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At least 343 records · Page 19

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DOE OSTI2022

Design Evaluation of a Next-Generation High-Temperature Particle Receiver for Concentrating Solar Thermal Applications

High-temperature particle receivers are being developed to achieve temperatures in excess of 700 °C for advanced power cycles and solar thermochemical processes. This paper describes designs and features of a falling particle receiver system that has been evaluated and tested at the National Solar Thermal Test Facility at Sandia National Laboratories. These advanced designs are intended to reduce heat losses and increase the thermal efficiency. Novel features include aperture covers, active air flow, particle flow obstructions, and optimized receiver shapes that minimize advective heat losses, increase particle curtain opacity and uniformity, and reduce cavity wall temperatures. Control systems are implemented in recent on-sun tests to maintain a desired particle outlet temperature using an automated closed-loop proportional–integral–derivative controller. These tests demonstrate the ability to achieve and maintain particle outlet temperatures approaching 800 °C with efficiencies between 60 and 90%, depending on incident power, mass flow, and environmental conditions. Lessons learned regarding the testing of design features and overall receiver operation are also presented.

Mills, Brantley H.↗

DOE OSTI2024

Detection of Control Injection Attacks using Energy Data Anomalies in CNC Machining

The widespread adoption of networked devices, sophisticated automation, and data-driven processes in the industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. With these benefits, however, comes a substantial increase in the attack surface of these systems. In addition to affecting the readiness and the quality of critical products, the attacks against manufacturing processes and systems carry the potential to have severe physical consequences, including human injury and death. In this paper we present the results of a remote network-based control injection attack on a CNC mill. Specifically, we focus on the impact of this type of the attack on the movement of CNC mill during operation. Evaluating the physical effect of these attacks on a workpiece, we provide machine agnostic, affordable, and scalable solution for their monitoring. We then demonstrate a simple threshold-based method for the detection of these attacks and evaluate the effectiveness of detection.

Taylor, Curtis↗

DOE OSTI2021

Commercial PV Inverter IEEE 1547.1 Ride-Through Assessments Using an Automated PHIL Test Platform

As more countries seek solutions to their de-carbonization targets using renewable energy (RE) technologies, interconnection standards and national grid codes for distributed energy resources (DER) are being updated to support higher penetrations of RE and improve grid stability. Common grid-code revisions mandate DER devices, such as solar inverters and energy storage systems, ride-through (RT) voltage and frequency disturbances. This is necessary because as the percentage of generation from DER increases, there is a greater risk power system faults will cause many or all DER to trip, triggering a substantial load-generation imbalance and possible cascading blackout. This paper demonstrates for the first time a methodology to verify commercial DER devices are compliant to new voltage, frequency, and rate of change of frequency (ROCOF) RT requirements established in IEEE Std. 1547-2018. The methodology incorporates a software automation tool, called the SunSpec System Validation Platform (SVP), in combination with a hardware-in-the-loop (HIL) system to execute the IEEE Std. 1547.1-2020 RT test protocols. In this paper, the approach is validated with two commercial photovoltaic inverters, the test results are analyzed for compliance, and improvements to the test procedure are suggested.

14 SOLAR ENERGY↗

DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗

DOE OSTI2022

Sizing Considerations for EV Dynamic Wireless Charging Systems with Integrated Energy Storage

Roadways with dynamic wireless charging systems (DWCS) enable charge-sustaining in-motion EV charging, which can reduce charging idle time while increasing range capabilities. Spatially distributed transmitter coils are controlled in response to traffic load that varies significantly minute to minute with high power levels, very short charging time, and low system utilization like wind turbine power. Traffic load estimation and localized analysis may guide effective sizing and topology adoption for feasible and scalable DWCS deployment. DWCS traffic load approximation is reviewed with measured Automated Traffic Recorder (ATR) data and statistical distributions being used to create a synthetic load analyzed using proposed metrics quantifying system utilization over time. Lumped coil section segmentation is compared between second-based distance and spatial density analysis methods, offering 17-27% greater system utilization. A peak load shifting method is proposed for traffic redirection across two tracks with optional BESS integration increasing system utilization by 50-60% depending on time-based and power reserve-based sizing and control.

Lewis, Donovin D.↗

DOE OSTI2022

Automated quantum error mitigation based on probabilistic error reduction

Current quantum computers suffer from a level of noise that prohibits extracting useful results directly from longer computations. The figure of merit in many near-term quantum algorithms is an expectation value measured at the end of the computation, which experiences a bias in the presence of hardware noise. A systematic way to remove such bias is probabilistic error cancellation (PEC). PEC requires a full characterization of the noise and introduces a sampling overhead that increases exponentially with circuit depth, prohibiting high-depth circuits at realistic noise levels. Probabilistic error reduction (PER) is a related quantum error mitigation method that systematically reduces the sampling overhead at the cost of reintroducing bias. In combination with zero-noise extrapolation, PER can yield expectation values with an accuracy comparable to PEC.Noise reduction through PER is broadly applicable to near-term algorithms, and the automated implementation of PER is thus desirable for facilitating its widespread use. To this end, we present an automated quantum error mitigation software framework that includes noise tomography and application of PER to user-specified circuits. We provide a multi-platform Python package that implements a recently developed Pauli noise tomography (PNT) technique for learning a sparse Pauli noise model and exploits a Pauli noise scaling method to carry out PER.We also provide software tools that leverage a previously developed toolchain, employing PyGSTi for gate set tomography and providing a functionality to use the software Mitiq for PER and zero-noise extrapolation to obtain error-mitigated expectation values on a user-defined circuit.

McDonough, Benjamin↗

DOE OSTI2022

Sequential Decision Making (SDM) for Mesh Refinement and Model Selection in Multiscale, Multi-Physics Applications

Intelligent automation and decision support are needed to enhance computational efficiency and robustness in multiscale and multi-physics problems, including materials science, manufacturing, and climate and weather modeling. Current scientific computing approaches for enabling decisions by scientists fail to explore the role of learning, reasoning, and probabilistic planning. Often these decisions are not performed in real-time during the computation but are made prior to the start of the computation, which must be interrupted in order to make changes to the prior choices. Such interruptions at different stages of the computation increase the total computing time and the need for a human expert to frequently monitor the results. State of art scientific computing methods consist of rule-based algorithms that cannot automatically adapt to a dynamically changing computing environment. The development of a Sequential Decision Making (SDM) framework will automate scientific computing by optimizing the policies for mesh refinement, time-stepping, model and algorithm selection, resource allocation, and pre and post-processing. Our agent SDM framework for scientific computing will consist of data-driven learning (Classifier), automated reasoning (contextual knowledge), and probabilistic planning (Reinforcement Learning). In this project, we focused on three problems to demonstrate our SDM framework on a set of ordinary and partial differential equations. Classification of Lorenz system regions using Feed-Forward Neural Networks examined learning in the SDM framework. On the other hand, reasoning and planning in the SDM framework were used in two problems: adaptive time-stepping for nonlinear ODEs using on-policy RL algorithms, and adaptive mesh refinement for 2-D PDEs using off-policy RL algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Automating Anomaly Detection for Target systems at Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory, produces the world’s most intense pulse neutrons beams. An accelerated proton beam is directed into a mercury target to generate neutrons via spallation. The target system accounted for over 40% of the overall downtime of the facility in 2022. Thus, early detection in anomalies in the target systems can enable taking corrective actions to avoid failures and reduce downtime. Fault prognostics and anomaly detection in accelerators, both at SNS and outside, has largely focused on the beam side. This paper presents one the first studies exploring leveraging machine learning to automate the detection of anomalies in the target system. The target system consists of over 30 different interconnected subsystems, and the present work focuses on the mercury process system as a use case. Analyzing data from 28 process variables from 2022 and 2023, tree-based and reconstruction-based algorithms are employed to detect anomalies in archived data. The algorithms detected previously unreported anomalies, several of which were deemed alert worthy by human experts, particularly those found by reconstruction-based algorithms. Using data from each production run in the accelerator increased the generalizability of the models in time. Efforts are now underway to implement a workflow for incorporating human feedback to update the models and evaluating performance on unseen data. The models will eventually be integrated into the existing System Tracking and Reliability system with a web interface for automated anomaly detection and reporting along with a pathway for incorporating human feedback for model updates.

Raj, Anant [ORNL] (ORCID:0000000306711244)↗

DOE OSTI2025

CASM Monte Carlo: Calculations of the thermodynamic and kinetic properties of complex multicomponent crystals

Monte Carlo techniques play a central role in statistical mechanics approaches that connect macroscopic thermodynamic and kinetic properties to the electronic structure of a material. This paper describes the implementation of Monte Carlo techniques for the study of multicomponent crystalline materials within the Clusters Approach to Statistical Mechanics (CASM) software suite, and demonstrates their use in model systems to calculate free energies and kinetic coefficients, study phase transitions, and construct phase diagrams from first principles. Many crystal structures are complex, with multiple sublattices occupied by differing sets of chemical species, along with the presence of vacancies or interstitial species. This imposes constraints on concentration variables, the form of thermodynamic potentials, and the values of kinetic transport coefficients. The framework used by CASM to formulate thermodynamic potentials and kinetic transport coefficients accounting for arbitrarily complex crystal structures is presented and demonstrated with examples of increasing complexity. Additionally, an overview of the capabilities of the CASM software specific to Monte Carlo methods is given, and a new CASM software package is introduced, casm-flow, which helps automate the setup, submission, management, and analysis of Monte Carlo simulations.

Cluster expansion↗

DOE OSTI2022

From fault-detection to automated fault correction: A field study

A fault detection and diagnostics (FDD) tool, as addressed by this study, is a tool that continuously identifies the presence of faults and efficiency improvement opportunities through a one-way interface to the building automation system and the application of automated analytics. Although FDD tools can inform operators of building operational faults, currently an action is always required to correct the faults to generate energy savings. Fault auto-correction integrating with commercial FDD technology offerings can close the loop between the passive diagnostics and active control, increase the savings generated by FDD tools, and reduce the reliance on human intervention. This paper presents the field study of seven fault auto-correction algorithms implemented in commercial FDD platforms. Implementation includes software changes in the FDD tools and additional controls hardware or software changes in the BAS that were required to enable the execution of different types of auto-correction algorithms in real buildings. The routines successfully and automatically correct faults and improve the operation of large built-up Heating, Ventilation, and Air Conditioning (HVAC) systems, common in most commercial buildings. The auto-correction algorithms are tested across four buildings and three different building automation systems, following a rigorous procedure to make sure they work properly and do not negatively impact the system and building occupants. Finally, technology benefits, market drivers, and scalability changes are drawn from the implementation effort and test results, to drive future research and industry engagement.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

A minor respiratory process with major global implications: is atmospheric methane oxidation in tree stems driven by stem respiration rather than microbial methanotrophy?

Tree stem surfaces are widely recognized as sites of carbon dioxide (CO₂) efflux and oxygen (O₂) influx, reflecting the dynamics of aerobic respiration of photosynthate substrates, such as sugars, delivered via the phloem. Stems are also largely considered passive conduits for methane (CH₄) produced in anoxic soils via microbial methanogenesis, where CH₄ is thought to be transported upward through the transpiration stream and/or diffusion and emitted through stem surfaces and the canopy. However, recent observations from dynamic stem chambers suggest that stems may also act as active sinks for atmospheric CH₄. Despite these findings, the extent and drivers of stem CH₄ consumption remain poorly characterized across biomes, species, and environmental gradients, and its quantitative relationship to stem respiration has not been established. Moreover, previous studies captured only snapshot fluxes, leaving diurnal patterns of CH₄ exchange uncharacterized. Here, we address these limitations by combining real-time measurements of stem CH₄ and O₂ uptake under ambient conditions in a California cherry tree, using a dynamic stem gas exchange system with three chambers receiving a continuous flow of ambient air and automated chamber and reference air sampling every 10 min. Our results confirm that stems of upland trees can actively consume both atmospheric CH₄ and O₂, but with decreasing temperature sensitivity as daily temperatures increase. Early mornings were marked by rapid influxes of both gases, followed by declining uptake as temperatures rose further. Methane uptake was tightly coupled with O₂ influx and represented a minor (0.012% ± 0.002%) fraction of stem respiratory activity, as determined by concurrent O₂ uptake. These findings suggest that while atmospheric CH₄ oxidation is a minor respiratory process in stems, it is strongly linked with stem physiological activity. This challenges the current assumption that terrestrial CH₄ uptake is driven solely by microbial methanotrophy and raises the possibility that living stem tissues may contribute to CH₄ oxidation through an as-yet-unidentified plant-based mechanism.

Atmospheric greenhouse gases↗

DOE OSTI2025

Exploring scenarios for enhanced fuel compression and performance on the National Ignition Facility with machine-learning-aided design techniques

Recent fusion experiments on the National Ignition Facility (NIF) have achieved ignition, producing multi-MJ fusion yields for input laser energies of roughly 2 MJ [Abu-Shawareb et al., Phys. Rev. Lett. 132, 065102 (2024)]. Building on the success of the target designs that have achieved ignition, we explore new implosion scenarios predicted to generate significantly more compression of the dense DT ice layer and correspondingly higher yields while preserving many of the key physics characteristics of present-day ignition designs. Our main result is a novel 3-shock implosion scheme that effectively minimizes the shock-induced entropy in the dense, accelerating DT shell and maximizes the resulting fuel compression subject to a fixed leading shock strength consistent with present-day ignition experiments, which is necessary to melt the crystalline high-density carbon ablator. Compared to the first NIF experiment to fulfill Lawson's ignition criterion, shot N210808 [Abu-Shawareb et al., Phys. Rev. Lett. 129, 075001 (2022)], our design exhibits a 40% increase in simulated peak areal density (ρR) and a 5× increase in 1D fusion yield using a 4% lighter ablator and identical DT payloads. We also present a complete integrated 2D hohlraum design and laser pulse specifications capable of generating the desired 3-shock drive and maintaining control of the low-mode capsule implosion symmetry, where the increase in simulated 2D yield relative to N210808 is > 10×. This new implosion regime was discovered with help from a machine-learning-enabled capsule design optimization framework. We outline the workflow this automated tool uses to identify improved design candidates by running several rounds of capsule simulations, constructing a surrogate model mapping input variations to key physics output quantities, and querying the resulting statistical model to propose adjustments to the x-ray drive and capsule to reach a set of physics objectives prescribed by the designer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Assessment of Commercial Building Lighting as a Frequency Regulation Resource

This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Global evidence for the acclimation of ecosystem photosynthesis to light

Photosynthesis responds quickly to changes in light, increasing with incoming photosynthetic photon flux density (PPFD) until the leaves become light saturated. This instantaneous response to PPFD, which is widely studied and incorporated into models of photosynthesis, is overlaid on non-instantaneous photosynthetic changes resulting from the acclimation of plants to average PPFD over intermediate timescales of a week to months ($\overline{PPFD}$). Such photosynthetic light acclimation is not typically incorporated into models, due to the lack of observational constraints. In this work, we use eddy covariance observations from globally distributed and automated sensor networks, along with photosynthesis estimates from nine terrestrial biosphere models (TBMs), to quantify and assess photosynthetic acclimation to light in natural environments. We also use recent theoretical developments to incorporate light acclimation in a TBM. Our results show widespread light acclimation of ecosystem photosynthesis. On average, a 1 μmol m -2 s -1 increase in $\overline{PPFD}_{10}$ (ten-day average PPFD) leads to a 0.031 ± 0.013 μmol C m -2 s -1 increase in the maximum photosynthetic assimilation rate (A max ), with croplands having stronger acclimation rates than grasslands and forests. Our analysis shows that the TBMs examined either neglect or substantially underestimate light acclimation. By updating a TBM to include photosynthetic acclimation, successfully reproducing the $\overline{PPFD}_{10}$-A max relationship, we provide a robust method for the incorporation of photosynthetic light acclimation in future models.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

A flexible test facility for liquid xenon detector development

As liquid xenon time projection chambers scale to ever-larger sizes, so too do the engineering challenges they pose. Here, we describe a large, flexible, multipurpose test facility capable of supporting the development of a number of key aspects of liquid xenon detector systems. Example applications of this facility include characterization of large-area light and charge sensor arrays, tests of xenon purification techniques and materials compatibility, and investigations into high-voltage phenomena. This facility uses an automated and remotely monitored cryo-cooling system based on immersion of the test chamber in a liquid bath rather than conductive coupling, leading to advantages in temperature and pressure stability, as well as increasing required response times in the case of cooling-power loss. Design advantages, operational procedures, and performance of the facility are described, as well as five examples of liquid xenon test chambers that use the facility.

Dark Matter detectors↗

DOE OSTI2025

SIDDA: SInkhorn Dynamic Domain Adaptation

Modern neural networks (NNs) often do not generalize well in the presence of a "covariate shift"; that is, in situations where the training and test data distributions differ, but the conditional distribution of classification labels remains unchanged. In such cases, NN generalization can be reduced to a problem of learning more domain-invariant features. Domain adaptation (DA) methods include a range of techniques aimed at achieving this; however, these methods have struggled with the need for extensive hyperparameter tuning, which then incurs significant computational costs. In this work, we introduce SIDDA, an out-of-the-box DA training algorithm built upon the Sinkhorn divergence, that can achieve effective domain alignment with minimal hyperparameter tuning and computational overhead. We demonstrate the efficacy of our method on multiple simulated and real datasets of varying complexity, including simple shapes, handwritten digits, and real astronomical observations. SIDDA is compatible with a variety of NN architectures, and it works particularly well in improving classification accuracy and model calibration when paired with equivariant neural networks (ENNs). We find that SIDDA enhances the generalization capabilities of NNs, achieving up to a ≈40% improvement in classification accuracy on unlabeled target data. We also study the efficacy of DA on ENNs with respect to the varying group orders of the dihedral group DN, and find that the model performance improves as the degree of equivariance increases. Finally, we find that SIDDA enhances model calibration on both source and target data--achieving over an order of magnitude improvement in the ECE and Brier score. SIDDA's versatility, combined with its automated approach to domain alignment, has the potential to advance multi-dataset studies by enabling the development of highly generalizable models.

Pandya, Sneh [Northeastern U.]↗

DOE OSTI2023

Probing individual single atom electrocatalyst sites by advanced analytical scanning transmission electron microscopy

Single atom electrocatalysts (SAEs) are promising next-generation materials for promoting a variety of important reactions, such as the oxygen reduction, nitrogen reduction, and CO 2 reduction reactions. While bulk characterization techniques such as X-ray absorption spectroscopy and Mössbauer spectroscopy have significantly enhanced our understanding of these catalysts, direct probing of individual single metal atom sites at the atomic scale is necessary to understand local variations in the properties of these sites and accelerate design and synthesis of improved SAEs. Aberration-corrected scanning transmission electron microscopy (STEM) has become a powerful tool for providing this type of atomic-scale information about SAE metal sites. These sites are typically unstable under the electron beam, however, which, in combination with conventional acquisition methods and detectors, has limited the type and quantity of information obtainable by spectroscopic STEM techniques. Here, we map multiple individual SAE metal sites in a nitrogen-doped carbon containing atomically dispersed Fe and Re (FeReNC) at the atomic scale by direct electron detection electron energy-loss spectroscopy (EELS). Direct electron detection provides an improved signal-to-noise ratio over conventional scintillator-based detectors and enables detection and real space localization of weak signals. In addition, we demonstrate an automated method for identification of metal atom positions, placement of the probe on these sites, and simultaneous EELS and energy dispersive X-ray spectroscopic (EDS) signal acquisition. This simultaneous acquisition of EELS and EDS provides access to the composition and bonding of a wide range of SAE metal sites. In this study, focusing the probe directly on the metal sites also increases the relevant data acquisition rate by more than an order of magnitude over two-dimensional mapping, enabling improved statistical measurements of site properties. The versatility, sensitivity, and speed that these techniques provide enhances our ability to probe the local elemental and chemical environment of a large number of individual SAE metal site structures at the atomic scale, enabling an improved understanding of the variations in the local properties of these electrocatalysts to be gained. As a result, significantly increased information about individual metal sites will be available to future electrochemical studies through these techniques, accelerating the development of advanced SAEs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗