SEARCH · Engineering Papers

Results for “Reduce order modeling”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,081 records · Page 60

All sources NASA NTRS DOE OSTI

DOE OSTI2026

Trisodium Phosphate Phases and Solubility in Alkaline Solutions Relevant to Radioactive Waste Processing

The U.S. Department of Energy’s Hanford Site faces significant challenges in managing millions of gallons of legacy radioactive waste, where phosphate precipitation can obstruct pipelines during retrieval and processing. To resolve long-standing inconsistencies in reported solubility and clarify factors governing trisodium phosphate hydrates, we examined the Na3PO4:NaOH:H2O system. Powder and single-crystal X-ray diffraction revealed that commercial precursors undergo transformations that produce multiple hydrates, including three previously unreported phases comprising an ordered polymorph of Na3(PO4)·12H2O·1/6NaOH, Na3PO4·5H2O, and Na3PO4·9H2O. Computational modeling indicated that the ordered dodecahydrate is more stable than its disordered counterpart, suggesting kinetic persistence of structural disorder. Solubility measurements were conducted with solutions prepared either from anhydrous Na3PO4 or from Na3(PO4)·12H2O·1/6NaOH and revealed that release of interstitial NaOH from the hydrate precursor elevated solution alkalinity, thereby significantly reducing phosphate solubility relative to solutions prepared with anhydrous Na3PO4 across 20–44 °C. These results begin to reconcile inconsistencies in prior solubility data and clarify how phase composition dictates phosphate precipitation under alkaline conditions.

Graham, Trenton R. (ORCID:0000000189078004)↗

DOE OSTI2021

Novel niobium-doped titanium oxide towards electrochemical destruction of forever chemicals

Electrochemical advanced oxidative processes (EAOP) are a promising route to destroy recalcitrant organic contaminants such as per- and polyfluoroalkyl substances (PFAS) in drinking water. Central to EAOP are catalysis-induced reactive free radicals for breaking the carbon fluorine bonds in PFAS. Generating these reactive species electrochemically at electrodes provides an advantage over other oxidation processes that rely on chemicals or other harsh conditions. Herein, we report on the performance of niobium (Nb) doped rutile titanium oxide (TiO 2 ) as a novel EAOP catalytic material, combining theoretical modeling with experimental synthesis and characterization. Calculations based on density functional theory are used to predict the overpotential for oxygen evolution at these candidate electrodes, which must be high in order to oxidize PFAS. The results indicate a non-monotonic trend in which Nb doping below 6.25 at.% is expected to reduce performance relative to TiO 2 , while higher concentrations up to 12.5 at.% lead to increased performance, approaching that of state-of-the-art Magnéli Ti 4 O 7 . TiO 2 samples were synthesized with Nb doping concentration at 10 at.%, heat treated at temperatures from 800 to 1100 °C, and found to exhibit high oxidative stability and high generation of reactive oxygen free radical species. The capability of Nb-doped TiO 2 to destroy two common species of PFAS in challenge water was tested, and moderate reduction by ~ 30% was observed, comparable to that of Ti 4 O 7 using a simple three-electrode configuration. We conclude that Nb-doped TiO 2 is a promising alternative EAOP catalytic material with increased activity towards generating reactive oxygen species and warrants further development for electrochemically destroying PFAS contaminants.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls

Here, this paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.

Frequency control↗

DOE OSTI2025

Overview of the SCEC/USGS Community Stress Drop Validation Study Using the 2019 Ridgecrest Earthquake Sequence

We present initial findings from the ongoing Community Stress Drop Validation Study to compare spectral stress-drop estimates for earthquakes in the 2019 Ridgecrest, California, sequence. This study uses a unified dataset to independently estimate earthquake source parameters through various methods. Stress drop, which denotes the change in average shear stress along a fault during earthquake rupture, is a critical parameter in earthquake science, impacting ground motion, rupture simulation, and source physics. Spectral stress drop is commonly derived by fitting the amplitude-spectrum shape, but estimates can vary substantially across studies for individual earthquakes. Sponsored jointly by the U.S. Geological Survey and the Statewide (previously, Southern) California Earthquake Center our community study aims to elucidate sources of variability and uncertainty in earthquake spectral stress-drop estimates through quantitative comparison of submitted results from independent analyses. The dataset includes nearly 13,000 earthquakes ranging from M 1 to 7 during a two-week period of the 2019 Ridgecrest sequence, recorded within a 1° radius. Here, in this article, we report on 56 unique submissions received from 20 different groups, detailing spectral corner frequencies (or source durations), moment magnitudes, and estimated spectral stress drops. Methods employed encompass spectral ratio analysis, spectral decomposition and inversion, finite-fault modeling, ground-motion-based approaches, and combined methods. Initial analysis reveals significant scatter across submitted spectral stress drops spanning over six orders of magnitude. However, we can identify between-method trends and offsets within the data to mitigate this variability. Averaging submissions for a prioritized subset of 56 events shows reduced variability of spectral stress drop, indicating overall consistency in recovered spectral stress-drop values.

58 GEOSCIENCES↗

DOE OSTI2025

Super-Resolution for Renewable Energy Resource Data with Wind from Reanalysis Data and Application to Ukraine

With a potentially increasing share of the electricity grid relying on wind to provide generating capacity and energy, there is an expanding global need for historically accurate, spatiotemporally continuous, high-resolution wind data. Conventional downscaling methods for generating these data based on numerical weather prediction have a high computational burden and require extensive tuning for historical accuracy. In this work, we present a novel deep learning-based spatiotemporal downscaling method using generative adversarial networks (GANs) for generating historically accurate high-resolution wind resource data from the European Centre for Medium-Range Weather Forecasting Reanalysis version 5 data (ERA5). In contrast to previous approaches, which used coarsened high-resolution data as low-resolution training data, we use true low-resolution simulation outputs. We show that by training a GAN model with ERA5 as the low-resolution input and Wind Integration National Dataset Toolkit (WTK) data as the high-resolution target, we achieved results comparable in historical accuracy and spatiotemporal variability to conventional dynamical downscaling. This GAN-based downscaling method additionally reduces computational costs over dynamical downscaling by two orders of magnitude. We applied this approach to downscale 30 km, hourly ERA5 data to 2 km, 5 min wind data for January 2000 through December 2023 at multiple hub heights over Ukraine, Moldova, and part of Romania. With WTK coverage limited to North America from 2007–2013, this is a significant spatiotemporal generalization. The geographic extent centered on Ukraine was motivated by stakeholders and energy-planning needs to rebuild the Ukrainian power grid in a decentralized manner. This 24-year data record is the first member of the super-resolution for renewable energy resource data with wind from the reanalysis data dataset (Sup3rWind).

17 WIND ENERGY↗

DOE OSTI2022

Optimal Sizing and Operation of a Hybrid Clean Energy Center

In this paper, we consider the deployment of battery energy storage systems (BESSs) paired with photovoltaics (PV) to form a hybrid clean energy center (HCEC), where the BESS is equipped for two main purposes: i) reducing the volatility of PV output and ii) shifting energy to meet an export limit. We develop a linear dynamical model to characterize the output of an HCEC and express the volatility and export power as functions of the battery's dispatch operations. We also propose an optimal sizing method via second-order cone programming (SOCP) to maximize the net benefits of the HCEC. Numerical studies are performed to illustrate and validate the proposed method.

Battery energy storage systems, photovoltaics, sec↗

DOE OSTI2023

DUNE-PRISM – A New Method to Measure Neutrino Oscillations

The Deep Underground Neutrino Experiment (DUNE) is a next-generation long baseline neutrino oscillation experiment designed to make precision measurements in a 1.2 2.4 MW neutrino beam, which is directed 1285 km from the Fermi National Accelerator Laboratory (Fermilab) to the Sandford Underground Research Facility (SURF) in South Dakota. Neutrinos are measured at two detector facilities: a near detector located at Fermilab close where the beam is produced and a far detector at SURF. The neutrino beam can be configured to be composed primarily of either muon or anti-muon neutrinos. DUNE measures the disappearance of muon and anti-muon neutrinos and appearance electron and anti-electron neutrinos in the neutrino beam. Measuring these neutrino flavour transitions provides DUNE with sensitivity to the neutrino mass ordering, $\delta_{CP}$, $\theta_{13}$, $\theta_{23}$ and the magnitude of $\Delta m^2_{23}$. The DUNE Precision Reaction Independent Spectrum Measurement (DUNE-PRISM) concept presents a novel way to perform a neutrino oscillation analysis, which has the potential to significantly reduce the impact of large systematic uncertainties in the neutrino interaction model. The PRISM method linearly combines measurements of off-axis neutrino interactions at the DUNE near detector to produce data-driven predictions of the oscillated neutrino event rate spectrum at the far detector. By building an oscillated far detector prediction directly from data, any unknown or poorly modelled neutrino interaction effects will be naturally incorporated into the measurement of the parameters of the neutrino oscillation model. This thesis presents the first complete neutrino oscillation analysis for DUNE using the PRISM method. Details of the methodology are fully explained and the prospects for further improvements to the techniques described are highlighted. The expected impact and relative importance of the neutrino flux, cross section and detector systematic uncertainties are described in detail. Finally, this thesis demonstrates that the PRISM method is capable of performing a measurement of the oscillation parameters that is robust against neutrino interaction modelling errors.

Hasnip, Ciaran↗

DOE OSTI2025

Harnessing Virtual Power Plants Reliably: Enabling tools for increased observability, controllability, operation, and aggregation of distributed energy resources

Harnessing virtual power plants enhances the integration of distributed energy resources into utility grids for a sustainable energy future. Virtual power plants (VPPs) aggregate DERs to enhance resource adequacy and reduce emissions. U.S. utilities are exploring various technologies to manage DERs effectively. FERC Order 2222 allows DERs to participate in both wholesale and retail markets. Enhancing observability and controllability of behind-the-meter (BTM) DERs is essential for reliable grid operations. A hierarchical control architecture can improve coordination among residential energy resources. Field tests showed nearly 20% energy savings and 30% peak power reduction during grid events. Effective management of DERs requires enhanced situational awareness to prevent grid congestion. Integrating DER management systems (DERMS) with existing planning tools can improve operational security. Near-real-time grid models can validate optimal resource set points against resource uncertainty. Traditional uninterruptible power supplies (UPS) can be upgraded to support grid services and become part of VPPs. Upgrading UPS systems can reduce costs by 75% and unlock significant battery capacity. New battery management systems and grid-aware controllers are essential for optimizing UPS performance. Continued research and development are necessary to address challenges in integrating DERs into utility grids. Encouraging customer participation in pilot programs is vital for the evolution of VPPs. Here, the shift towards price-responsive DERs and VPPs is expected to enhance energy distribution efficiency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Microwave-Assisted Plastic Upcycling: Dynamic Data Reconciliation, Parameter Estimation, and Kinetic Modeling

Microwave (MW)-assisted catalytic pyrolysis offers a promising pathway for efficient plastic upcycling. This work develops an integrated modeling framework combining dynamic data reconciliation, a temperature-dependent rate model, and a yield model to represent the time-varying production rate of components in MW-assisted LDPE pyrolysis conducted in a batch reactor. An Arrhenius-type rate model with a temperature-dependent reaction order is developed. A biexponential correlation is proposed for the yield of gaseous products that enables to capture the evolving product formation behavior during conversion. In the yield correlation, one term is used to represent the initial increase in yield, reflecting the rapid formation of intermediate or primary products at the early stages of the reaction when a larger fraction of the reactant remains available. As conversion progresses, the influence of this term gradually diminishes. The other term accounts for the subsequent decrease in the predicted yield, representing secondary reactions such as further cracking or coke formation that reduce the concentration of certain products at higher conversion. The model is found to accurately represent reconciled experimental flow rate profiles from an in-house MW-assisted catalytic batch reactor for major products, including ethylene, ethane, 1-butene, and benzene, across 250−350 °C. Ethylene remains the dominant product but decreases from about 41.95% at 250 °C to 30.14% at 350 °C, while heavier products increase significantly, with 1-butene rising to nearly 8.37% and benzene reaching 2.17% at intermediate temperatures. The model shows that the ethylene production rate can be maximized at around 270 °C. The models developed in this work can be utilized for process optimization, reactor design and scale-up of microwave-assisted plastic conversion technologies, and economic analysis.

Damahe, Harish [West Virginia Univ., Morgantown, W↗

DOE OSTI2026

Joint scheduling of energy, fast and primary frequency response reserves in integrated transmission–distribution networks

Inverter-based distributed energy resources (DERs) connected to distribution networks (DNs) can provide fast frequency support, but their reserve deliverability depends on feeder constraints and differs from synchronous primary frequency response (PFR). Existing transmission–distribution coordination studies usually treat reserve generically or neglect feeder-level feasibility, while frequency-security scheduling studies rarely represent distribution feeders explicitly. This paper develops a bi-level day-ahead scheduling framework for integrated transmission–distribution networks that jointly clears energy, transmission-side PFR, and distribution-side fast frequency response (FFR) under exogenous hourly inertia and largest-loss inputs from an external unit commitment (UC) schedule. The transmission problem is modeled with DC-optimal power flow (OPF) and closed-form second-order cone (SOC) frequency-security constraints, whereas each DN is represented by a reserve-aware branch-flow AC-OPF so that scheduled fast reserves remain deliverable during activation. The bi-level problem is reformulated through Karush–Kuhn–Tucker (KKT) conditions into a mixed-integer SOC program, and a penalty term is used to tighten the distribution-network relaxation. In the reduced test system, lower exogenous inertia increased the required primary response from 179.64 MW to 191.08 MW, distribution-side fast response reduced total frequency-response procurement by up to 4.9%, and neglecting distribution constraints overstated the combined distribution-side energy and reserve award by up to 18%. In the expanded study, the largest case was solved in 2.02 s with a 0.00% optimality gap. Time-domain simulations kept the frequency nadir above 59.0 Hz in all tested hours. These results demonstrate the value of fast-response modeling and distribution-feasible reserve delivery in coordinated market clearing.

Noh, Seung-Gil↗

DOE OSTI2024

Sterically Encumbered Aryl Isocyanides Extend Excited-State Lifetimes and Improve the Photocatalytic Performance of Three-Coordinate Copper(I) β-Diketiminate Charge-Transfer Chromophores

Copper(I) complexes are prominent candidates to replace noble metal-based photosensitizers. We recently introduced a three-coordinate design for copper(I) charge-transfer chromophores that pair β-diketiminate ligands with aryl isocyanides. The excited-state lifetime in these compounds can be extended using a bichromophoric “triplet reservoir” strategy, which comes at the expense of a decrease in excited-state energy and reducing power. In this work, we introduce a complementary, sterically driven strategy for increasing the excited-state lifetimes of these photosensitizers, which gives a higher-energy, more strongly reducing charge-transfer triplet state than does the bichromophore approach. The compounds presented (Cu1–Cu4) have the general formula Cu(CyNacNac Me )(CN-Ar), where CyNacNac Me is a cyclohexyl-substituted β-diketiminate and CN-Ar is an aryl isocyanide with a variable steric profile. Their structural features and electrochemical and photophysical properties are described. The complexes with sterically encumbered 2,6-diisopropylphenyl or m-terphenyl isocyanide ligands (Cu2–Cu4) exhibit prolonged excited-state lifetimes relative to those of the parent 2,6-dimethylphenyl isocyanide compound Cu1. Specifically, one of the m-terphenyl isocyanide compounds, Cu3, displays an excited-state lifetime of 276 ns, approximately 30 times longer than that of Cu1 (9.3 ns). The photoluminescence quantum yield of Cu3 (0.09) also increases by two orders of magnitude compared to that of Cu1 (0.0008). Furthermore, the strong excited-state reducing power (*E ox = –2.4 V vs Fc +/0 ) and long lifetime of Cu3 lead to higher yields in photoredox and photocatalytic isomerization reactions, which include dehalogenation and/or hydrodgenation of benzophenone substrates, C–O bond activation of a lignin model substrate, and photocatalytic E/Z isomerization of stilbene.

14 SOLAR ENERGY↗

DOE OSTI2025

A Novel Secondary Frequency Regulation with Optimal Priority Selection of AGC Contributions

Automatic generation control (AGC) is used to maintain acceptable frequencies during operation owing to fluctuations in load and variable resources. In conventional industry applications, the AGC signal is allocated to each generator according to the predispatched frequency regulation capacity or the order of economic efficiency. However, with the increasing integration of inverter-based resources (IBRs), the retirement of conventional synchronous generators (SGs) has posed new challenges to frequency control schemes because fewer of them are optional for AGC regulation. In this paper, we propose a novel model predictive control (MPC)-based frequency regulation model to reduce control cost and ensure stability, by considering different critical dynamic factors when optimally selecting the AGC units. The proposed control model – developed in a general form – comprehensively embeds characteristics such as generator ramping rates, reserve capacity, and operation cost. The model predictive control–based two-timescale AGC scheme enhances the capability of immunizing the power disturbance from types of resources by coordinating the control signals between faster IBRs and slower SGs. The case study’s proposed model is verified to be effective in synergistically enforcing different dynamic properties of AGC units into the frequency regulation scheme.

Jiang, Sufan [The University of North Carolina at ↗

DOE OSTI2023

Enhancement of charge density wave correlations in a Holstein model with an anharmonic phonon potential

The Holstein Hamiltonian describes itinerant electrons whose site density couples to local phonon degrees of freedom. In the single-site limit, at half filling, the electron-phonon coupling results in a double-well structure for the lattice displacement, favoring empty or doubly occupied sites. In two dimensions and on a bipartite lattice in d ≥ 2, intersite hopping causes these doubly occupied and empty sites to alternate in a charge density wave (CDW) pattern when the temperature is lowered. Because a discrete symmetry is broken, this occurs in a conventional second-order transition at finite T cdw . In this paper, we investigate the effect of changing the phonon potential energy to one with an intrinsic double-well structure even in the absence of electron-phonon coupling. While this aids in the initial process of pair formation, the implications for subsequent CDW order are nontrivial. One expects that, when the electron-phonon coupling is too strong, the double wells become deep and the polaron mass gets large, an effect which reduces T cdw . Here, we show here the existence of regions of parameter space where the double-well potential, while aiding local pair formation, does so in a way which also substantially enhances long-range CDW order.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Replace Human Intelligence with Fast and Smart Geometric Reasoning and Graph Neural Network to Accelerate Next Gen ModSim Workflows

We present an agent-guided approach to CAD geometry decomposition that automates hex/hybrid meshing with graph neural networks (GNNs) to accelerate next-generation ModSim workflows. Our end-to-end pipeline (i) reduces 3D boundary-representation (B-Rep) models to a 2D chordal axis skeleton (CAT) and then to a 1D bipartite graph of surface and curve nodes, (ii) assigns per node labels as Cubit® WebCut actions, (iii) trains a multi-action GNN under supervised learning, and (iv) predicts five surface-node and three curve-node actions on out-of-distribution test geometries. Each graph node carries geometric, topological, and meshing attributes drawn from the B-Rep “skin” and CAT “skeleton,” with two-way mappings across 3D↔2D↔1D representations to maintain traceability back to 3D CAD. The supervised learning model exhibits stable convergence of the binary cross-entropy loss and achieves 98.7% accuracy on unseen lattice models. To operationalize decision-making, we rank predicted commands by geometric significance and prototyped the agent-guided workflow through the Cubit® Meshing PowerTool GUI. As a stretch goal, we explore reinforcement learning (RL) to reduce or remove label requirements and to learn policies for action sequences that maximize total reward (e.g., size of hex-meshable regions and resulting hex mesh quality). When all-hex meshing is not feasible, the agent assists in producing hybrid meshes—prioritizing hex in critical regions and transitioning to tetrahedral elements (tets) elsewhere—maintaining fidelity while ensuring robustness. The overarching objective is to replace manual, heuristics-based decomposition with data-driven, reproducible automation, cutting meshing turnaround time by orders of magnitude. We anticipate direct impact on simulation workflows through intelligent, scalable decomposition of complex CAD models into hex-meshable subdomains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Closing the Loop between In Situ Stress Complexity and EGS Fracture Complexity

42 ENGINEERING↗

DOE OSTI2021

Highly Ordered Hierarchical Anodes for Extreme Fast Charging Batteries (Final Report)

The goal of this project is to enable extreme fast charging (XFC) of Li-ion batteries, which was accomplished through a combination of 1) rational design and manufacturing of hierarchically structured anode architectures; 2) blending graphite/hard carbon into a bulk hybrid anode; 3) engineering artificial solid-electrolyte interphase (SEI) coatings with reduced interphase impedance; 4) computational modeling of coupled transport, kinetic, and electrochemical phenomena; and 5) improved fundamental understanding of lithium plating through operando analysis. This work integrated structural, compositional, and surface modification of graphite anodes, multi-physics modeling of ion transport, electrochemical activity, and heat transfer, advanced strategies to detect Li plating, and semi-automated roll-to-roll cell assembly. The unique facilities at the University of Michigan (UM) and Sandia National Laboratories (SNL) were leveraged to manufacture, prototype, and characterize commercially relevant >2Ah and >180 Wh/kg cells with a target of <20% capacity fade over 500 XFC cycles.

25 ENERGY STORAGE↗

DOE OSTI2023

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Ultrathin Magnesium-Based Coating as an Efficient Oxygen Barrier for Superconducting Circuit Materials

Scaling up superconducting quantum circuits based on transmon qubits necessitates substantial enhancements in qubit coherence time. Over recent years, tantalum (Ta) has emerged as a promising candidate for transmon qubits, surpassing conventional counterparts in terms of coherence time. However, amorphous surface Ta oxide layer may introduce dielectric loss, ultimately placing a limit on the coherence time. In this study, a novel approach for suppressing the formation of tantalum oxide using an ultrathin magnesium (Mg) capping layer is presented. Synchrotron-based X-ray photoelectron spectroscopy studies demonstrate that oxide is confined to an extremely thin region directly beneath the Mg/Ta interface. Additionally, it is demonstrated that the superconducting properties of thin Ta films are improved following the Mg capping, exhibiting sharper and higher-temperature transitions to superconductive and magnetically ordered states. Moreover, an atomic-scale mechanistic understanding of the role of the capping layer in protecting Ta from oxidation is established based on computational modeling. Further, this work provides valuable insights into the formation mechanism and functionality of surface tantalum oxide, as well as a new materials design principle with the potential to reduce dielectric loss in superconducting quantum materials. Ultimately, the findings pave the way for the realization of large-scale, high-performance quantum computing systems.

36 MATERIALS SCIENCE↗