Feedbacks between size and density determine rapid eco-phenotypic dynamics
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Environmental Sciences & Ecology↗
SEARCH · Engineering Papers
Results for “experimental evolution”
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.
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Reconstruction of the Room D, B, G, and Q Closure Histories at the Waste Isolation Pilot Plant
Experimental measurements of room closure in salt repositories are valuable for understanding the evolution of the underground and for validating geomechanical models. Room closure was measured during a number of experiments at the Waste Isolation Pilot Plant (WIPP) during the 1980's and 1990's. Most rooms were excavated using a multi-pass mining sequence, where each pass necessarily destroyed some of the mining sequence closure measurement points. These destroyed points were promptly reinstalled to capture the closure after the mining pass. After the room was complete, the mining sequence closure measurement stations were supplemented with remotely read closure measurement stations. Although many aspects of these experiments were thoroughly documented, the digital copies of the closure data were inadvertently destroyed, the non-trivial process of zeroing and shifting the raw closure measurements after each mining pass was not precisely described, the various closure measurements within a given room were not directly compared on the same plot, and the measurements were collected for several years longer than previously reported. Consequently, the hand-written mining sequence closure measurements for Rooms D, B, G, and Q were located in the WIPP archives, digitized, and reanalyzed for this report. The process of reconstructing the mining sequence closure histories was documented in detail and the raw data can be found in the appendices. Within the mid-section of a given room, the reconstructed closure histories were largely consistent with other mining sequence and remotely read closure histories, which builds confidence in the experiments and suggests that plane strain is an appropriate modeling assumption. The reconstructed closure histories were also reasonably consistent with previously published results, except in one notable case: the reconstructed Room Q closure histories 30 days after excavation were about 45 % less than the corresponding closures reported in Munson's 1997 capstone paper.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
A Multi-Scale Computational Platform for Predictive Modeling of Corrosion in Al-Steel Joints (Final Report)
The research team proposed to develop innovative multi-scale models to predict corrosion and the resulting mechanical performances in aluminum-steel joints. The methods of joining considered are resistance spot welding, self-piercing riveting, and rivet-welding, all suitable for mass production applications. The multi-scale models integrate high throughput first-principle calculations based on density functional theory (DFT), high throughput calculation of phase diagrams (CALPHAD) modeling, and finite element method (FEM) simulations. These models are to be validated through laboratory experiments. Furthermore, the models are available as open source so as to enable scientists and engineers in the community to adapt and contribute to the development and application. The approaches rely on the research team’s extensive experience on the prediction of properties of individual phases at finite temperatures and variable compositions through DFT calculations, and our broad expertise on dissimilar material joining and their corrosion. The proposed computational framework enables high throughput computations for improved predictions of corrosion and the associated mechanical performance in dissimilar material joints, resulting in significant reduction in computational time needed by the current state-of-the-art methods. With the participation of researchers from three universities, an auto manufacturer, two manufacturing technology/equipment suppliers, and a software developer/vendor, the interdisciplinary research team applies the technical development on both phase-based modeling and laboratory experiments into the automobile body joining processes for validation and technology demonstration. The global cost of corrosion was estimated at about 3.4% of the global GDP in 2013. By using available corrosion control practices, it is estimated a saving between 15-35% of the cost of corrosion. In the U.S., more than $276 billion is spent repairing corrosion damage. Prediction of the corrosion and its impact on performance of the dissimilar material joints is critical for reducing the massive number of the current corrosion-based recalls for automobiles. Thus, the project goal is to develop models to enable predictive maintenance and end-of-life planning of multi-metal joints with risk of corrosion under different conditions such as exposure to high temperatures in summer and salt solutions in winter, quantified through its pH. An academia-industry consortium led by the University of Michigan and including Pennsylvania State University, University of Illinois Urbana-Champaign, University of Georgia, General Motors Company, Livermore Software Technology Corporation, and Optimal Process Technologies, LLC. created multi-scale models for prediction of corrosion in aluminum-steel joint structures such of them used in vehicle subassemblies – chassis and transmission systems. Starting from the first principle calculations, the team developed mathematical and data-driven models to predict the metallic components, which are formed during joining of two metals, for example aluminum and steel - a lightweight multilateral system which is currently used in more than 60% car bodies. These models were used for simulating chemical reactions that are happening when the joining metallic components are exposed to high temperatures and different pH values. The team was able to predict how the corrosion installs on the metallic components and how they lead to a sudden failure of components in cars. Newly developed machine learning algorithms combining Science, Technology, Engineering and Math disciplines, advanced finite element simulation and experimental validations have been integrated in a platform for prediction of the corrosion evolution and prediction the failure of joints under mechanical loadings and fatigue. Moreover, based on machine learning and inverse analysis, the team proposed solutions for designing new metallic alloys less susceptible to corrosion when joining multi-material assembles. An average of 4% error compared with experiments was achieved for the most common joints that are used in vehicle subassemblies.
36 MATERIALS SCIENCE↗
Activated Lattice Transitions of Six and Twelve-fold Anisotropic Vortex Lattices [Slides]
MD Simulations match experimental SANS data, showing an approximate logarithmic decay with slight upwards concavity. Domain evolution occurs continuously in large domain sections, but domain walls dissolve randomly as penetration depth oscillates.
97 MATHEMATICS AND COMPUTING↗
Effects of dose rate on the microstructure and deuterium retention in γ-LiAlO 2 pellets
This report presents experimental findings obtained from November 2024 to September 2025. Defect accumulation and microstructural evolution during ion irradiation at elevated temperatures are governed by two competing processes: defect production, driven by dose rate, and defect recovery, controlled by defect diffusion, interaction, and annihilation. At a given dose, the resulting microstructural evolution depends on both the dose rate and irradiation temperature. As a continuation of our FY24 tritium science project, which investigated temperature effects at a fixed dose rate, this study focuses on dose-rate effects at a fixed temperature to provide deeper insights into the irradiated microstructure and compositional changes in γ-LiAlO 2 pellets. The study aims to elucidate the impact of dose rate on microstructure, precipitate morphology, deuterium retention, and lithium volatility in γ-LiAlO 2 pellets under sequential 120 keV He + and 80 keV D 2 + ion irradiation. Three dose rates of 7.3×10 4 , 2.9×10 4 and 6.8×10 5 dpa/s were applied to achieve the same total ion fluence of 2×10 17 (He + +D + )/cm 2 at 500 °C, corresponding to a maximum combined dose of 7.55 dpa at ~255 nm. The irradiated pellets were subsequently characterized using scanning transmission electron microscopy (STEM) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). The microstructural response of γ-LiAlO 2 to ion irradiation was found to be strongly dose-rate dependent. At medium and high dose rates, irradiation produced a surface amorphized layer and an underlying crystalline layer containing LiAl 5 O 8 precipitates, with implanted gases accumulating and forming blisters at the crystalline-amorphous interface. It remains to be investigated whether the amorphized layer was produced by He + ion irradiation prior to D 2 + ion irradiation. In contrast, at low dose rates, the material remained crystalline, with cavities, likely gas-filled, distributed around precipitates, within the γ-LiAlO 2 matrix, and along grain boundaries. While precipitate morphology exhibited anisotropy, their size showed little sensitivity to dose rate in the applied range of this study. This result, however, does not rule out the possibility that further reductions in dose rate could influence precipitate size. High dose-rate irradiation enhanced protonium–deuterium isotopic exchange and lithium depletion in the amorphized region. Collectively, the results show that dose rate governs amorphization, gas redistribution, isotopic exchange, and lithium depletion, providing important insights into the mechanisms underlying structural evolution in γ-LiAlO 2 pellets under reactor-relevant irradiation conditions.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Evaluation of Corrosion and Its Impact on the Mechanical Performance of Al–Steel Joints
Aluminum–steel joints are increasingly used in the automotive industry to meet the requirements for energy saving and emission reduction. Among various joining technologies, self-pierce riveting (SPR) and resistance spot welding (RSW) are two well-established technologies for fabricating dissimilar joints with stable and high mechanical performance. However, corrosion will occur in these joints inevitably due to different electrochemical properties, which can degrade the surface quality and the mechanical performance, such as strength. This paper presents a method of understanding the corrosion mechanisms in joining aluminum and steel. For this understanding, a hybrid method combining experimental observations, mechanical properties identification, and analytical approaches was used to assess the evolution of the impact of corrosion on the joining performance, such as traction separation curves. The study was conducted on common combinations used in the vehicles, e.g., a 1.2 mm thickness aluminum alloy (AA 6022) and 2.0 mm thickness hot deep galvanized steel (HDG HSLA 340) joined by SPR and RSW. After the fabrication of these joints, accelerated cyclic corrosion tests of up to 104 cycles were performed, which reproduced the environmental conditions to which a vehicle was exposed. By investigating the microstructural evolution within the joints, the corrosion mechanisms of SPR and RSW joints were revealed, including the initiation and propagation. Moreover, the intrinsic impact of the corrosion on the mechanical performance, including the strength, axial stiffness, and crashworthiness, was analyzed by performing a lap-shear test. It showed that as corrosion proceeds, the fracture modes and mechanical performance are affected significantly.
Chemistry↗
Insights into the Structure and Energy of DNA Nanoassemblies
Since the pioneering work of Ned Seeman in the early 1980s, the use of the DNA molecule as a construction material experienced a rapid growth and led to the establishment of a new field of science, nowadays called structural DNA nanotechnology. Here, the self-recognition properties of DNA are employed to build micrometer-large molecular objects with nanometer-sized features, thus bridging the nano- to the microscopic world in a programmable fashion. Distinct design strategies and experimental procedures have been developed over the years, enabling the realization of extremely sophisticated structures with a level of control that approaches that of natural macromolecular assemblies. Nevertheless, our understanding of the building process, i.e., what defines the route that goes from the initial mixture of DNA strands to the final intertwined superstructure, is, in some cases, still limited. In this review, we describe the main structural and energetic features of DNA nanoconstructs, from the simple Holliday junction to more complicated DNA architectures, and present the theoretical frameworks that have been formulated until now to explain their self-assembly. Deeper insights into the underlying principles of DNA self-assembly may certainly help us to overcome current experimental challenges and foster the development of original strategies inspired to dissipative and evolutive assembly processes occurring in nature.
59 BASIC BIOLOGICAL SCIENCES↗
Extreme shear-deformation-induced modification of defect structures and hierarchical microstructure in an Al–Si alloy
Abstract Extreme shear deformation is used for several material processing methods and is unavoidable in many engineering applications in which two surfaces are in relative motion against each other while in physical contact. The mechanistic understanding of the microstructural evolution of multi-phase metallic alloys under extreme shear deformation is still in its infancy. Here, we highlight the influence of shear deformation on the microstructural hierarchy and mechanical properties of a binary as-cast Al-4 at.% Si alloy. Shear-deformation-induced grain refinement, multiscale fragmentation of the eutectic Si-lamellae, and metastable solute saturated phases with distinctive defect structures led to a two-fold increase in the flow stresses determined by micropillar compression testing. These results highlight that shear deformation can achieve non-equilibrium microstructures with enhanced mechanical properties in Al–Si alloys. The experimental and computational insights obtained here are especially crucial for developing predictive models for microstructural evolution of metals under extreme shear deformation.
36 MATERIALS SCIENCE↗
Off-Stoichiometric Restructuring and Sliding Dynamics of Hexagonal Boron Nitride Edges in Conditions of Oxidative Dehydrogenation of Propane
Boron-containing materials, such as hexagonal boron nitride (h-BN), recently shown to be active and selective catalysts for the oxidative dehydrogenation of propane (ODHP), have been shown to undergo significant surface oxyfunctionalization and restructuring. Although experimental ex situ studies have probed the change in chemical environment on the surface, the structural evolution of it under varying reaction conditions has not been established. Herein, we perform global optimization structure search with a grand canonical genetic algorithm to explore the chemical space of off-stoichiometric restructuring of the h-BN surface under ambient as well as ODHP-relevant conditions. A grand canonical ensemble representation of the surface is established, and the predicted 11B solid-state NMR spectra are consistent with previous experimental reports. In addition, we investigated the relative sliding of h-BN sheets and how it influences the surface chemistry with ab initio molecular dynamics simulations. Furthermore, the B–O linkages on the edges are found to be significantly strained during the sliding, causing the metastable sliding configurations to have higher reactivity toward the activation of propane and water.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
A complete grain-level assessment of the stress-strain evolution and associated deformation response in polycrystalline alloys
Polycrystalline alloys are used pervasively across structural applications contingent upon extensive experimental testing. A statistically representative number of tests are required to expose the variability in the material's performance, as a result of non-uniform microstructures and associated micromechanical fields. In a more direct means of capturing this pertinent information, multi-modal experimental techniques are presented to measure and track the complete micromechanical state, evolving during loading, of each and every grain within the regions of interest. Specifically, a combination of high-energy X-ray diffraction microscopy and digital image correlation coupled with electron backscatter diffraction are conducted on a specimen for each of the alloys, Haynes 282 and Ti7Al. The results of the multi-modal analysis definitively demonstrate that the degree of heterogeneity increases with deformation level and is used to assess the number of grains necessary for a representative volume element description of the stress state for each of these materials. Moreover, higher resolution imaging is used for identification of the slip system activity and subsequently used to study slip transmission events. An accurate knowledge of the resolved shear stress in adjacent grains (grain interactions) is demonstrated to be a key descriptor of the slip transmission events.
36 MATERIALS SCIENCE↗
Electromagnetic & Weak Probes: Experimental Overview
Electromagnetic and electroweak probes are the most versatile probes in the study of heavy-ion collisions. Produced at every stage in the evolution of QCD matter, its messengers are practically inert to the strongly interacting medium they travel through. In this contribution, I will discuss a selection of new results from experiments at the LHC, RHIC, and SIS facilities, spanning almost four orders of magnitude in beam energy. Finally, I will conclude with a brief overview of the experimental landscape in the near future.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Formation of Carbon–Carbon Interlinkage Bonds under High Pressure
The formation of carbon–carbon interlinkage bonds (CCIBs) via the chemical binding of interlayer carbon atoms of many sp 2 -bonded carbon precursors is an essential step for synthesizing various diamond and diamond-like materials. Although the existence of CCIBs may be reasonably assumed under high-pressure conditions, direct experimental evidence has been scarce. Micro-Raman spectroscopy is here employed to track in situ the evolution of C–C bonds in a pressure range from ambient to 54 GPa. A pressure-induced two-stage (polynomial and linear) shift of the G peak and new generation of the CCIB peak at about 1550 cm –1 are observed in multiple types of layer-structured carbon precursors, including glassy carbon, natural graphite, and carbon nanotubes. In conclusion, the experimental discovery of CCIBs holds significance in comprehending phase transitions of sp 2 -bonded carbon materials and has implications for the advancement of novel carbon structures.
Anode materials↗
Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor
With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .
42 ENGINEERING↗
Structural evolution of liquid silicates under conditions in Super-Earth interiors
Molten silicates at depth are crucial for planetary evolution, yet their local structure and physical properties under extreme conditions remain elusive due to experimental challenges. In this study, we utilize in situ X-ray diffraction (XRD) at the Matter in Extreme Conditions (MEC) end-station of the Linear Coherent Linac Source (LCLS) at SLAC National Accelerator Laboratory to investigate liquid silicates. Using an ultrabright X-ray source and a high-power optical laser, we probed the local atomic arrangement of shock-compressed liquid (Mg,Fe)SiO 3 with varying Fe content, at pressures from 81(9) to 385(40) GPa. We compared these findings to ab initio molecular dynamics simulations under similar conditions. Results indicate continuous densification of theO-O and Mg-Si networks beyond Earth’s interior pressure range, potentially altering melt properties at extreme conditions. This could have significant implications for early planetary evolution, leading to notable differences in differentiation processes between smaller rocky planets, such as Earth and Venus, and super-Earths, which are exoplanets withmasses nearly three times that of Earth.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Dyson maps and unitary evolution for Maxwell equations in tensor dielectric media
The propagation and scattering of electromagnetic waves in dielectric media is of theoretical and experimental interest in a wide variety of fields. An understanding of observational results generally requires a numerical solution of Maxwell equations—usually implemented on conventional computers using sophisticated numerical algorithms. In recent years, advances in quantum information science and in the development of quantum computers have piqued curiosity about taking advantage of these resources for an alternate numerical approach to Maxwell equations. This requires a reformulation of the classical Maxwell equations into a form suitable for quantum computers which, unlike conventional computers, are limited to unitary operations. In this paper, a unitary framework is developed for the propagation of electromagnetic waves in a spatially inhomogeneous, passive, nondispersive, and anisotropic dielectric medium. For such a medium, generally, the evolution operator in the combined Faraday-Ampere equations is not unitary. There are two steps needed to convert this equation into a unitary evolution equation. In the first step, a weighted Hilbert space is formulated in which the generator of dynamics is a pseudo-Hermitian operator. In the second step, a Dyson map is constructed which maps the weighted-physical-Hilbert space to the original Hilbert space. Furthermore, the resulting evolution equation for the electromagnetic wave fields is unitary. Utilizing the framework developed in these steps, a unitary evolution equation is derived for electromagnetic wave propagation in a uniaxial dielectric medium. The resulting form is suitable for quantum computing.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Benchmarking of Different Inverse Point Kinetics Implementations for an Autocorrected Reactimeter Algorithm
In November 2017, the Transient Reactor Test Facility returned to operation. Since that time, many transient test series have been completed; each has provided valuable data for materials performance, reactor safety that can be applied in future designs. During each experimental series, detector count rates provided important information on the core behavior during transients. However, a limitation of these data is that variations in the neutron distribution during experiments cause errors when attempting to infer reactivity evolution from detector signals. Neutron physics codes can be used to compute the flux shape variations. However, this is a poor solution when the experimental data is used to do verification, validation and uncertainty quantification (VVUQ) on codes. Indeed, if the output of the code is used both as a reference and to correct what the reference is compared to, the circular dependency limits the quality of the VVUQ approach. To overcome this problem, an Autocorrected Reactimeter Algorithm (ACRA) has been developed. This approach infers time-dependent reactivity evolution by testing different spatial corrections and selecting the one that minimizes reactivity variations when the core is in a frozen configuration (i.e. when there is no variation in parameters affecting reactivity). However, the scope of this method was limited to transients where there were negligible thermal feedback. Indeed, the core is never in a frozen configuration when the fuel temperature varies during the whole transient. This is our motivation for the development of an improved version of the ACRA which does not require frozen configurations
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Bulk‐Boundary Correspondence of Semimetal Ru 3 Sn 7 and Topological Surface States on Chemically Realistic Terminations
Ru 3 Sn 7 is experimentally demonstrated as an efficient catalyst, with potential utilization of topological surface states for hydrogen evolution reaction. Despite its promising catalytic performance, the topological nature of Ru 3 Sn 7 remains uncertain. Particularly, the bulk-boundary correspondence has not yet been established, hence hindering a rigorous justification of its topologically-protected surface states. In this work, the bulk topology of Ru 3 Sn 7 is detailed using first-principles calculations and the topological quantum chemistry formalism. Ru3Sn7 turns out to be an enforced semimetal possessing symmetry-protected crossings within a set of bands near the Fermi level, which are enforced and prescribed by the violations of symmetry-prescribed compatibility relations. Moreover, the surface states and the associated origin from the same set of entangled bands are identified, thereby establishing the bulk-boundary correspondence. To evaluate the effects of chemical modifications, the response of topological surface states to various surface terminations, stoichiometry, and oxidation is examined. The surface structures are globally optimized, and the phase diagrams for various experimental conditions are built. It is shown that, due to changes in the local chemical environment, the original surface states are significantly altered. Modified surface bands can be observed near the Fermi level on surface terminations that preserve the C 4v symmetry.
36 MATERIALS SCIENCE↗
Self-Learning Kinetic Monte Carlo Simulations of Radiation Damage in Nuclear Fuels
Understanding how irradiation affects the thermo-physical and mechanical properties of nuclear materials, such as thermal conductivity degradation in fuels and embrittlement of structural components, is critical to the safety and efficiency of nuclear reactors. These effects are largely governed by the formation and evolution of atomic-scale point defects and defect clusters. Due to their small sizes, however, these defects are invisible under high-resolution scanning transmission electron microscopy. This project aims to fill this experimental knowledge gap by integrating density functional theory (DFT), machine learning interatomic potential (MLIP), and kinetic Monte Carlo (KMC) techniques to predict longtime evolution of irradiation-induced defects in nuclear fuels.
36 - MATERIALS SCIENCE↗