, however, there are also inherent vulnerabilities and threats associated with its use and risk mitigations must be built into any AI/ML-enabled systems. This work provides a background on AI and ML and different data types used in the field, including open-source intelligence information (OSINT) that is discoverable by AI tools and application data that are used by AI tools for decision-making and automation. Current and potential AI applications and vulnerabilities related to their use within the nuclear security regime are also discussed.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2019

Execution of an Alternative Modeling Strategy for Closure of the Rainier Mesa Corrective Action Unit at the Nevada National Security Site - 19099

The Underground Test Area (UGTA) Project of the U.S Department of Energy (DOE) is investigating radionuclide contaminant movements in groundwater derived from a decades-long period of underground nuclear testing at the Nevada National Security Site (NNSS) to support regulatory corrective action and closure requirements. The project is built around an integrated campaign of drilling and sampling, data interpretation, computer modeling, and broader monitoring activities conducted under a joint Federal Facilities Agreement and Consent Order (1996 as amended) (FFACO) between the State of Nevada, the DOE, and the U.S. Department of Defense. The FFACO strategy is applied to individual Corrective Action Units (CAUs) that roughly envelop disparate geographic testing areas at the NNSS. The Rainier Mesa/Shoshone Mountain (RMSM) CAU is a geologically and hydrologically complex system lying in a remote up-gradient section of the NNSS. It was the site of 67 underground tests conducted between 1957 and 1992, 61 at Rainier Mesa proper, and 6 at Shoshone Mountain, isolated to its south. The original strategy to close the RMSM CAU required a simulation-based investigation to define the evolution of contaminated volumes of groundwater (in 3D) under uncertainty as a means to define a surface-projected contaminant boundary and to serve as a basis for longer term monitoring activities. Early models developed under this strategy proved to be quite complex and difficult to calibrate. Additional costs required to better characterize the system were judged to be out of balance with the low risks associated with this particular CAU. As a result, an Alternative Modeling Strategy (AMS) was adopted. The AMS removes the requirement for developing ensembles of 3D simulations and replaces it with a simpler one requiring the generation of multiple 1D streamline models. These are meant to encompass a range of alternative, yet plausible release and transport scenarios as a means to bound the overall lateral extent of contaminant transport. This paper will review the modeling approach developed under the AMS and its application to the Rainier Mesa portion of the CAU. Preliminary model results made under different parametric assumptions and boundary condition scenarios will be presented. Additional refinements of the model and its further application will be discussed. (authors)

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

Machine Learning for Anomaly Detection in Neural Network Security and SRF Cavities

This dissertation explores the development and deployment of machine learning approaches to address critical challenges in anomaly detection across two distinct domains: neural network security in federated learning settings and cavity behavior analysis in particle accelerator operations at Jefferson Lab in Newport News, Virginia. Anomaly detection identifies deviations from expected patterns, safeguarding systems in cybersecurity, industry, and research against malicious activities and failures. This dissertation demonstrates how our machine learning approaches enhance detection accuracy and efficiency in both neural network security and industrial applications. First, we investigate vulnerabilities in deep neural networks deployed in federated learning. Although federated learning preserves user privacy by training models locally, it remains vulnerable to backdoor attacks, in which malicious participants embed hidden triggers that induce targeted misbehavior. We propose a self-supervised contrastive learning framework to detect and mitigate such backdoor attacks. In our experiments, this method achieves higher detection accuracy and lower false positive rates than existing defenses, while operating without access to local model updates or original training data and thus preserving the privacy guarantees of the federated setting. Second, we address the operational reliability of superconducting radio-frequency (SRF) cavities at the Continuous Electron Beam Accelerator Facility (CEBAF). Our research leverages an unsupervised learning approach, combined with Principal Component Analysis (PCA) and k-means clustering, to identify anomalous behaviors in SRF cavities. Our method detects subtle anomalous behavior by analyzing SRF signal data. This knowledge allows for the early detection and resolution of potential faults, significantly improving the efficiency and reliability of operations. Third, we extend these insights to time-series anomaly detection more broadly. We design a contrastive-learning based model tailored to increasingly dynamic environments and academic research. This model improves detection accuracy in settings that require real-time monitoring and predictive maintenance. Our research underscores the broader applicability and impact of advanced machine learning techniques in anomaly detection. By extracting meaningful patterns from complex data, machine learning can significantly enhance security in distributed neural networks and improve the efficiency of particle accelerator operations. This dissertation serves as a stepping stone for future investigations into the vast possibilities of anomaly detection, inspiring further exploration and development of machine learning techniques in this field.

Ferguson, Hal [Old Dominion University]↗
NASA NTRS1994

Robotic tape library system level testing at NSA: Present and planned

In the present of declining Defense budgets, increased pressure has been placed on the DOD to utilize Commercial Off the Shelf (COTS) solutions to incrementally solve a wide variety of our computer processing requirements. With the rapid growth in processing power, significant expansion of high performance networking, and the increased complexity of applications data sets, the requirement for high performance, large capacity, reliable and secure, and most of all affordable robotic tape storage libraries has greatly increased. Additionally, the migration to a heterogeneous, distributed computing environment has further complicated the problem. With today's open system compute servers approaching yesterday's supercomputer capabilities, the need for affordable, reliable secure Mass Storage Systems (MSS) has taken on an ever increasing importance to our processing center's ability to satisfy operational mission requirements. To that end, NSA has established an in-house capability to acquire, test, and evaluate COTS products. Its goal is to qualify a set of COTS MSS libraries, thereby achieving a modicum of standardization for robotic tape libraries which can satisfy our low, medium, and high performance file and volume serving requirements. In addition, NSA has established relations with other Government Agencies to complete this in-house effort and to maximize our research, testing, and evaluation work. While the preponderance of the effort is focused at the high end of the storage ladder, considerable effort will be extended this year and next at the server class or mid range storage systems.

Shields, Michael F.↗
DOE OSTI2019

Transitioning from Cobalt-60 to X-Ray or E-Beam for Medical Sterilization: Filling Data and Education Gaps

The Office of Radiological Security (ORS) within the U.S. DOE’s National Nuclear Security Administration (NNSA) is charged to “work with government, law enforcement, and businesses across the globe to protect radioactive sources used for medical, research, and commercial purposes; remove and dispose of disused radioactive sources; and reduce the global reliance on high activity radioactive sources through the promotion of viable non-radioisotopic alternative technologies.” In this effort, the NNSA/ORS, along with the Office of Defense Nuclear Nonproliferation R&D, has been working with government and private entities that utilize gamma-ray irradiators in areas that include blood irradiation, radiotherapy, biology research, well logging, and radiation processing to consider alternative technologies that do not depend upon radioactive materials. In the medical device industry, nearly 50% of products are sterilized with ethylene oxide, 40.5% with cobalt-60 gamma-rays, 4.5% with electron-beam (e-beam), and <1% with X-ray.(1,2) The cobalt-60 irradiation facilities use an estimated 150 MCi of isotope to sterilize approximately 200 million cubic feet of product annually.(3) Due to growing safety regulations governing cobalt-60 use, continued advancements in e-beam and X-ray accelerator technology, and the greater irradiation durations and costs for cobalt-60 irradiation compared to e-beam and X-ray, switching to these non-radionuclide modalities is increasingly economically competitive. However, this breakdown of 40.5% cobalt-60 and 5.5% accelerator for product sterilization illustrates the difficulty that market forces and industry players have had in overcoming barriers in transitioning to non-nuclide alternatives. Besides those associated with construction of new e-beam and X-ray facilities, significant financial and regulatory obstacles are also associated with the irradiation and comprehensive testing of product constituent polymers and elastomers. The standards associated with product material performance testing are the Association for the Advancement of Medical Instrumentation (AAMI) Technical Information Report 17 (TIR 17), and its referenced American Society for Testing and Materials (ASTM) polymer testing standards. In the study now underway, and the subject of this article, the NNSA/ORS provided R&D funding to the Pacific Northwest National Laboratory (PNNL) in partnership with university researchers, leading medical product manufacturers, accelerator manufacturers, and contract sterilizers. The resulting Team Nablo (in memory of Sam Nablo) includes individuals representing PNNL, Becton Dickinson (BD), Johnson & Johnson, Stryker, Texas A&M University (TAMU), Steri-Tek, Mevex, Ion Beam Applications (IBA), and Sterigenics. The goals of this study are to 1) Identify specific polymer and/or elastomer-based medical products that are currently sterilized with gamma-rays, and for which there are data gaps and would be of greatest industry impact, 2) Work with relevant ASTM subcommittees for guidance and for potential standard revision, 3) Work with an appropriate Food & Drug Administration (FDA) office for potential collaboration on a guidance document for device manufacturers, 4) Measure the physical effects that product materials exhibit when they are given sterilization-level radiation doses from cobalt-60, e-beam or X-ray, and determine whether these effects would preclude the use of e-beam or X-ray for sterilization, 5) Communicate the resulting scientific datasets and findings via industry conferences, peer-reviewed journals, and relevant trade magazines, and 6) Post the resulting datasets on a publicly accessible website, which can be built on in future years. The end objective is to identify technical barriers preventing the adoption of X-ray and e-beam technologies, and share the results to encourage X-ray and e-beam adoption where feasible.

sterilization, alternate technologies, gamma, X-ra↗