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At least 1,387 records · Page 77

DVMDOSTEM v0.8.3: a terrestrial ecosystem model designed to represent arctic, boreal and permafrost ecosystem dynamics

The impacts of climate change on natural ecosystems are the result of complex physical and ecological processes operating and interacting at a variety of spatio-temporal scales, that can be represented in process-based ecosystem models. DVMDOSTEM is an advanced process-based terrestrial ecosystem model (TEM) designed to study ecosystem responses to climate changes and disturbances. It has a particular focus on permafrost regions (i.e. regions characterized by soils that stay partially frozen all year round for at least two consecutive years), encompassing boreal, arctic, and alpine landscapes. The model couples two previous versions of the Terrestrial Ecosystem Model (TEM) (McGuire et al., 1992): DVMTEM that includes a dynamic vegetation module (DVM) (E. S. Euskirchen et al., 2009), and DOSTEM that includes a dynamic organic soil module (DOS) (H. Genet et al., 2013; Yi et al., 2010). DVMDOSTEM simulates processes at yearly and monthly scales, with some physical processes operating at an even finer temporal resolution. Its versatility allows for site-specific to regional simulations, making it valuable for predicting shifts in permafrost, vegetation, and carbon (C) and nitrogen (N) dynamics. While DVMDOSTEM has been described in the methods sections of many manuscripts, this paper is the first stand alone description of DVMDOSTEM, independent of a particular scientific investigation.

Carman, Tobey B. [Univ. of Alaska, Fairbanks, AK (↗

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale that has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify highvalue data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies.

58 GEOSCIENCES↗

Electrochemical Control for Corrosion in Molten Chlorides During CSP Plant Operation

The Liquid Pathway of the Concentrating Solar Power Generation 3 (CSP Gen3) program proposed low-cost molten chloride salt for energy storage. However, online corrosion control was identified as a remining major risk of the Liquid Pathway approach. This project addressed that risk. Electrochemical solutions for corrosion mitigation during CSP plant operation were investigated and their feasibility and scalability were evaluated. The leading cause of corrosion in molten chloride salt systems was identified as corrosive impurities that form within the salt upon exposure to trace amounts of air and moisture. Leveraging electrochemistry, reduction/oxidation reactions can be employed to remove these corrosive impurities. In Phase 1 of this project, a bench-scale batch electrochemical reactor was designed, fabricated, and used to assess the kinetics and thermodynamics of electrochemical salt purification. In Phase 2, a laboratory-scale flow reactor was designed, fabricated, and used to assess the efficacy of the electrochemical method under flowing conditions. Results show that under proposed operating conditions for the Liquid Pathway Gen3 Pilot Plant, the electrochemical method is significantly more effective at removing impurities than alternative chemical and thermal methods, and that the electrochemical method produces less harmful byproducts. A key advance made in the course of this project was the development of a 2-electrode method for electrochemical purification that is more scalable than previously developed 3 electrode methods. This novel method is based on Magnesium (Mg) electrowinning. A provisional patent based on this invention has been submitted (USPTO Application No. 63/480,355). Additional key advances made during this project include assessment of the effect of dissimilar alloys on corrosion, kinetic and thermodynamic evaluation of thermolysis reactions of impurities within the molten salt, characterization of byproducts of purification reactions, and generation of IP focused on isolating value-added products using molten salt-based electrochemistry that could be deployed to valorize the process (USPTO Application No. 63/478,806). Ultimately, this project represented a step toward feasibility of Liquid Pathway Gen3 CSP. The method developed under this project could significantly reduce capital expenses and operating costs and increase plant profitability by enabling use of less expensive alloys, decreasing maintenance, and increasing plant longevity. Key focus areas for follow-on work have been identified as 1) evaluation of the efficacy of the electrochemical method under turbulent conditions in a larger flow system, such as the FASTR loop, 2) development of methods for removal of purification byproducts, 3) modeling pilot and industrial scale performance of electrochemical salt purification during plant operation and 4) further assessment of the effect of impurities on salt vapor phase.

14 SOLAR ENERGY↗

Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are identifying hidden geothermal resources in the USA and designing profitable enhanced geothermal systems (EGS). Many non-obvious processes and parameters could characterize geothermal resources and could control the ultimate energy potential of geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize geothermal resources, but this data is sparse and multi-scale. This has hindered attempts to leverage the datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) give promise to overcome these issues. Modern ML methods and tools can (1) analyze large datasets, (2) assimilate model ensembles that include a multitude of inputs and outputs, (3) process sparse datasets, (4) perform transfer learning between sites with different data quality, (5) extract hidden geothermal signatures from field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. In this work, we implement ML-based geothermal exploration and an enhanced geothermal systems (EGS) design tool to achieve the above goals. Our exploration tool is GeoThermalCloud (GTC) EGS design tool is GeoDT-ML. GTC (github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. It enables the identification of critical measurements needed to identify geothermal resource signatures. GeoDT-ML (github.com/SmartTensors/GeoThermalCloud.jl/tree/master/) adds coupling to GeoDT (https://github.com/GeoDesignTool/GeoDT.git) for stochastic EGS design optimization and performance prediction. GeoDT-ML leverages recent advances in deep learning and high-performance computing. Contributors to this effort include LANL, PNNL, Google, Stanford, and Julia Computing.

15 GEOTHERMAL ENERGY↗

Nuclear fuel irradiation testbed for nuclear security applications

The nuclear security community has long been interested in the identification and quantification of nuclear material signatures to understand a material’s provenance, use, and ultimate application. New forensics signatures and methods intended for non-traditional or advanced nuclear fuel applications require fuel irradiation experiments to demonstrate viability and validity. Integral fuel irradiations have historically required significant costs and long timelines to design, irradiate, and characterize. This paper describes how a recently developed nuclear fuel irradiation testbed can be used to provide a low cost, rapid turnaround, modular test environment for irradiation and evaluation of nuclear fuel specimens for nuclear security applications. The irradiation testbed houses six small ‘MiniFuel’ samples within hermetically sealed capsules inside targets that can be removed in between each ∼25-day operating cycle of the High Flux Isotope Reactor (HFIR). As many as nine targets can be irradiated using a single irradiation position (reflector region) in HFIR, allowing for varying irradiation temperatures and burnups. A suite of hot cell capabilities have been established to perform post-irradiation examination for measuring performance (e.g., fuel swelling, fission gas release) and facilitating experiment disassembly for subsequent property measurements, microstructural analysis, or chemical assay. This new testbed allows fuel irradiations to be conducted on an accelerated timeframe to enable rapid proof of concept testing and to provide reference material for nuclear fuel security applications. Recent applications using this testbed include the testing of isotopic taggants in UO 2 fuel (intentional forensics), testing of U-10Mo fuel for down-conversion of highly enriched uranium–fueled reactors, and the production of irradiated UO 2 fuel material for signature analysis of its isotopic composition (plutonium, fission gases, etc.).

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Metasurfaces with Multipolar Resonances and Enhanced Light–Matter Interaction

Metasurfaces, composed of engineered nanoantennas, enable unprecedented control over electromagnetic waves by leveraging multipolar resonances to tailor light–matter interactions. This review explores key physical mechanisms that govern their optical properties, including the role of multipolar resonances in shaping metasurface responses, the emergence of bound states in the continuum (BICs) that support high-quality factor modes, and the Purcell effect, which enhances spontaneous emission rates at the nanoscale. These effects collectively underpin the design of advanced photonic devices with tailored spectral, angular, and polarization-dependent properties. This review discusses recent advances in metasurfaces and applications based on them, highlighting research that employs full-wave numerical simulations, analytical and semi-analytic techniques, multipolar decomposition, nanofabrication, and experimental characterization to explore the interplay of multipolar resonances, bound and quasi-bound states, and enhanced light–matter interactions. A particular focus is given to metasurface-enhanced photodetectors, where structured nanoantennas improve light absorption, spectral selectivity, and quantum efficiency. By integrating metasurfaces with conventional photodetector architectures, it is possible to enhance responsivity, engineer photocarrier generation rates, and even enable functionalities such as polarization-sensitive detection. The interplay between multipolar resonances, BICs, and emission control mechanisms provides a unified framework for designing next-generation optoelectronic devices. This review consolidates recent progress in these areas, emphasizing the potential of metasurface-based approaches for high-performance sensing, imaging, and energy-harvesting applications.

Kerker effect↗

Report Documenting Activity for Second Alloy 709 Commercial Heat

SUMMARY Qualification of the advanced austenitic stainless steel material Alloy 709 for use in Section III Division 5 of the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel Code is being pursued by the US Department of Energy Advanced Reactor Technology Program. Qualification of this alloy will allow its use in design and construction of components for elevated temperature nuclear reactor systems. Code qualification requires extensive characterization of three commercial scale heats of the alloy. This report describes progress in procuring the second of three required heats of Alloy 709 plate from ATI Flat Rolled Products.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Development and preliminary results of 270 GHz microwave forward scattering diagnostic system on the experimental advanced superconducting tokamak (EAST)

To measure localized (intermediate, high) poloidal wavenumber electron density fluctuations, a tangential millimeter-wave collective scattering system has been designed and successfully developed on the experimental advanced superconducting tokamak (EAST). This innovative system employs a 270 GHz mm-wave probe beam, emitted from the mid-plane of Port K and directed towards Port P after passing through a combination of two in-vessel mirrors. Here these two ports are located on the EAST device’s midplane and are 110° apart. The downward scattered signals pass through two in-vessel mirrors, and exit through the P window. The received multi-channel separated scattering signals are arranged along the poloidal direction. This optical arrangement makes the monitored turbulent wave number is poloidal dominant and up to 40 cm −1 . The tangential microwave scattering scheme enables this diagnostic to monitor local density fluctuations, with typically a radial spatial resolution △ R = 5 cm, and a wavenumber resolution of △ k = 0.4 cm −1 . The scattering system underwent comprehensive laboratory testing in 2023, with installation finalized in 2024. The system features steerable launch and receiver optics, allowing remote control to position the scattering volume from normalized radius ρ –0 to the pedestal region on a shot-by-shot basis. Effective experimental data, characterized by distinct off-center spectral peaks, have been successfully obtained in neutral beam-heated plasmas on EAST. The beam tracing forward modeling has been employed for experimental data interpretation. The advanced millimeter-wave scattering system provides fluctuation measurement capability from ion temperature gradient to electron temperature gradient scale on low field side. Preliminary experimental density fluctuation data have been successfully obtained in neutral beam-heated plasmas on EAST. The localized measurement will be used for kinetic turbulence transport numerical simulation validation.

high-k collective scattering↗

Toward extreme high-temperature supercritical CO 2 power cycles: Leakage characterization of ceramic 3D-printed heat exchangers

We report future supercritical carbon dioxide (sCO 2 ) Brayton power cycles demand high-performance gas-to-gas heat exchangers (HXs) operating under extreme temperature and pressure conditions at which most existing superalloy materials fail to function safely. Ceramic HXs are deemed excellent candidates for advanced sCO 2 power plants as they can withstand high-temperature working environments. Particularly, ceramic 3D printing enables compact HX topologies employing complex and efficient heat transfer features. However, ceramic 3D-printed walls separating hot and cold flow streams are susceptible to a through-plane leakage inherent to a powder-based manufacturing process, including ceramic 3D printing. A potential leakage through ceramic separating walls poses a significant challenge in developing reliable ceramic 3D-printed HXs and could deteriorate thermal performance. In this study, various parameters, including feedstock slurry, 3D-printing direction, and post-processing conditions, are considered, for the first time, to characterize the argon gas leakage rate associated with alumina 3D-printed parts. Three 3D-printed ceramic structures of flat plates, curved tubes, and small-scale plate-and-frame HXs with various thicknesses are systematically studied to determine powder and ceramic 3D-printing conditions to eliminate the through-plane leakage. The results showed that an alumina 3D-printed plate with a thickness of 0.75 mm demonstrates a permeability of 6 x 10 -4 milli-darcy. An alumina 3D-printed tube with a wall thickness of 0.9 mm revealed a permeability of 9.6 x 10 -7 milli-darcy. Furthermore, leakage test results of functional 3D-printed modules showed a dependency on the 3D-printing direction. Particularly, alumina cell-scale HXs employing 1.5-mm-thick horizontal and vertical 3D-printed separating walls demonstrated impermeability and gas permeability of 7.2 x 10 -5 millidarcy, respectively. Insights gained from the present study facilitate the development of complex ceramic 3D-printed HXs and other balance of plant components for next-generation high-temperature high-pressure sCO 2 power cycles.

36 MATERIALS SCIENCE↗

Versatile Liquid Crystal Elastomer Formulations Using Amine-Acrylate Chemistry and Processing for Advanced Manufacturing

This study aims to tune rheological properties of liquid crystal elastomers (LCEs) through a strategic approach, leveraging the differing reaction rates of thiols and primary and secondary amines with acrylate monomers or the inclusion of processing additives to enable amenability to advanced manufacturing. We found that varying the time of oligomerization at elevated temperatures of amine-functional monomers with acrylate-functional monomers enabled simple control of rheological properties. Additionally, we could also control rheological properties by introducing reinforcing fillers or organic solvents. By varying the oligomerization conditions, we achieved a broad range of viscosities, from 1.1 Pa s to 560 Pa s as measured at 60 °C. When the rheological properties were varied through the incorporation of various solid and liquid inclusions, viscosities could range from less than 1 Pa s to nearly 1000 Pa s when measured at 60 °C. The shape morphing capabilities that are intrinsic to LCEs and the mechanical properties were also characterized. Here, we find that the oligomerization conditions and the addition of fillers influence shape morphing and energy absorption, as characterized by the stress–strain characteristics, further suggesting their potential to be used in energy dissipation applications where performance can be turned. Last, we demonstrate how the range of accessible rheological properties grants formulations amenable to various advanced manufacturing techniques.

Liquid chromatography↗

Characterizing Variability in Lignocellulosic Biomass: A Review

Feedstock variability is a significant barrier to the scale-up and commercialization of lignocellulosic biofuel technologies. Variability in feedstock characteristics and behavior creates numerous challenges to the biorefining industry by affecting continuous operation and biofuels yields. Recently, feedstock variability is understood and explained largely on the basis of chemical composition. Physical and mechanical properties and behavior of lignocellulosic feedstock in various unit operations, studied through advanced analytical methods, can further explain variability. Such studies will enable us in developing processes and designing equipment to improve operation and conversion performance. In this perspective, we review several advanced analytical methods that measure density, moisture content, thermal properties, flowability, grindability, rheology properties, and micromorphological characteristics. We also discuss the correlations and interactions among these properties that reflect the complexity of lignocellulosic biomass as a feedstock and the associated quality metrics and logistics of supplying consistent quality feedstock to a biorefinery. We further examine methods that have not traditionally been used to characterize lignocellulosic feedstocks but have the potential to bridge the gap in our explanation of feedstock variability.

09 BIOMASS FUELS↗

Structural and spectroscopic characterization of photoactive yellow protein and photoswitchable fluorescent protein constructs containing heavy atoms

Photo-induced structural rearrangements of chromophore-containing proteins are essential for various light-dependent signaling pathways and optogenetic applications. Ultrafast structural and spectroscopic methods have offered insights into these structural rearrangements across many timescales. However, questions still remain about exact mechanistic details, especially regarding photoisomerization of the chromophore within these proteins femtoseconds to picoseconds after photoexcitation. Instrumentation advancements for time-resolved crystallography and ultrafast electron diffraction provide a promising opportunity to study these reactions, but achieving enough signal-to-noise is a constant challenge. In this work, we present four new photoactive yellow protein constructs and one new fluorescent protein construct that contain heavy atoms either within or around the chromophore and can be expressed with high yields. Structural characterization of these constructs, most at atomic resolution, show minimal perturbation caused by the heavy atoms compared to wild-type structures. Spectroscopic studies report the effects of the heavy atom identity and location on the chromophore’s photophysical properties. None of the substitutions prevent photoisomerization, although certain rates within the photocycle may be affected. Overall, these new proteins containing heavy atoms are ideal samples for state-of-the-art time-resolved crystallography and electron diffraction experiments to elucidate crucial mechanistic information of photoisomerization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays

This is the final technical report for the Virginia Tech Center for Photonics Technology (VT-CPT) research project entitled “Fully Distributed Acoustic and Magnetic Field Monitoring Via a Single Fiber Line for Optimized Production of Unconventional Resource Plays”. In the the four-year effort, VT-CPT collaborated with the Departments of Mathmatics at VT and Sentek Instrument, LLC (Sentek) to develop a fiber-optic sensing system capable of real-time simultaneous distributed measurement of multiple subsurface, drilling, and production parameters. The ultra-sensitive fiber optic distributed acoustic sensing technology, picoDAS, developed by Sentek was integrated with a novel multi-material optical sensing fiber fabricated by CPT-VT to obtain distributed acoustic and magnetic field measurements with ultrahigh sensitivity and high spatial resolution. The sensing technology successfully developed and demonstrated under this research program is truly unique has application beyond subsurface imaging to include carbon storage and electrical grid monitoring, as well as healthcare and nuclear fusion. The project, sponsored by the Advanced Technology Program, began in 2019 and had the original goal of developing the next generation of harsh environment sensing systems. Exhaustive theoretical modeling was performed to optimize the multi-material optical fiber design. VT developed the processing techniques employed to successfully fabricate single mode fibers with metal cladding wires. Sensing fibers with nickel and Metglas cladding wires were fully characterized, inscribed with sensors, and fully integrated with the picoDAS system. Laboratory scale testing was performed to demonstrate performance upon exposure to lateral and transverse magnetic fields. Preliminary field trials were performed for prototype magnetic and acoustic sensing systems upon near surface deployment at VT. The distributed acoustic and magnetic field sensing system was advanced from a (Technology Readiness Level) TRL=2 to a TRL=5 via laboratory scale system validation in relevant environments. The truly one-of-its kind distributed magnetic and acoustic sensing system is expected to find immediate applications that require subsurface monitoring, to include carbon storage site monitoring, and has the potential to be a distributive technology in healthcare and electric grid markets. The technologies developed by Virginia Tech’s Center for Photonics Technology will support the mission of the National Energy Technology Laboratory (NETL) to drive innovation and deliver solutions for an environmentally sustainable and prosperous energy future by ensuring affordable, abundant, and reliable energy that drives a robust economy and national security. Technical accomplishments during the program are summarized briefly here and described in detail in the remainder of the report.

02 PETROLEUM↗

Micro-photoluminescence mapping and Chemometrics for the rapid classification of rare earth materials

This article introduces advancements in chemically mapping rare earth materials using photoluminescence (PL) and chemometrics. By leveraging the high sensitivity and selectivity of PL compared to alternative optical techniques, as well as its compatibility with microscopy, we present enhanced capabilities for noninvasive material screening and characterization. Exemplary PL spectra of samarium(III) and europium(III) in oxide, nitrate, and chloride forms demonstrated the ability to extract detailed chemical information of diverse rare earth particles. Additionally, we introduced efficient PL mapping sequences capable of covering a 9 mm diameter carbon tab within minutes, which highlighted the benefits of rapid, large-area imaging. Furthermore, an integrated approach combining PL mapping with principal component analysis and a random forest classifier enabled the resolution of overlapping spectral peaks from different chemistries and provided accurate material classification. In conclusion, these advancements underscored the versatility and robustness of PL for chemically mapping rare earth materials, with the potential to support applications in mining, energy, environmental monitoring, isotope production and beyond.

Chemometrics↗

Enhanced alcohol production over binary Mo/Co carbide catalysts in syngas conversion

Recently metal carbide catalysts have attracted attention as alternatives to pure metals for the conversion of syngas to higher oxygenates, a process which could enable the sustainable production of fuels, polymers, and chemicals. Although Mo and Co carbides have both shown promise for higher oxygenate production, they have not achieved the requisite activity and selectivity for practical implementation. In this work we synthesize and characterize a binary Mo and Co carbide catalyst that exhibits improved activity and oxygenate selectivity relative to either pure metal carbide. In this work, we apply a combination of advanced electron microscopy and X-ray diffraction to show that the binary Mo/Co carbide catalyst forms uniformly mixed amorphous nanoparticles. Through in situ X-ray absorption spectroscopy studies, we determine that the structure of the mixed metal carbide catalyst under reaction conditions consists of both carbidic and bimetallic components. By testing the catalytic properties of a series of Mo/Co carbide catalysts prepared by different synthesis methods, we find that the Mo and Co sites must be in close contact to achieve improved syngas conversion to higher alcohols. Through in situ DRIFTS measurements, both Mo and Co atoms at the surface of the catalyst are identified as adsorption sites for reactive species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Techno-economics of hydrocarbon fuel production and recyclables recovery from landfill-destined municipal solid waste: AI-enhanced materials recovery facility design

Sustainable aviation fuels (SAF) production from cellulosic paper fractions of municipal solid waste (MSW) destined for landfills has strong potential to advance environmental, social, and economic sustainability across the aviation and waste sectors. This study proposes an artificial intelligence-enabled material recovery facility (AI-MRF) design to efficiently characterize, separate, process, and convert recovered paper waste from MSW into intermediate chemicals and SAF. The AI-MRF, designed to process 233,091 metric tons of MSW annually, integrates smart manufacturing technologies including AI, visual and hyperspectral imaging, multi-sensor data, and traditional sorting systems. Well-characterized and sorted cellulosic paper waste was utilized for chemical and fuel production scenarios, while clean plastics, metals, and glass were considered for recycling. Conversion of paper waste into intermediate sugars achieved a net present value (NPV) of up to $\$67$ million. For sugar-to-SAF production scenarios, the minimum fuel selling price (MFSP) was calculated at $\$6.11$ per gasoline gallon equivalent (GGE) when excluding recyclable revenue, and $\$4.03$ per GGE when halving recyclable revenue. The MFSP was further reduced to $\$1.96$ per GGE when accounting for SAF sales and recyclables. Nationally, this approach could yield about 2 billion GGE of hydrocarbon fuel annually from available MSW in the United States.

09 BIOMASS FUELS↗

Dislocation dynamics in heterogeneous nanostructured materials

Crystalline materials can be strengthened by introducing dissimilar phases that impede dislocation glide. At the same time, the changes in microstructure and chemistry usually make the materials less ductile. One way to circumvent the strength–ductility dilemma is to take advantage of heterogeneous nanophases which simultaneously serve as dislocation barriers and sources. Owing to their superior mechanical properties, heterogeneous nanostructured materials (HNMs) have attracted a lot of attention worldwide. Nevertheless, it has been difficult to characterize dislocation dynamics in HNMs using classical continuum models, mainly due to the challenges in describing the elastic and plastic heterogeneity among the phases. Here, in this work, we advance a phase-field dislocation dynamics (PFDD) model to treat multi-phase materials, consisting of phases differing in composition, structural order, and size in the same system. We then apply the advanced PFDD model to exploring two important but divergent materials design problems in HNMs: dislocation/obstacle interactions and dislocation/interface interactions. Results show that the interactions between a dislocation and distribution of obstacles varying in structure and composition cannot be understood by simply interpolating from their individual interactions with a dislocation. It is also found that materials containing interfaces with nanoscale thicknesses and compositional gradients have a much higher dislocation bypass stress than those with sharp interfaces, providing an explanation for the simultaneous high strength and toughness of thick interface-containing nanolaminates as observed in recent experiments.

36 MATERIALS SCIENCE↗

Exploring the Limits of Dative Boratrane Bonding: Iron as a Strong Lewis Base in Low-Valent Non-Heme Iron-Nitrosyl Complexes

We previously reported the synthesis and preliminary characterization of a unique series of low-spin (ls) {FeNO} 8–10 complexes supported by an ambiphilic trisphosphineborane ligand, [Fe(TPB)(NO)] +/0/– . In this study, we use advanced spectroscopic techniques and density functional theory (DFT) calculations to extract detailed information as to how the bonding changes across the redox series. We find that, in spite of the highly reduced nature of these complexes, they feature an NO + ligand throughout with strong Fe–NO π-backbonding and essentially closed-shell electronic structures of their FeNO units. This is enabled by an Fe–B interaction that is present throughout the series. In particular, the most reduced [Fe(TPB)(NO)] – complex, an example of a ls-{FeNO} 10 species, features a true reverse dative Fe → B bond where the Fe center acts as a strong Lewis-base. Hence, this complex is in fact electronically similar to the ls-{FeNO} 8 system, with two additional electrons “stored” on site in an Fe–B single bond. The outlier in this series is the ls-{FeNO} 9 complex, due to spin polarization (quantified by pulse EPR spectroscopy), which weakens the Fe–NO bond. These data are further contextualized by comparison with a related N 2 complex, [Fe(TPB)(N 2 )] – , which is a key intermediate in Fe(TPB)-catalyzed N 2 fixation. Our present study finds that the Fe → B interaction is key for storing the electrons needed to achieve a highly reduced state in these systems, and highlights the pitfalls associated with using geometric parameters to try to evaluate reverse dative interactions, a finding with broader implications to the study of transition metal complexes with boratrane and related ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗