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DOE OSTI2026

Energy-efficient scientific computing using chemical reservoirs

The rapid growth of computing demands driven by scientific computing, data analytics, and artificial intelligence (AI) advancements has exposed the limitations of traditional digital processing systems. These systems are nearing physical energy barriers, making significant gains in energy efficiency increasingly unattainable. As we advance toward post-exascale computing, disruptive approaches are critical to overcoming these limitations. Among emerging analog solutions, biochemical computing offers a transformative path for achieving orders-of-magnitude improvements in energy efficiency. By leveraging the natural optimization capabilities of chemical reaction networks (CRNs), biochemical systems have the potential to meet high-performance computing needs through natural scalability. However, numerous challenges remain, including theoretical limitations in mapping computational problems to CRNs and practical barriers in implementing biochemical computing devices. In this paper, we present a framework for chemical computation using biochemical systems and introduce key components of our approach for energy-efficient scientific computing. We showcase the feasibility of this framework by solving a system of ordinary differential equations by emulating a chemical reservoir device, demonstrating its potential for addressing modern computing challenges. This work lays a foundational step toward harnessing the computational power of chemistry to design energy-efficient, scalable, high-performance next-generation computing systems.

Johnson, Connah G. M. [Pacific Northwest National ↗

DOE OSTI2022

Prediction of strain, inter-layer interaction and critical current in CORC ® wires under axial strain by T-A modeling

Superconducting conductors on round core (CORC ® ) cables and wires can meet the needs of large high-field magnets, such as particle accelerators and compact nuclear fusion machines, due to their simple cabling process, high current-carrying capacity and reliable operation under high mechanical stresses. Many high-field magnets require CORC ® cables to carry a current of thousands of amperes in a background magnetic field exceeding 20 T. As a result, the large electromagnetic forces will deform the cable in the axial direction due to hoop stress and in the transverse direction by compressive stress. Therefore, it is essential to determine the irreversible deformation limit of the CORC ® cable under axial tensile load and optimize the cabling parameters to potentially extend this limit. Analytical and numerical methods are developed to assess the performance degradation of CORC ® wires under axial tensile load. The strain level, interlayer contact pressure and friction and their impact on the critical current are calculated by combining the mechanical response and the T-A method. Analyzing the results shows that the winding angle of the tape and the Poisson’s ratio of the inner core are key factors affecting the irreversible tensile strain limit of CORC ® wires. The smaller the winding angle and the higher the Poisson’s ratio of the inner core, the higher the irreversible tensile strain limit. For multi-layer CORC ® wires, the initial contact pressure caused by the cabling process must also be considered. The inter-layer interaction is coupled with the tape strain of each layer. The results of this research can serve as a basis for optimizing and designing CORC ® wires with extended irreversible strain limits.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Assess and Benchmark Magneto-Inertial Fusion (MIF) Scaling. Final Technical Report for the SNL/Rochester ALPHA Follow-on Project

This project was a follow-on to the Sandia National Laboratories (SNL) and the Laboratory for Laser Energetics (LLE) ARPA-E ALPHA project entitled “Demonstrating Fuel Magnetization and Laser Heating Tools for Low-Cost Fusion Energy”. The primary purpose of this follow-on project was to obtain additional data at the OMEGA facility to help better understand how MagLIF, a platform that has already demonstrated the scientific viability of magneto-inertial fusion, scales across a factor of 1000 in driver energy. A secondary aspect of this project was to extend simulations and analysis at SNL to cover a wider magneto-inertial fusion (MIF) parameter space and test scaling of those models across this wide range of input energies and conditions of the target. This work was successful in improving understanding of how key physics elements of MIF scales and improves confidence in setting requirements for fusion gain with larger drivers. The OMEGA experiments at the smaller scale verified the hypothesis that preheating the fuel plays a significant role in introducing wall contaminants that mix into the fuel and significantly degrade fusion performance. This contamination not only impacts target performance but the optimal input conditions for the target. However, analysis at the Z-scale showed that target performance at high preheat levels is limited by the Nernst effect, which advects magnetic flux from the hot spot, reducing magnetic insulation and consequently reduces the temperature of the fuel. The combination of MagLIF experiments at the disparate scales of OMEGA and Z along with a multiscale 3D simulation analysis has led to new insight into the physical mechanisms responsible for limiting target performance and provides important benchmarks to assess target scaling more generally for MIF schemes. Finally, in addition to the MagLIF related work, a semi-analytic model of liner driven Field Reversed Configuration (FRC) was developed that predicts the fusion gain for such systems. This model was also validated with 2D radiation magneto-hydrodynamic simulations and predicts that fusion gains of near unity could be driven by the Z machine.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗

DOE OSTI2026

Facilitating Data Collection of Maintenance Events to Populate the Hydrogen Component Reliability Database (HyCReD)

The Hydrogen Component Reliability Database (HyCReD) is a collaborative project between the National Renewable Energy Laboratory, the University of Maryland, and hydrogen stakeholders to improve safety and reliability for hydrogen facilities by implementing component reliability data taxonomies that support hydrogen infrastructure failure rate analysis. The project aims to quantify failure rates of hydrogen components through high-quality data collection and analysis on root causes and maintenance needed. HyCReD provides a common database for cataloging hydrogen component failures which exists for reliability research in many other mature industries [2]. The database fills a gap for the hydrogen community by providing a scientifically rigorous approach to quantitative risk assessment (QRA), prognostic health management (PHM), and reliability-centered maintenance (RCM) analysis. High level results will be aggregated and anonymized to protect company sensitive information; detailed results will be used to help address issues of hydrogen components. These advanced analytics will support accelerated deployment of hydrogen infrastructure by enabling better: design and safety of projects (safety codes and standards development), infrastructure reliability and cost (component failure rates, maintenance protocols), and component R&D needs (robust supply chain). A key to a successful HyCReD implementation is facilitating the ease of reporting and data quality in the database that can be used for analysis. Maintenance data was a previously identified gap in initial efforts to populate and validate the database taxonomies [3]. Collection of maintenance data will be instrumental in identifying failure modes and rates, identifying incipient component failures or reduced performance, cataloging best practices for maintenance routines and methods for prognostic health management, and quantifying the risk and effect of different failure modes. Several key priorities are identified for streamlined data collection to achieve quality and detailed failure data: Applicability, Ease of Use, Accessibility, and Information Security. The HyCReD team has now begun deployment of the database to several companies and groups that have signed non-disclosure agreements to facilitate the data collection of failures in industry hydrogen refueling station infrastructure. This paper will provide an update into the process of HyCReD deployment including the development of a coding guide for facility personnel to reference and ensure data quality and consistency from one station to another as well as implementation of contextually dependent data fields of system taxonomy and formatted entries to provide ease of use. The goal is to communicate the lessons learned from the roll-out to technicians and engineers in the field, and the addition of need for high level of security to protect all stakeholders.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

High-throughput screening to predict highly active dual-atom catalysts for electrocatalytic reduction of nitrate to ammonia

Ammonia is an essential chemical owing to its importance in fertilizer production and other industrial applications. Electrocatalytic nitrate reduction to ammonia (NO 3 RR) holds great promise for low-temperature ammonia production while simultaneously addressing nitrate-based environmental concerns. To provide the mechanistic understanding needed to design an effective electrocatalyst, we systematically investigated the catalytic performance of metal-based dual-atom catalysts (DACs) anchored on two-dimensional (2D) expanded phthalocyanine (Pc) for NO 3 RR. We found that NO 3 RR can efficiently produce ammonia on Cr 2 -Pc, V 2 -Pc, Ti 2 -Pc, and Mn 2 -Pc surfaces with low limiting potentials of – 0.02, – 0.25, – 0.34, and – 0.41 V RHE , respectively. Moreover, using the free energy difference of *NO 3 - and *H as a descriptor, we found that the hydrogen evolution reaction is significantly suppressed on the DAC surface due to an ensemble effect in which the two metal atoms cooperate to selectively form ammonia. We performed high-throughput screening to develop an efficient metal-based DAC for NO 3 - reduction, followed by a mechanistic study to elucidate the NO3RR pathway on the DAC. Finally, this work provides design information for advancing sustainable ammonia synthesis under ambient conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

The evolution of model Rh/Fe 3 O 4 (001) catalysts in hydrogen environments

Single metal atoms dispersed on oxides are a new emerging class of catalysts owing to their unique electronic and chemical properties. Here, in this study, we have prepared a series of model single-atom catalysts possessing well-characterized Rh sites that include Rh adatoms (Rh ad ), mixed surface layers with octahedrally-coordinated Rh (Rh oct ), as well as metallic Rh clusters and nanoparticles (Rh met ) on Fe 3 O 4 (001). Using X-ray photoelectron spectroscopy (XPS) and scanning tunneling microscopy (STM), we investigated the activity of such model systems towards H2 and their stability in reducing environments. Our results show that the atomically dispersed Rhad and Rhoct species do not activate H 2 , which would result in the formation of surface hydroxyls on Fe 3 O 4 (001). In contrast, the presence of Rh met in H 2 results in the formation of hydroxyls and subsequent etching of the Fe 3 O 4 (001) at higher temperatures (≥ 500 K) due to water formation via the Mars-van Krevelen mechanism. Additionally, such surface etching leads to the release of the Rh oct from the surface lattice and their sintering to Rh met . To bridge the material gap between the surface science models and high surface area catalysts, we perform parallel studies on powder Rh/Fe 3 O 4 catalysts. The XPS characterization shows remarkable similarities between these systems. Further, our surface science studies provide an atomistic picture of the behavior of high surface area catalysts in the H 2 atmosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Adsorption and Catalytic Activity of Gold Nanoparticles in Mesoporous Silica: Effect of Pore Size and Dispersion Salinity

The assembled state of nanoparticles (NPs) within porous matrices plays a governing role in directing their biological, electronic, and catalytic properties. However, the effects of the spatial confinement and environmental factors, such as salinity, on the NP assemblies within the pores are poorly understood. In this study, we use adsorption isotherms, spectrophotometry, and small-angle neutron scattering to develop a better understanding of the effect of spatial confinement on the assembled state and catalytic performance of gold (Au) NPs in propylamine-functionalized SBA-15 and MCM-41 mesoporous silica materials (mSiO 2 ). We carry out a detailed investigation of the effect of pore diameter and ionic strength on the packing and spatial distribution of AuNPs within mSiO 2 to get a comprehensive insight into the structure, functioning, and activity of these NPs. We demonstrate the ability of the adsorbed AuNPs to withstand aggregation under high salinity conditions. We attribute the observed preservation of the adsorbed state of AuNPs to the strong electrostatic attraction between oppositely charged pore walls and AuNPs. The preservation of the structure allows the AuNPs to retain their catalytic activity for a model reaction in high salinity aqueous solution, here, the reduction of p-nitrophenol to p-aminophenol, which otherwise is significantly diminished due to bulk aggregation of the AuNPs. This fundamental study demonstrates the critical role of confinement and dispersion salinity on the adsorption and catalytic performance of NPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Integrated Reference Electrodes in Anion-Exchange-Membrane Electrolyzers: Impact of Stainless-Steel Gas-Diffusion Layers and Internal Mechanical Pressure

Alkaline-membrane electrolyzers operating in pure water might provide scalable low-cost H 2 production but currently lag in performance and durability compared to commercial technologies. Typically, membrane–electrode assemblies (MEAs) are optimized in electrolyzers by changing one parameter at a time and assessing the resulting system performance via two-electrode polarization and impedance measurements. These approaches are limited in their ability to assign performance changes and durability to specific electrodes or processes. We integrate a reference electrode with the MEA to separate anode and cathode responses in both polarization and impedance measurements. Here we illustrate the power of the approach by showing how the fiber diameter of stainless-steel gas-diffusion layers (GDLs) affects performance solely at the anode, while changing the thickness of the cathode GDL simultaneously affects the performance of the anode and cathode due to changes in internal pressure from mechanical compression. This finding was obscured in conventional two-electrode measurements. The work thus guides both high-performance alkaline-membrane water-electrolyzer development and illustrates a useful strategy to study structure–activity relationships in the MEA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Two-Factor Phase Separations in Mixed-Halide Quasi-2D Perovskite LEDs: Dimensionality and Halide Segregations

Quasi-2D halide perovskites have attracted much interest as a promising material for light-emitting diodes (LEDs) due to their tunability in quantum confinement and halide alloy formation to modulate the energy bandgap and emission color. However, two-factor phase separations with respect to heterogeneous quantum-well thicknesses and halide segregation are still crucial issues in quasi-2D perovskite LEDs, leading to low external quantum efficiencies (EQEs) and color shifts. In this work, we compare quasi-2D perovskite films using different cations to unveil the key contributions from the chemical design of organic cations. While mixing halide ions in conventional quasi-2D perovskite films induces micrometer-scale heterogeneity, new extended and twisted conjugated cations suppress the two-factor phase separations, leading to high EQEs of over 25% and controllable emission wavelengths across red and near-infrared regions. The fundamental insights in this work will provide guidance for advancing materials design and device performance in the future.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Enzyme Engineering for Expanded Product Scope (CRADA Final Report)

As part of the Cyclotron Road program, Aralez Inc. investigated methods for producing chemicals sustainably, with a particular focus on engineering enzymes to make novel chemical products or accept new substrates that aren’t found in nature. We used directed evolution and other protein engineering techniques to create enzymes that perform abiological chemistry or make chemical products through a non-natural synthetic route. This work was an important step towards increasing the scope of compounds that can be manufactured in a sustainable fashion, which in turn will alleviate the high energy consumption, costly emissions, and waste that are hallmarks of current methods in chemical manufacturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

The diversity of aluminum-based drinking water treatment residuals for use in environmental remediation

Drinking water treatment residuals (DWTRs) are complex mixtures of organic and inorganic phases generally disposed of as waste materials. However, their strong sorptive properties could be further exploited to immobilize contaminants. To characterize these materials, we applied a range of analytical techniques to a set of aluminum-based DWTRs. Here we determined surface areas, elemental compositions using CHNS analysis and X-ray fluorescence (XRF), performed thermogravimetric analyses paired with mass spectrometry (TGA-MS), and used synchrotron-based methods: X-ray diffraction (XRD) and X-ray absorption spectroscopy (XAS). Elemental analyses and specific surface area measurements – that vary between 7.23 ± 0.03 and 197.6 ± 0.9 m 2 g –1 – indicate that non-coagulant additives must play an important role in controlling sorption. High resolution powder XRD reveals the presence of only a few crystalline minerals mostly derived from source waters or additives. Fe was detected by XRF in all samples, whereas Mn was present in significant levels in samples with potassium permanganate as additive. The chemical speciation of these elements was characterized by performing spectral decompositions of XAS spectra. The spectral features of Fe are consistent with iron(III) oxides/hydroxides and structural iron in clays. On the other hand, Mn is predominantly present under its reduced form, Mn(II).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Beyond Contrast Transfer: Spectral SNR as a Finite-Dose Metric for STEM Phase Retrieval

The contrast transfer function (CTF) is widely used to evaluate phase retrieval methods in scanning transmission electron microscopy (STEM), including center-of-mass imaging, parallax imaging, direct ptychography, and iterative ptychography. However, the CTF reflects only the maximum usable signal, neglecting the effects of finite electron fluence and the Poisson-limited nature of detection. As a result, it can significantly overestimate practical performance, especially in low-dose regimes. Here, we employ the spectral signal-to-noise ratio (SSNR), as a finite-dose statistical framework to evaluate the recoverable signal as a function of spatial frequency. Using numerical reconstructions of white-noise objects, we show that center-of-mass, parallax, and direct ptychography exhibit dose-independent SSNRs, with close-form analytic expressions. In contrast, iterative ptychography exhibits a surprising dose dependence: at low fluence, its SSNR converges to that of direct ptychography; at high fluence, it saturates at a value consistent with the maximum detective quantum efficiency predicted by recent quantum Fisher information bounds. The results highlight the limitations of CTF-based evaluation and motivate SSNR as a more accurate, finite-dose metric for assessing STEM phase retrieval methods.

STEM phase retrieval↗

DOE OSTI2025

Current-based metrology with two-terminal mesoscopic conductors

The traditional approach to quantum parameter estimation focuses on the quantum state, deriving fundamental bounds on precision through the quantum Fisher information. In most experimental settings, however, performing arbitrary quantum measurements is highly unfeasible. In open quantum systems, an alternative approach to metrology involves the measurement of stochastic currents flowing from the system to its environment. However, the present understanding of current-based metrology is mostly limited to Markovian master equations. Considering a parameter estimation problem in a two-terminal mesoscopic conductor, we identify the key elements that determine estimation precision within the Landauer-Büttiker formalism. Crucially, this approach allows us to address arbitrary coupling and temperature regimes. Furthermore, we obtain analytical results for the precision in linear-response and zero-temperature regimes. For the specific parameter estimation task that we consider, we demonstrate that the boxcar transmission function is optimal for current-based metrology in all parameter regimes.

Landauer formula↗

DOE OSTI2020

Ultrafast quantum photonics enabled by coupling plasmonic nanocavities to strongly radiative antennas

Quantum emitters coupled to plasmonic nanostructures can act as exceptionally bright sources of single photons, operating at room temperature. Plasmonic mode volumes supported by these nanostructures can be several orders of magnitude smaller than the cubic wavelength, which leads to dramatically enhanced light–matter interactions and drastically increased photon production rates. However, when increasing the light localization further, these deeply subwavelength modes may in turn hinder the fast outcoupling of photons into free space. Plasmonic hybrid nanostructures combining a highly confined cavity mode and a larger antenna mode circumvent this issue. We establish the fundamental limits for quantum emission enhancement in such systems and find that the best performance is achieved when the cavity and antenna modes differ significantly in size. We experimentally support this idea by photomodifying a nanopatch antenna deterministically assembled around a nanodiamond known to contain a single nitrogen–vacancy (NV) center. As a result, the cavity mode shrinks, further shortening the NV fluorescence lifetime and increasing the single-photon brightness. Our analytical and numerical simulation results provide intuitive insight into the operation of these emitter–cavity–antenna systems and show that this approach could lead to single-photon sources with emission rates up to hundreds of THz and efficiencies close to unity.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Scalable, Dual-Mode Occupancy Sensing for Commercial Venues

The ability to establish accurate occupancy is a key enabler for improved energy efficiency in human-occupied indoor spaces. The accurate counts can be used to adjust, in real-time, air exchange, heating, and cooling, to closely match the environmental needs of the occupants rather than to use a single setting based on maximum room occupancy. This project focuses on the use of overhead fisheye cameras and artificial intelligence (AI) algorithms as the technology to realize accurate people counting in large commercial spaces. We developed Computational Occupancy Sensing SYstem ("COSSY''), which supports large-scale deployment (thousands of square feet) and straightforward installation (using conventional power over ethernet), while providing high accuracy and low cost. COSSY has been validated at Boston University and by a third party, demonstrating people-counting accuracy of at least 89.5% in large spaces (2,000 sqft). A cost/benefit analysis performed for Boston commercial market indicates that a full payback of COSSY deployment costs due to energy savings would occur within 3-6 months from installation. Applications of COSSY reach far beyond saving energy. The precise people counting in real time could also be useful for emergency response (e.g., fire), in spatial analytics (e.g., office space management) and in some health applications (e.g., social distance assessment during pandemics).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Highly Active and Durable Air Electrodes for Reversible Protonic Ceramic Electrochemical Cells Enabled by an Efficient Bifunctional Catalyst

The commercialization of reversible protonic ceramic electrochemical cells is hindered by the lack of highly active and durable air electrodes exposed to high concentration of steam under operating conditions. We report findings that dramatically enhance the electrocatalytic activity and stability of a conventional (La 0.6 Sr 0.4 ) 0.95 Co 0.2 Fe 0.8 O 3-δ (LSCF) air electrode by a multiphase catalyst coating composed of a conformal Pr 1-x Ba x CoO 3-δ thin film and exsolved BaCoO 3-δ nanoparticles, are reported. At 600 °C, the catalyst coating decreases the polarization resistance of the LSCF air electrode by a factor of 25 (from 1.09 to 0.043 Ω cm 2 ) in air and the degradation rate by two orders of magnitude (from 1.0 × 10 -2 to 1.8 × 10 -4 Ω cm2 h -1 in humidified air with 30 vol% H2O). Further, a single cell with the catalyst-coated LSCF air electrode at 600 °C demonstrates a high peak power density of 1.04 W cm -2 in the fuel cell mode and a high current density of 1.82 A cm -2 at 1.3 V in the electrolysis mode. The significantly enhanced performance of the LSCF air electrode is attributed mainly to the high rate of surface oxygen exchange, fast surface proton diffusion, and the rapid H 2 O and O 2 dissociation on the catalysts.

36 MATERIALS SCIENCE↗