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At least 919 records · Page 51

Widening of Wind Stress Anomalies Amplifies ENSO in a Warming Climate

Abstract Climate change simulations generally indicate the strengthening of El Niño–Southern Oscillation (ENSO) sea surface temperature (SST) variability through the twenty-first century, yet a robust physical mechanism explaining this change across different models is still lacking, and the projections for ENSO amplitude exhibit a large spread. Most commonly, changes in the background state of the tropical Pacific are invoked to explain these changes of ENSO. Here, we show that changes in the structure of wind stress anomalies associated with ENSO are potentially as important as these background state changes. Specifically, changes in the magnitude, meridional width, and zonal structure of wind stress anomalies can explain approximately 53% of the intermodel variance in the projected change of ENSO magnitude through the twenty-first century as well as 43% in ENSO periodicity changes. Among these changes in the wind structure, the meridional widening of wind anomalies plays the most important role. To demonstrate that these changes are indeed critical, we develop a hybrid model of ENSO based on the Community Earth System Model, version 2, which incorporates a dynamical ocean coupled to a simplified statistical atmosphere within the tropical Pacific. In the absence of external forcing and corresponding mean-state changes, the imposed changes in wind stress anomalies in this hybrid model result in an increase of ENSO amplitudes of nearly 10% along the equator. Our results are also theoretically supported by a recharge-oscillator model that incorporates the meridional wind structure. Thus, changes in the structure of wind stress anomalies, together with changes in the mean state, likely play a critical role in the projected strengthening of ENSO.

Stuivenvolt-Allen, Jacob [Yale University, New Hav↗

Factors driving metabolic diversity in the budding yeast subphylum

Associations between traits are prevalent in nature, occurring across a diverse range of taxa and traits. Individual traits may co-evolve with one other, and these correlations can be driven by factors intrinsic or extrinsic to an organism. However, few studies, especially in microbes, have simultaneously investigated both across a broad taxonomic range. Here we quantify pairwise associations among 48 traits across 784 diverse yeast species of the ancient budding yeast subphylum Saccharomycotina, assessing the effects of phylogenetic history, genetics, and ecology. We find extensive negative (traits that tend to not occur together) and positive (traits that tend to co-occur) pairwise associations among traits, as well as between traits and environments. These associations can largely be explained by the biological properties of the traits, such as overlapping biochemical pathways. The isolation environments of the yeasts explain a minor but significant component of the variance, while phylogeny (the retention of ancestral traits in descendant species) plays an even more limited role. Positive correlations are pervasive among carbon utilization traits and track with chemical structures (e.g., glucosides and sugar alcohols) and metabolic pathways, suggesting a molecular basis for the presence of suites of traits. In several cases, characterized genes from model organisms suggest that enzyme promiscuity and overlapping biochemical pathways are likely mechanisms to explain these macroevolutionary trends. Interestingly, fermentation traits are negatively correlated with the utilization of pentose sugars, which are major components of the plant biomass degraded by fungi and present major bottlenecks to the production of cellulosic biofuels. Finally, we show that mammalian pathogenic and commensal yeasts have a suite of traits that includes growth at high temperature and, surprisingly, the utilization of a narrowed panel of carbon sources. These results demonstrate how both intrinsic physiological factors and extrinsic ecological factors drive the distribution of traits present in diverse organisms across macroevolutionary timescales.

59 BASIC BIOLOGICAL SCIENCES↗

Temporal deposition of copper and zinc in the sediments of metal removal constructed wetlands

The objective of this study was to explore the effects of time, seasons, and total carbon (TC) on Copper (Cu) and Zinc (Zn) deposition in the surface sediments. This study was performed at the H-02 constructed wetland on the Savannah River Site (Aiken, SC, USA). Covering both warm (April-September) and cool (October-March) seasons, several sediment cores were collected twice a year from the H-02 constructed wetland cells from 2007 to 2013. Total concentrations of Cu and Zn were measured in the sediments. Concentrations of Cu and Zn (mean ± standard deviation) in the surface sediments over 7 years of operation increased from 6.0 ± 2.8 and 14.6 ± 4.5 mg kg -1 to 139.6 ± 87.7 and 279.3 ± 202.9 mg kg -1 dry weight, respectively. The linear regression model explained the behavior and the variability of Cu deposition in the sediments. On the other hand, using the generalized least squares extension with the linear regression model allowed for unequal variance and thus produced a model that explained the variance properly, and as a result, was more successful in explaining the pattern of Zn deposition. Total carbon significantly affected both Cu ( p = 0.047) and Zn ( p < 0.001). Time effect on Cu deposition was statistically significant ( p = 0.013), whereas Zn was significantly affected by the season ( p = 0.009).

54 ENVIRONMENTAL SCIENCES↗

Quantile-specific heritability of plasma fibrinogen concentrations

Fibrinogen is a moderately heritable blood protein showing different genetic effects by sex, race, smoking status, pollution exposure, and disease status. These interactions may be explained in part by “quantile-dependent expressivity”, where the effect size of a genetic variant depends upon whether the phenotype (e.g. plasma fibrinogen concentration) is high or low relative to its distribution. Determine whether fibrinogen heritability (h 2 ) is quantile-specific, and whether quantile-specific h 2 could account for fibrinogen gene-environment interactions. Plasma fibrinogen concentrations from 5689 offspring-parent pairs and 1932 sibships from the Framingham Heart Study were analyzed. Quantile-specific heritability from offspring-parent (β OP , h 2 = 2β OP /(1+r spouse )) and full-sib regression slopes (β FS , h 2 = {(1+8r spouse β FS ) 0.05 –1}/(2r spouse )) were robustly estimated by quantile regression with nonparametric significance assigned from 1000 bootstrap samples. Quantile-specific h 2 (±SE) increased with increasing percentiles of the offspring’s age- and sex-adjusted fibrinogen distribution when estimated from β OP (P trend = 5.5x10 -6 ): 0.30±0.05 at the 10 th , 0.37±0.04 at the 25 th , 0.48±0.05 at the 50 th , 0.61±0.06 at the 75 th , and 0.65±0.08 at the 90 th percentile, and when estimated from β FS (P trend = 0.008): 0.28±0.04 at the 10 th , 0.31±0.04 at the 25 th , 0.36±0.03 at the 50 th , 0.41±0.05 at the 75 th , and 0.50±0.06 at the 90 th percentile. The larger genetic effect at higher average fibrinogen concentrations may contribute to fibrinogen’s greater heritability in women than men and in Blacks than Whites, and greater increase from smoking and air pollution for the FGB -455G>A A-allele. It may also explain greater fibrinogen differences between: 1) FGB -455G>A genotypes during acute phase reactions than usual conditions, 2) GTSM1 and IL-6 -572C>G genotypes in smokers than nonsmokers, 3) FGB -148C>T genotypes in untreated than treated diabetics, and LPL PvuII genotypes in macroalbuminuric than normoalbuminuric patients. Fibrinogen heritability is quantile specific, which may explain or contribute to its gene-environment interactions. The analyses do not disprove the traditional gene-environment interpretations of these examples, rather quantile-dependent expressivity provides an alternative explanation that warrants consideration.

59 BASIC BIOLOGICAL SCIENCES↗

Modeling the emergence of viral resistance for SARS-CoV-2 during treatment with an anti-spike monoclonal antibody

To mitigate the loss of lives during the COVID-19 pandemic, emergency use authorization was given to several anti-SARS-CoV-2 monoclonal antibody (mAb) therapies for the treatment of mild-to-moderate COVID-19 in patients with a high risk of progressing to severe disease. Monoclonal antibodies used to treat SARS-CoV-2 target the spike protein of the virus and block its ability to enter and infect target cells. Monoclonal antibody therapy can thus accelerate the decline in viral load and lower hospitalization rates among high-risk patients with variants susceptible to mAb therapy. However, viral resistance has been observed, in some cases leading to a transient viral rebound that can be as large as 3–4 orders of magnitude. As mAbs represent a proven treatment choice for SARS-CoV-2 and other viral infections, evaluation of treatment-emergent mAb resistance can help uncover underlying pathobiology of SARS-CoV-2 infection and may also help in the development of the next generation of mAb therapies. Although resistance can be expected, the large rebounds observed are much more difficult to explain. We hypothesize replenishment of target cells is necessary to generate the high transient viral rebound. Thus, we formulated two models with different mechanisms for target cell replenishment (homeostatic proliferation and return from an innate immune response antiviral state) and fit them to data from persons with SARS-CoV-2 treated with a mAb. We showed that both models can explain the emergence of resistant virus associated with high transient viral rebounds. We found that variations in the target cell supply rate and adaptive immunity parameters have a strong impact on the magnitude or observability of the viral rebound associated with the emergence of resistant virus. Both variations in target cell supply rate and adaptive immunity parameters may explain why only some individuals develop observable transient resistant viral rebound. Our study highlights the conditions that can lead to resistance and subsequent viral rebound in mAb treatments during acute infection.

60 APPLIED LIFE SCIENCES↗

Global scale environmental control of plant photosynthetic capacity

Photosynthetic capacity, determined by light harvesting and carboxylation reactions, is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models (ESMs) at a reference temperature, it is either a fixed value for a given plant functional type or derived from a linear function of leaf nitrogen content. In this study, we conducted a comprehensive analysis that considered correlations of environmental factors with photosynthetic capacity as determined by maximum carboxylation (V c,m ) rate scaled to 25°C (i.e., V c,25; μmol CO 2 ·m –2 ·s –1 ) and maximum electron transport rate (Jmax) scaled to 25°C (i.e., J 25 ; μmol electron·m –2 ·s –1 ) at the global scale. Our results showed that the percentage of variation in observed Vc,25 and J25 explained jointly by the environmental factors (i.e., day length, radiation, temperature, and humidity) were 2–2.5 times and 6–9 times of that explained by area-based leaf nitrogen content, respectively. Environmental factors influenced photosynthetic capacity mainly through photosynthetic nitrogen use efficiency, rather than through leaf nitrogen content. The combination of leaf nitrogen content and environmental factors was able to explain ~56% and ~66% of the variation in V c,25 and J 25 at the global scale, respectively. As a result, our analyses suggest that model projections of plant photosynthetic capacity and hence land–atmosphere exchange under changing climatic conditions could be substantially improved if environmental factors are incorporated into algorithms used to parameterize photosynthetic capacity in ESMs.

54 ENVIRONMENTAL SCIENCES↗

Evaluating the Importance of Barometric Pumping for Subsurface Gas Transport Near an Underground Nuclear Test Site

Core Ideas Gas transport modeling can estimate ground surface breakthrough after an underground nuclear explosion. Comparison tracer‐injection experimental and model results reveals parameter sensitivity. Barometric pumping may explain gas breakthrough following an underground nuclear explosion. Amplitude and period of barometric pressure signal are key in controlling breakthrough. An underground nuclear explosion (UNE) generates and distributes radioactive gases that can be transported to the ground surface though preexisting and explosion‐induced fractures over timescales of hours to months. If detected, the presence of short‐lived radionuclides in gas is evidence of a recent UNE. Numerical modeling can provide estimates of surface arrival times that can help inform gas monitoring strategies at suspected foreign test sites. Efforts are underway at historic US UNE sites to better understand subsurface gas‐transport processes following a UNE by geologic characterization of the near‐field damage structures, field‐scale tracer experiments, and subsurface air pressure monitoring. The development of numerical models using historical and experimental datasets from former UNE sites can improve predictions by testing conceptual models, highlighting key processes, and constraining parameter ranges. The models developed in this study represent the U20az site at the Nevada National Security Site where the Barnwell device was expended in December 1989. A two‐phase (water and air), dual‐permeability flow and transport model of the U20az site was built to investigate gas transport processes under recent experimental conditions and following the Barnwell nuclear event. Results indicate that the model can explain both the lack of arrival of radioactive gas tracers in a 2013 field experiment as well as the observed arrival of radioactive gases following the 1989 Barnwell event using barometric pressure records from the respective periods, even when additional advective processes associated with the Barnwell detonation are ignored. The results demonstrate that the character of the barometric records may be a key factor in explaining the differences in transport behavior.

58 GEOSCIENCES↗

Metadata for a systematic description of signal data

This chapter aims to provide a comprehensive overview of metadata types that may be useful during system design, optimization, and automation. Metadata are grouped into three main categories: (a) metadata describing signal generation, (b) metadata describing signal quality, and (c) contextual information in the form of annotations. Each of these categories is introduced and explained in three separate sections. Importantly, this chapter mainly answers what is considered metadata. To a lesser degree, recommendations are made regarding the selection of metadata for long-term storage. Chapter 4 will explain where and how to store metadata. Chapters 5 and 6 explain how to collect certain metadata through dedicated sensor validation tests (Chapter 5) or algorithmic analysis (Chapter 6).

Alferes, Janelcy↗

Final Technical Report for "High Energy Physics at The University of Iowa"

Particle Physics explores the very fundamental building blocks of our universe: the nature of forces, of space and time. By exploring very energetic collisions of sub-nuclear particles with sophisticated detectors at the colliding beam accelerators (as well as others), experimental particle physicists have established the current theory known as the Standard Model (SM), one of the several theoretical postulates to explain our everyday world. It explains all phenomena known up to a very small fraction of a second after the Big Bang to a high precision; the Higgs boson, discovered recently, was the last of the particle predicted by the SM. However, many other phenomena, like existence of dark energy, dark matter, absence of anti-matter, the parameters in the SM, neutrino masses etc. are not explained by the SM. So, in order to find out what lies beyond the SM, i.e., what conditions at the earliest fractions of the first second of the universe gave rise to the SM, we constructed the Large Hadron Collider (LHC) at CERN after the Tevatron collider at Fermi National Accelerator Laboratory. Each of these projects helped us push the boundary further with new insights as we explore a yet higher energy regime. The experiments are extremely complex, and as we push the boundaries of our existing knowledge, it also requires pushing the boundaries of our technical knowhow. So, not only do we pursue humankind’s most basic intellectual pursuit of knowledge, we help develop technology that benefits today’s highly technical society. Our trained Ph.D. students become experts at fast computing, manipulation of large data volumes and databases, developing cloud computing, fast electronics, advanced detector developments, and complex interfaces in several of these areas. Many of the Particle physics Ph.D.s build their careers at various technology and computing facilities, even financial institutions use some of their skills of simulation and statistical prowess. Additionally, last but not least, today’s discoveries make for tomorrow’s practical uses of an improved life style, case in point, internet technology, fiber optics, and many such things. At The University of Iowa we are involved in the LHC experiments, ATLAS and CMS, building equipment, with calibration and maintenance, supporting the infrastructure in hardware, software and analysis as well as participating in various aspects of data analyses. Our theory group works on fundamentals of field theories and on exploration of non-accelerator high energy neutrinos and possible dark matter searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Sensitivity to electronvolt-scale sterile neutrinos at a 3.8-GeV/c muon decay ring

The liquid-scintillator neutrino-detector (LSND) and mini booster neutrino experiment (MiniBooNE) experiments claim to observe the oscillation $\bar{v}$ μ → $\bar{v}$ e , which can only be explained by additional neutrinos and is a claim that must be further tested. This thesis proposes a new accelerator and experiment called neutrinos from stored muons ( STORM) to refute or confirm the oscillation these claims by studying the CPT-equivalent channel v e → v μ . A 3.8-GeV/c muon decay ring is proposed with neutrino detectors placed 20 m and 2000 m from the decay ring. The detector technology would be a magnetized iron sampling calorimeter, where the magnetic field is induced by a superconducting transmission line. In a frequentist study, the sensitivity of this experiment after 5 years would be >10σ . The range of the thesis discussion starts with the proton front-end design and ends with neutrino parameter estimation. After describing the phenomenology of sterile neutrinos, the facility and detector performance work is presented. Finally, the systematics are explained before the sensitivity and parameter-estimation works are explained

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Chemical Frustration: A Design Principle for the Discovery of New Complex Alloy and Intermetallic Phases (Final Report)

A limiting factor in the design of new metallic materials for energy applications is the inability to predict or control the crystal structures of intermetallic phases. These compounds exhibit an immense structural diversity, which continues to expand with progress in synthetic exploration. In this project, a concept has been developed through experimental and theoretical endeavors to explain and predict how complex structures can arise in these systems: chemical frustration. Competing interactions were induced synthetically through the selection of ternary combinations of elements, in which incompatibilities are anticipated to arise between the preferred bonding or packing modes of the binary subsystems. These experiments led to a variety of new compounds which were structurally characterized and interpreted in terms of the chemical frustration concept. Theoretical models based on Density Functional Theory (DFT) and semiempircal tightbinding calculations were also developed to account for new compounds encountered during these experimental investigations, as well as relevant intermetallic structures in the literature. Examples include the μ3-acidity model and the reversed approximation Molecular Orbital approach. This research has contributed to the field by proposing an approach to navigating the structural diversity of intermetallics, which has long been difficult to explain or control, and demonstrating its usefulness in the discovery of new compounds. At the same time, it has provided new tools for explaining structural phenomena in metals and forged deeper connections between the fields of materials and molecular chemistry. In terms of technical effectiveness and economic feasibility, the chemical frustration approach is promising. Several of the compounds discovered illustrate the structural features expected for frustration, and can be prepared simply from the elemental metals as thermodynamically stable phases. The future effectiveness of this approach will require criteria for reliably determining which systems will yield chemically-frustrated phases, and which will result in simple phase segregation. One outcome of the project is the refined guidelines for identifying such systems, which will be tested in future research. In addition to the scientific impacts of this research, there are also long-term benefits to the public: this project has opened routes to compounds based on intergrowths of simple intermetallics. When applied to the intergrowth of intermetallic phases with useful properties, this approach could yield new functional composites whose nanostructuring is extremely resilient as it is dictated by thermodynamics.

36 MATERIALS SCIENCE↗

Deformed Materials: Towards a Theory of Materials Morphology Dynamics

This grant supported work on the response of crystals to external stress. Our primary work described how disordered structural materials break in two (statistical models of fracture in disordered materials), studied models of deformation bursts (avalanches) that mediate deformation on the microscale, and developed continuum dislocation dynamics models for plastic deformation (as when scooping ice cream bends a spoon, Fig. 9). Glass is brittle -- it breaks with almost atomically smooth fracture surfaces. Many metals are ductile -- when they break, the fracture surface is locally sheared and stretched, and it is this damage that makes them hard to break. Bone and seashells are made of brittle material, but they are strong because they are disordered -- lots of little cracks form as they are sheared and near the fracture surface, diluting the external force. We have studied materials like bone and seashells using simulations, mathematical tools, and statistical mechanics models from physics. In particular, we studied the extreme values of fracture strengths (how likely will a beam in a bridge break far below its design strength), and found that the traditional engineering tools could be improved greatly. We also studied fascinating crackling-noise precursors -- systems which formed microcracks of a broad range of sizes before they broke. Ductile metals under stress undergo irreversible plastic deformation -- the planes of atoms must slide across one another (through the motion of dislocations) to change the overall shape in response to the external force. Microscopically, the dislocations in crystals move in bursts of a broad range of sizes (termed 'avalanches' in the statistical mechanics community, whose motion is deemed 'crackling noise'). In this grant period, we resolved a longstanding mystery about the average shape of avalanches of fixed duration (using tools related to an emergent scale invariance), we developed the fundamental theory describing the shapes of avalanches and how they are affected by the edges of the microscope viewing window, we found that slow creep of dislocations can trigger an oscillating response explaining recent experiments, we explained avalanches under external voltage, and we have studied how avalanches in experiments on the microscale relate to deformation of large samples. Inside the crystals forming the metal, the dislocations arrange into mysterious cellular structures, usually ignored in theories of plasticity. Writing a natural continuum theory for dislocation dynamics, we found that it spontaneously formed walls -- much like models of traffic jams and sonic booms. These walls formed rather realistic cellular structures, which we examined in great detail -- our walls formed fractal structures with fascinating scaling properties, related to those found in turbulent fluids. We found, however, that the numerical and mathematical tools available to solve our equations were not flexible enough to incorporate materials-specific information, and our models did not show the dislocation avalanches seen experimentally. In the last year of this grant, we wrote an invited review article, explaining how plastic flow in metals shares features with other stressed materials, and how tools of statistical physics used in these other systems might be crucial for understanding plasticity.

36 MATERIALS SCIENCE↗

Beam asymmetry for photoproduced omega mesons off bound protons in deuterons

Quantum chromodynamics (QCD) is the fundamental theory underlying the strong interaction. Great effort has been dedicated to explain hadron interactions and the hadron composition in terms of the QCD degrees of freedom (quarks and gluons). However, this task has been largely complicated by confinement, an intrinsic feature of QCD itself, which prevents us from a direct experimental study of the dynamics of quarks and gluons. Instead, QCD-inspired models have been developed to explain the hadron spectrum, and while there has been success in explaining important features of it, many of the states that have been predicted by these models have not been found experimentally. Furthermore, the hadronic spectrum is much more complicated to analyze than the atomic one because of a high number of excited states of the nucleon which have large widths that cause resonances to overlap. Also, these resonances may decay into a multitude of decay channels involving either mesons or baryons. Additionally, we will need measurements of observables, coming from polarized beam and polarized target experiments, to obtain a complete measurement of all the helicity amplitudes. My current research aims at contributing to a larger experimental program that seeks to shed light on the evolving status of the proton spectrum. I focus on the photoproduction of ω mesons off the bound proton, which is of great interest in providing information about N* resonances as ω is an isospin filter. I have extracted a preliminary quasi-free right-arrow gamma delta → ωp(n) photon beam asymmetry polarization observable from CLAS data. The ω meson was identified through its charged decay ω → π + π - π 0 where the π 0 subsequently decays in two photons. The data were taken during the E06-103 experiment with the CLAS detector in Hall B at the Thomas Jefferson National Accelerator Laboratory (JLab). The experiment used the Hall-B Coherent Bremsstrahlung Facility to provide a high quality beam of linearly-polarized photons in the energy range from 1.1 to 2.3 GeV.

Source record↗

Nuclear Criticality Safety Fundamentals [Slides]

Objectives of this presentation are: 1. Explain the difference between radiation safety vs. criticality safety, 2. Define criticality, 3. Explain the three types of chain reactions, 4. Define two terms scientists use to characterize criticality, 5. Explain what occurs during a criticality accident, 6. Identify prevention measures for avoiding a criticality accident, and 7. Identify the parameters that effect criticality of a system.

61 RADIATION PROTECTION AND DOSIMETRY↗

Semi-supervised Bayesian Low-shot Learning

Deep neural networks (NNs) typically outperform traditional machine learning (ML) approaches for complicated, non-linear tasks. It is expected that deep learning (DL) should offer superior performance for the important non-proliferation task of predicting explosive device configuration based upon observed optical signature, a task which human experts struggle with. However, supervised machine learning is difficult to apply in this mission space because most recorded signatures are not associated with the corresponding device description, or “truth labels.” This is challenging for NNs, which traditionally require many samples for strong performance. Semi-supervised learning (SSL), low-shot learning (LSL), and uncertainty quantification (UQ) for NNs are emerging approaches that could bridge the mission gaps of few labels and rare samples of importance. NN explainability techniques are important in gaining insight into the inferential feature importance of such a complex model. In this work, SSL, LSL, and UQ are merged into a single framework, a significant technical hurdle not previously demonstrated. Exponential Average Adversarial Training (EAAT) and Pairwise Neural Networks (PNNs) are chosen as the SSL and LSL methods of choice. Permutation feature importance (PFI) for functional data is used to provide explainability via the Variable importance Explainable Elastic Shape Analysis (VEESA) pipeline. A variety of uncertainty quantification approaches are explored: Bayesian Neural Networks (BNNs), ensemble methods, concrete dropout, and evidential deep learning. Two final approaches, one utilizing ensemble methods and one utilizing evidential learning, are constructed and compared using a well-quantified synthetic 2D dataset along with the DIRSIG Megascene.

97 MATHEMATICS AND COMPUTING↗

Effect of Microstructural Bands on the Localized Corrosion of Laser Surface-melted 316L Stainless Steel

The localized corrosion of laser surface melted (LSM) 316L stainless steel is investigated by a combination of potentiodynamic anodic polarization in 0.1M HCl and microscopic investigation of the initiation and propagation of localized corrosion. The pitting potential of LSM 316L is significantly lower than the pitting potential of wrought 316L. The LSM microstructure is highly banded as a consequence of the high laser power density and high linear energy density. The bands are composed of zones of changing modes of solidification, cycling between very narrow regions of primary austenite solidification and very wide regions of primary ferrite solidification. Pits initiate in the outer edge of each band where the mode of solidification is primary austenite plane front solidification and primary austenite cellular solidification. The primary austenite regions have low chromium concentration (and possibly low molybdenum concentration), which explains their susceptibility to pitting corrosion. The ferrite is enriched in chromium, which explains the absence of pitting in the primary ferrite regions. The presence of the low chromium regions of primary austenite solidification explains the lower pitting resistance of LSM 316L relative to wrought 316L. The influence of banding on localized corrosion is applicable to other rapidly solidified processes such as additive manufacturing.

36 MATERIALS SCIENCE↗

Flexoelectricity and New Phenomena

It is easy to miss the scientific implications of our recent work on Triboelectricity. Everyone knows that rubbing and contact can produce static electricity; less appreciated is that the thermodynamic driver has been an open question since static electricity was first observed by Thales of Miletus around 585 BC. People think they understand it, for instance one common explanation that can still be found in the current literature is that differences in the work function drives charge transfer, often called the Volta-Helmholtz hypothesis. As summarized in 1967 by Harper, this fails to explain many experimental observations, for instance that charging can occur when two pieces of the same material are rubbed against each other. We are the first to place triboelectricity on a solid foundation rooted in quantum mechanics – the flexoelectric effect. We were able to explain a significant number of previously unexplained phenomena: 1) Bipolar tribocurrents associated with stick-slip, due to the change in sign of the strain gradients. 2) A one-third power scaling of tribocurrents with indentation force. 3) Tribocharging when two identical materials are used, these being due to local variations in the asperities so there are usually local potential differences. 4) Inhomogeneous charging of insulators, related to the statistical nature of asperities. 5) An experimentally observed reversal in the sign of charge transfer for negative and positive curvature, which is related to a change in the sign of the strain gradient. Exploiting our DOE prior funded work on flexoelectricity, we obtained semi-quantitative matching to existing experimental measurements of the surface charge in triboelectric experiments. The work has been well received in the literature. The work has been the focus of a number of popular science press articles, and also formed the basis for a Podcast for children 6-10 “The Rise and Fall of Static Man” posted in December 2019 by NPR as part of their “Wow in the World” series. I was also briefly interviewed by the Chicago PBS station in January 2020. This work are significant for a wide range of energy applications; to quote from an independent source: "Triboelectric power has plenty of potential, says Wenzhuo Wu, an assistant professor of engineering at Purdue. If the basics of static electricity are better understood, we could maximize the efficiency of wind or wave power generators, Wu says. The body's own movement could be used to power internal medical devices. Imagine being able to create a roof shaped to harness the power of a raindrop — the friction of the rain passing over the surface — to generate triboelectricity, powering the building below it." This is the start of new science, some of which we already partially understand such as the role of band bending in charge transfer. We need to understand charge transfer combining elasticity, quantum mechanics, band bending and defect states. These directly involve several of the DOE Grand Challenges: "How do we control material processes at the level of electrons? How do remarkable properties of matter emerge from complex correlations of the atomic or electronic constituents and how can we control these properties? How do we characterize and control matter away—especially very far away— from equilibrium?" I will argue that this work truly falls into the class of disruptive science; it is not just a simple extension, linear science. Not everyone will accept the approach. Since we explain far more about triboelectricity than anyone before, the preponderance of evidence supports the model. The feedback I have received is that many agree with the work, to quote: "The model makes sense, says Michael McAlpine, a professor of engineering at the University of Minnesota. "It's such a simple explanation, I was surprised I didn't put my finger on that," McAlpine says." The proposal received strong reviews. It was also publicized on the Department of Energy Web Site.

16 TIDAL AND WAVE POWER↗

Understanding Twinning and Deformation in High Entropy Alloys

A combination of high strength and high ductility has been observed in multi-principal element alloys due to twin formation attributed to low stacking fault energy (SFE). In the pursuit of low SFE alloys, a key bottleneck is the lack of understanding of the composition–SFE cor- relations that would guide tailoring SFE via alloy composition. Using density functional theory (DFT), we show that dopant radius, which have been postulated as a key descriptor for SFE in dilute alloys, does not fully explain SFE trends across different host metals. Instead, charge density is a much more central descriptor. It allows us to (1) explain contrasting SFE trends in Ni and Cu host metals due to various dopants in dilute concentrations, (2) explain the large SFE variations observed in the literature even within a given alloy composition due to the nearest neighbor environments in “model” concentrated alloys, and (3) develop a machine learning model that can be used to predict SFEs in multi-elemental alloys. This model opens a possibility to use charge density as a descriptor for predicting SFE in alloys. Furthermore, a descriptor-less machine learning (ML) model based only on charge density images extracted from density functional theory (DFT) is developed to predict stacking fault energies (SFE) in concentrated alloys. The model is based on convolutional neural networks (CNNs) as one of the promising ML techniques for dealing with complex images and data. Identification of correct descriptors is a key bottleneck to develop ML models for predicting materials properties. Often, in most ML models, textbook physical descriptors such as atomic radius, valence charge and electronegativity are used as descriptors which have limitations because these properties change in concentrated alloys when multiple elements are mixed to form a solid solution. We illustrate that, within the scope of DFT, the search for descriptors can be circumvented by electronic charge density, which is the backbone of the Kohn-Sham DFT and describes the system completely. The performance of our model is demonstrated by predicting SFE of concentrated alloys with an RMSE and R2 of 6.18 mJ/m2 and 0.87, respectively, validating the accuracy of the proposed approach.

36 MATERIALS SCIENCE↗