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DOE OSTI2026

Entanglement Throughput Measurements and Capacity Estimates for Aerial-Inground Fiber

The throughput of entangled qubit pairs per second (eqps) is a basic performance metric of quantum networks that provide the entanglement distribution capability. Over fiber connections, it is measured using specialized instruments, including photonic entanglement sources and single-photon detectors. Extensive theory has been developed to estimate the capacity of a generic quantum channel, which in turn is applied to estimate the maximum achievable eqps over a fiber connection. However, there is a gap in relating these two performance metrics, in part due to their disparate nature, namely, mathematical formulae of the channel capacity and specialized instrumentation for eqps measurements. We describe eqps measurements collected over testbed connections composed of aerial and inground fiber of lengths up to 45 km. We estimate the normalized capacity using the transmissivity parameter derived from single photon detector measurements. These estimates are then converted to capacity bounds on the connection's eqps using the source eqps rate derived from co-located detector measurements. The results indicate a consistency between eqps measurements and their capacity estimates, and provide insights into relating the parameters of analytic capacity estimates to physical measurements.

Rao, Nageswara [ORNL] (ORCID:0000000234085941)↗

DOE OSTI2025

Fast and Invertible Simplicial Approximation of Magnetic‐Following Interpolation for Visualizing Fusion Plasma Simulation Data

We introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.

Ren, Congrong [The Ohio State Univ., Columbus, OH ↗

DOE OSTI2026

Ride Pingo to Transit

In this project, we developed an on-demand microtransit first- and last-mile service. To integrate the service with fixed-route transit, we developed a feature called Transit Connect that prioritized riders’ on-time arrival at the transit station over other service requirements. We first prototyped service and related algorithms in a simulated environment, and then piloted the service in the city of Kent, Washington. Our algorithm incorporates request-specific hard drop-off deadlines to ensure timely arrivals for transit transfers. In the pilot, these constraints were obtained from GTFS Realtime data to accurately determine the schedule of the transit and the location of the stations. This approach introduced the ability to accept or decline new requests based on the timing of transit connections for these new requests and connection status of onboarding customers. The pilot (called “Ride Pingo to Transit”) deployed a fleet of three 14-person vans, ran from September 2021 to March 2023, and served a total of 21,329 trips. Transit Connect was offered for drop-offs at both Kent Station and the Kent Valley hub. In total, 2,844 such trips were completed. This dataset was collected from our pilot, which includes the following: - Requests: List of all trip requests, including those that were actually served and those not materialized. - Fleet: Daily vehicle service logs. - Service details: Daily vehicle stop logs (boarding and alighting). - Trip types: First mile, last mile, or point-to-point. ![pingo to transit](pingo-to-transit.jpg)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Countering Weapons of Mass Destruction (CWMD) Device Cybersecurity Characterization Process and Profile

Countering Weapons of Mass Destruction (CWMD) recognizes that threats in the cyberspace domain continue to grow, which requires CWMD devices and supporting systems to be both cybersecure (ability to protect or defend from cyber-attacks) and resilient (ability to maintain required capability in the face of adversity) to cyber threats. The CWMD cybersecurity characterization approach in this document supports existing cyber resilience activities within the Acquisition Lifecycle Framework. Similarly, this process supports existing Department of Homeland Security Cyber Resilience Test and Evaluation activities, which consist of iterative processes, starting at the initiation of system acquisition and continuing throughout the entire device and system life cycle. Cyber resilience is the ability of an information system to continue to operate while under attack, even if in a degraded or debilitated state,1 and to rapidly recover operational capabilities for essential functions after a successful attack.2 The goal of the security characterization task for CWMD is to support the development of a CBRN device-dependent profile that aligns with device network capabilities and maps to recommended security controls to create a characterization security profile impact levels. The impact levels for CWMD devices should be characterized as Low (L), Moderate (M), High (H) to align with the low, moderate, high control baselines. To estimate the impact levels, the device’s security-related attributes are translated into the security objectives: Confidentiality (C), Integrity (I), and Availability (A), known as the CIA triad. The potential impact for each device can be L, M, H, for devices that connect and transmit different types of data and may have different impact levels. National Institute of Standards and Technology Federal Information Processing Standards Publication 199 states, “the potential impact values assigned to the respective security objectives shall be the highest value from among those security categories that have been determined for each type of information resident on the information system.”3 As CWMD is determining the cybersecurity impact levels of CBRN devices based on network connections and data transfers, the impact levels are aligned with the associated attributes of network connections and communications. For example, if the device system is connected to a wireless network and transmits different data types based on the confidentiality of the data, the highest impact value for each security objective should represent the device’s CIA impact level. This document is intended to be used by test managers, test team, and program managers.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Queued Up: 2025 Edition – Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2024 [Slides]

Electric transmission system operators (ISOs, RTOs, or utilities) require proposed power plants seeking to connect to the transmission grid to undergo a series of impact studies before they can be built. This process establishes what new transmission equipment or upgrades may be needed before a project can connect to the system and assigns the costs of that equipment. The lists of projects in this process are known as “interconnection queues”. In collaboration with interconnection.fyi, Berkeley Lab compiled, aggregated, and cleaned interconnection queue data from >50 transmission grid operators (7 ISO/RTOs and 49 non-ISO balancing areas), which collectively represent ~97% of currently installed U.S. electric generating capacity. The dataset includes requests submitted to queues through the end of 2024, and only includes requests seeking to connect to the transmission grid (not distribution-connected or behind-the-meter projects). The files below include both a PDF report and an Excel data file. The PDF report analyzes interconnection data and metrics through the end of 2024. The Excel data file includes (a) the full project-level interconnection queue dataset through 2024, (b) a codebook (data dictionary) describing each data field, and (c) 35 additional tabs featuring tables summarizing a range of interconnection metrics. Key highlights from the Queued Up: 2025 Edition (featuring data through 2024) include: • As of the end of 2024, there were ~10,300 projects actively seeking grid interconnection in the U.S., representing 1,400 GW of generation and approximately 890 GW of storage. • Historic withdrawal rates alongside relatively fewer new requests resulted in a 12% decrease in total active queue volume compared to the prior year. • Active natural gas capacity (136 GW, +72% year-over-year) increased in 2024, while solar (956 GW, -12%), storage (890 GW, -13%), and wind (271 GW, -26%) capacity decreased. • 408 GW of capacity already has a draft or executed interconnection agreement (IA) but has not yet reached commercial operations. • The time projects spend in queues before reaching COD is increasing. For the regions with available data, the median duration from IR to COD has doubled from <2 years for projects built in 2000-2007 to over 4 years for those built in 2018-2024. • Ultimately, most of this proposed capacity will not be built. Only 13% of capacity that submitted interconnection requests from 2000-2019 had reached commercial operations by the end of 2024; 77% of that capacity had been withdrawn and 10% was still active. • FERC Order 2023 and various other reforms are being implemented. These are important measures to reduce interconnection bottlenecks and enhance grid system reliability, but it is too early to measure and assess their full impact. • New additions for the 2025 edition include: (a) additional detail on data processing and gaps; (b) updates on interconnection reforms; (c) new analysis on interconnection agreements, and more.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Database Performance Monitoring for DUNE

This project improves Checkmk monitoring for DUNE Rucio PostgreSQL database services by adding clearer dashboard visibility for connection and lock behavior. The work began with a request to monitor database performance metrics such as connection usage, configured limits, lock activity, wait behavior, query performance, storage trends, and saturation alerts. Existing Checkmk PostgreSQL checks were reviewed, and gaps were identified in how connection states and lock modes were displayed. To address these gaps, two DSG-specific local checks were added for dune_rucio_prod: one for connection-state monitoring and one for lock-state monitoring. These checks report active, idle, idle-in-transaction, total, usage-percent, lock-mode, waiting-lock, and wait-age metrics. The added metrics supplement built-in Checkmk monitoring and provide DUNE application developers with clearer service states, history graphs, dashboard widgets, and alerts.

Bowers, Elliot [Cabrillo Coll.]↗

DOE OSTI2024

Myna

The additive manufacturing (AM) community has been developing digital factory tools over the past decade to better leverage the multi-modal process data coming out of the advanced manufacturing process. As a result, numerous databases of additive manufacturing process data exist in the literature and in the archival storage of disparate research groups. While some efforts have been made to create a standard ontology for storing and sharing AM data, in practice a variety of data structures are used to store AM build data, even within a single institution. This causes many problems for maintainability and extensibility when attempting to integrate computational modeling tools with experimental data to either validate models or to provide further insight into results and trends. Myna is a Python-based framework that aims to decrease the effort needed to connect individual computational models to the variety of AM process data that exist in different research groups and institutions. This type of software is sometimes referred to as "middleware" or “glueware,” in that it connects disparate databases and applications into a single computational ecosystem. Instead of maintaining unique interfaces between each application and each database, developers can create a single interface from each application to Myna and thereby gain access to the implemented database connections. Similarly, developing a database connection in Myna provides access to the developed simulation applications. This framework greatly simplifies the maintainability of model applications that rely on experimental data. Using external simulation tools, users will also be able to run pre-configured workflows using the built-in workflow manager. Several examples of input files are provided with Myna for different workflows, including melt pool geometry predictions and detailed melt pool and solidification microstructure predictions.

Knapp, GerryL. [Oak Ridge National Laboratory (ORN↗

DOE OSTI2024

Automating the detection of hydrological barriers and fragmentation in wetlands using deep learning and InSAR

The loss of hydrological connectivity and fragmentation of natural wetlands is a widespread driver of wetland degradation. Understanding where and how natural connectivity is impaired is essential for managing, protecting and remediating these ecosystems. Wetland Interferometric Synthetic Aperture Radar (Wetland InSAR) can provide information on surface flow orientation in wetlands at a high spatial resolution, which can be used for barrier detection. However, the broad application of this approach is constrained by the labour-intensive manual delineation of barriers based on mapped water levels. This study presents the first deep learning-based methodology for the automated detection of hydrological barriers. We trained a deep convolutional network to segment edge features of hydrological barriers in 25 image pairs captured by ALOS PALSAR-1 L-Band InSAR between 2006 and 2011. The training dataset consists of manually labelled and delineated barriers showing abrupt changes in water surface elevation and wrapped interferograms with high coherence. We tested this method across three wetland sites: the Everglades and southern Louisiana wetlands (United States) and the Cienaga de Zapata (Cuba). Across these sites, the convolutional network detected hydrological barriers with up to 84% accuracy. The model performed particularly well for linear hydrological barriers such as roads, dikes, and channels. Notably, some barriers impede flow only seasonally, appearing during low water levels and disappearing when water levels rise. Our automated approach to detecting and assessing wetland hydrologic connectivity can be applied more broadly to support the effective management of fragmented wetland ecosystems.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Resilient stellarator divertor characteristics in the Helically Symmetric eXperiment

Resilient divertor features connected to open chaotic edge structures in the Helically Symmetric eXperiment are investigated. For the first time, an expanded vessel wall was considered that would give space for implementation of a physical divertor target structure. The analysis was done for four different magnetic configurations with very different chaotic plasma edges. A resilient plasma wall interaction pattern was identified across all configurations. This manifests as qualitatively very similar footprint behavior across the different plasma equilibria. Overall, the resilient field lines of interest with high connection length L C lie within a helical band along the wall for all configurations. This resiliency can be used to identify the best location of a divertor. The details of the magnetic footprint’s resilient helical band is subject to specific field line structures which are linked to the penetration depth of field lines into the plasma and directly influence the heat and particle flux patterns. The differences arising from these details are characterized by introducing a new metric, the minimum radial connection min $(\delta_N)$ of a field line from the last closed flux surface. The relationship, namely the deviation from a scaling law, between min $(\delta_N)$ and L C of the field lines in the plasma edge field line behavior suggests that the field lines are associated with structures such as resonant islands, cantori, and turnstiles. This helps determine the relevant magnetic flux channels based on the radial location of these chaotic edge structures and the divertor target footprint. These details will need to be taken into account for resilient divertor design.

divertor↗

DOE OSTI2024

Improving the Freight Productivity of a Heavy-Duty, Battery Electric Truck by Intelligent Energy Management

This project aimed to enhance the range and reduce the operating costs of battery electric Class 8 trucks traveling over 250 miles daily. This was achieved through the development and implementation of an intelligent-Energy Management System (i-EMS) that leverages vehicle and operations data, physics-aware machine learning algorithms, and vehicle-to-cloud (V2C) connectivity. The project hypothesized that advanced machine learning algorithms and real-time data analytics could significantly improve the energy efficiency and range of these trucks. Key objectives included developing a physics-aware machine learning algorithm, implementing an i-EMS with V2C connectivity and physics-aware spatial data analytics (PSDA), and validating the system’s effectiveness with fleet partners HEB Companies and Murphy Logistics. Extensive data collection from vehicle operations, including vehicle characteristics, road conditions, and payload, was conducted. A machine learning algorithm was developed to predict energy consumption and enable proactive decision-making. The i-EMS was implemented on two Volvo VNR BEVs, with operators receiving charging and routing recommendations. Charging stations were installed at depot locations in Texas and Minnesota, with an additional on-route charger in Minnesota. Significant findings included a 14% range improvement for Murphy Logistics on a highway-driving eco-route and a 22% range improvement for HEB Companies on a city-driving eco-route. The i-EMS utilized rule-based methods and physics-based algorithms to predict and reduce energy consumption, with real-time monitoring and analysis through V2C connectivity enabling proactive decision-making. The project demonstrated the feasibility and economic viability of battery electric Class 8 trucks for long-haul operations, showcasing the potential of physics-aware machine learning in optimizing energy management. The successful implementation of the i-EMS in real-world scenarios validates its practical application and effectiveness, paving the way for the widespread adoption of battery electric vehicles in the freight transportation industry.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

Circles and triangles, the NLSM and Tr(Φ 3 )

A surprising connection has recently been made between the amplitudes for Tr(Φ 3 ) theory and the non-linear sigma model (NLSM). A simple shift of kinematic variables naturally suggested by the associahedron/stringy representation of Tr(Φ 3 ) theory yields pion amplitudes at all loops. In this note we provide an elementary motivation and proof for this link going in the opposite direction, starting from the non-linear sigma model and discovering its formulation as a sum over triangulations of surfaces with simple numerator factors. This uses an ancient connection between “circles” and “triangles”, interpreting the equation y = $\sqrt{1 – x^2}$ both as parametrizing points a circle as well as generating the number of triangulations of polygons. A further simplification of the numerator factors exposes them as arising from the kinematically shifted Tr(Φ3) theory, and gives rise to novel tropical representations of NLSM amplitudes. The connection to Tr(Φ 3 ) theory defines a natural notion of “surface-soft limit” intrinsic to curves on surfaces. Remarkably, with this definition, the soft limit of pion amplitudes vanishes directly at the level of the integrand, via obvious pairwise cancellations. We also give simple, explicit expressions for the multi-soft factors for tree and loop-level integrands in the limit as any number of pions are taken “surface-soft”.

Scattering Amplitudes↗

DOE OSTI2024

Capacity contributions of Southern Oregon offshore wind to the Pacific Northwest and California

Variable renewable energy generation poses unique capacity challenges, which increasingly depend on weather events at varying timescales. Facilitated by transmission planning, geographic and technological diversity of the generation fleet may provide a mitigation to capacity shortfalls. In this work, offshore wind (OSW) energy is sited in the areas off the West Coast between Coos Bay, Oregon, and Crescent City, California. Three generation and transmission scenarios are modeled within the Western Interconnection: (i) 3.4 gigawatts (GW) of installed OSW capacity connected to Southern Oregon through a High Voltage Alternating Current (HVAC) Radial Topology in 2030; (ii) 12.9 GW of installed OSW capacity connected to Washington, Oregon, and California through a High Voltage Direct Current (HVDC) Radial Topology post-2030, and (iii) the same 12.9 GW connected to the same locations through a Multi-terminal DC (MTDC) Backbone Topology post-2030. Zonal dispatch simulations assuming coincident wind, solar, and hydropower production and loads over 18 meteorological years, accounting for temperature-dependent equipment derating and forced outages, serve as inputs to the Associated System Capacity Contribution (ASCC) methodology. The capacity credit is 33%, 25% and 34% for the 2030 HVAC Radial Topology, 2030+ HVDC Radial Topology, and 2030+ MTDC Backbone Topology, respectively. Transmission design is shown to mitigate the typical erosion of marginal capacity contribution as more OSW is developed, underscoring the opportunity for grid modernization while decarbonizing the generation mix.

17 WIND ENERGY↗

DOE OSTI2025

Solvent Transport in Disordered and Dynamic Membrane Pores: Implications for Reverse Osmosis and Nanofiltration Membranes

Pressure-driven separations with nanoporous membranes, such as reverse osmosis and nanofiltration, play a vital role in addressing water scarcity and enabling resource recovery. Understanding water or solvent transport in membrane pores is essential for advancing membrane separation technologies. A key question in transport modeling is to establish a relationship between solvent permeability and membrane porous structure properties, such as porosity or pore size. The nano- and subnanometer pores in polymeric membranes such as reverse osmosis and nanofiltration membranes are highly tortuous and dynamically connected, which challenges the conventional methods of transport modeling. This study addresses this challenge by developing a theoretical framework to describe solvent transport through membranes with dynamic and disordered porous structure. Specifically, we propose a lattice model to describe the pore network, while preserving the viscous nature of solvent permeation. We further establish a relationship between solvent permeability and membrane porosity or pore size, which is validated by molecular dynamics simulations and experimental data. By integrating this relationship into the solution-friction model, we define pore connectivity and local friction coefficient to quantify the impact of pore structure on solvent permeability. Our analysis highlights the dominant influence of pore connectivity on the permeability of reverse osmosis and nanofiltration membranes, particularly when the pore size approaches the dimensions of solvent molecules. Overall, this study provides critical insights into water and solvent transport mechanisms in nanoporous membranes, opening the door for strategies to substantially enhance membrane performance.

membranes↗

DOE OSTI2024

Effects of Structural Constraints on Excited-State Properties in Dimeric Cu(I) Diimine Complexes

Copper(I) bis-diimine complexes have played important roles in light-activated processes that can lead to their potential applications in photocatalysis and chemical sensing. Their metal-to-ligand charge-transfer (MLCT) excited-state properties are tunable by various structural factors. Dimeric Cu(I) complexes with connecting diimine derivative ligands offer another structural tuning platform for the excited-state properties. Here, we investigate excited-state properties in two covalently connected dimeric Cu(I)'s with varying structural constraints exerted by the number of carbons in the polyethylene bridge (C0 and C4) connecting the two copper(I) diimine moieties. An interesting feature of Cu(I) diimine complexes is their ability to flatten following a photoinduced structural change. Herein, we observe larger structural constraints and more structural rearrangement required upon excitation of the longer bridged complex C4 to achieve a conformation toward a more flattened tetrahedral coordination geometry compared to the shorter bridged C0. Vibrational wavepacket analysis of these complexes further supports the effect of these structural constraints where we observe a more rapid dephasing of the C0 complex, as opposed to the C4 complex, despite similar normal mode vibrations. The experimental results were supplemented by TDDFT calculations. In conclusion, the studies provide insight into using metal-metal interactions through constraints to tune excited-state dynamics and pathways.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Catalytic Consequences of Hierarchical Pore Architectures within MFI and FAU Zeolites for Polyethylene Conversion

The benefits of hierarchical zeolites for the conversion of bulky molecules like polymeric waste have been reported in the literature; however, the impact of mesopore sizes and connectivities on rates, product selectivities, and catalyst deactivation in the context of plastic upcycling has not been systematically probed. Here, in this study, we synthesized a suite of hierarchical MFI and FAU zeolites via desilication under varying conditions for metal-free polyethylene conversion reactions under batch and flow conditions (473–523 K). Polyethylene (solid) conversion rates (normalized by Bro̷nsted acid site density) were higher on hierarchical than parent microporous MFI regardless of mesopore connectivities, i.e., open or constricted, suggesting that the incorporation of mesopores facilitates diffusion of intermediate products to access medium-pore protons for successive scission events. Furthermore, higher branched:linear gaseous product ratios were produced on hierarchical than parent MFI, since mesopores allow for egress of bulkier molecules without undergoing further secondary events, e.g., isomerization back to linear alkanes/alkenes or beta scission. Solid conversion rates on hierarchical FAU synthesized via desilication with cetyltrimethylammonium bromide (CTABr), however, were not higher than parent FAU, likely because the presence of CTABr facilitates recrystallization of leached species to form composites (hierarchical FAU and ordered mesoporous materials) with more isolated mesopores. The stagnation in rates, despite increased mesopore volumes (>0.22 cm 3 g –1 ), highlights the importance of confinement effects provided by micropores for cleaving C–C bonds at modest reaction conditions. In situ 1 H MAS NMR performed on polyethylene with MFI zeolite show that PE isomerizes (and potentially deconstructs) at temperatures near 450 K, highlighting the role of Bro̷nsted acid sites in activating C–C bonds under mild reaction conditions. Catalyst recyclability studies showed that all catalysts undergo deactivation during plastic upcycling reactions, but to varying extents. Overall, hierarchical materials have better catalyst stability than parent materials, although the differences in stability between hierarchical and parent FAU are smaller than those for MFI. Taken together, these findings demonstrate how rates, selectivities, and catalyst deactivation from plastic upcycling reactions can be controlled via fine-tuning the identity and connectivity of mesopores.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Torsional Flexibility Tuning of Hexa-Carboxylate Ligands to Unlock Distinct Topological Access to Zirconium Metal–Organic Frameworks

Zirconium-based metal–organic frameworks (Zr-MOFs) exhibit remarkable structural diversity and functionality. However, uncovering new topological types within this family remains a considerable challenge today. Herein, we report two new hexa-topic ligands designed through the introduction of torsional flexibility, which enable the construction of two Zr-MOFs featuring rare network topologies. The (4,4′,4″,4‴,4‴′,4‴′′-((2-carboxybenzene-1,3,5-triyl)tris(9H-carbazole-9,3,6-triyl))hexabenzoic acid ligand (BTCH)) was obtained by replacing the rigid triptycene core in the H6PET-1 ligand (4,4′,4″,4‴,4‴′,4‴′′-(9,10-dihydro-9,10-[1,2]benzenoanthracene-2,3,6,7,14,15-hexayl)hexabenzoic acid) with a benzene-tricarbazole unit. Owing to its torsionally flexible core that allows rotational freedom to the arms, this ligand directs the construction of NU-2620 (NU represents Northwestern University), a Zr-MOF with 8-connected Zr6 clusters and the rare nuh topology. Further flexibilization of the carbazole units to benzene rings yielded the even more torsionally flexible 5′,5‴-bis(4-carboxyphenyl)-5″-(4,4″-dicarboxy-[1,1′:3′,1″-terphenyl]-5′-yl)-[1,1′:3′,1″:3″,1‴:3‴,1‴′-quinquephenyl]-4,4‴′-dicarboxylic acid ligand (CCTT), which forms NU-2630 featuring 6-connected clusters and the pcu topology. Both frameworks exhibit good chemical stability, prompting evaluation of their performance in CO2 photoreduction catalysis. Under low-concentration CO2 conditions, NU-2620 displays markedly higher catalytic activity than its benzene-based analogue, NU-2630, thanks to the abundance of photoactive carbazole units within its structure. These results demonstrate that introducing torsional flexibility in high-connected linkers can unlock access to new topologies and accelerates the reticular expansion of Zr-MOFs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Structure of molten ytterbium aluminum garnet

Rare earth aluminum garnets are important materials in optical, dielectric, and thermal barrier applications. To advance the understanding of their melt processing and glass forming ability, we report the atomic structure of molten Yb 3 Al 5 O 12 over 1770–2630 K, which spans the equilibrium and supercooled liquid regimes. The melt density at T m = 2283 K is 5.50 g cm –3 , measured via silhouette imaging of electrostatically levitated drops over 1010–2420 K. Four separate structure measurements were made with aerodynamically levitated melts using x-ray and neutron diffraction with isotope substitution of Yb ( 172 Yb, 174 Yb, or nat Yb). Empirical potential structure refinement models were developed, which are in excellent agreement with the experiments. Coordination environments for Al–O are predominantly 4- and 5-coordinate, with a mean coordination of n AlO = 4.43(8), while Yb–O environments mostly range from 5- to 8-coordinate, with n YbO = 6.26(8). The cation–oxygen polyhedra are connected primarily by corner-sharing, with edge-sharing constituting up to ~1/3 of the connectivity among polyhedra with Yb or higher-coordinated Al–O. Structurally, the –Al–O– network in molten Yb 3 Al 5 O 12 appears conducive to glass formation: n OAl = 1.85(3), there are 1.86 AlO x –AlO x connections per Al atom (e.g., a mixture of Q 3 and Q 4 units), and the modal ring size is six cations. These characterize a network that is somewhat less constrained compared to SiO 2 glass, yet Yb 3 Al 5 O 12 cannot be quenched into crystal-free glass. Here, aluminum garnet compositions with larger rare earth cations do form glass, so these characterizations help reveal the structural characteristics corresponding to the limit of glass forming ability in rare earth aluminates.

36 MATERIALS SCIENCE↗