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DOE OSTI2023

Online Tracking of Two Dominant Inter-Area Modes of Oscillation in the Eastern Interconnection

Reliable power system operation requires that small-signal stability be maintained at all times. Mode meters are measurement-based tools that provide operators with situational awareness of the system's stability margin. They operate by continually tracking the inter-area modes of oscillation that govern small-signal stability. This paper reports on the deployment of mode meters for online monitoring of two dominant modes of oscillation in the United States' Eastern Interconnection (EI). The use of measurements from system operators across the interconnection to provide continuous tracking is novel in the EI. Results from over four months of analysis reveal diurnal patterns in the modes and demonstrate that they can be tracked through a variety of system conditions. The results from this study continue to build an understanding of the EI's modes that will inform future modeling and monitoring efforts.

Follum, James D.↗

DOE OSTI2022

A Fast and Accurate Transient Stability Assessment Method Based on Deep Learning: WECC Case Study

Transient stability is one of the critical aspects of power system stability assessment. The increasing integration of inverter-based resources and the retirement of conventional synchronous generators result in the decreasing system inertia and growing complexity of system operating conditions. Using a few selected typical operating conditions cannot guarantee system transient stability in all operating conditions, and the time-domain simulation of all operating conditions requires tremendous time and is often infeasible. This paper proposes a more efficient transient stability assessment method based on deep learning. The binary search method is used to determine the critical clearing time (CCT) in creating training databased by time-domain simulation. This method is fast and accurate with 1 ms resolution. The buses whose CCTs are lower than 200 ms are considered critical buses. Buses close to each other are grouped based on their mutual admittance matrix to reduce the search space of the critical buses. This paper also proposes the generator feature normalization based on the physical model. Case study on the reduced 240-bus WECC system model demonstrates that the proposed method can predict CCT accurately and efficiently.

critical clearing time↗

DOE OSTI2020

Model predictive control for voltage restoration in microgrids using temporal logic specifications

Power system operation will encounter numerous voltage variabilities as the proliferation of renewable energy continues. Real-time monitoring and communication technologies can potentially improve voltage stability by enabling the rapid detection of voltage deviations and the implementation of corrective actions. These corrective actions will only be effective in restoring stability if they are chosen in a timely and scalable manner. This paper considers the problem of power systems’ load voltage control in order to simultaneously address both magnitude and time voltage specifications. In order to comply with grid codes and avoid unnecessary relay protection actions, a model predictive control (MPC)-based control strategy employing temporal logic specifications (TLSs) is proposed. The TLSs strategy is introduced as a formalism to control the voltage variation at a critical load bus against operational bounds over time. The control objective is to schedule optimal control input signals from the available supportive energy storage systems, which provide reactive power injections, leading to satisfying the specified finite-time restoration described by the TLSs with minimal control efforts. The simulation results display that the TLSs strategy for power systems’ voltage control synthesis is extremely significant.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗

DOE OSTI2020

Chatter Stability of Machining Operations

Here, this paper reviews the dynamics of machining and chatter stability research since the first stability laws were introduced by Tlusty and Tobias in the 1950s. The paper aims to introduce the fundamentals of dynamic machining and chatter stability, as well as the state of the art and research challenges, to readers who are new to the area. First, the unified dynamic models of mode coupling and regenerative chatter are introduced. The chatter stability laws in both the frequency and time domains are presented. The dynamic models of intermittent cutting, such as milling, are presented and their stability solutions are derived by considering the time-periodic behavior. The complexities contributed by highly intermittent cutting, which leads to additional stability pockets, and the contribution of the tool's flank face to process damping are explained. The stability of parallel machining operations is explained. The design of variable pitch and serrated cutting tools to suppress chatter is presented. The paper concludes with current challenges in chatter stability of machining which remains to be the main obstacle in increasing the productivity and quality of manufactured parts.

42 ENGINEERING↗

DOE OSTI2024

A Pseudo Planar Heterojunction Structure for Eco‐Friendly Printable Organic Solar Cells Achieving 19.05% Efficiency

Abstract Obtaining a well‐accurate vertical distribution active layer morphology through the air‐printing process is an essential task for achieving efficient scalable large‐area organic solar cells (OSCs). In this target, the desired and controllable pseudo planar heterojunction (PPHJ) active layer structure with suitable phase separation is developed by pre‐deposited D18‐Cl layer under the PM6:BTP‐eC9 film via an eco‐friendly manufacturing method. The addition of the D18‐Cl regulates molecular crystallization and leads to an ideal vertical stratification while simultaneously suppressing voltage loss, optimizing energetic disorder, and carrier management. Impressively, the optimal PPHJ devices perform superior power conversion efficiencies (PCEs) of 19.05% (100 nm), 17.33% (300 nm), and 14.14% (4 cm 2 ) compared to the BHJ devices. Importantly, the PPHJ OSCs also exhibit an impressive extrapolated T 80 (the time required to reach 80% of initial PCE) of long‐time storage and operational stability, as well as thermal stability.

Chemistry↗

DOE OSTI2020

Perovskite-Type Solid Solution Nano-Electrocatalysts Enable Simultaneously Enhanced Activity and Stability for Oxygen Evolution

A trade-off between catalytic activity and structural stability generally exists in oxygen evolution electrocatalysis, especially in acidic environment. This dilemma limits the development of higher-performance electrocatalysts that are required by next-generation electrochemical technologies. In this work it is demonstrated that the inverse catalytic activity–structural stability relation can be broken by alloying catalytically inert strontium zirconate with the other catalytically active perovskite, strontium iridate. This strategy results in an alloyed perovskite electrocatalyst with simultaneously improved iridium mass activity and structural stability, by about five times, for the oxygen evolution reaction under acidic conditions. The experimental and theoretical results suggest that the alloying strategy generates multiple positive effects, mainly including the reduction of catalyst size, the decrease of catalyst covalency, and the weakening of surface oxygen-binding ability. The synergistic optimization of bulk and surface properties, as a result, enhances the intrinsic activity and availability of surface iridium sites, whilst significantly inhibiting the surface cation corrosion during electrocatalysis.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Application of Poly(ether sulfone)‐Based Membranes in Clean Energy Technology

Abstract Poly(ether sulfone) (PES) is a kind of polymer materials with excellent electrical insulation and acid/alkali stability. PES can be operated at high temperature continuously for a long time and still maintain excellent property stability in the environments with rapidly changed temperature, namely, great thermostability. Moreover, PES has low molding shrinkage, good dimensional stability and excellent film‐forming characteristics. Compared with inorganic membranes, PES‐based membranes have lower cost, which have received more attention and wide recognition in the field of clean energy technologies in recent years, such as flow batteries, fuel cells, water treatment, and gas separation. Therefore, this review summarizes the research status and prospect of the utilization of PES‐based membranes in clean energy fields, in order to further promote their development and application.

Chemistry↗

DOE OSTI2024

P‐Doping Modulated RuIr Nanoparticles Anchored on Co/N/C Catalysts with Improved Alkaline Hydrogen Evolution Activity and Stability

Achieving efficient and stable hydrogen evolution reactions in alkaline conditions is crucial for hydrogen production. In this study, a RuIr/Co (SA) NC-P catalyst featuring RuIr alloys alongside P-doping and CoNx sites is developed. RuIr alloying optimizes the electronic structure between Ru and Ir, promoting electron transfer from Ru to Ir. P-doping further modulates the electronic properties of RuIr alloys, optimizing hydrogen binding energy and weakening Ru─OH binding energy, facilitating rapid H 2 generation and OH ad transfer. Meanwhile, CoNx promotes water dissociation, providing a fast proton delivery path for RuIr alloys. The catalyst exhibits enhanced HER activity with a low overpotential of 20 mV at 10 mA cm −2 , a Tafel slope of 20.4 mV dec −1 , and a turnover frequency of 19.5 H 2 s −1 at 150 mV overpotential. Moreover, catalyst stability is improved 8 times by mitigating RuIr alloy dissolution/agglomeration via P-doping. In conclusion, this work introduces a promising approach for developing efficient and stable HER electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Within‐field soil moisture variability and time‐invariant spatial structures of agricultural fields in the US Midwest

Abstract Understanding soil moisture variability and estimating high‐resolution soil moisture at subfield to field scales is critical for agricultural research and applications. However, systematic investigation of subfield scale soil moisture variability over cropland is still lacking from both measurement and satellite remote sensing. In this study, we aim to investigate (1) the characteristics of within‐field soil moisture distribution over typical cropland in the US Midwest and (2) the capabilities of satellite remote sensing in capturing the spatiotemporal variabilities of soil moisture at subfield scale. Specifically, we conducted soil moisture field experiments in three typical commercial agricultural fields (∼85 acres per field) in central Illinois, representing typical commercial farmlands in the US Midwest, and compared the soil moisture measurements with satellite remote sensing data from optical and active microwave sensors. In each field, dense soil moisture samples (spaced at 50–60 m) were obtained for two dry down events in May and July 2021, and multiple long‐term soil moisture stations were installed. We found prominent time‐invariant spatial structures of soil moisture at within‐field scales both during the dry down period and over longer time scales, and the stability is minimally affected by plant water use during the growing season. Comparing the field campaign measurements with satellite remote sensing data, we found that surface reflectance of shortwave infrared bands, such as SWIR1 (1610 nm) from Sentinel‐2, can capture relative surface soil moisture patterns at within‐field scales, but their relationships with soil moisture are field specific. These findings and the improved understanding of within‐field soil moisture dynamics could potentially help future research on high‐resolution soil moisture estimation with multi‐source remote sensing data.

Yang, Yi↗

DOE OSTI2022

Dynamic calibration of differential equations using machine learning, with application to turbulence models

We present a methodology for calibration of parametric ordinary and partial differential equation models, using off-the-shelf software for back-propagation in Neural Networks (NN). As a prototypical example, we consider calibration of a Reynolds-averaged Navier-Stokes (RANS) turbulence closure model, against ground truth data from direct numerical simulations (DNS) of two different turbulent flows. Numerical time integration is represented as a custom NN, where only the RANS model parameters are trainable. A loss function is defined to quantify the mismatch between the NN prediction and the ground truth over a predefined, finite time integration window. This loss function is then minimized using a gradient descent method utilizing the back-propagation algorithm. Furthermore, this dynamic approach to training is to be contrasted with a static approach, wherein a least square regression estimate for parameters is obtained in the limit of an infinitesimal time integration window. In a first test of static and dynamic approaches against ground truth data generated by the model, the former proves to be significantly faster and more accurate than the latter at recovering the parameters. When both calibration approaches are tested against DNS data, for which it is known that the model cannot achieve a perfect fit, the static approach yields a good prediction only for short times, while the dynamic approach results in physical and stable predictions over the entire integration window. After optimization of the dynamic approach for time step, spatial resolution, stability, and physics-based constraints, we obtain a 50% improvement of outcomes over those obtained from the existing, manually calibrated set of parameters, demonstrating the merits of this systematic and automated procedure.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Elongated Fullerene Unlocks Oxygen-Resistant Perovskite Solar Minimodules

State-of-the-art encapsulation cannot prevent the permeation of oxygen into perovskite solar cells (PSCs). Here, we report the finding that p-i-n structured PSCs degrade quickly under oxygen exposure. Oxidation of C60, rather than perovskites, dominates the degradation. Chemical absorption of oxygen by C60 is identified, which impairs its electron transport property. We modify the electron transport layers to address the instability under oxygen exposure. The elongated fullerene C70 is found to react with oxygen orders of magnitude slower than C60. In combination with a compact SnO2 buffer fabricated by atomic layer deposition, which can slow down the oxygen diffusion, the resulting unencapsulated PSCs with C70 retained 90% of their initial efficiency after 1-sun illumination in pure oxygen for 1,200 h at 70 degrees C, improving stability by hundreds of times. Testing of unencapsulated perovskite minimodules of different perovskite compositions with C70 gives extrapolated lifetimes of 17-41 years at 50 degrees C.

14 SOLAR ENERGY↗

DOE OSTI2021

Evolution of Protrusions on Lithium Metal Anodes Stabilized by a Solid Block Copolymer Electrolyte Studied Using Time-Resolved X-ray Tomography

The growing demand for rechargeable batteries with higher energy densities has motivated research focused on enabling the lithium metal anode. A prominent failure mechanism in such batteries is short circuiting due to the uncontrolled propagation of lithium protrusions that often have a dendritic morphology. In this paper, the electrodeposition of metallic lithium through a rigid polystyrene- b -poly(ethylene oxide) (PS- b -PEO or SEO) block copolymer electrolyte was studied using hard X-ray microtomography. In this system, protrusions were approximately ellipsoidal globules: we take advantage of this simple geometry to quantify their growth as a function of polarization time and electrolyte salt concentration. The growth of 47 different globules was tracked with time to obtain average velocities of globule growth into the electrolyte. The globule diameter was a linear function of globule height in the electrolyte with a slope of about 6, independent of time and electrolyte salt concentration.

25 ENERGY STORAGE↗

DOE OSTI2024

Stabilization of the Active Ruthenium Oxycarbonate Phase for Low-Temperature CO 2 Methanation

Interstitial carbon-doped RuO 2 catalyst with the newly reported ruthenium oxycarbonate phase is a key component for low-temperature CO 2 methanation. However, a crucial factor is the stability of interstitial carbon atoms, which can cause catalyst deactivation when removed during the reaction. In this work, the stabilization mechanism of the ruthenium oxycarbonate active phase under reaction conditions is studied by combining advanced operando spectroscopic tools with catalytic studies. Three sequential processes: carbon diffusion, metal oxide reduction, and decomposition of the oxycarbonate phase and their influence by the reaction conditions, are discussed. We present how the reaction variables and catalyst composition can promote carbon diffusion, stabilizing the oxycarbonate catalytically active phase under steady-state reaction conditions and maintaining catalyst activity and stability over long operation times. In addition, insights into the reaction mechanism and a detailed analysis of the catalyst composition that identifies an adequate balance between the two phases, i.e., ruthenium oxycarbonate and ruthenium metal, are provided to ensure an optimum catalytic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics

Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO 3 , with results showing that, with just 20% of the area sampled, most high-response clusters were captured. Furthermore, this approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Thermoplastic moulding of regenerated silk

Early insights into the unique structure and properties of native silk suggested that β-sheet nanocrystallites in silk would degrade prior to melting when subjected to thermal processing. Since then, canonical approaches for fabricating silk-based materials typically involve solution-derived processing methods, which have inherent limitations with respect to silk protein solubility and stability in solution, and time and cost efficiency. In this study, we report a thermal processing method for the direct solid-state moulding of regenerated silk into bulk ‘parts’ or devices with tunable mechanical properties. At elevated temperature and pressure, regenerated amorphous silk nanomaterials with ultralow β-sheet content undergo thermal fusion via molecular rearrangement and self-assembly assisted by bound water to form a robust bulk material that retains biocompatibility, degradability and machinability. This technique reverses presumptions about the limitations of direct thermal processing of silk into a wide range of new material formats and composite materials with tailored properties and functionalities.

36 MATERIALS SCIENCE↗

DOE OSTI2019

A highly-selective chloride microelectrode based on a mercuracarborand anion carrier

The chloride gradient plays an important role in regulating cell volume, membrane potential, pH, secretion, and the reversal potential of inhibitory glycine and GABAA receptors. Measurement of intracellular chloride activity, $a^i_{Cl}$, using liquid membrane ion-selective microelectrodes (ISM), however, has been limited by the physiochemical properties of Cl - ionophores which have caused poor stability, drift, sluggish response times, and interference from other biologically relevant anions. Most importantly, intracellular $HCO^-_3$ may be up to 4 times more abundant than Cl - (e.g. skeletal muscle) which places severe constraints on the required selectivity of a Cl - - sensing ISM. Previously, a sensitive and highly-selective Cl - sensor was developed in a polymeric membrane electrode using a trinuclear Hg(II) complex containing carborane-based ligands, [9]-mercuracarborand-3, or MC3 for short. Here, we have adapted the use of the MC3 anion carrier in a liquid membrane ion-selective microelectrode and show the MC3-ISM has a linear Nernstian response over a wide range of a Cl (0.1 mM to 100 mM), is highly selective for Cl - over other biological anions or inhibitors of Cl - transport, and has a 10% to 90% settling time of 3 sec. Importantly, over the physiological range of aCl (1 mM to 100 mM) the potentiometric response of the MC3-ISM is insensitive to $HCO^-_3$ or changes in pH. Finally, we demonstrate the biological application of an MC3-ISM by measuring intracellular aCl, and the response to an external Cl-free challenge, for an isolated skeletal muscle fiber.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

The nature and suppression strategies of interfacial reactions in all-solid-state batteries

Solid-state Li batteries are promising energy storage devices owing to their high safety and high theoretical energy density. However, the serious interfacial reaction between solid state electrolytes and cathodes deteriorates the battery performance, impeding the realization of long-term cyclability. The buried nature of the interphase layer presents a significant challenge in achieving a comprehensive understanding of the underlying interfacial reaction mechanisms. Herein, we systematically explore the interfacial reaction evolutions and interphase compositions and electronic properties between the popular oxide cathodes and sulfide solid electrolytes (SSEs). This includes analysis of the chemical and electrochemical reactions between cathodes/coatings and SSEs, as well as the electrochemical self-decomposition of SSEs by thermodynamic phase equilibrium analysis. Herein, we disclose that the driving force of the electrochemical reaction at the chemical potential of μ Li is much stronger than that of the chemical reaction, which dominates the interfacial reaction. Preventing the formation of an electronically conductive interphase is crucial in inhibiting the continuous interfacial degradation during long-term cycling, which can be achieved through the optimized combination of cathodes and SSEs, as well as the introduction of functional coatings between them. Based on these findings, the percentage of molar fraction ( f ) of electronically conductive species in the formed interphase is proposed as a key factor for indicating the interfacial stability for the first time. Furthermore, we propose a specific high-throughput screening scheme to filter the functional coating materials by comprehensively evaluating their functionality. The tiered screening identifies 48 coating materials with optimal properties. The work highlights the significant roles of rational coupling of the cathodes and SSEs, and optimizing interfacial coating materials for solid-state batteries. It opens new avenues for engineering an interphase with improved interfacial compatibility to realize long-term cyclability.

25 ENERGY STORAGE↗