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219 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Mechanisms of Retention and Transport of Fission Products in Virgin and Irradiated Nuclear Graphite

To fulfill the design and licensing requirements for advanced reactors, the U.S. Department of Energy (DOE) is specifically interested in targeted research to quantify the mechanisms of fission product transport and retention in graphitic grades that will likely be used in next-generation very/high temperature reactors, fluoride salt-cooled high temperature reactors and molten salt reactors. We have examined an integrated experimental-computational approach to determine the diffusivities of selected fission products – ruthenium (Ru) and silver (Ag) in five nuclear graphite grades – POCO AXF-5Q, POCO ZXF-5Q, PCEA, IG110, and NBG-18. Experimental investigation for diffusion of other fission products (iodine, cesium, and krypton) in graphite could not be carried out due to extreme evaporation due to high vapor pressure, despite several attempts incorporating various experimental schemes. Experimental investigation with irradiated graphite could not be carried out because necessary approval to transport irradiated graphite could not be obtained due to procedural reasons at ORNL and national policy change in UK. An effort was made to “produce” by ion sputtering, but it was discontinued because the outcome did not adequately represent irradiated graphite. Thin film and “cup-and-cap” methods were employed to deposit fission products, diffusion annealed were carried out in an argon atmosphere, and concentration profiles were determined by dynamic secondary ion mass spectroscopy depth profiling technique. Regardless of graphite type, anomalous diffusion behavior of Ru (i.e., lower than expected based on Arrhenius temperature dependence) at an intermediate temperature, around 600 to 700 °C, was observed. Diffusion behavior of silver on the other hand agreed well to the Arrhenius temperature dependence although the excessive evaporation of silver hampered the accurate determination of diffusivities at temperature higher than 900 °C. In general, silver was observed to diffuse faster than Ru in the temperature range compared from 500 to 900 °C, had consistently had higher pre-exponential factor corresponding to higher jump frequency. To understand the abnormal diffusional behavior of Ru in graphite in intermediate temperature range, a detailed microstructural analysis was carried out. Clustering of Ru into particles on the surface of graphite was observed around 700 °C corresponding to de-wetting and spheroidization. This change would influence the boundary condition of diffusion, although features of clustering was much smaller (<1 mm) than the SIMS raster size (120 x 120 mm). Raman spectroscopy also demonstrated that Raman peak intensity for Ru decreased significantly for samples annealed higher than 700 °C and presence of Ru in graphite altered the crystallinity of graphite. To further elucidate the abnormal diffusion behavior observed in intermediate temperature investigated, the vibrational properties of graphene, bilayer graphene and Kr atoms in bilayer graphene were investigated with molecular dynamics. Krypton, which is a fission product, was introduced between a bilayer graphene system to evaluate (i) whether the phonon dispersion curves change with high temperature in bilayer graphene (ii) whether the addition of Kr atoms between the bilayer introduces any significant change in the vibrational properties, and (iii) whether any change in the vibrational properties can be correlated to the diffusive behavior of Kr atoms. The results show that the Kr atoms indeed exhibit an anomalous diffusive behavior at 550°C. Phonon analysis shows that a few phonon frequencies are enhanced as much as 20 to 30 % with the addition of just 0.2 at.% Kr. So, this may explain the unexpected self-diffusivity of fission products.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Highly efficient, rapid, and concurrent removal of toxic heavy metals by the novel 2D hybrid LDH–[Sn 2 S 6 ]

According to a United Nations report, by 2050 nearly six billion people worldwide will suffer from clean water scarcity. This is mostly because of the exponential proliferation of world population, urbanization, industrialization, and water pollution. Heavy metals are common water pollutants that can pose grave public health consequences. Existing water purification systems are lack of materials that have the potential for quick, simultaneous, efficient, and cost-efficient removal of numerous toxic metals from wastewater. Here, in this work, we report the design and synthesis of an economically viable Layered Double Hydroxides - Stannic Sulfide, LDH–[Sn 2 S 6 ] that exhibits a rapid, efficient, selective, and concurrent removal of Cu 2+ , Ag + , Cd 2+ , Pb 2+ , and Hg 2+ from parts per million (ppm) level to below 5 parts per billion (ppb) satisfying World Health Organization’s (WHO) safe drinking water limit. Moreover, LDH–[Sn 2 S 6 ] shows exceptionally high removal efficiencies of the above metals in acidic, neutral, and basic conditions. LDH–[Sn 2 S 6 ] also demonstrates enormous sorption capacities of 378, 978, 332, 579, and 666 mg/g for Cu 2+ , Ag + , Cd 2+ , Pb 2+ , and Hg 2+ , respectively. Remarkably, LDH–[Sn 2 S 6 ] displays extraordinary tolerance to the concentrations of Na + , Ca 2+ , Mg 2+ , Cl - , CO 3 2– , NO 3 – ,and SO 4 2- , and other constituents in tap and river water, it efficiently sequestrates Cu 2+ , Ag + , Cd 2+ , Pb 2+ , and Hg 2+ from ppm to safe drinking water levels in minutes. LDH–[Sn 2 S 6 ] shows pseudo-second-order sorption kinetics suggesting chemisorption adsorption mechanism involving M–S bonding. Altogether, the regeneratable LDH–[Sn 2 S 6 ] becomes an exceptional material that shows ultrahigh removal, unprecedented selectivity, rapid adsorption kinetics, wide pH stability, and a massive adsorption capacity. The integration of these features places LDH–[Sn 2 S 6 ] at the top of all adsorbents known to date and thus could be used for wastewater purifications.

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

Even Higher-Level Synthesis: An Exploration of AI Hardware Accelerators using HLS4ML

With the rise of artificial intelligence, the popularization of deep learning, and a constantly evolving industry, the demand for flexible and efficient tools has never been greater. As algorithms grow more complex, their runtime and energy consumption increase exponentially. Customized hardware accelerators, long used for specific mathematical operations, remain essential for managing modern applications' computational and power demands. Hardware accelerators can speed up complex computations by orders of magnitude, but their manual design and verification processes are often challenging and time-consuming. High-Level Synthesis (HLS) provides a solution by transforming high-level algorithm descriptions, typically written in C++ or SystemC, into synthesizable RTL suitable for hardware implementation. This approach reduces development time for RTL engineers while offering flexibility beyond what traditional handwritten RTL can provide. We extended this capability to the machine-learning domain with the open-source framework hls4ml, which allows neural networks trained in Python frameworks like Tensorflow or PyTorch to be synthesized into efficient hardware representations for the traditional FPGA and ASIC flows. This breakthrough addresses the growing need for reduced design turnaround and easy verification of ML hardware accelerators with low latency and power efficiency constraints. During this tutorial, we will demonstrate how Python complements HLS by simplifying the ML design process, bridging the gap between software and hardware development. Attendees will explore how we translate neural networks modeled in Python into fixed-point C++ models suitable for HLS workflows. We will dive into strategies like Value-Range Analysis and Quantization-Aware Training, which optimize these designs for deployment and evaluate their accuracy, power consumption, and energy efficiency. To exemplify these concepts, experts from Fermilab will share their experiences applying this technology to high-energy physics experiments, where real-time, low-latency processing is critical. Over the years, Fermilab engineers have demonstrated how deep neural networks, optimized for hardware using hls4ml, can meet the stringent requirements of trigger systems at the CERN Large Hadron Collider. These systems rely on rapid decision-making to process immense data volumes while retaining only the most relevant events for further analysis. The application of hls4ml has also been extended to innovative technologies like smart pixel arrays. These smart pixels integrate ML inference capabilities directly into sensor devices, enabling localized data processing at the pixel level. This approach drastically reduces the need to transmit raw data to external processing units, significantly decreasing power consumption and latency. By embedding neural networks within the pixel architecture, the smart pixels can identify and prioritize relevant data in real time, providing a highly efficient solution for edge computing in scenarios such as particle detectors and imaging systems. Fermilab's work highlights the potential of hardware-accelerated ML in scenarios where both speed and power efficiency are mission-critical. Through this tutorial, attendees will gain valuable insights into the challenges and solutions of deploying ML in hardware. Understanding how HLS and hls4ml streamline the development of neural network-based hardware accelerators is fundamental for the industry's future. Participants will learn how these technologies are shaping the future of AI and scientific computing.

Di Guglielmo, Giuseppe [Fermilab]↗