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DOE OSTI2023

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Fundamental Investigations into the Properties and Performance of Advanced Materials

Intelligent engineering designs require an accurate understanding of material behavior, since any uncertainties or gaps in knowledge must be counterbalanced with heightened factors of safety, leading to overdesign. Therefore, building better structures and pushing the performance of new components requires an improved understanding of the thermomechanical response of advanced materials under service conditions. This dissertation provides fundamental investigations of several advanced materials: thermoset polymers, a common matrix material for fiber-reinforced composites and nanocomposites; aluminum alloy 7075-T6 (AA7075-T6), a high-performance aerospace material; and ceramic matrix composites (CMCs), an advanced composite for extreme-temperature applications. To understand matrix interactions with various interfaces and nanoinclusions at their fundamental scale, the properties of thermoset polymers are studied at the atomistic scale. An improved proximity-based molecular dynamics (MD) technique for modeling the crosslinking of thermoset polymers is carefully established, enabling realistic curing simulations through its ability to dynamically and probabilistically perform complex topology transformations. The proximity-based MD curing methodology is then used to explore damage initiation and the local anisotropic evolution of mechanical properties in thermoset polymers under uniaxial tension with an emphasis on changes in stiffness through a series of tensile loading, unloading, and reloading experiments. Aluminum alloys in aerospace applications often require a fatigue life of over 109 cycles, which is well over ii the number of cycles that can be practically tested using conventional fatigue testing equipment. In order to study these high-life regimes, a detailed ultrasonic cycle fatigue study is presented for AA7075-T6 under fully reversed tension-compression loading. The geometric sensitivity, frequency effects, size effects, surface roughness effects, and the corresponding failure mechanisms for ultrasonic fatigue across different fatigue regimes are investigated. Finally, because CMCs are utilized in extreme environments, oxidation plays an important role in their degradation. A multiphysics modeling methodology is thus developed to address the complex coupling between oxidation, mechanical stress, and oxygen diffusion in heterogeneous carbon fiber-reinforced CMC microstructures.

Schichtel, Jacob J.↗

DOE OSTI2023

Unitary limit and its far-reaching impact on neutron matter

This paper explores low-density neutron matter and its behavior in proximity to the unitary limit. To that end, unitary nucleon-nucleon potentials, with infinite 1 S 0 neutron-neutron scattering lengths, are constructed and the Berstch parameter is discussed in relation to results from ultra-cold atomic gases. A focal point is unitarity as a constraint for neutron matter and the symmetry energy, a topic that has been discussed in the literature. I revisit some of those arguments and emphasize the relevance of keeping a fi rm link with low-energy nuclear physics for robust predictions of neutron-rich systems. Here, the predictions are obtained from realistic few-nucleon forces based on chiral effective fi eld theory at N 3 LO.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Expanding the Solar Workforce through the Illinois Community College System

The Illinois Green Economy Network (IGEN) is a consortium of all 39 Illinois community college districts working to grow the green economy in the state. The network aims to build capacity in sustainability and transform education by connecting business/industry and educators to resources, curating information and tools, cultivating partnerships, and advocating for change. Led by IGEN, the goal of the “Expanding the Solar Workforce through the Illinois Community College System” project was to create and sustain a comprehensive, statewide strategy that equips education and training providers, workers, businesses, and communities with resources and tools to prepare a solar workforce. A cohort of five Illinois community colleges participated in the program based on their capacity and interest, as well as their potential for and proximity to industry growth in the state: City Colleges of Chicago, John A. Logan College, Kankakee Community College, Lewis and Clark Community College, and Lincoln Land Community College. The project had three Objectives, 1) Address an unmet need by increasing the pool of skilled solar workers, 2) Keep pace with evolving needs of the solar workforce by improving programs to be more accessible and industry-driven, 3) Strengthen IGEN’s capacity as a statewide resource for solar education, training, and workforce initiatives. Overall, over 400 students were trained in solar installation practices, with over 40 businesses engaged. Additionally. the project spurred a number of innovations and the development of effective practices related to establishing and maintaining a solar installation program at a community college. This project sought to identify effective and scalable practices for developing a solar installation program which included the following components: enrolling students into aligned programs; developing curriculum and courses which meet industry needs; placing students in solar installation or related employment; and sustaining programming past grant funding. Through this project- IGEN was able to identify critical components of a solar installation program. Detailed further in this report these components describe 1) Necessary conditions for establishing a solar installation program, 2) Program structures use cases for different contexts, 3) Effective practices related to student pipeline development and program throughout. As a result of this project- all five participating community colleges have expressed support for continuing to offer solar installation training to their students. Additionally, three nonparticipating colleges in IGEN have expressed interest in adopting practice, courses, or curriculum developed through this program. IGEN has also committed to making the curriculum, course structure and other materials available on their website for accelerated adoption of effective practices both state and nationwide.

14 SOLAR ENERGY↗

DOE OSTI2024

Achieving Unprecedented CO 2 Utilization InCO 2 Concrete™: System Design, Product Development and Process Demonstration

Anthropogenic sources of carbon dioxide are generated from a number of sources, but the key among these are ordinary Portland cement (OPC) production and combustion of fossil fuels. Cement production is the largest global CO 2 source from the mineral decomposition of carbonates. This is due to the clinkering process whereby limestone (mainly consisting of CaCO 3 ) is decomposed into CaO and CO 2 , and combined with silica rich clays at high temperatures to form clinkers (i.e. the four key minerals that comprise cement). The high temperature range of 1400 – 1550°C required for this process accounts for up to 60% of the generated CO 2 from cement production. Combination of the limestone decomposition and thermal requirements of the clinkering process causes cement production to contribute 8-9% of annual global CO 2 emissions. Combustion of fossil fuels (coal, oil and gas) was shown to contribute a much larger portion of global CO 2 emissions. As of 2018, combustion of fossil fuels accounted for 65% of global CO 2 , where 41% was derived from stationary sources for electricity and heat generation and the other 24% was related to transport. To reduce these contributions, key steps forward in CO 2 utilization technologies are required. Therefore, a CO 2 mineralization technology (CO 2 mineralization concrete) to reduce the OPC content in concrete, while utilizing flue gas emissions from fossil fuel combustion has been developed to address both areas simultaneously. This Reversa™ technology utilizes low-carbon cementation agents produced by in situ CO 2 mineralization (“mineral carbonation reactions”) to offer a promising alternative to OPC. CO 2 mineralization relies upon the reaction of dissolved CO 2 with inorganic alkaline reactants to precipitate mineral carbonates (e.g., CaCO 3 ), which bind proximate particles and achieve cementation. Herein, a concrete green body, which is composed of a mixture of binder, water, and mineral aggregates, is exposed to CO 2 borne in industrial flue gas streams. This manner of CO 2 mineralization allows the production of construction components that feature equivalent engineering attributes as their OPC-based counterparts while featuring a much smaller embodied carbon intensity (eCI). The purpose of this project is to demonstrate the feasibility of the Reversa process evolving from a TRL-3 technology at the bench-scale up to TRL-6 technology at the pilot-scale. The reliability of the Reversa technology was tested to prove the effective production of three standard industrial concrete products selected during the course of the project. The results detailed herein will demonstrate the evolution of this technology to the industrial scale. The culmination of this work resulted in 9 production runs completed at the National Carbon Capture Center (NCCC), Wilsonville, AL, using natural gas (NG) flue gas as the CO 2 source. Over the course of the production runs at NCCC, the CO 2 utilization as a function of time, 24-h CO 2 uptake, electricity usage, and 28-d net area compressive strength recorded for each run. Collection of this data will be used to determine the success of the demonstration goals: (1) achieving in excess of 0.2gCO 2 /g reactant , (2) achieving greater than 50% reduction in global warming potential compared to standard produced units, and (3) ensuring compliance of carbonated concrete with industry standard specifications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Sampling off-axis neutrino fluxes with the short-baseline near detector

The short-baseline near detector (SBND), the near detector in the short-baseline neutrino program at Fermi National Accelerator Laboratory, is located just 110 m from the booster neutrino beam target. Thanks to this close proximity, relative to its 4 m × 4 m front face, neutrinos enter SBND over a range of angles from 0° to approximately 1.6°, enabling the detector to sample variations in the neutrino flux as a function of the angle—a technique known as precision reaction-independent spectrum measurement (PRISM), referred to here as SBND-PRISM. In this paper, we show how muon- and electron-neutrino fluxes vary as a function of the neutrino beam axis angle and how this can be exploited to expand the physics potential of SBND. We make use of a model that predicts an angle-dependent electron-neutrino excess signal to illustrate this effect, such as 𝜈 𝜇 → 𝜈 𝑒 oscillations. We present how SBND-PRISM provides a method to add robustness against uncertainties in cross-section modeling and, more generally, uncertainties that do not depend on the spatial position of neutrino interaction inside the detector. The fluxes, along with their associated covariance matrices, are made publicly available with this publication.

Abratenko, P. [Tufts University]↗

DOE OSTI2022

Genetic mapping of sorghum resistance to an Illinois isolate of Colletotrichum sublineola

Anthracnose leaf blight (ALB) is an economically important disease of sorghum [Sorghum bicolor (L.) Moench] caused by the fungal pathogen Colletotrichum sublineola Henn. ex Sacc. & Trotter. Although qualitative and quantitative resistance have been identified for ALB, the usefulness of resistance loci differs depending on the pathogen pathotype. Identifying resistance effective against unique pathogen pathotypes is critical to managing ALB, as the disease is managed primarily through the deployment of host resistance. We isolated C. sublineola from ALB-infected leaves collected in Illinois and found that the strain was a novel pathotype, as it produced a unique combination of virulence against a set of differential lines. Using this isolate, we inoculated 579 temperate-adapted sorghum conversion lines in 2019 and 2020. We then conducted a genome-wide association study (GWAS) and a metabolic pathway analysis using the Pathway Associated Study Tool (PAST). We identified 47 significant markers distributed across all chromosomes except chromosome 8. We identified 32 candidate genes based on physical proximity with significant markers, some of which have a known role in host defense. We identified 47 pathways associated with ALB resistance, indicating a role for secondary metabolism in defense to ALB. Our results are important to improve the understanding of the genetic basis of ALB resistance in sorghum and highlight the importance of developing durable resistance to ALB.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Analysis and prediction of intersection traffic violations using automated enforcement system data

We report that the automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model’s prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

42 ENGINEERING↗

DOE OSTI2024

AWAKEN Dual-Doppler Lidar (ADDLidar) Field Campaign Report

The AWAKEN Dual-Doppler Lidar (ADDLidar) experiment was conducted as part of the larger AWAKEN field campaign (https://www.nrel.gov/wind/awaken.html). The American Wake Experiment (AWAKEN) is an international, multi-institutional wind energy field campaign that was conducted from May 2022 to 2024, in the vicinity of the King Plains wind farm in north central Oklahoma. The goal of AWAKEN was to provide observations to better understand interactions between wind turbines in a wind farm and the interactions between the wind farm as a whole and the atmosphere. The focus of the ADDLidar campaign was to provide height-resolved measurements of wind speed and direction at key locations upwind of the wind farm to characterize the inflow and possible blockage effects upwind of the farm. Specifically, dual-Doppler scanning methods were employed to create a number of so-called virtual towers (Calhoun et al 2006, Debnath et al. 2017, Fernando et al. 2019, Hill et al. 2010, Newman et al. 2016, Newsom et al. 2008, 2015) upwind of the farm. The ADDLidar campaign involved the deployment of two U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility scanning Doppler lidars (S/N 236 and 237) to AWAKEN sites A4 (36.361894°, -97.356352°) and A7 (36.347259°, -97.389968°). Both sites are located approximately 29 km south of the ARM Southern Great Plains (SGP) observatory C1 site, as shown in Figure 1. These sites were chosen for their close proximity to the most southerly row of turbines in the King Plains wind farm.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

AWAKEN Dual-Doppler Lidar (ADDLidar) Field Campaign Report

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Complex pH-Dependent Interactions between Weak Polyelectrolyte Block Copolymer Micelles and Molecular Fluorophores

Amphiphilic block copolymers with weak polyelectrolyte blocks can assemble stimulus-responsive nanostructures and interfaces. Applications of these materials in drug delivery, biomimetics, and sensing largely rely on the well-understood swelling of polyelectrolyte chains upon deprotonation, often induced by changes in pH or ionic strength. This deprotonation can also tune interfacial interactions between the polyelectrolyte blocks and surrounding solution, an effect which is less studied than morphological swelling of polyelectrolytes but can be just as critical for intended function. Here, we investigate whether the pH-driven morphological response of polyelectrolyte-bearing nanostructures also affects the interactions of these nanostructures with molecules in solution, using micelles of a short-chain polybutadiene-block-poly(acrylic acid) (pBd–pAA) as a model system. Here we introduce a Förster resonance energy transfer (FRET) approach to probe interactions between micelles and fluorescent molecular solutes as a function of solution pH. As expected, the pAA corona of these pBd–pAA micelles increases in thickness monotonically as a function of pH. However, FRET efficiency, which provides a metric of the spatial proximity of fluorescently labeled micelles and freely diffusing fluorophores, exhibits complex nonmonotonic behavior as a function of pH, indicating that the average separation of micelles and acceptor fluorophores is not strictly correlated with micelle swelling. Dialysis experiments quantify the affinity of fluorophores for micelles as a function of pH, confirming that changes in FRET are driven almost entirely by the pH-dependent affinity of the pAA block for the investigated molecular fluorophores, not simply by a shape change of the pAA corona. This study provides key insights into the interfacial interactions between weak-polyelectrolyte-bearing nanostructures and molecular solutes, of importance for the development of their stimulus-responsive applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Mycobacterium tuberculosis CitA activity is modulated by cysteine oxidation and pyruvate binding

As an adaptation for survival during infection, Mycobacterium tuberculosis becomes dormant, reducing its metabolism and growth. Two types of citrate synthases have been identified in Mycobacterium tuberculosis, GltA2 and CitA. Previous work shows that overexpression of CitA, the secondary citrate synthase, stimulates the growth of Mycobacterium tuberculosis under hypoxic conditions without showing accumulation of triacylglycerols and makes mycobacteria more sensitive to antibiotics, suggesting that CitA may play a role as a metabolic switch during infection and may be an interesting TB drug target. To assess the druggability and possible mechanisms of targeting CitA with small-molecule compounds, the CitA crystal structure was solved to 2.1 Å by X-ray crystallography. The solved structure shows that CitA lacks an NADH binding site that would afford allosteric regulation, which is atypical of most citrate synthases. However, a pyruvate molecule is observed within the analogous domain, suggesting pyruvate may instead be the allosteric regulator for CitA. Further, the R149 and R153 residues forming the charged portion of the pyruvate binding pocket were mutated to glutamate and methionine, respectively, to assess the effect of mutations on activity. Protein thermal shift assay shows thermal stabilization of CitA in the presence of pyruvate compared to the two CitA variants designed to decrease pyruvate affinity. Solved crystal structures of both variants show no significant structural changes. However, the catalytic efficiency of the R153M variant increases by 2.6-fold. Additionally, we show that covalent modification of C143 of CitA by Ebselen completely arrests enzyme activity. Similar inhibition is observed using two spirocyclic Michael acceptor containing compounds, which inhibit CitA with IC app 50 values of 6.6 and 10.9 μM. A crystal structure of CitA modified by Ebselen was solved, but significant structural changes were lacking. Considering that covalent modification of C143 inactivates CitA and the proximity of C143 to the pyruvate binding site, this suggests that structural and/or chemical changes in this sub-domain are responsible for regulating CitA enzymatic activity.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Evidence for I 2 loss from the perovskite–gas interface upon light-induced halide segregation

Sunlight-induced halide segregation in (CH 3 NH 3 )Pb(Br x I 1−x ) 3 (1 > x > 0.2), which limits obtainable voltages from solar cells with these perovskite absorbers, reverses upon resting in the dark. However, sustained illumination at ca. 1 sun opens a new decomposition pathway, leading to irreversible I 2 loss in an open system. We conclusively show I 2 off-gassing from halide-segregated (CH 3 NH 3 )Pb(Br 0.75 I 0.25 ) 3 by trapping gaseous I 2 and tracking the electronic conductivity of the perovskite, which increases from electron-doping as iodides are oxidized to iodine. Importantly, we show that this reaction occurs across the perovskite-air solid–gas interface, without confounding effects from solvent or reactive solid interfaces. This characterization was conducted under a nitrogen atmosphere, avoiding vacuum- and oxygen-driven I 2 loss pathways. Consistent observations of I 2 loss upon light-soaking CsPb(Br 0.75 I 0.25 ) 3 films show that this reaction is intrinsic to the inorganic framework. We propose that the disruption of iodide-rich domains in the halide-segregated films through I 2 loss can masquerade as a light-induced healing or apparent remixing of the segregated film, when in fact it leads to irreversible decomposition. Although I 2 off-gassing is less likely in bromide-rich solid solutions, light-induced halide segregation brings the iodides into proximity and forms electronic states that are energetically poised to trap and accumulate holes, providing a driving force for I 2 loss. Thus, even bromide-rich mixed-halide perovskite absorbers will benefit from I 2 -impermeable encapsulation for long-term stability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Model-based Hierarchical Reinforcement Learning for Improved Physical Security Design: A Prototype

Prior work in FY24 developed an adversarial AI agent aid in path analysis of physical protection systems. This agent, trained using a model-based reinforcement learning algorithm, was able to successfully learn the most vulnerable path in facilities. It was able to extend the current state of practice for physical protection design by exhibiting dynamic behavior based on current environmental conditions. Whereas PathTrace largely performs a static, graph-based analysis, the AI agent was able to make decisions based on relative position in the facility, current conditions (was the adversarial agnet discovered?), and proximity to secondary targets. The agent demonstrated some novel capabilities, but had limitations that need to be resolved before it can be used for production purposes. For example, the adversarial agent generalizes poorly and takes a relatively long time to train. Nonetheless, there is still considerable promise for developing the adversarial agent further in order to explore even richer, more dynamic behaviors (e.g., adversary motivations, environmental debris, and more). This work considers a complementary idea; development of a planning agent. The planning agent is envisioned as an auto-complete-like tool that can help accelerate security system design by human experts. The agent would respect existing barriers and sensors placed by a human expert while offering cost-effective suggestions (i.e., implicitly balancing effectiveness with cost) to improve the design. The goal is for this agent to be part of an expert’s toolbox, not to totally upend the current state-of-practice, or to displace human experts. The ultimate goal would be concurrent training of both the adversarial and planning agent together, to learn entirely through self-play. This would represent an entirely new way of performing system deign. We selected a hierarchical, model-based reinforcement learning algorithm to serve as the planning agent. This is an extension of concepts used in the prior FY24 adversarial agent work. There, we had a single agent acting an environment. Here, we have two different sub-agents (policies), working together, to form a complete agent. There is a manager policy, which can select abstract goals on slower time scales, and a worker, which performs primitive actions to reach goals selected by the manager. It is worth noting that this class of algorithm is challenging to work with. From our understanding, our work is one of the first successful uses of model-based reinforcement learning (MBRL) in nuclear energy1 , and likely the first hierarchical model-based reinforcement learning application in nuclear energy. Further, this work is one of the first known attempts to apply AI to perform a design tasks in nuclear energy. Consequently, there were significant implementation challenges and the bulk of the work was focused on successful implementation and algorithm design. The results presented here are very low technology readiness level as a consequence of the lack of related literature, but still represent a significant step forward in the pursuit of applied AI for design.

42 ENGINEERING↗

DOE OSTI2024

Hyperspectral Reflectance-Based High Throughput Phenotyping to Assess Water-Use Efficiency in Cotton

Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.

Agriculture↗

DOE OSTI2020

Do Dominant Ants Affect Secondary Productivity, Behavior and Diversity in a Guild of Woodland Ants?

The degree to which competition by dominant species shapes ecological communities remains a largely unresolved debate. In ants, unimodal dominance–richness relationships are common and suggest that dominant species, when very abundant, competitively exclude non-dominant species. However, few studies have investigated the underlying mechanisms by which dominant ants might affect coexistence and the maintenance of species richness. In this study, we first examined the relationship between the richness of non-dominant ant species and the abundance of a dominant ant species, Formica subsericea, among forest ant assemblages in the eastern US. This relationship was hump-shaped or not significant depending on the inclusion or exclusion of an influential observation. Moreover, we found only limited evidence that F. subsericea negatively affects the productivity or behavior of non-dominant ant species. For example, at the colony-level, the size and productivity of colonies of non-dominant ant species were not different when they were in close proximity to dominant ant nests than when they were away and, in fact, was associated with increased productivity in one species. Additionally, the number of foraging workers of only one non-dominant ant species was lower at food sources near than far from dominant F. subsericea nests, while the number of foragers of other species was not negatively affected. However, foraging activity of the non-dominant ant species was greater at night when F. subsericea was inactive, suggesting a potential mechanism by which some non-dominant species avoid interactions with competitively superior species. Gaining a mechanistic understanding of how patterns of community structure arise requires linking processes from colonies to communities. Our study suggests the negative effects of dominant ant species on non-dominant species may be offset by mechanisms promoting coexistence.

Formicidae↗

DOE OSTI2026

Mechanics of pore array collapse and interaction in shock-compressed polymethyl methacrylate (PMMA)

Recent studies on dynamic pore collapse have revealed significant development of shear localization, which can lead to material failure in porous structures and hot spot generation in energetic materials. These findings have dramatically improved the understanding of failure mechanisms during pore collapse but also prompt further investigation of realistic porous materials. In particular, porous media consist of many pores and porous networks. Even in low-porosity materials, pores can form in close proximity during the manufacturing process, leading to the critical question of pore–pore interaction during collapse under dynamic loading conditions. This study investigates, via plate impact experiments coupled with high-speed internal digital image correlation and shadowgraphy techniques, the collapse of two pores in shock-compressed PMMA at stresses between 0.4 and 1 GPa. The results of these experiments provide new insights into shear localization in pore collapse, in addition to distinct interactions between pores. Shadowgraphy measurements reveal novel, direct visualization of shear band development and crack evolution from pore surfaces. Spacing between adiabatic shear bands is measured over a range of impact stresses and is predicted accurately by the Grady–Kipp model. Pore interactions are found to effect a transition in the impact stress threshold at which different failure mechanisms initiate and are also found to possibly influence preferential sites for shear cracking. Throughout the study, numerical and theoretical models are leveraged to understand shear localization behavior. The role of baroclinicity and wave interactions between the pores is used to elucidate interaction mechanisms between pores.

Lawlor, Barry P. [California Institute of Technolo↗

DOE OSTI2023

Linear and Nonlinear Optical Properties of Iridium Nanoparticles Grown via Atomic Layer Deposition

Nonlinear optical phenomena enable novel photonic and optoelectronic applications. Especially, metallic nanoparticles and thin films with nonlinear optical properties offer the potential for micro-optical system integration. For this purpose, new nonlinear materials need to be continuously identified, investigated, and utilized for nonlinear optical applications. While noble-metal nanoparticles, nanostructures, and thin films of silver and gold have been widely studied, iridium (Ir) nanoparticles and ultrathin films have not been investigated for nonlinear optical applications yet. Here, we present a combined theoretical and experimental study on the linear and nonlinear optical properties of iridium nanoparticles deposited via atomic layer deposition (ALD). Linear optical constants, such as the effective refractive index and extinction coefficient, were evaluated at different growth stages of nanoparticle formation. Both linear and nonlinear optical properties of these Ir ALD coatings were calculated theoretically using the Maxwell Garnett theory. The third-order susceptibility of iridium nanoparticle samples was experimentally investigated using the z-scan technique. According to the experiment, for an Ir ALD coating with 45 cycles resulting in iridium nanoparticles, the experimentally determined nonlinear third-order susceptibility is about χ Ir (3) = (2.4 – i2.1) × 10 –17 m 2 /V 2 at the fundamental wavelength of 700 nm. The theory fitted to the experimental results predicts a 5 × 10 6 -fold increase around 230 nm. This strong increase is due to the proximity to the Mie resonance of iridium nanoparticles.

36 MATERIALS SCIENCE↗