Results for “Group Processes” — Page 27

DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Data-Informed Evaluation Framework for Integrated Energy Systems: Insights from Power, Process Heat, and Hydrogen Production Applications

The multi-criteria decision analysis (MCDA) framework provides a systematic evaluation of the diverse preferences and performance metrics associated with alternative solutions. This approach is advantageous over a single-criterion methodology, which are only valid under conditions that assume ceteris paribus or an "apples-to-apples" comparison. However, selecting suitable technologies for integrated energy systems (IES) can be likened to an "apples-to-oranges" comparison, given the heterogeneous factors at stake. These factors include economics and performance parameters, geological compatibility, and environmental impacts. Consequently, past research has often employed a mixture of qualitative and quantitative criteria tailored to the specific interests of each study. While the method proves effective in handling the intricate interplay of criteria, the resulting rankings and scores can vary from study to study. This inconsistency is introduced from the use of subjectively defined thresholds and weights. As a result, decision-makers frequently find it challenging to establish clear connections between specific criteria and the resulting scores, as the transformation of criteria into ordinal scores results in a substantial loss of information. To address this challenge, we introduce a data-informed IES evaluation framework that offers comprehensive, interpretable, and traceable evaluations backed by quantifiable rationale. First, we identified key IES evaluation criteria from a decade of literature, focusing on relevant IES applications in power, process heat, and hydrogen production. We leveraged state-of-the-art cost estimates from the Idaho National Laboratory (INL) and technical data from 78 reactor designs from the International Atomic Energy Agency (IAEA) and the Organization for Economic Co-operation and Development - Nuclear Energy Agency (OECD-NEA). Lastly, we established thresholds by analyzing the mean, variance, root mean square, and slope of values across alternatives, categorizing the preferences of decision-makers into distinct utility functions, such as linear, saturating, exponential, and stepwise. Our approach yielded two main outcomes: (1) it provided consistent assessments across different stakeholder groups and (2) it visualized uncertainties in the decision-making context via comprehensive sensitivity analysis. To demonstrate the impact of our framework, we conducted case studies on 6 reactor designs (AP1000, NuScale, BWRX-300, Xe-100, eVinci, iMSR) for the three applications. Our data-driven framework proved to be highly effective in addressing heterogenous uncertainties faced by varied decision-makers? preferences and IES applications, as well as cost and technical estimates of advanced reactors.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Construction Methodology Transformation for the Benefit of Workforce Development

Construction is a key economic engine driving both national and global economies. While manual, onsite construction methods dominate the U.S. construction industry, a major shift towards offsite methods has been underway due to its efficiency, speed, and potential cost savings. The workforce necessary for offsite construction growth does not exist in its current form because of the focus on onsite methodologies and the lack of exposure to offsite building methods at all levels of a student’s learning journey. The growth of the U.S. construction industry and competitiveness in an increasingly global construction market over the coming decades can only be supported by a dramatic increase in the use of offsite methods, which requires ramping up workforce training for certain skillsets. The goal of Construction Methodology Transformation for the Benefit of Workforce Development was to understand the opportunities and barriers in both education and industry and to identify best practices for offering curriculum and training to educators, industry, and students that would support skills needed for careers in offsite construction. Our team proposed combining three offsite construction workforce development needs: content development, exposure and training, and job placement - under a single Platform model that would increase experience and career opportunities for students and help match them with potential industry members. Through our proposed solution we expected to see: developed offsite curriculum being utilized by educators and students; an increase in the identification of construction technology and offsite construction methods; an average increase in knowledge gain of at least 25% after participation in pilots; better equipped candidates who are prepared for jobs in offsite construction; and a beta workforce development platform that helps build more pathways for students looking for careers in offsite construction. The two pilots included almost 250 students and resulted in an average knowledge gain of 37 percent. Our research has identified areas of opportunity, for both education and industry to make collaborative training programs more efficient and successful. The chosen techniques for this program are extremely effective when both the school and factory have solid processes and cultures in place to accept students into training programs. This project serves as an important stepping stone to industrywide collaboration to move workforce development for offsite construction forward across the country. With continued collaboration programs like this can provide much needed early exposure and training in offsite construction and we can begin to fill important positions for the future of construction.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Controlling the Crystal Packing and Morphology of Metal–Organic Macrocycles through Side-Chain Modification

Supramolecular nanotubes constructed from the self-assembly of conjugated metal–organic macrocycles provide a unique collection of materials properties, including solution processability, porosity, and electrical conductivity. Here we show how small modifications to the macrocycle periphery subtly alter the noncovalent interactions governing self-assembly, leading to large changes in crystal packing, crystal morphology, and materials properties. Specifically, we synthesized five distinct copper-based macrocycles that differ in either the steric bulk, polarity, or hydrogen-bonding ability of the peripheral side chains. We show that the electrical conductivity of these macrocycles is highly sensitive to steric bulk, decreasing by 3 orders of magnitude upon introduction of peripheral neopentyl substituents. Here, we further show that the introduction of hydrogen-bonding groups leads to more ordered packing and a dramatic increase in crystallite size. Together, these results establish side-chain engineering as a rich toolkit for controlling the packing structure, particle morphology, and bulk properties of conjugated metal–organic macrocycles.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Ligand Controls Excited Charge Carrier Dynamics in Metal-Rich CdSe Quantum Dots: Computational Insights

Small metal-rich semiconducting quantum dots (QDs) are promising for solid-state lighting and single-photon emission due to their highly tunable yet narrow emission line widths. Nonetheless, the anionic ligands commonly employed to passivate these QDs exert a substantial influence on the optoelectronic characteristics, primarily owing to strong electron–phonon interactions. In this work, we combine time-domain density functional theory and nonadiabatic molecular dynamics to investigate the excited charge carrier dynamics of Cd 28 Se 17 X 22 QDs (X = HCOO – , OH – , Cl – , and SH – ) at ambient conditions. These chemically distinct but regularly used molecular groups influence the dynamic surface-ligand interfacial interactions in Cd-rich QDs, drastically modifying their vibrational characteristics. The strong electron–phonon coupling leads to substantial transient variations at the band edge states. The strength of these interactions closely depends on the physicochemical characteristics of passivating ligands. Consequently, the ligands largely control the nonradiative recombination rates and emission characteristics in these QDs. Our simulations indicate that Cd 28 Se 17 (OH) 22 has the fastest nonradiative recombination rate due to the strongest electron–phonon interactions. Conversely, QDs passivated with thiolate or chloride exhibit considerably longer carrier lifetimes and suppressed nonradiative processes. The ligand-controlled electron–phonon interactions further give rise to the broadest and narrowest intrinsic optical line widths for OH and Cl-passivated single QDs, respectively. Finally, obtained computational insights lay the groundwork for designing appropriate passivating ligands on metal-rich QDs, making them suitable for a wide range of applications, from blue LEDs to quantum emitters.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Precision three-dimensional imaging of nuclei using recoil-free jets

In this study, we explore the azimuthal angle decorrelation of lepton-jet pairs in e-p and e-A collisions as a means for precision measurements of the three-dimensional structure of bound and free nucleons. Utilizing soft-collinear effective theory, we perform the first-ever resummation of this process in e-p collisions at NNLL accuracy using a recoil-free jet axis. Our results are validated against Pythia simulations. In e-A collisions, we address the complex interplay between three characteristic length scales: the medium length L, the mean free path of the energetic parton in the medium λ, and the hadronization length L h . We demonstrate that in the thin-dilute limit, where L $\ll$ L h and L ~ λ, this process can serve as a robust probe of the three-dimensional structure for bound nucleons. We conclude by offering predictions for future experiments at the Electron-Ion Collider within this limit.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

One-loop analysis of β decays in SMEFT

We perform a loop-level analysis of charged-current (CC) processes involving light leptons and quarks within the Standard Model Effective Field Theory (SMEFT). This work is motivated by the high precision reached in experiment and Standard Model calculations for CC decays of mesons, neutron, and nuclei, and by a lingering tension in the Cabibbo universality test. We identify the SMEFT operators that induce the largest loop-level contributions to CC processes. These include four-quark and four-fermion semileptonic operators involving two third-generation quarks. We discuss the available constraints on the relevant effective couplings and along the way we derive new loop-level bounds from K → πν$\overline{v}$ on four-quark operators involving two top quarks. We find that low-energy CC processes are quite competitive with other probes, set constraints that do not depend on flavor-symmetry assumptions, and probe operators involving third-generation quarks up to effective scales of Λ ≃ 8 TeV. Finally, we briefly discuss single-field ultraviolet completions that could induce the relevant operators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Modeling of Vertical Motor-driven Pump for Simulation of a Fault Signature \\ for Condition Monitoring

As part of the ongoing effort to transition from preventive maintenance strategies to condition-based maintenance strategies in nuclear power plants, there is significant reliance on using machine learning techniques. To develop a robust machine learning model that can diagnose all the fault modes of a vertical motor-driven pump, data capturing the unique signature of each fault mode is required. In practice, it is difficult to collect or capture data that captures all the fault modes from a single plant site. So to address this situation, a computational model of a vertical motor-driven pump is developed using the multipurpose finite element software COMSOL Multiphysics. The developed model is used to generate simulated data under normal operation and is compared with the vibration data collected using vibration sensors. Once the simulation model is verified under normal operating condition, simulated data for the fault mode for which minimal or no evidence is available in historical plant process data is developed. This simulated data is used to develop fault signatures to achieve robust predictive models. This paper presents modeling details and verification of the model that can used to generate data for fault modes that are not available at a plant site for condition monitoring purpose.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

DOE OSTI2025

Illuminating the Role of Alcohol Substrate in Nickel Photoredox Catalysis via Ground State and Transient Absorption Spectroscopy

Direct excitation of nickel photocatalysts has gained popularity over the last several years following the successful activation of nickel by Ir and Ru photosensitizers. The ability to pair these photosensitizers with Ni catalysts facilitates the formation of crucial chemical bonds under relatively mild conditions. However, there are still gaps in our understanding of what influences the performance of these Ni photocatalysts. Previous work from our group identified a nickel photocatalyst supported by a tridentate pyridinophane ligand that undergoes wavelength-dependent cross-coupling reactivity. Herein, we report detailed studies of the wavelength dependence of the C–O cross-coupling reactivity, illustrating that the catalytic activity has an optimal wavelength, and excitation on both the blue and red sides of this optimal wavelength leads to a reduction in product yield. We also reveal a crucial catalyst-alcohol preirradiation interaction that drastically alters the catalyst’s optical properties. Furthermore, we investigate both wavelength dependency and alcohol dependency via optical transient absorption spectroscopy and reveal a substrate binding process responsible for the observed wavelength-dependent cross-coupling catalysis.

alcohol↗

DOE OSTI2024

Small‐Molecule Mixed Ionic‐Electronic Conductors for Efficient N‐Type Electrochemical Transistors: Structure‐Function Correlations

Abstract The fundamental challenge in electron‐transporting organic mixed ionic‐electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6‐type/U‐shaped non‐fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1 g to 3 g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Y n series: a U‐shaped/planar core, close π–π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion‐electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high transconductance of 16.5 mS, an on/off current ratio of ~10 6 , and a turn‐on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY‐based OECT performance in terms of denser n gY OEG content, increased crystallite dimensions with decreased long‐range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small‐molecule‐based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small‐molecule OMIECs.

Cho, Yongjoon↗

DOE OSTI2024

Small‐Molecule Mixed Ionic‐Electronic Conductors for Efficient N‐Type Electrochemical Transistors: Structure‐Function Correlations

Abstract The fundamental challenge in electron‐transporting organic mixed ionic‐electronic conductors (OMIECs) is simultaneous optimization of electron and ion transport. Beginning from Y6‐type/U‐shaped non‐fullerene solar cell acceptors, we systematically synthesize and characterize molecular structures that address the aforementioned challenge, progressively introducing increasing numbers of oligoethyleneglycol (OEG; g) sidechains from 1 g to 3 g, affording OMIECs 1gY, 2gY, and 3gY, respectively. The crystal structure of 1gY preserves key structural features of the Y n series: a U‐shaped/planar core, close π–π molecular stacking, and interlocked acceptor groups. Versus inactive Y6 and Y11, all of the new glycolated compounds exhibit mixed ion‐electron transport in both conventional organic electrochemical transistor (cOECT) and vertical OECT (vOECT) architectures. Notably, 3gY with the highest OEG density achieves a high transconductance of 16.5 mS, an on/off current ratio of ~10 6 , and a turn‐on/off response time of 94.7/5.7 ms in vOECTs. Systematic optoelectronic, electrochemical, architectural, and crystallographic analysis explains the superior 3gY‐based OECT performance in terms of denser n gY OEG content, increased crystallite dimensions with decreased long‐range crystalline order, and enhanced film hydrophilicity which facilitates ion transport and efficient redox processes. Finally, we demonstrate an efficient small‐molecule‐based complementary inverter using 3gY vOECTs, showcasing the bioelectronic applicability of these new small‐molecule OMIECs.

Cho, Yongjoon↗

DOE OSTI2025

Comparative Performance Evaluation of Large Language Models for Extracting Molecular Interactions and Pathway Knowledge

Understanding the interactions and regulatory relationships among biomolecules is essential for deciphering complex biological systems and elucidating the mechanisms behind diverse biological functions. Traditionally, the collection of such molecular interaction data has relied on expert curation, a process that is both time-consuming and labor-intensive. To address these limitations, this study explores the use of large language models (LLMs) to automate the genome-scale extraction of molecular interaction knowledge. Here, we evaluate the performance of various LLMs on key biological tasks, including the identification of protein-protein interactions, detection of genes associated with pathways influenced by low-dose radiation, and inference of gene regulatory relationships. Our findings demonstrate that larger LLMs tend to perform better, particularly in extracting intricate gene and protein interactions. Despite their strengths, these models face challenges in recognizing functionally diverse gene groups and highly correlated regulatory relationships. Through a comprehensive analysis using established molecular interaction and pathway databases, we show that LLMs possess the potential to identify relevant biomolecules and predict their interactions, offering valuable insights and marking a significant step toward AI-driven biological knowledge discovery.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2025

Analysis of Tar and Oil Derived from Pyrolysis and Copyrolysis of Waste Plastics and Biomass

Pyrolysis has been proposed as a potential technology for managing the growing volume of plastic waste generated worldwide. Co-pyrolysis of plastic waste with biomass is a promising technology for generating fuel and chemical products. However, this process generates tar as a waste product. The chemical properties of this tar have yet to be thoroughly analyzed. Further, this study presents the results of gas chromatography–mass spectrometry (GC–MS), Fourier-transform infrared spectroscopy (FTIR), and thermogravimetric analysis (TGA) of oil and tar obtained from the pyrolysis of pure plastics including high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene (PE), polystyrene (PS), and plastic-biomass mixtures. GC–MS analysis revealed the presence of C 7 –C 37 carbon-containing hydrocarbons, which include alkanes and alkenes as the dominant products. FTIR data revealed the presence of various functional groups, including alcohols, aldehydes, ketones, and carboxylic acids, indicating the complexity of the pyrolysis and copyrolysis oil obtained from waste plastics and biomass. TGA data show that tar from all four plastics has a higher decomposition rate, suggesting the presence of heavier hydrocarbons compared with their corresponding oils. This research will be of interest to researchers looking to advance the study of plastic and biomass waste management.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Source of Processable Vitrimer Viscosities: Swap Frequencies and Steric Factors

Vitrimers exhibit high, processable viscosities, where other polymers do not, and are among the most promising polymers for closed-loop material circularity. We sought to investigate the underlying chemical kinetic factors that result in high viscosities for vitrimers, which are crucial to designing vitrimers with tunable viscosity. To interrogate these factors, we achieved the first simulated predictions of real vitrimer viscosities, using a novel kinetic Monte Carlo molecular dynamics method, overcoming the time and length scale gaps to predict experimental bulk viscosities. The vitrimer architecture investigated is based on poly(dimethylsiloxane) chains and vinylogous urethane bond swaps. We probed the effects of the extent of free swapping groups, %F, the activation energy, E A , and the steric factor, ρ. The steric factor is related to the intrinsic reaction probability for molecules with sufficient energy. All three factors were found to be significant, but the role of ρ was found to be the biggest and also the most underappreciated. The results show that the inclusion of accurate ρ is of critical importance for viscosity predictions, with the evidence suggesting that the typical assumption of ρ = 1 is not valid for vitrimers and that, indeed, very low steric factors are present in bond-swap vitrimers such that values of ρ < 10 –10 may be typical. This greatly influences the bond exchange rates and, ultimately, the viscosities. Recognition of this result is necessary for the prediction of vitrimer viscosities from molecular simulations and to make vitrimers by design from molecular dynamics. We also investigated the effects that E A , ρ, and the number of free swapping groups have upon vitreous range temperatures, TV, with respect to achieving a specific viscosity (η V = 1 × 10 8 Pa·s), as well as for a commonly reported higher viscosity extrapolation (η V = 1 × 10 12 Pa·s). The evidence suggests that vitrimers may follow universal curves for E A vs T V , as a function of ρ. Finally, this study achieves the first of these comparisons of molecular simulations to experiments and reveals critical insights toward creating vitrimers by design, while providing a route for the prediction of T V from kinetic Monte Carlo molecular dynamics simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

DeFault: DEep‐Learning‐Based FAULT Delineation Using the IBDP Passive Seismic Data at the Decatur CO2 Storage Site

Abstract The carbon capture, utilization, and storage (CCUS) framework is an essential component in reducing greenhouse gas emissions, with its success hinging on the comprehensive knowledge of subsurface geology and geomechanics. Passive seismic event relocation and fault detection offer vital insights into subsurface structures and the ability to monitor fluid migration pathways. Accurate identification and localization of seismic events, however, face significant challenges, including the necessity for high‐quality seismic data and advanced computational methods. To address these challenges, we introduce a novel deep learning method, , specifically designed for passive seismic source relocation and fault delineating for passive seismic monitoring projects. By leveraging data domain‐adaptation, allows us to train a neural network with labeled synthetic data and apply it directly to field data. Using , the passive seismic sources are automatically clustered based on their recording time and spatial locations, and subsequently, faults and fractures are delineated accordingly. We demonstrate the efficacy of on a field case study involving injection related microseismic data from Decatur, Illinois area. Our approach accurately and efficiently relocated passive seismic events, identified faults and could aid in potential damage induced by seismicity. Our results highlight the potential of as a valuable tool for passive seismic monitoring, emphasizing its role in ensuring CCUS project safety. This research bolsters the understanding of subsurface characterization in CCUS, illustrating machine learning’s capacity to refine these methods. Ultimately, our work has significant implications for CCUS technology deployment, an essential strategy in combating climate change. Plain Language Summary In our quest to tackle climate change, we use a strategy known as carbon capture, utilization, and storage (CCUS) to keep greenhouse gases out of the atmosphere. This strategy relies heavily on our ability to understand what's happening deep under the earth's surface. To make sure we store super critical safely, we need to accurately map out the geological structure, especially faults, but this is tough without high‐quality data and complex computer programs. We've developed a new tool called “DeFault,” which uses advanced machine learning to improve how we find and map these underground features. “DeFault” is smart enough to learn from numerically simulated data and then apply what it’s learned to real‐world situations. It groups together seismic activity—tiny tremors and shifts in the earth—based on when and where they happen, which helps us spot where there might be cracks or faults. We tested “DeFault” in Illinois, where CO 2 is injected underground, and it successfully pinpointed where these tremors occurred and mapped out the faults, helping to prevent accidents accurately in the future. Our study shows that “DeFault” will be a powerful ally in making CCUS safer and more effective, especially for the Illinois Basin Decatur Project. Key Points Faults and fractures introduced by carbon storage can be monitored by passive seismicity DeFault algorithm enables an automatic process for accurate and efficient passive seismic event locating and clustering

58 GEOSCIENCES↗

DOE OSTI2024

Holographic entropy inequalities and multipartite entanglement

Abstract We study holographic entropy inequalities and their structural properties by making use of a judicious grouping of terms into certain multipartite information quantities. This allows us to recast cumbersome entropic expressions into much simpler ones which share interestingly rigid structures. By performing a systematic search over some of these structures, we are able to discover more than 1800 novel entropy inequalities for six parties, thereby demonstrating that these recastings provide a fruitful generating technique for uncovering new holographic entropy inequalities. In attempting to interpret the corresponding sign-definite quantities as correlation measures, we also obtain a no-go result: the superbalance property of holographic entropy inequalities turns out to preclude them from being monotonic under partial tracing. In the process, we also comment on the geometrical significance of multipartite information quantities and present various structural relations amongst them.

Physics↗

DOE OSTI2025

Origin of Stabilization of Ligand-Centered Mixed Valence Ruthenium Azopyridine Complexes: DFT Insights for Neuromorphic Applications

Redox-driven conductance changes are critical processes in molecular- and coordination-complex-based memristive thin films and devices that are envisioned for neuromorphic technologies, but fundamental mechanisms of conductance switching are not fully understood. Here, we explore charge disproportionation (CD) processes in [Ru II L 2 ](PF 6 ) 2 molecular systems that intrinsically involve interfragment charge transfer (IFCT). Using a combination of ab initio molecular dynamics simulation (AIMD), time-dependent density functional theory (TD-DFT), and density functional theory (DFT) calculations, we investigate the electron transfer mechanisms and the roles of temperature and cell volumetric expansion in facilitating the counterion movements and electronic transitions required for low-cost IFCT and charge redistribution. A detailed analysis of the density of states and TD-DFT calculations highlights that unpaired electrons play a crucial role in low-energy transitions, with the azo (N=N) groups of the ligand serving as the primary sites for electronic transport between molecular fragments, further stabilizing the asymmetric state. Localization of added electrons on azo ligands occurs with negligible change at the Ru centers, supported by atomic volume expansions up to +4.74 bohr 3 , and goes along with a progressive reduction of the HOMO−LUMO gap across redox states, suggesting enhanced conductivity. The TD-DFT analysis reveals a dominant IFCT excitation at 2082.76 nm in the doubly reduced (22) state, while a stabilization energy of 1.20 eV of the asymmetric (13) state relative to the symmetric (22) state is predicted by constrained DFT. Periodic DFT and AIMD simulations emulating a molecular film show that the stabilization of the asymmetric state, relative to a symmetric one, translates in net charge separation values (order of ∼0.33 e) that are strongly linked to increased counterion mobility (average counterion displacements exceeding 0.7 Å per atom during CD events) and the involvement of azo groups in electron redistribution. These findings, which align with previously reported experimental and computational data, provide key insights into the IFCT mechanisms and electronic transport facilitated by azo groups, with important implications for redox-driven memristive and neuromorphic technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Barge Site - Avian Radar System / Derived Data

This is a combined data set of 67,410 bird/bat tracks from an avian radar system deployed on a research barge (MERLIN True3D, DeTect, Panama City, Florida, USA) and concurrent wind measurements from two scanning lidars (WindCube v2.1, Vaisala, Vantaa, Finland, and Halo XR+, Halo Photonics, Lannion, France). The research barge (16.5 m x 61 m) was deployed as part of the Wind Forecast Improvement Project (WFIP-3) off the northeast coast of the United States south of Massachusetts (40.9 deg N, 70.79 deg W). This data set comprises 5 weeks of data between August 27th 2024 and September 27th 2024. Radar data were provided by DeTect and Lidar data were accessed through the Wind Data Hub (wfip3/barg.WINDPROF.z01.a0) The data have been filtered and sorted into two size groups ("big" and "small") based on a clustering approach. See Snortland, A., Clerc, J., Hein, C., & Cotter, E. (2025). Wind as Driver of Bird and Bat Abundance, Flight Direction, Altitude, and Speed on the North Atlantic Shelf. arXiv preprint arXiv:2511.14983 for complete details. Data are provided in 2 files: "Birds" and "Birds_hourly" Birds: This file contains information about each of the 67,410 flying animal tracks detected by the radar during the data collection period, including parameters measured by the radar and wind information interpolated from the lidar wind measurements. We note that the raw radar dataset contained 301,618 tracks; tracks in this processed dataset were filtered based on the requirements described in Snortland et al. (2025). Birds_hourly: This file contains timeseries of the number of tracks detected per hour over the course of the data collection period, including wind conditions and sun position for each hour. These data were used for generalized additive modeling in Snortland et al. (2025).

17 WIND ENERGY↗