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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.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Distributed Temperature Monitoring of Tundish Refractory Lining Using Optical Fiber Sensors

Distributed temperature monitoring can provide useful insights into the thermal conditions in the continuous caster tundish that can affect operating stability and cast quality, such as preheat conditions, superheat uniformity, fill height, and refractory wear. In the current study, fiber optic sensors were embedded into the refractory lining of a lab-scale tundish to record the temperature profiles during dry-out, preheating, and direct molten steel exposure to demonstrate sensor performance. Additional trials were performed with fiber optic sensors embedded in an industrial tundish used in production. Furthermore, the results demonstrate that fiber optic sensors using Rayleigh technology can provide accurate distributed temperature measurements in refractory lined vessels and provide useful information about the process.

Continuous Caster↗
DOE OSTI2022

Manganese Oxidation States in Volcanic Soils across Annual Rainfall Gradients

Manganese (Mn) exists as Mn(II), Mn(III), or Mn(IV) in soils, and the Mn oxidation state controls the roles of Mn in numerous environmental processes. However, the variations of Mn oxidation states with climate remain unknown. Here we determined the Mn oxidation states in highly weathered bulk volcanic soils (primary minerals free) across two rainfall gradients covering mean annual precipitation (MAP) of 0.25–5 m in the Hawaiian Islands. With increasing MAP, the soil redox conditions generally shifted from oxic to suboxic and to anoxic despite fluctuating at each site; concurrently, the proportions of Mn(IV) and Mn(II) decreased and increased, respectively. Mn(III) was low at both low and high MAP, but accumulated substantially, up to 80% of total Mn, in soils with prevalent suboxic conditions at intermediate MAP. Mn(III) was likely hosted in Mn(III,IV) and iron(III) oxides or complexed with organic matter, and its distribution among these hosts varied with soil redox potentials and soil pH. Soil redox conditions and rainfall-driven leaching jointly controlled exchangeable Mn(II) in soils, with its concentration peaking at intermediate MAP. The Mn redox chemistry was at disequilibrium, with the oxidation states correlating with long-term average soil redox potentials better than with soil pH. The soil redox conditions likely fluctuated between oxic and anoxic conditions more frequently at intermediate than at low and high MAP, creating biogeochemical hot spots where Mn, Fe, and other redox-sensitive elements may be actively cycled.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Coordinated Optimal Control of PV Inverters and HVAC Loads in Distribution Systems

The increasing integration of distributed energy resources (DERs), such as photovoltaics (PVs) and smart buildings into distribution systems complicate power system operation and controls. This paper proposes a coordinated optimal control strategy for PV inverters and Heating, ventilation, and air conditioning (HVAC) loads in smart buildings to minimize the total network loss in a distribution system. For the HVAC units, we enforce minimum on and off time constraints to avoid frequent switching that can degrade the unit. The proposed control will dispatch optimal control signals of active and reactive power to PV inverters and on/off commands to HVAC units while maintaining the nodal voltage within a secure range and the temperature of HVAC units at a comfort level. The simulation results on a modified IEEE 33-node distribution system demonstrate that the proposed coordinated control scheme can reduce the network loss.

Pani, Naveen↗
DOE OSTI2023

Predicting climate change impacts on poikilotherms using physiologically guided species abundance models

Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Ensemble learning-iterative training machine learning for uncertainty quantification and automated experiment in atom-resolved microscopy

Deep learning has emerged as a technique of choice for rapid feature extraction across imaging disciplines, allowing rapid conversion of the data streams to spatial or spatiotemporal arrays of features of interest. However, applications of deep learning in experimental domains are often limited by the out-of-distribution drift between the experiments, where the network trained for one set of imaging conditions becomes sub-optimal for different ones. This limitation is particularly stringent in the quest to have an automated experiment setting, where retraining or transfer learning becomes impractical due to the need for human intervention and associated latencies. Here we explore the reproducibility of deep learning for feature extraction in atom-resolved electron microscopy and introduce workflows based on ensemble learning and iterative training to greatly improve feature detection. This approach allows incorporating uncertainty quantification into the deep learning analysis and also enables rapid automated experimental workflows where retraining of the network to compensate for out-of-distribution drift due to subtle change in imaging conditions is substituted for human operator or programmatic selection of networks from the ensemble. This methodology can be further applied to machine learning workflows in other imaging areas including optical and chemical imaging.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Unified ELM Suppression on KSTAR and DIII-D via Adaptive Feedback Control Strategies

This paper reports on the extension of our amplitude-based resonant magnetic perturbation (RMP) edge localized mode (ELM) controller to support phasing control (relative toroidal phases of RMP waveforms between rows of coils), multiple toroidal mode numbers, and new ‘jump’ and ‘probing’ strategies, all deployed on KSTAR and DIII-D. By treating the control algorithm as device-independent and adjusting only the real-time interfaces to sensors and power supplies, we have confirmed that the same finite state machine—based feedback logic can be ported between machines with minor modifications. In experiments using n = 2 RMPs on KSTAR and n = 3 on DIII-D, the controller successfully modulated RMP amplitudes in real time to sustain ELM suppression while minimizing confinement degradation. Phasing control broadened the suppression window, as it permitted the system to avoid locked-mode regions and safely access ELM-free conditions. A rotating RMP phasing scheme, integrated into the same framework, distributes divertor heat loads more uniformly, making it a promising strategy for protecting plasma-facing components during long discharges. New ‘jump’ and ‘probing’ techniques demonstrate the possibility for the controller to preempt imminent ELMs and refine the minimum required RMP amplitude without returning to ELMy conditions. Taken together, these upgrades enable extended ELM-free operation while mitigating confinement degradation, providing a practical framework for real-time ELM control in future high-performance tokamaks.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Spatial distribution of sp 3 defects in carbon fibers via time-of-flight secondary ion mass spectrometry

Defects play a significant role in the material properties of carbon fibers (CF). Several defects result in the formation of sp 3 bonds in an otherwise sp 2 -dominant graphitic structure. Understanding the distribution of these defects within CF provides insight into their properties and the effect of manufacturing conditions. Reports showed time-of-flight secondary ion mass spectrometry (ToF-SIMS) is capable of characterizing the spatial distribution of sp 2 and sp 3 content in carbon materials. Here, ToF-SIMS was utilized to investigate the spatial distribution of sp 3 defects in T700, T1000, and M46 CF. M46 had the lowest sp 3 content. Center-to-edge analysis revealed that T700 CF had a gradient of sp 3 defects starting from the center and increasing to the edge, whereas M46 CF had a sudden increase in sp 3 defects roughly 1 μm from the edge. Comparatively, T1000 CF had a relatively uniform radial distribution of sp 3 defects, except for a newly identified sp 2 rich region at 0.8 μm from the center. This is hypothesized to originate from a skin–core structure that forms during CF manufacturing. As a result, this work demonstrates the utility of ToF-SIMS for characterizing the spatial distribution of sp 3 defects within CF, establishing new ways to understand CF formation.

Carbon fibers↗
DOE OSTI2023

Cyber Physical Subsystems under Adverse Conditions

The United States power grid is built upon complex cyber-physical systems (CPS), including many distributed energy resources (DERs) and microgrid structures. Due to cross-disciplinary domains that govern the interplay between cyber and physical technologies that sustain DERs, it is difficult to confidently predict how these systems will react under adverse conditions such as faults and cyberattacks. This project utilized power hardware in the loop (PHiL) to gain system behavior knowledge and robust understanding through experimentation and observational science. It also produced a large volume of datasets to feed into other efforts to advance high-fidelity model simulations and to support the design of novel control approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Spatially resolved performance and degradation in a perfluorinated anion exchange membrane fuel cell

Anion exchange membrane fuel cells may enable future operation with non-precious metal-based catalysts. These systems have a delicate sensitivity to operating conditions such as humidification levels and the presence of CO2 in the air oxidant stream. We present spatially resolved in-situ performance results that shed light on phenomena that are unique to anion exchange membrane fuel cells. For cell construction, a highly conductive perfluorinated anion exchange polymer was used as the membrane and the material in powder form as the ionomer. Experiments were conducted to investigate the effects of humidification, fuel/oxidant concentration, and carbonation effects on the performance and its distribution in the cell. The results indicated that (i) dry conditions at the cathode have a stronger effect than at the anode on overall cell performance, (ii) performance significantly suffered when humidification was below 90%, (iii) fuel and oxidant dilution effects lead mass-transport losses and were stronger than flow rate effects, (iv) CO2 in the cathode feed stream creates an equilibration disparity between the inlet and outlet sections and CO2 purging is affected by flooding conditions, and (v) after >500 h of operation, performance deteriorates predominantly at the inlet.

2+1D fuel cell model↗
DOE OSTI2022

Vapor-Phase Aggregation of Cerium Oxide Nanoparticles in a Rapidly Cooling Plasma

Local conditions, such as temperature and oxygen availability, have a pronounced effect on the formation and evolution of fallout following a nuclear explosion. While the behavior of nuclear-relevant materials such as uranium has begun to be explored under a wider range of environments, little is known about the behavior of plutonium. Here, using cerium as a surrogate, we track the vapor-phase aggregation of cerium oxide nanoparticles created in a plasma flow reactor under conditions of controlled temperature at two different oxygen fugacities. In situ optical emission spectroscopy is used to measure the variations in the spectral intensity of atomic and molecular species with temperature and oxygen content. We find that the relative rate of gas-phase oxidation of cerium is highly dependent on both temperature and local redox conditions within the flow reactor, to the extent that doubling the oxygen availability effectively doubles the amount of vapor-phase cerium monoxide at high temperatures (>2000 K). Condensed cerium oxide nanoparticles are also collected and analyzed ex situ via transmission electron microscopy and grazing-incidence small-angle X-ray scattering to determine their elemental composition, crystal structure, and size distribution. The size and morphology of the condensed nanoparticles are independent of local redox conditions, forming the same crystal type with the same size distribution regardless of oxygen availability. Postcondensation particle evolution, however, is found to be predominantly driven by temperature, with the average particle size increasing as particles cool and subsequently aggregate. These results expand our understanding of the chemical and physical behavior of refractory oxides that form during the early stages of fallout formation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Modeling and Simulation of Fuel Dispersal During the Loss-of-Coolant Accident

This document is the compilation of the milestone portion to a larger end of project NEUP report. The executive summary of the modeling portion is provided below: In the event of cladding rupture during a postulated LOCA in a pressurized water reactor, fuel particles, along with fission gases, can be expelled into the reactor core from the fractured fuel rod, a phenomenon referred to as fuel dispersal. The initial stage of fuel dispersal is strongly influenced by the high-pressure ejection of fuel fragments, the size and geometry of the ruptured cladding, and the depressurization history of the fuel rod during the postulated LOCA transient. Depending on the location of the burst orifice relative to the quench front, the dispersal event represents an intricate three-phase flow and heat transfer phenomenon, where high-temperature fuel particles carried by the fission gases interact with the coolant within the narrow subchannels of the fuel assemblies, inducing localized phase change. Given the unique multiphysics nature of this phenomena, the current study develops a dedicated computational framework to predict the mass distribution and cooling of dispersing fuel particles, facilitating post-accident assessment and management of the fuel assemblies. Considering the scale of nuclear reactor applications, a continuum three-fluid model is proposed for simulating the transport of solids within the reactor core. With high-temperature fuel fragments within the liquid media, nucleation sites inducing phase changes are dispersed within the flow domain. Coupled with the fact that the transient dispersal event occurs on different time scales than other three-phase flow applications, this study derives a time-averaged three-fluid flow model without losing generality. The assumptions regarding the continuum treatment of the solid phase and the modeling of fuel dispersal behavior are incorporated to simplify the governing equations and derive applicable closure relations. The computational validation of the model was conducted using adiabatic experimental results obtained from ongoing research at Oregon State University, focusing on characterizing fuel dispersal behavior during simulated LOCA conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched the probability density functions reported in experimental studies. The transport of fuel particles within a scaled 5 × 5 lattice of a pressurized-water reactor rod bundle geometry was modeled through a two-fluid Eulerian framework. The required boundary conditions were evaluated from the fuel performance code BISON in a postulated large-break LOCA scenario. The modeling framework considered solid fuel particles as granular matter, interacting with the gaseous dry steam phase and fission gases through the governing interfacial momentum exchange between the participating fluids. The simulation results provided the volume fraction of the solids obtained at the bottom surface of the enclosing tank geometry. Postulated LOCA leading to fuel dispersal phenomena involves the strong coupling between fuel thermomechanics, cladding deformation, thermal-hydraulics, and fuel particle transport. Incorporation of such a strong coupling in numerical simulation is performed by coupling the multiphysics solvers. In the case of fuel dispersal, a strong coupled simulation can be performed by coupling the BISON code for fuel performance, the TRACE code for system-level thermal hydraulics, and fuel particle transport in Multiphysics Object-Oriented Simulation Environment (MOOSE). For such intricate infrastructure, the MOOSE Framework eases the data transfer between codes. The recent version of MOOSE has incorporated the Navier-Stokes module for the fluid flow. An exploratory exercise was done to gain familiarity with finite volume capabilities in the MOOSE framework to incorporate the Spalart-Allmaras (SA) turbulence model. New finite-volume and auxiliary kernels were introduced to assemble the SA transport equation, compute turbulent viscosity, and evaluate wall distance and diagnostic turbulence terms, fully integrated with existing Navier-Stokes modules. A turbulent lid-driven cavity at a Reynolds number of approximately 10,000 is used for verification. MOOSE shows the robust solver convergence and produces the turbulent features. But it underpredicts the velocity profile and turbulent quantities, emphasizing the need to develop improved SA near-wall treatments (e.g., low-Re corrections or wall functions) as a key direction for future work.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Toward Addressing the Challenge to Predict the Heat Capacities of RDX and HMX Energetic Materials

Availability of heat capacity as function of pressure and temperature is an essential prerequisite for development of a computational multiscale strategy capable to address the evolution of microstructure and energy release in advanced high energy density materials. In the case of 1,3,5-trinitro-1,3,5-triazinane (RDX) and 1,3,5,7-tetranitro-1,3,5,7-tetrazocane (HMX) systems as two of the most studied energetic materials, there are substantial gaps in experimental data, with available heat capacities values distributed only in a region close to standard ambient conditions. In this study we demonstrate how these major experimental limitations can be addressed in the case of the RDX and HMX systems based on the combined use of classical and quantum mechanical calculations. We show that by considering ideal gas properties evaluated using quantum mechanical methods, and residual properties obtained from molecular simulations using fully flexible atomistic force field models, excellent agreement can be obtained for the predicted heat capacities to the most recent experimental values. An important advantage of the current computational methodology is that it allows evaluation of both constant-volume and constant-pressure heat capacities for a broad interval of temperatures and pressures, which encompasses solid and liquid phases conditions. In the case of the solid α and γ phases of RDX and the β phase of HMX, the predicted results follow closely both the available experimental data at standard ambient conditions and the results obtained using density functional theory calculations at high pressures, a regime where experimental data are not available. A perspective to expand the current methodology is also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Wickability-optimized textured liquid-desiccant air dehumidifiers for independent moisture management in energy-efficient buildings

Liquid-desiccant-based air conditioning systems are envisioned to enable independent humidity management, thereby improving the energy efficiency of future buildings. Existing liquid-desiccant-based air conditioning concepts, however, suffer from a poor liquid flow distribution deteriorating moisture removal rate. They are consequently flooded with the liquid-desiccant solution, which significantly degrades the energy efficiency of the dehumidification process. Here, in this work, the capillary forces and wickability effect of textured air dehumidifier surfaces are altered to minimize the liquid-desiccant flow rate of the fully wetted state, thereby transforming the physics of interfacial desiccant flow distribution. Consequently, the wickability-optimized air dehumidifier surface maximizes both moisture removal rate and dehumidification energy efficiency. It was interestingly found that the length scale of a textured air dehumidifier surface concept is optimized at an intermediate pattern density. Dry solid-air menisci appear at length scales exceeding the optimum pattern distance while the effective liquid–air interfacial area is reduced at smaller length scales, both of which degrade the moisture removal rate. At the optimum pattern density, the effective liquid–air interfacial area increases with the solution flow rate, thereby increasing the dehumidification rate. At a water vapor pressure potential of 3 kPa and a solution flow rate of 2.8 g/s, experimental results indicated a moisture removal rate of 0.16 g/m2-s for a textured surface concept with a capillary length scale of 3 mm, a 28% improvement compared with that of smooth-plate dehumidifier surfaces. A high moisture removal rate of the textured surface at a low desiccant flow rate led to a high thermal efficiency of 0.75 at a water vapor pressure potential of 5.6 kPa and a LiBr flow rate of 2.8 g/s. The insights gained from the present study accelerate the development of advanced textured surface concepts for next-generation liquid-desiccant-based air dehumidification systems offering independent humidity management for future energy-efficient buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

A discrete dislocation dynamics study of precipitate bypass mechanisms in nickel-based superalloys

Order strengthening in nickel-based superalloys is associated with the extra stress required for dislocations to bypass the $\gamma'$ precipitates distributed in the $\gamma$ matrix. Depending on the operating conditions and microstructure, a rich variety of bypass mechanism has been identified, with various shearing and Orowan looping processes gradually giving way to climb bypass as the operating conditions change from the low/intermediate temperatures and high stress regime, to the high temperature and low stress regime. When anti phase boundary (APB) shearing and Orowan looping mechanisms operate, the classical picture is that, at for a given volume fraction, the bypass mechanism changes from shearing to looping with increased particle size and within a broad coexistence size window. Another possibility, which is supported by indirect experimental evidence, is that a third “hybrid” transition mechanism may operate. Here, in this paper we use discrete dislocation dynamics (DDD) simulations to study dislocation bypass mechanisms in Ni-based superalloys. We develop a new method to compute generalized stacking fault forces in DDD simulations, based on a concept borrowed from complex analysis and known as the winding number of a closed curve about a point. We use this method to study the mechanisms of bypass of a square lattice of spherical $\gamma'$ precipitates by $a/2\langle110\rangle$ {111} edge dislocations, as a function of the precipitates volume fraction and size. We show that not only the hybrid mechanism is possible, but also that it operates as the transition mechanism between the shearing and looping regimes over a wide range of precipitates volume fraction and radii. Based on our simulation results, we propose a simple model for the strength of this mechanism. We also consider the effects of a $\gamma$/$\gamma'$ lattice misfit on the bypass mechanisms, which we approximate by an additional precipitate stress computed according to Eshelby’s inclusion theory. We show that in the shearing and hybrid looping-shearing regimes, a lattice misfit generally results in an increased bypass stress. For sufficiently high lattice misfit, the critical bypass configuration in attractive dislocation-precipitates interactions changes dramatically, and the bypass stress is controlled by the pinning of the trailing dislocation on the exit side of the precipitates, similar to what has been reported in the high-temperature creep literature.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Building energy efficiency and load flexibility optimization using phase change materials under futuristic grid scenario

This work investigates the energy savings and load flexibility capacity of phase change material (PCM) integrated into building envelope under a future energy generation scenario, where 80% of the energy load comes from renewable sources. Using community-scale modeling, we first determine the net thermal storage required to fully manage the demand variability at a utility grid, and then using whole-building simulations we optimize operating parameters - such as PCM thickness, latent heat, interior setpoint profile, PCM distribution, and heat transfer coefficient - to determine the optimal conditions required for maximum energy reduction and load flexibility without compromising occupants' thermal comfort. The optimal PCM-integrated envelope proposed in this study can provide annual load flexibility up to 33.6% and annual energy savings up to 10.8% in a lightweight residential building located in Baltimore, MD. This study is relevant given the increasing contributions of renewable energy in the total energy generation mix, leading to a significant time-imbalance between peak energy demand and peak energy production. While thermal energy storage using PCM is a recognized technique, there is no known prior study dedicated to examining the thermal performance of PCM-integrated envelope under future energy generation scenarios. This study bridges the research gap by investigating a practical way to implement PCMs in the buildings and maximize the energy efficiency as well as load flexibility related benefits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Identifying factors controlling the selective ethane dehydrogenation on Pt-based catalysts from DFT based micro-kinetic modeling

Herein, a detailed EDH reaction mechanism on Pt(111), Pt(211), Pt 3 Sn(111) and Pt 3 Sn(211) surfaces has been systematically computed with DFT. A mean-field micro-kinetic model has been established to simulate the product distribution and selectivity on each surface at experimentally relevant conditions. We have uncovered a number of different factors controlling the catalytic performance of Pt-based EDH catalysts, thus leading to a better understanding of experimentally detected phenomenon. In this study, the (111) and (211) model surfaces of Pt and Pt 3 Sn catalysts were chosen to represent the terrace and step-edge sites when simulating the EDH reaction mechanisms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Redistribution of radionuclides in irradiated AGR-1 UCO TRISO fuel after 1800 °C safety testing

Release of radionuclides from intact tristructural-isotropic (TRISO) coated particle fuel at normal and accident conditions is a primary metric of fuel performance. The distribution of fission products and actinides in the TRISO layers of individual particles provides insight on radionuclide transport and release behavior and was determined using scanning electron microscopy analysis. Particles were isolated from an irradiated fuel compact (AGR-1 Compact 4-4-2) and analyzed as-irradiated or after individual particle safety-testing at 1800 °C for 650 h. Particles were selected for comparison based on their remaining 110mAg fission product inventory. These comparisons corroborated the observation that the 110m Ag inventory is a marker for relative irradiation temperature based on observed radionuclide distribution in the SiC layer. The comparison also indicated that the in-pile behavior influences the fission product and actinide species interactions with the TRISO layers during high temperature exposure after irradiation. The analysis confirms both palladium and uranium diffusion, as well as other species, are active in the UCO TRISO fuel system at 1800 °C and that palladium transport is active at lower temperatures relative to uranium. While diffusion across the SiC layer was observed, the intact nature of the SiC layer after the 1800 °C, 650-h exposure indicates the SiC layer maintained its functionality as a fission product barrier by mitigating release of radionuclides at beyond accident margin temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Bifunctional Electrocatalysts with High-Entropy Alloys: Bridging Hydrogen Evolution and Oxygen Reduction

High-entropy alloys (HEAs) have emerged as a promising class of bifunctional electrocatalysts capable of simultaneously driving the hydrogen evolution reaction (HER) and the oxygen reduction reaction (ORR) with high activity and durability. Their near-equiatomic multicomponent compositions give rise to unique physicochemical characteristics, including lattice distortion, sluggish diffusion, high-entropy stabilization, and pronounced electronic heterogeneity, that collectively generate diverse and synergistic active sites inaccessible in conventional alloys. This review summarizes recent progress in HEA-based bifunctional electrocatalysis, with a focus on the fundamental mechanisms governing HER and ORR activity, stability, and selectivity. We discuss advances in synthesis strategies, ranging from confined growth and step-alloying to scalable continuous-flow methods, that enable precise control over composition, size, and surface structure. Complementary computational and data-driven approaches, including density functional theory, machine-learning-assisted screening, and descriptor development, are highlighted as essential tools for navigating the vast HEA design space and establishing structure−property relationships. Particular attention is paid to adsorption-energy distributions, multisite cooperativity, and environmental effects under realistic electrochemical conditions. Finally, we outline current challenges and future opportunities for integrating mechanistic understanding with AI-guided, closed-loop design frameworks to accelerate the discovery of next-generation HEA bifunctional electrocatalysts for sustainable energy conversion.

Alloys↗