Fabrication of radioactive and non-radioactive titanate and zirconate ceramics for immobilization of
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The thermal and mechanical properties of martensitic stainless steel 422 were evaluated for suitability as a drop-in replacement for 4140 steel in next generation heavy-duty diesel engine (HDDE) pistons. The time and temperature of the austenitization and tempering steps were studied to achieve optimum materials performance in piston applications, including the balance of thermal and mechanical properties and resistance to long-term thermal aging. Reducing the tempering temperature from 700 to 600 °C caused a substantial increase in elevated temperature strength from 25 to 600 °C, but had no significant influence on thermal conductivity, suggesting that thermal conductivity in 422 is dominated largely by composition and distribution of alloying elements and mostly independent of the sub-grain structure size and precipitate size. Compared to the current HDDE piston alloy 4140, 422 exhibits substantially higher elevated temperature strength and lower thermal conductivity, the latter which will cause 422 to operate at higher temperatures in pistons, possibly requiring a piston redesign to take advantage of the improved high temperature strength of 422. Piston material selection and alloy design strategies with potential to mitigate some of the shortcomings of martensitic stainless steels, such as 422, as drop-in replacements are discussed.
In this work, dissimilar rotary inertia friction welds between 422 martensitic stainless steel and 4140 martensitic low-alloy steel were made to fabricate prototype heavy-duty diesel engine pistons. The influence of the inertia friction welding process and post weld heat treatment (PWHT) temperature on the interfacial microstructure evolutions and corresponding effects on mechanical properties of the 422/4140 welds were evaluated in detail. Carbon diffused from the 4140 side to the 422 side during PWHT at 650 °C for 1.5 h, causing the formation of a hard carbide-rich layer on the 422 side, and a softer but discontinuous C-depleted layer the 4140 side. PWHT at 700 °C for 1.5 h greatly accelerated C diffusion across the interface relative to 650 °C, resulting in a thicker hard carbide-rich layer and a relatively thick and continuous layer of coarse C-depleted grains (ferrite) on the 4140 side. In addition, the PWHT temperature greatly influenced the tensile properties and fracture behavior of the welds, with the 650 °C PWHT-ed samples failing predominately in a ductile manner in the 4140 heat affected zone during tensile testing. Conversely, the 700 °C PWHT specimens exhibited a strength reduction compared with the 650 °C PWHT specimens because of additional coarsening of the interfacial ferrite layer and softening of the base materials during PWHT, with brittle fracture between the hard and soft layers the predominate failure mechanism. Based on the findings, a reduced PWHT temperature and/or time, minimizing the hardness differential of the base metals, and pre-heating the 422 steel prior to welding are the potential pathways to achieve a more optimal balance between desirable tempering and stress relief of the weld microstructure and undesirable C migration across the weld interface, and to reduce the strength mismatch across the weld.
Vertical Fixed Field Accelerators (vFFAs) feature complex and highly non-linear magnetic fields, which require simulation codes allowing step-wise particle tracking. Methods to model the 3D magnetic field of scaling vFFAs have been developed in the ray-tracing code Zgoubi. The field modeling and particle tracking methods include the field non-linearities, the fringe fields, and the field superposition of neighboring magnets. The procedure implements the vFFA analytical field expressions, allowing design studies and parameter optimizations using the Zgoubi built-in fit method. The vFFA procedure has been applied to a ten-fold symmetry ring with a triplet focusing structure designed to accelerate protons from 3 MeV to 12 MeV and studied under the ISIS-II proton driver prototype project. Results from particle tracking in externally generated 3D semi-analytical field maps and the developed vFFA analytical model are shown to be in excellent agreement.
Molten salt reactors (MSRs) are gaining attention due to their potential for safe, carbon-free nuclear energy with reduced waste. However, licensing these reactors is hindered by limited experimental data on fueled salts, both pre- and post-irradiation. Here, the novel Molten-salt Research Temperature-controlled Irradiation (MRTI) vehicle was designed to address knowledge gaps in irradiating enriched‑uranium-bearing salts. The MRTI experiment irradiated 13 cm 3 of UCl₃-NaCl (93 % U-235) salt in the Neutron Radiography (NRAD) Reactor, achieving a burnup of 0.196 GWd/MTU over 390 h. Despite a heater failure, the thermocouple data suggested fission heat kept the salt molten. The MRTI assembly was remotely disassembled for nondestructive post-irradiation examination (PIE), which included precision gamma-ray scanning (PGS) and neutron radiography. Radiograph images showed the location of the salt and the solidification pattern. PGS results provided an early indication that activated materials of construction did not increase in relative intensity in the region of the capsule where the salt was in contact with the material of construction. Additionally, PGS data showed the presence of several gamma emitting fission products, such as Nb-95, Zr-95, Ru-103, Ce-141, and La-140, where Ru-103 had the highest counts at the bottom of the capsule. Computational fluid dynamics modeling supported observations of salt solidification patterns and proved to be a valuable tool to inform PIE activities. The MRTI experiment has thus far provided critical data and lessons learned for fuel salt PIE activities, essential for advancing the technical readiness of MSRs.
Molten salt reactors (MSRs) have garnered increasing attention recently with several demonstration efforts on the way. A key challenge to the licensing basis for these reactors is the lack of experimental data on fueled salts. This is expected to be crucial to the safety evaluation and licensing basis of reactors of this type deployed in the future. While capability for irradiating molten salts has started being reestablished in the recent decade, no enriched fuel irradiation capability has been developed and tested as of yet. A new experiment vehicle under development at Idaho National Laboratory (INL) is presented here. The Molten-salt Research Temperature-controlled Irradiation (MRTI) experiment was developed to host enriched-uranium bearing salt samples to be irradiated at a test reactor within the lab complex. One of the key scientific objectives is to provide irradiated salt samples for post irradiation examination (PIE) to study the impact of fission product generation and neutron/gamma radioactivity on the salt solution and salt-facing wall material. This paper provides a detailed overview of the mechanical design of the experiment, followed by an overview of the fabrication and assembly of an initial prototype vehicle (with non-fuel bearing salt). A summary of the key analyses conducted as a part of the performance and safety evaluation is then provided. Lastly, an overview of the test conducted in prototypic out-of-pile (non-neutron) environment are shown. These evaluations provide the foundation for a planned irradiation of and enriched uranium-bearing chloride salt sample in the near term. The upcoming irradiation will contain 13cm 3 of UCl 3 -NaCl salt (93% enrichment) generating around 20 W/cm 3 of fission energy during irradiation and a temperature range that can be contained between bounds of 525-900°C.
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Pyrolysis of polyvinyl chloride (PVC) is considered an alternative to traditional, mechanical methods of recycling. However, there is insufficient research conducted on the thermal decomposition pathways of PVC, particularly the fate of chlorinated hydrocarbons generated during the chemical recycling process. One significant product from the pyrolysis of PVC is (2- chloroethyl)benzene. Using a hyperthermal tubular reactor and matrix-isolation FTIR techniques, the pyrolysis products of gasphase (2-chloroethyl)benzene were identified. Following pyrolysis at 1400 K, the FTIR spectra indicated the formation of HCl, styrene, phenylacetylene, benzene, vinylacetylene, acetylene, propyne, ethylene, and propargyl radical.
The main protease of SARS-CoV-2 (M pro ) is the most promising drug target against coronaviruses due to its essential role in virus replication. With newly emerging variants there is a concern that mutations in M pro may alter the structural and functional properties of protease and subsequently the potency of existing and potential antivirals. We explored the effect of 31 mutations belonging to 5 variants of concern (VOCs) on catalytic parameters and substrate specificity, which revealed changes in substrate binding and the rate of cleavage of a viral peptide. Crystal structures of 11 M pro mutants provided structural insight into their altered functionality. Additionally, we show M pro mutations influence proteolysis of an immunomodulatory host protein Galectin-8 (Gal-8) and a subsequent significant decrease in cytokine secretion, providing evidence for alterations in the escape of host-antiviral mechanisms. Accordingly, mutations associated with the Gamma VOC and highly virulent Delta VOC resulted in a significant increase in Gal-8 cleavage. Importantly, IC50s of nirmatrelvir (Pfizer) and our irreversible inhibitor AVI-8053 demonstrated no changes in potency for both drugs for all mutants, suggesting M pro will remain a high-priority antiviral drug candidate as SARS-CoV-2 evolves.
With rising consumer demands, society is tapping into wastewater as an innovative source to recycle depleting resources. Novel reclamation technologies have been recently explored for this purpose, including several that optimize natural biological processes for targeted reclamation. However, this emerging field has a noticeable dearth of synthetic material technologies that are programmed to capture, release, and recycle specified targets; and of the novel materials that do exist, synthetic platforms incorporating biologically inspired mechanisms are rare. We present here a prototype of a materials platform utilizing peptide amphiphiles that has been molecularly engineered to sequester, release, and reclaim phosphate through a stimuli-responsive pH trigger, exploiting a protein-inspired binding mechanism that is incorporated directly into the self-assembled material network. This material is able to harvest and controllably release phosphate for multiple cycles of reuse, and it is selective over nitrate and nitrite. We have determined by simulations that the binding conformation of the peptide becomes constrained in the dense micelle corona at high pH such that phosphate is expelled when it otherwise would be preferentially bound. However, at neutral pH, this dense structure conversely employs multichain binding to further stabilize phosphate when it would otherwise be unbound, opening opportunities for higher-order conformational binding design to be engineered into this controllably packed corona. With this work, we are pioneering a new platform to be readily altered to capture other valuable targets, presenting a new class of capture and release materials for recycling resources on the nanoscale.
Abstract This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.
Ferroelectric materials have technological applications in information storage and electronic devices. The ferroelectric polar phase can be controlled with external fields, chemical substitution and size-effects in bulk and ultrathin film form, providing a platform for future technologies and for exploratory research. In this work, we integrate spin-polarized density functional theory (DFT) calculations, crystal structure databases, symmetry tools, workflow software, and a custom analysis toolkit to build a library of known, previously-proposed, and newly-proposed ferroelectric materials. With our automated workflow, we screen over 67,000 candidate materials from the Materials Project database to generate a dataset of 255 ferroelectric candidates, and propose 126 new ferroelectric materials. We benchmark our results against experimental data and previous first-principles results. The data provided includes atomic structures, output files, and DFT values of band gaps, energies, and the spontaneous polarization for each ferroelectric candidate. Finally, we contribute our workflow and analysis code to the open-source python packages atomate and pymatgen so others can conduct analogous symmetry driven searches for ferroelectrics and related phenomena.
Section 405(d) of the Clean Water Act requires the US Environmental Protection Agency to review sewage sludge regulations every two years to identify any additional pollutants that may occur in biosolids and to set regulations for pollutants identified in biosolids if sufficient scientific evidence shows they may harm human health or the environment. To date, EPA has conducted eight biennial reviews to identify chemical and microbial pollutants and three national sewage sludge surveys to identify pollutants and obtain concentration data for chemicals found in biosolids. Prior to 2021, there was inconsistent reporting of chemicals identified and EPA did not cumulatively track chemicals in biosolids. Through the efforts presented here, EPA produced a list of 726 chemicals and structure-based classes found in biosolids based on biennial reviews and national sewage sludge surveys. Summary statistics of concentration data are also reported for the 484 chemicals found in the three national sewage sludge surveys. The creation of the Biosolids List supports EPA in assessing the potential risk of chemical pollutants found in biosolids.
Heat-resistant steels with high chromium additions (≥5 weight percent) are critical for many high temperature energy and manufacturing applications, including heat exchangers, pistons for engines, and dies for metal working and casting. However, while high chromium additions increase oxidation resistance at elevated temperatures, they also compromise thermal conductivity, resulting in a metallurgical trade-off between these two important properties. Here we show that a microstructure with both higher thermal conductivity and improved oxidation resistance at elevated temperatures is achieved in a unique steel with only 1 weight percent chromium, thereby overcoming the long-standing metallurgical trade-off. This is accomplished through a tailored thermal treatment that produces a tempered martensitic matrix with low solute content and a fine dispersion of copper precipitates and molybdenum enriched carbides. A further discovery is that the resultant thermally grown oxide includes an iron-copper-manganese-enriched outer layer that provides high-temperature oxidation protection equivalent to heat-resistant steels with five times the chromium content and 25% lower thermal conductivity.
Here we report that two-dimensional (2D) excitons arise from electron-hole confinement along one spatial dimension. Such excitations are often described in terms of Frenkel or Wannier limits according to the degree of exciton spatial localization and the surrounding dielectric environment. In hybrid material systems, such as the 2D perovskites, the complex underlying interactions lead to excitons of an intermediate nature, whose description lies somewhere between the two limits, and a better physical description is needed. Here, we explore the photophysics of a tuneable materials platform where covalently bonded metal-chalcogenide layers are spaced by organic ligands that provide confinement barriers for charge carriers in the inorganic layer. We consider self-assembled, layered bulk silver benzeneselenolate, [AgSePh] ∞ , and use a combination of transient absorption spectroscopy and ab initio GW plus Bethe-Salpeter equation calculations. We demonstrate that in this non-polar dielectric environment, strongly anisotropic excitons dominate the optical transitions of [AgSePh] ∞ . We find that the transient absorption measurements at room temperature can be understood in terms of low-lying excitons confined to the AgSe planes with in-plane anisotropy, featuring anisotropic absorption and emission. Finally, we present a pathway to control the exciton behaviour by changing the chalcogen in the material lattice. Our studies unveil unexpected excitonic anisotropies in an unexplored class of tuneable, yet air-stable, hybrid quantum wells, offering design principles for the engineering of an ordered, yet complex dielectric environment and its effect on the excitonic phenomena in such emerging materials.
Tragically, the death toll from the COVID-19 pandemic continues to rise, and with variants being observed around the globe new therapeutics, particularly direct-acting antivirals that are easily administered, are desperately needed. Studies targeting the SARS-CoV-2 3CL protease, which is critical for viral replication, with different peptidomimetics and warheads is an active area of research for development of potential drugs. To date, however, only a few publications have evaluated the nitrile warhead as a viral 3CL protease inhibitor, with only modest activity reported. This article describes our investigation of P3 4-methoxyindole peptidomimetic analogs with select P1 and P2 groups with a nitrile warhead that are potent inhibitors of SARS-CoV-2 3CL protease and demonstrate in vitro SARS-CoV-2 antiviral activity. A selectivity for SARS-CoV-2 3CL protease over human cathepsins B, S and L was also observed with the nitrile warhead, which was superior to that with the aldehyde warhead. A co-crystal structure with SARS-CoV-2 3CL protease and a reversibility study indicate that a reversible, thioimidate adduct is formed when the catalytic sulfur forms a covalent bond with the carbon of the nitrile. Furthermore, this effort also identified efflux as a property limiting antiviral activity of these compounds, and together with the positive attributes described these results provide insight for further drug development of novel nitrile peptidomimetics targeting SARS-CoV-2 3CL protease.
Neutron and x-ray scattering represent two classes of state-of-the-art materials characterization techniques that measure materials structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems from catalysts to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and x-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and x-ray techniques, including neutron scattering, x-ray absorption, x-ray scattering, and photoemission. We highlight the integration of machine learning methods into the typical workflow of scattering experiments, focusing on problems that challenge traditional analysis approaches but are addressable through machine learning, including leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials representations, mitigating spectral noise, and others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.
Although drift-ordered fluid models are widely applied in tokamak edge turbulence simulations, the models used are acknowledged not to conserve energy or even electrical charge. The present paper aims to remove many of the existing pitfalls in drift-fluid models, however, with the objective of finding a solution simple enough to be implemented in numerical applications. Our main result is an improved version of the drift-Braginskii equations involving a generalized vorticity function. In the new drift-Braginskii system, the quasi-neutrality condition translates into a transport equation for a generalized vorticity, expressed in conservation form, and related to the total mass-weighted circulation. It is found that kinetic energy conservation can be achieved if the polarization flow is defined recursively. In conclusion, the resulting model conserves the kinetic energy associated with E×B and diamagnetic flows and retains the associated perpendicular kinetic energy flux.