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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Evaluation of the performance of distributed and centralized biomass technologies in rural China

Biomass fermentation and gasification are two types of biomass utilization technologies for residential households. Government plays an essential role in the diffusion of improved technologies. Cost-effectiveness analysis can provide the cost and abatement potentials of each alternative under a single abatement strategy; however, it is difficult for policy-makers to make an integrated assessment when multiple objectives are involved. This paper evaluates the performance of three distributed alternatives and two centralized alternatives for residential biomass use in rural China, including centralized biogas and centralized gasifier systems. An integrated assessment approach is built, which combines cost-benefit analysis, an entropy-weighting method, and a preference-ranking organization method for enrichment evaluations using the PROMETHEE approach. A total of 686 sample households in three regions of China were surveyed in this study. The centralized gasifier system and distributed household biogas were the top two alternatives for further development to meet the government’s 2020 development goal for biogas technology. The overall biomass utilization amount in the prioritized scenario is about three times that of the business-as-usual scenario by 2020. Furthermore, this study can provide significant insights for China’s policy-makers to draft strategic policies and countermeasures that will promote the development of renewable energy in China.

09 BIOMASS FUELS↗
DOE OSTI2018

Magnetocaloric Effect in La{sub 1−x}Li{sub x}MnO{sub 3}

In this work, we investigate the effect of Li concentration on magnetocaloric effect (MCE) in La{sub 1−x}Li{sub x}MnO{sub 3} (LLMO) using a phenomenological model (PM). The results show that the maximum value of change of magnetic entropy decreases slightly with low Li concentration and does not change with high Li concentration. However, the full width at half maximum and maximum value of change of specific heat increase slightly with low-Li concentration and decrease at high-Li concentration samples. In addition, the relative cooling power does not change monotonously with Li concentration. The results recommend that LLMO samples have potential application for magnetic refrigeration (MR) in an extended temperature range.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Large magnetocaloric effect in rapidly quenched Mn 50- x Co x Ni 40 In 10 nanomaterials

The effect of Co addition on magnetic hysteresis, martensitic transformation temperature, and magnetic entropy change of rapidly-quenched Mn 50- x Co x Ni 40 In 10 alloy nanomaterials has been investigated. The melt-spun Mn 50 Ni 40 In 10 sample exhibits a small magnetic hysteresis which is further reduced by Co doping as measured between 0 and 2 T. The martensitic transformation temperature increases linearly with the electron concentration in the alloy from 195 K for Mn 50 Ni 40 In 10 to 378 K for Mn 45 Co 5 Ni 40 In 10 . The Mn 47 Co 3 Ni 40 In 10 alloy, which has phase-transition temperature close to room temperature, exhibits a substantial peak entropy change of 29.7 J kg -1 K -1 at magnetic field change of 2 T. Our results demonstrate that Mn 47 Co 3 Ni 40 In 10 nanomaterial exhibits promising magnetocaloric properties for near-room-temperature magnetic refrigeration.

Physics↗
DOE OSTI2023

Off-policy deep reinforcement learning with automatic entropy adjustment for adaptive online grid emergency control

Electric overloading conditions and contingencies put modern power systems at risk of voltage collapse and blackouts. Load shedding is crucial to maintain voltage stability for grid emergency control. However, the rule- or model-based schemes rely on accurate dynamic system models and face considerable challenges in adapting to various operating conditions and uncertain event occurrences. Here, to address these issues, this paper proposes a novel deep reinforcement learning (DRL)-based voltage stability control algorithm with automatic entropy adjustment (AEA) for grid emergency control. Various dynamic network components for complex system operations are modeled to construct the DRL environment. An off-policy soft actor-critic architecture is developed to maximize the expected reward and policy entropy simultaneously. The AEA mechanism is proposed to facilitate the policy maximum entropy procedure, and the proposed method can automatically provide effective discrete and continuous actions against various fault scenarios. Our approach accomplishes high sampling efficiency, scalability, and auto-adaptivity of the control policies under high uncertainties. Comparative studies with the existing DRL-based control methods in IEEE benchmarks indicate salient performance improvement of the proposed method for dynamic system emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

An uncertainty visualization framework for large-scale cardiovascular flow simulations: A case study on aortic stenosis

We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated on a patient-specific stenosed aorta. The framework combines EasyVVUQ for parameter sampling with large-eddy simulation turbulence modeling in HemeLB, and executes ensembles on the Frontier exascale supercomputer. Spatially resolved metrics, including entropy and isosurface-crossing probability, are used to map uncertainty in pressure and wall shear stress fields directly onto vascular geometries. Two sources of model variability are examined: inlet peak velocity and the Smagorinsky constant. Inlet velocity variation produces high uncertainty downstream of the stenosis where turbulence develops, while upstream regions remain stable. Smagorinsky constant variation has little effect on the large-scale pressure field but increases WSS uncertainty in localized high-shear regions. In both cases, the stenotic throat manifests low entropy, indicative of robust identification of elevated WSS. By linking quantitative UQ measures to three-dimensional anatomy, the framework improves interpretability over conventional 1D UQ plots and supports clinically relevant decision-making, with broad applicability to vascular flow problems requiring both accuracy and spatial insight.

Hemodynamics↗
DOE OSTI2015

Magnetothermopower, magnetic entropy change, and magnetoresistance in ferromagnetic cobaltites: R{sub 0.7}Sr{sub 0.3}CoO{sub 3} (R = La and Gd)

We compare the impact effect of external magnetic fields on the magnetic entropy (S{sub m}), electrical resistivity, and thermopower in two metallic oxides R{sub 0.7}Sr{sub 0.3}CoO{sub 3} that become ferromagnetic below T{sub C} = 240 K and 117 K for R = La{sup 3+}(4f{sup 0}) and Gd{sup 3+}(4f{sup 7}), respectively. While Gd{sub 0.7}Sr{sub 0.3}CoO{sub 3} shows unusually high values of negative magnetothermopower (≈−35.5%), magnetic entropy change (ΔS{sub m} ≈ −12 J/kg K), and negative magnetoresistance (≈−3.7%) at 10 K for a field change of 5 T, these effects are absent in the La sample at 10 K. We suggest that the large ΔS{sub m} arises from the field-induced suppression of spin fluctuations associated with localized 4f{sup 7} moments of Gd{sup 3+} ions, but magnetic interaction among Co ions dictates magnetoresistance and magnetothermopower. Magnetization data indicate that while Co{sup 3+} ions adopt intermediate spin state (S = 1) in the La compound, major fraction of them transforms into low spin state (S = 0) in the Gd sample. Hence, the Gd sample is microscopically heterogeneous with a mixture of ferromagnetic metallic and paramagnetic metallic clusters. Spin dependent scattering at the surface of ferromagnetic clusters may be the cause for the coexistence of magnetothermopower and magnetoresistance in this compound. A close correlation between the magnetoresistance and the magnetothermopower is reported.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach

Exploring water fluxes between hydrological model (HM) components is essential to assess and improve model realism. Many classical metrics for HM diagnosis rely solely on streamflow and hence provide limited insights into model performance across processes. This study applies an information theory measure known as “transfer entropy” (TE) to systematically quantify the transfer of information among major HM components. To test and demonstrate the benefits of TE, we use the Framework for Understanding Structural Errors (FUSE) model to mimic and compare four commonly used HM structures, VIC, PRMS, SACRAMENTO, and TOPMODEL, across 671 catchments spanning a variety of hydrologic regimes in the conterminous United States. We explore connections between HM components and catchment landscape characteristics (e.g., climate, topography, soil, and vegetation) and characterize their nonlinear associations using distance correlation and Spearman correlation coefficients. Our results indicate that while the information transferred from precipitation to runoff is similar across model structures (likely as a result of calibration), the information transferred among other components can vary significantly from a FUSE structure to another. We find that aridity, precipitation duration and frequency, snow fraction, mean elevation, forest area, and leaf area index are often significantly associated with TE between the main HM components. Here, we propose that the presence of meaningful nonlinear associations can be used to diagnose process representation in HMs. Our results highlight the necessity to enhance the conventional streamflow-only calibration approach for a more realistic representation of water dynamics in the models.

large-sample hydrology↗
DOE OSTI2026

Score-based deterministic density sampling

We propose a deterministic sampling framework using Score-Based Transport Modeling for sampling an unnormalized target density π given only its score ∇ log π. Our method approximates the Wasserstein gradient flow on KL($f_t$∥π) by learning the time-varying score ∇ log $f_t$ on the fly using score matching. While having the same marginal distribution as Langevin dynamics, our method produces smooth deterministic trajectories, resulting in monotone noise-free convergence. We prove that our method dissipates relative entropy at the same rate as the exact gradient flow, provided sufficient training. Numerical experiments validate our theoretical findings: our method converges at the optimal rate, has smooth trajectories, and is often more sample efficient than its stochastic counterpart. Experiments on high-dimensional image data show that our method produces high-quality generations in as few as 15 steps and exhibits natural exploratory behavior. The memory and runtime scale linearly in the sample size.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Vortex entropy and superconducting fluctuations in ultrathin underdoped Bi 2 Sr 2 CaCu 2 O 8+x superconductor

Vortices in superconductors can help identify emergent phenomena but certain fundamental aspects of vortices, such as their entropy, remain poorly understood. Here, we study the vortex entropy in underdoped Bi 2 Sr 2 CaCu 2 O 8+x by measuring both magneto-resistivity and Nernst effect on ultrathin flakes (≤2 unit-cell). We extract the London penetration depth from the magneto-transport measurements on samples with different doping levels. It reveals that the superfluid phase stiffness p s scales linearly with the superconducting transition temperature T c , down to the extremely underdoped case. On the same batch of ultrathin flakes, we measure the Nernst effect via on-chip thermometry. Together, we obtain the vortex entropy and find that it decays exponentially with T c or p s . We further analyze the Nernst signal above T c in the framework of Gaussian superconducting fluctuations. The combination of electrical and thermoelectric measurements in the two-dimensional limit provides fresh insight into high temperature superconductivity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

High-Throughput Combinatorial Development of High-Entropy Alloys For Light-Weight Structural Applications

The primary limitation of today’s lightweight structural alloys is that specific yield strengths (SYS) higher than 200MPa x cc/g (typical value for titanium alloys) are extremely difficult to achieve. This holds true especially at a cost lower than 5dollars/kg (typical value for magnesium alloys). Recently, high-entropy alloys (HEA) have shown promising SYS, yet the large composition space of HEA makes screening compositions complex and time-consuming. Over the course of this 2-year project we started from 150 billion compositions and reduced the number of potential low-density (<5g/cc), low-cost (<5dollars/kg) high-entropy alloy (LDHEA) candidates that are single-phase, disordered, solid-solution (SPSS) to a few thousand compositions. This was accomplished by means of machine learning to guide design for SPSS LDHEA based on a combination of recursive partitioning, an extensive, experimental HEA database compiled from 24 literature sources, and 91 calculated parameters serving as phenomenological selection rules. Machine learning shows an accuracy of 82% in identifying which compositions of a separate, smaller, experimental HEA database are SPSS HEA. Calculation of Phase Diagrams (CALPHAD) shows an accuracy of 71-77% for the alloys supported by the CALPHAD database, where 30% of the compiled HEA database is not supported by CALPHAD. In addition to machine learning, and CALPHAD, a third tool was developed to aid design of SPSS LDHEA. Phase diagrams were calculated by constructing the Gibbs-free energy convex hull based on easily accessible enthalpy and entropy terms. Surprisingly, accuracy was 78%. Pursuing these LDHEA candidates by high-throughput experimental methods resulted in SPSS LDHEA composed of transition metals (e.g. Cr, Mn, Fe, Ni, Cu) alloyed with Al, yet the high concentration of Al, necessary to bring the mass density below 5.0g/cc, makes these materials hard and brittle, body-centered-cubic (BCC) alloys. A related, yet multi-phase BCC alloy, based on Al-Cr-Fe-Ni, shows compressive strain >10% and specific compressive yield strength of 229 MPa x cc/g, yet does not show ductility in tensile tests due to cleavage. When replacing Cr in Al-Cr-Fe-based 4- and 5-element LDHEA with Mn, hardness drops 2x. Combined with compression test results, including those on the ternaries Al-Cr-Fe and Al-Mn-Fe suggest that Al-Mn-Fe-based LDHEA are still worth pursuing. These initial results only represent one compressive stress-strain curve per composition without any property optimization. As such, reproducibility needs to be followed by optimization to show their full potential. When including Li, Mg, and Zn, single-phase Li-Mg-Al-Ti-Zn LDHEA has been found with a specific ultimate compressive strength of 289MPa x cc/g. Al-Ti-Mn-Zn showed a specific ultimate compressive strength of 73MPa x cc/g. These initial results after hot isostatic pressing (HIP) of the ball-milled powders represent the lower end of what is possible, since no secondary processing (e.g. extrusion) has been performed to optimize strength and ductility. Compositions for multi-phase (e.g. dual-phase) LDHEA were identified largely by automated searches through CALPHAD databases, while screening for large face-centered-cubic (FCC) volume fractions, followed by experimental verification. This resulted in several new alloys. Li-Mg-Al-Mn-Fe and Mg-Mn-Fe-Co ball-milled powders upon HIP show specific ultimate compressive strengths of 198MPa x cc/g and 45MPa x cc/g, respectively. Several malleable quarternary Al-Zn-based alloys have been found upon arc/induction melting, yet with limited specific compressive yield strength (<75 MPa x cc/g). These initial results are all without any optimization for strength and/or ductility. High-throughput experimentation allowed us to triple the existing experimental HEA database as published in the past 10 years in less than 2 years which happened at a rate 10x higher than previous methods. Furthermore, we showed that high-throughput thin-film combinatorial methods can be used to get insight in isothermal phase diagram slices. Although it is straightforward to map hardness as a function of composition for sputtered, thin-film, compositional gradients by nano-indentation and compare the results to micro-indentation on bulk samples, the simultaneous impact of composition, roughness, film density, and microstructure on hardness requires monitoring all these properties as a function of location on the compositional gradient, including dissecting the impact of these 4 factors on the hardness map. These additional efforts impact throughput significantly. This work shows that a lot of progress has been made over the years in predicting phase formation that aids the discovery of new alloys, yet that a lot of work needs to be done to predict phases more accurately for LDHEA, whether done by CALPHAD or by other means. More importantly, more work needs to be done to predict mechanical properties of novel alloys, like yield strength, and ductility. Furthermore, this work shows that there is a need for the generation of an empirical alloy database covering strategic points in a multi-dimensional composition space to allow for faster and more accurate predictive interpolations to identify the oasis in the dessert more quickly. Finally, this work suggests that it is worth pursuing a ductile alloy with a SYS > 300 MPa x cc/g in a mass density range of 6-7 g/cc, since the chances for a single-phase or majority-phase FCC increase significantly. Today’s lightweight steels are in this density range.

36 MATERIALS SCIENCE↗
DOE OSTI2016

An Integrated Study on a Novel High Temperature High Entropy Alloy

This report summarizes our recent works of theoretical modeling, simulation, and experimental validation of the simulation results on the new refractory high entropy alloy (HEA) design and oxide doped refractory HEA research. The simulation of the stability and thermal dynamics simulation on potential thermal stable candidates were performed and related HEA with oxide doped samples were synthesized and characterized. The HEA ab initio density functional theory and molecular dynamics physical property simulation methods and experimental texture validation techniques development, achievements already reached, course work development, students and postdoc training, and future improvement research directions are briefly introduced.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Enhancement of magnetocaloric properties in (1 − x)La{sub 0.7}Ca{sub 0.2}Sr{sub 0.1}MnO{sub 3}/xLa{sub 0.7}Ca{sub 0.15}Sr{sub 0.15}MnO{sub 3} composite system (0 ≤ x ≤ 1)

Highlights: • The parent compounds were elaborated by solid-state reaction. • Continuous curves were investigated by the numerical calculations in (1 − x)La{sub 0.7}Ca{sub 0.2}Sr{sub 0.1}MnO{sub 3}/xLa{sub 0.7}Ca{sub 0.15}Sr{sub 0.15}MnO{sub 3} system. • The optimum magnetocaloric effect has been found for the composition with x = 0.45. - Abstract: The present work reveals an especial agreement between the experimental results and the continuous curves investigated by the numerical calculations in (1 − x)La{sub 0.7}Ca{sub 0.2}Sr{sub 0.1}MnO{sub 3}/xLa{sub 0.7}Ca{sub 0.15}Sr{sub 0.15}MnO{sub 3} composite system. Moreover, the ability to adjust the magnetic entropy change in a large temperature range is revealed to be possible by the mixture of both individual phases La{sub 0.7}Ca{sub 0.2}Sr{sub 0.1}MnO{sub 3} and La{sub 0.7}Ca{sub 0.15}Sr{sub 0.15}MnO{sub 3}. Our samples have been synthesised using the solid state method. Rietveld refinements of the X-ray diffraction patterns show that both samples are single phase. The Curie temperature T{sub C} is found to be 308 K and 338 K for La{sub 0.7}Ca{sub 0.2}Sr{sub 0.1}MnO{sub 3} and La{sub 0.7}Ca{sub 0.15}Sr{sub 0.15}MnO{sub 3}, respectively. Moreover, the magnetic entropy change and the Relative Cooling Power (RCP) values were estimated. We have shown that the optimum magnetocaloric effect at μ{sub 0}H = 2T has been found for the composition with x = 0.45, which exhibits a table-like ΔS curve with a peak value slightly lowered and strongly expanded when comparing with the parent compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Magnetic and magnetocaloric properties of La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}Mn{sub 1−x}Fe{sub x}O{sub 3} (0≤x≤0.3) manganites

The La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}Mn{sub 1−x}Fe{sub x}O{sub 3} (x=0, 0.1, 0.2 and 0.3) samples have been elaborated by the solid-state reaction method. X-ray powder diffraction shows that all the samples crystallize in a rhombohedric phase with R3{sup ¯}c space group. The variation of magnetization as a function of temperature and applied magnetic field was carried out. The samples for x=0 and 0.1 exhibit a FM–PM transition at the Curie temperature T{sub C}, however, for x=0.2 and 0.3 exhibit an AFM–PM one at the Neel temperature T{sub N}, when the temperature increases. A magneto-caloric effect has been calculated in terms of isothermal magnetic entropy change. A large magneto-caloric effect has been observed, the maximum entropy change, |ΔS{sub M}{sup max}|, reaches the highest value of 3.28 J/kgK under a magnetic field change of 5 T with an RCP value of 220 J/kg for La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}MnO{sub 3} composition, which will be an interesting compound for application materials working as magnetic refrigerants near room temperature. - Graphical abstract: Magnetic entropy change versus temperature and applied magnetic field for x=0.1 (a) and RCP versus applied magnetic field for x=0, 0.1 (b). - Highlights: • The La{sub 0.6}Pr{sub 0.1}Sr{sub 0.3}Mn{sub 1−x}Fe{sub x}O{sub 3} (0≤x≤0.3) polycrystalline samples were prepared by the solid state reaction method. • Crystalline and magnetic structures were investigated using DRX and magnetization measurements. • The magnetocaloric (MC) effect was estimated versus magnetic field and temperatures. • Compounds with x=0, 0.1 exhibit great potential for magnetic refrigeration at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Critical behavior and magnetocaloric effect of Pr{sub 1−x}Ca{sub x}MnO{sub 3}

The critical behavior of Pr{sub 1−x}Ca{sub x}MnO{sub 3} samples with x = 0.25, 0.27, and 0.29 has been investigated. Detailed analyses of magnetic-field dependences of magnetization at temperatures around the paramagnetic-ferromagnetic transition, M(H, T), reveal that the samples undergo a second-order magnetic phase transition. The Arrott plot method predicts the values of critical parameters to be T{sub C} ≈ 118 K, β = 0.351 ± 0.003, γ = 1.372 ± 0.002, and δ = 4.90 ± 0.02 for x = 0.25; T{sub C} ≈ 116 K, β = 0.362 ± 0.002, γ = 1.132 ± 0.004, and δ = 4.09 ± 0.03 for x = 0.27; and T{sub C} ≈ 110 K, β = 0.521 ± 0.002, γ = 0.912 ± 0.005, and δ = 2.71 ± 0.02 for x = 0.29. The values of β = 0.351 (for x = 0.25) and β = 0.362 (for x = 0.27) are close to the value β = 0.365 expected for the 3D Heisenberg model, proving an existence of short-range ferromagnetic interactions in these samples. A slight increase in Ca-doping content (x = 0.29) leads to the shift of the β value (=0.521) towards that of the mean-field theory (with β = 0.5) characteristic of long-range ferromagnetic interactions. The samples also exhibit a magnetocaloric effect: around T{sub C} of Pr{sub 1−x}Ca{sub x}MnO{sub 3} compounds, magnetic-entropy change reaches the maximum values of about 5.0, 4.1, and 2.5 J kg{sup −1} K{sup −1} for x = 0.25, 0.27, and 0.29, respectively, under an applied-field change of 50 kOe. Magnetic-field dependences of the maximum magnetic-entropy change (ΔS{sub max}) obey a power law |ΔS{sub max}(H)| ∝ H{sup n}, where exponent values n = 0.68–0.74 are close to those obtained from the theoretical relation n = 1 + (β − 1)/(β + γ)

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2000

Qualitative Discovery in Medical Databases

Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that: (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.

Maluf, David A.↗
DOE OSTI2021

In situ neutron diffraction study of fatigue behavior of CrFeCoNiMo 0.2 high entropy alloy

In situ neutron diffraction measurement was applied to study the low-cycle fatigue behavior of CrFeCoNiMo 0.2 high entropy alloy. A two-step stress-controlled fatigue test with a stress range of 100–460 and 20–500 MPa was employed. The as-cast sample was cycled 129,000 and 61,000 times, respectively, before fracture. The evolution of the lattice strain and peak intensity demonstrates that the main deformation mechanism is dislocation slip, while no evidence of stacking fault was found. A three-stage ratcheting was clearly observed. Under the stress-controlled mode, the experimentally determined dislocation density increases linearly with the ratcheting strain. Here, the increase of the dislocation density results in a decay of the ratcheting strain rate, which stabilizes the structure and is beneficial for fatigue resistance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Manganese-rich high entropy oxides for lithium-ion batteries:materials design approaches to address voltage fade

Lithium- and manganese-rich oxides are of interest as lithium-ion battery cathode materials as Mn is earth abundant, low cost, and can deliver high capacity. Herein, a high entropy strategy was used to prepare Mn rich high entropy oxide (HEO) materials by including four additional metals (Ni, Co, Fe and Al) in the compositions using a mild co-precipitation method. Two HEOs (Li x Ni 0.1 Mn 0.6 Co 0.1 Al 0.1 Fe 0.1 O y , where x = 1.5 for HEO-L and x = 0.5 for HEO-H) with layered and spinel-layered hybrid structures were investigated where the morphology, elemental composition, structure, atomic level phase distribution, and electrochemistry were determined. The HEO-L samples involve a Li 2 TMO 3 layered structure with ~39% stacking faults. HEO-H is a hybrid structure comprised of 80 wt% spinel and 20 wt% LiMO 2 layered structure. The high entropy manganese-rich HEO-L showed higher capacity and 93% retention of the average voltage after 100 cycles while HEO-H showed higher capacity retention and near 100% average voltage retention. Finally, operando X-ray absorption spectroscopy revealed that the Ni, Co, and Mn are redox active in both materials while the Fe center remains at the Fe 3+ oxidation state throughout cycling, where the changes in the oxidation states for both materials during discharge were consistent with the delivered electrochemical capacity rationalizing the observed electrochemistry.

25 ENERGY STORAGE↗