Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “exploration medical capability”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Empowering Lineworkers: The Case for Active Exoskeletons in Utility Work

Exoskeletons have evolved from early medical prototypes to advanced systems capable of addressing physical demands in various industries. This report explores the potential of active exoskeleton technology within the utility sector, focusing on its application for linemen who face significant risks of work-related musculoskeletal disorders (WMSDs). By analyzing existing literature on exoskeletons across industries such as construction, manufacturing, and military, the study identifies a gap in utility-specific applications. Task-specific design features like gravity compensation, limb support, and advanced safety measures, improve exoskeletons’ potential to alleviate physical strain, reduce workplace injuries, and enhance productivity. This review emphasizes the need for targeted research and development to optimize exoskeleton designs for the utility sector to provide benefits for workers, companies, and the broader community.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Public Health Response and Medical Management of Internal Contamination in Past Radiological or Nuclear Incidents: A Review

Following a radiological or nuclear emergency, workers, responders and the public may be internally contaminated with radionuclides. Screening, monitoring and assessing any internal contamination and providing necessary medical treatment, especially when a large number of individuals are involved, is challenging. Experience gained and lessons learned from the management of previous incidents would help to identify gaps in knowledge and capabilities on preparedness for and response to radiation emergencies. In this paper, eight largescale and five workplace radiological and nuclear incidents are reviewed cross 14 technical areas, under the broader topics of emergency preparedness, emergency response and recovery processes. The review findings suggest that 1) new strategies, algorithms and technologies are explored for rapid screening of large populations; 2) exposure assessment and dose estimation in emergency response and dose reconstruction in recovery process are supported by complementary sources of information, including ‘citizen science’; 3) surge capacity for monitoring and dose assessment is coordinated through national and international laboratory networks; 4) evidence-based guidelines for medical management and follow-up of internal contamination are urgently needed; 5) mechanisms for international and regional access to medical countermeasures are investigated and implemented; 6) long-term health and medical follow up programs are designed and justified; and 7) capabilities and capacity developed for emergency response are sustained through adequate resource allocation, routine nonemergency use of technical skills in regular exercises, training, and continuous improvement.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2023

Second Report of the Nuclear Data Subcommittee of the Nuclear Science Advisory Committee

The central importance of the nuclear data curated by the US Nuclear Data Program (USNDP) for clean energy generation, national security, nonproliferation, medical applications, and space exploration as well as basic science was described in a prior report issued by the DOE/NSF Nuclear Science Advisory Committee subcommittee on Nuclear Data (NSAC-ND) in September 2022. In this report, we present a set of fourteen (14) recommendations that will enhance and advance DOE-NP's stewardship of nuclear data. The first three recommendations focus on the existing core USNDP capabilities, namely: 1) Support the nuclear structure evaluation workforce to improve the currency, consistency, and accessibility of the Evaluated Nuclear Structure Data File (ENSDF); 2) Enhance nuclear reaction evaluation within the USNDP in support of the Evaluated Nuclear Data File (ENDF) through expansion of the workforce and integration of high-performance computing, automation, and machine learning and; 3) Continue atomic mass evaluation in support AME and NUBASE databases. This is followed by eight (8) recommendations representing new cross-cutting initiatives involving both measurement and evaluation to address outstanding nuclear data needs. These new initiatives require a highly trained, diverse workforce that includes personnel with expertise from both inside and outside the nuclear physics community from which evaluators have traditionally been recruited. As such, many of these initiatives are accomplished via a Topical Nuclear Data Collaborations (TNDC). A TNDC is made up of domestic and international stakeholders, subject matter and nuclear data experts, and nuclear data evaluators and features a workforce development plan to ensure that nuclear data evaluators maintain currency in the relevant applications and are seen as equity partners in the endeavor. These include: 1) Establish a coordinated effort to improve evaluation and modeling in nuclear astrophysics for stellar dynamics, multi-messenger astronomy and nucleosynthesis; 2) Initiate a TNDC to develop and maintain nuclear structure evaluation beyond discrete states, including nuclear level densities, photon strength functions and photonuclear data for improved reaction modeling, and exploring nuclear structure at finite temperature; 3) Create a TNDC to perform correlated fission data evaluation, including cross sections, fragment yields, v(A), v(E n ) for nuclear energy, national security, nonproliferation and basic science; 4) From a panel of subject matter experts to establish and annually update a roster of key decay data to nurture its accelerated dissemination including both measurement and evaluation for targeted high-value nuclides for national security, nonproliferation and medical applications; 5) Comprehensive, consistent neutron-induced structure and reaction data for nuclear energy, national security, nonproliferation and planetary nuclear spectroscopy; 6) Charged-particle stopping powers for detector design, space effects and ion beam therapy; 7) High-energy reactions for space exploration and medical nuclide production, and; 8) The creation of an infrastructure for open data and data preservation for use by the entire nuclear physics community. All told, these initiatives require approximately $6.5M increase in NP support of the USNDP in fiscal year 2023 dollars and would require at least 3-5 years to carry out due to the length of time needed to recruit and train new nuclear data researchers. This relatively modest investment would help ensure that the fruits of the nuclear data research carried out by DOE-NP and its collaborators would be brought to bear to address some of the most important needs of our nation and the world. To ensure effective execution of this plan, we present an overview of recruitment, training, and retention goals for the USNDP, the centerpiece of which is a mutually agreed upon code of conduct. Finally, we identify the facility and instrumentation needed to perform the recommended experimental activities. This includes a short review of target fabrication capabilities, reactors, neutron beam, light- and heavy-stable ion, gamma-ray, high-energy and radioactive ion beam facilities. Lastly, a more complete appendix of experimental facilities previously compiled is included with new input provided for 6 facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

CRADA Number NFE-19-07859 with Purist, Inc. (CRADA Final Report)

Cooperative Research and Development Agreement (CRADA) NFE-19-07859 between Oak Ridge National Laboratory (ORNL) and Purist Inc. (Purist) focused on developing a technology for production of high purity radioisotopes using small-scale underutilized research nuclear reactors. Purist is developing a novel production mechanism that will produce, and efficiently isolate radioisotopes with high specific activity at small-scale research reactors. The goal of this technology is to enable underutilized smaller scale reactors, typically not capable of producing medical grade radioisotopes, to be utilized as a production supply source. Such radioisotope production facilities will complement production efforts of the few larger production facilities to reduce and prevent risk in the medical isotope supply chain. The work done under this CRADA explored target development, to maximize isotope production and separation using Purist’s technology to ultimately obtain a radioisotope product with high specific activity for use in medical applications. Furthermore, under this CRADA post production, capture, concentration and encapsulation of the radioisotope product was studied by developing post irradiation midi to microscale processes to capture, store and release the separated radioisotopes from large volumes of the capture matrix after production, via automated High-Pressure Ion Chromatography (HPIC) and microfluidic purification/separation systems. This will enable concentration of the radioisotope product obtained into small volumes, which will aid in packaging and transporting the final radioisotope product to users.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary

Deep learning (DL) is a subdomain of artificial intelligence algorithms capable of automatically evaluating subtle graphical features to make highly accurate predictions, which was recently popularized in multiple imaging-related tasks. Because of its capabilities to analyze medical imaging such as radiology scans and digitized pathology specimens, DL has significant clinical potential as a diagnostic or prognostic tool. Coupled with rapidly increasing quantities of digital medical data, numerous novel research questions and clinical applications of DL within medicine have already been explored. Similarly, DL research and applications within hematology are rapidly emerging, although these are still largely in their infancy. Given the exponential rise of DL research for hematologic conditions, it is essential for the practising hematologist to be familiar with the broad concepts and pitfalls related to these new computational techniques. This narrative review provides a visual glossary for key deep learning principles, as well as a systematic review of published investigations within malignant and non-malignant hematologic conditions, organized by the different phases of clinical care. In order to assist the unfamiliar reader, this review highlights key portions of current literature and summarizes important considerations for the critical understanding of deep learning development and implementations in clinical practice.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

An Evaluation and Qualification of U.S.-Based Research Reactors for Irradiation Capabilities Supporting Advanced Nuclear Systems

Irradiation experiments are a prerequisite for evaluating nuclear reactor system designs, analyzing the performance of these systems, and obtaining licenses. Likewise, irradiation facilities are necessary for producing the radioisotopes used in industrial and medical applications. Recent developments in modeling and simulation capabilities and advancements in computational resources have further enabled the design of irradiation experiments for evaluating radiation-induced phenomena and determining nuclear fuel, material, and system design and safety criteria pertaining to both normal and accident scenarios. These computational tools and models require comprehensive experimental datasets acquired under prototypic radiation conditions—for exploring material and system performance under the uniquely harsh environments found in nuclear reactors—to enable verification and validation for qualification and licensing purposes. However, qualification of irradiation experimental facilities, primarily research and test reactors (RTRs), necessitates that their performance be evaluated based on the irradiation environment (e.g. flux, power, testing capabilities) using an appropriate scoring matrix. Although many university campus RTRs are available for research and development (R&D) activities and initiatives, this study focuses on evaluating and qualifying the irradiation facilities (mostly RTRs) within the United States that are suitable for advanced nuclear fuel, material, and system irradiation experiments aimed at establishing operational-performance limits and informing component and fuel designs so as to improve operational efficiencies and mitigate proliferation vulnerabilities, as well as for radioisotope production aimed at multipurpose applications. As a result, the findings of the present study support the acceleration of nuclear fuel and material qualifications, thus hastening new and advanced nuclear energy system demonstrations and radioisotope production efforts by using extended R&D.

irradiation experiment↗
DOE OSTI2024

A Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function

A collaborative team from Texas A&M AgriLife Research, ABQMR Inc., the Soil Health Institute, the Athinoula A. Martinos Center for Biomedical Imaging, and NIST developed low-field magnetic resonance imaging (LF-MRI) instrumentation capable of imaging intact soil-root systems. The system measured root biomass, architecture, 3D mass distribution, and growth rates, providing a non-destructive means to evaluate ideal plant characteristics based on root metrics. It also successfully generated three-dimensional images of soil water content, a key property influencing root growth and exploration. Operating much like an MRI used in a medical setting, the system functioned in field conditions without damaging plants, overcoming the limitations of traditional methods such as trenching, soil coring, and root excavation. Over the course of the project, the team designed and built three functional prototype systems. These prototypes provided new insights into root–water–soil interactions that drive processes such as nutrient uptake, water use, and carbon management. This information contributed to efforts to optimize plants for carbon sequestration without sacrificing economic yield. The project also supported the identification of desirable traits for energy sorghum, including high root growth rates, more vertical root angles, and enhanced drought resilience under water-limiting conditions.

09 BIOMASS FUELS↗
DOE OSTI2025

Direct ink writing of aqueous-based Gadolinium (III) oxide slurries

Gadolinium (III) oxide (gadolinia, Gd 2 O 3 ) has recently been identified as an intriguing material for applications in the medical, solid oxide fuel cell, and nuclear industries. This interest drives the need for developing and understanding manufacturing techniques that can produce dense Gd 2 O 3 structures. Direct ink writing (DIW), an extrusion-based additive manufacturing method, has also garnered interest because of its capability to produce dense ceramic parts with increased complexity in an economical manner. In this study, DIW was explored as a manufacturing technique for Gd 2 O 3 . Experiments were performed to develop Gd 2 O 3 bearing inks capable of being processed via DIW. Ink solids loading and sintering temperatures were varied to assess their impact on the final density and microstructure. Optimum sintering conditions are proposed and were experimentally verified at a dwell temperature of 1500°C. Gd 2 O 3 samples were successfully manufactured using DIW, achieving densities greater than 96 % of the theoretical density.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Impurity-enhanced core valence luminescence via Zn-doping in cesium magnesium chlorides

Scintillators with faster timing capabilities are currently in high demand for use in radiation detection systems in the fields of nuclear and medical physics. The limited number of suitable materials that meet the performance criteria of next generation detection systems presents an opportunity for discovery of new fast scintillator materials. In this work, the effects of doping several ultrafast core-valence luminescent (CVL) scintillators with divalent Zn is explored. Three compounds are investigated – CsMgCl 3 , Cs 2 MgCl 4 , and Cs 3 MgCl 5 – and single crystals of each doped with 5 mol% Zn are grown via the Bridgman method. Additionally, mixing across the full range of concentrations (from 0 % to 100 % Zn) is explored in the Cs 2 Mg 1-x Zn x Cl 4 and Cs 3 Mg 1-x Zn x Cl 5 systems. For low concentrations of Zn, light yields of all three compounds are enhanced (by up to ~60 %) compared to the pure crystals, achieving what we believe to be the brightest known CVL, CsMgCl 3 :Zn 5 % (3400 ± 170 ph/MeV light yield). More importantly, Zn doping does not affect the ultrafast timing properties, with each composition maintaining a single-component decay time around 1–3 ns. A sub-100 ps coincidence time resolution (CTR) is also achieved with CsMgCl 3 :Zn 5 %. The results of this work reveal a new avenue towards obtaining brighter CVL materials, which could open up possibilities for more advanced ultrafast scintillators to be discovered moving forward.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
DOE OSTI2023

Millimeter-Thick Liquid Crystalline Elastomer Actuators Prepared by-Surface-Enforced Alignment

Here, liquid crystalline elastomers (LCE) are thermally cyclable, compliant actuators with compelling mechanical properties. The large and programmable deformation of LCE has led to numerous functional examinations spanning optics, medical devices, and robotics. A well-established method to prepare complex LCE actuators is to utilize surface-enforced photoalignment. Herein, a facile and scalable approach is reported to circumvent the physical limits of surface-enforced alignment (e.g., samples that are 50 µm or less) to amplify the achievable force output in LCE. Applying an approach termed direct layering, the thermomechanical response of LCE elements prepared with +1 disclination patterns in a range of compositions and thicknesses is contrasted. The design and preparation of +1 disclination patterns and arrays is explored to assess the contribution of sample geometry and overlap to deformation and force output. The methodology detailed in this contribution allows for the preparation of elements ≈1 mm in thickness that are capable of actuating large objects. Furthermore, the fabrication of these elements uniquely enables the realization of mechanical instabilities to hasten the actuation rate in response to thermal change and to enable leaping.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Triggering cell death in cancers using self-illuminating nanocomposites

Bioinspired photocatalysis has resulted in efficient solutions for many areas of science and technology spanning from solar cells to medicine. Here we show a new bioinspired semiconductor nanocomposite (nanoTiO 2 DOPA-luciferase, TiDoL) capable of converting light energy within cancerous tissues into chemical species that are highly disruptive to cell metabolism and lead to cell death. This localized activity of semiconductor nanocomposites is triggered by cancer-generated activators. Adenosine triphosphate (ATP) is produced in excess in cancer tissues only and activates nearby immobilized TiDoL composites, thereby eliminating its off-target toxicity. The interaction of TiDoL with cancerous cells was probed in situand in real-time to establish a detailed mechanism of nanoparticle activation, triggering of the apoptotic signaling cascade, and finally, cancer cell death. Activation of TiDoL with non-cancerous cells did not result in cell toxicity. Exploring the activation of antibody-targeted semiconductor conjugates using ATP is a step toward a universal approach to single-cell-targeted medical therapies with more precision, efficacy, and potentially fewer side effects.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN—an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Science & Technology - Other Topics↗
DOE OSTI2024

A Simulation Modeling Approach to Optimizing Nuclear Waste Dispositioning

The dispositioning of nuclear waste generated at facilities across the country is an ongoing battle that affects us all. National laboratories and research centers dealing in medical research, clean energy, and other nuclear activities such as the Department of Energy (DOE) facilities face the need to properly manage and dispose of nuclear waste. A dynamic modeling solution would enable the DOE and others to make decisions on waste disposal and technological options. In doing so, this research explores modeling techniques using available data to address these situations. The focus being on developing an initial robust and adaptable discrete event model using the ExtendSim tool. This modeling effort will target the dispositioning of transuranic waste at the Savannah River National Laboratory (SRNL) which can be expanded to represent the current state of disposition process for waste generated at other DOE facilities. The model aims to assess resource allocation and waste processing options to stabilize productivity and cut the backlog of nuclear waste. By assessing the results of different scenarios, this research aims to provide actionable insights for the DOE. This approach has the potential to significantly improve the management of radioactive waste, offering the capability of evaluating options for optimizing the process for nuclear waste disposal. The findings of this study can serve as a valuable resource for decision-makers and other national laboratories, or research entities engaged in nuclear operations by enabling them to make more informed choices.

Andaverde, Alexis↗
DOE OSTI2023

Measurement and applications: Exploring the challenges and opportunities of hierarchical federated learning in sensor applications

Sensor applications have become ubiquitous in modern society as the digital age continues to advance. AI-based techniques (e.g., machine learning) are effective at extracting actionable information from large amounts of data. An example would be an automated water irrigation system that uses AI-based techniques on soil quality data to decide how to best distribute water. However, these AI-based techniques are costly in terms of hardware resources, and Internet-of-Things (IoT) sensors are resource-constrained with respect to processing power, energy, and storage capacity. These limitations can compromise the security, performance, and reliability of sensor-driven applications. To address these concerns, cloud computing services can be used by sensor applications for data storage and processing. Unfortunately, cloud-based sensor applications that require real-time processing, such as medical applications (e.g., fall detection and stroke prediction), are vulnerable to issues such as network latency due to the sparse and unreliable networks between the sensor nodes and the cloud server [1]. As users approach the edge of the communications network, latency issues become more severe and frequent. A promising alternative is edge computing, which provides cloud-like capabilities at the edge of the network by pushing storage and processing capabilities from centralized nodes to edge devices that are closer to where the data are gathered, resulting in reduced network delays [2], [3].

Po-Leen Ooi, Melanie↗
DOE OSTI2025

Nuclear excitation functions for medical isotope production: Targeted radionuclide therapy via nat IR$(d, x)$ 193m Pt

193m Pt is an Auger emitting radionuclide which may have therapeutic potential, particularly when labeled to the chemotherapeutic drug cisplatin. One challenge to broader explorations of its clinical potential is the need for production routes with high specific activity. As part of a larger campaign to address gaps in reaction data for emerging medical radionuclides, this work seeks to characterize the nat Ir(d,x) reactions as a potential production pathway for 193m Pt. A stacked target irradiation, consisting of natural iridium, iron, nickel, and copper foils, was performed using a 33 MeV deuteron beam at the Lawrence Berkeley National Laboratory 88-Inch Cyclotron. This measurement, along with previous experimental data, suggests an energy window between 11 to 18 MeV to maximize the production and radiopurity of 193m Pt. This experiment has yielded cross sections for 43 channels of deuteron-induced reactions from threshold to 30 MeV, including the first experimental results of nat Ir(d,x) 188m1+g,190m1+g Ir (cumulative), nat Ni(d,x) 56,57,58 m,58g Co (independent), nat Cu(d,x) 61 Co (cumulative) and nat Fe(d,x) 53 Fe, 48 V (cumulative). The results were compared with literature data, the TENDL-2023 database, and default theoretical calculations from the TALYS-2.04, CoH-3.6.0, EMPIRE 3.2.3, and ALICE-2020 reaction modeling codes. Here, this work presents another example of the lack of predictive capabilities for this set of modern nuclear-reaction modeling codes, and highlights the unsatisfactory modeling of experimental cross sections. Experimental data are important to improve the codes in general, and new experimental results can be used to improve the models. Finally, this measurement has revealed the need for an updated evaluation of the nat Cu(d,x) 63 Zn deuteron monitor reaction.

193mPt↗
DOE OSTI2024

Porous Polymer Structures with Tunable Mechanical Properties Using a Water Emulsion Ink

Recently, the manufacturing of porous polydimethylsiloxane (PDMS) with engineered porosity has gained considerable interest due to its tunable material properties and diverse applications. An innovative approach to control the porosity of PDMS is to use transient liquid phase water to improve its mechanical properties, which has been explored in this work. Adjusting the ratios of deionized water to the PDMS precursor during blending and subsequent curing processes allows for controlled porosity, yielding water emulsion foam with tailored properties. The PDMS-to-water weight ratios were engineered ranging from 100:0 to 10:90, with the 65:35 specimen exhibiting the best mechanical properties with a Young’s Modulus of 1.17 MPa, energy absorption of 0.33 MPa, and compressive strength of 3.50 MPa. This led to a porous sample exhibiting a 31.46% increase in the modulus of elasticity over a bulk PDMS sample. Dowsil SE 1700 was then added, improving the storage capabilities of the precursor. The optimal storage temperature was probed, with -60 °C resulting in great pore stability throughout a three-week duration. The possibility of using these water emulsion foams for paste extrusion additive manufacturing (AM) was also analyzed by implementing a rheological modifier, fumed silica. Fumed silica’s impact on viscosity was examined, revealing that 9 wt% of silica demonstrates optimal rheological behaviors for AM, bearing a viscosity of 10,290 Pa·s while demonstrating shear-thinning and thixotropic behavior. This study suggests that water can be used as pore-formers for PDMS in conjunction with AM to produce engineered materials and structures for aerospace, medical, and defense industries as sensors, microfluidic devices, and lightweight structures.

36 MATERIALS SCIENCE↗