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DOE OSTI2025

Polyanion-type iron-based sulfate cathode materials: From fundamental research to industrialization

Sodium-ion batteries (SIBs) with the merits of abundant sodium resources and low cost are considered significant supplements to lithium-ion batteries in large-scale energy storage systems. Among the discovered cathode materials for SIBs, iron-based sulfate materials have attracted widespread attention due to their high operating voltage, superior structural stability, and high safety. Here, in this review, we summarize the research achievements of iron-based sulfate cathode materials in fundamental research, as well as provide a comprehensive overview of its industrialization progress for the first time. Meanwhile, effective modification strategies (including morphology design, hybridization with carbon materials, and construction of heterostructure) and further research directions to boost the sodium storage performance of iron-based sulfate cathode materials are also proposed. This review can provide valuable guidance for developing high-performance iron-based sulfate cathode materials for practical application.

cathode materials↗

DOE OSTI2022

Cooperative fault management for resilient integration of renewable energy

Cooperative fault management (CFM) is designed herein to control different types of renewable energy resources cooperatively during electrical faults. This paper studies systems with a high penetration of photovoltaic (PV) energy and wind energy. First, CFM leverages power converters of PV farms to boost the ride-through capability of nearby doubly-fed induction generators (DFIGs). By controlling PV farms’ output voltages to change smoothly during both fault initiation and fault clearance, the widely used crowbar in DFIGs is less likely to be activated. Crowbar activation adversely makes DFIGs lose controllability and absorb reactive power. The second contribution is the development of a software-defined CFM controller and a controller in-the-loop demonstration of the real-time performance of this optimization-based CFM. CFM capitalizes on distributed optimization formulation to enable flexibility, plug-and-play, and privacy-preserving. Computation time, however, is a major concern for optimization-based dynamics control. Here, real-time controller-in-the-loop simulation results show optimization-based CFM can output reference values around 60 ms and is quick enough for dynamic control.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Metal hydride composition-derived parameters as machine learning features for material design and H 2 storage

Though hydrogen is a promising energy carrier for a green future, many challenges persist. One is the difficulty in engineering storage solutions, with metal hydrides being a leading contender among solid-state strategies. To facilitate efficient searching of candidate materials, ridge regression, simple decision trees, random forest ensembles, and gradient boosting ensembles were employed to predict the energy of formation, with the random forest ensemble resulting in the lowest test set error. First, two public databases, Materials Project and HydPark, were searched for metal hydrides. Feature engineering was performed before the models were developed, resulting in electronegativity, density, atomic density, d-character, f-character, band gap, hydrogen weight fraction, magnetization, temperature, and pressure being retained. The models were then benchmarked by the lowest test error before a random forest ensemble was used to populate entries missing energy of formation. Furthermore, all were then scored by hydrogen storage capacity and energy of formation suitability. Readily available features including several derived from only the chemical formula which were found to be highly predictive. and so are promising for high-throughput screening of arbitrary novel hydride formulations and blends for thermodynamic feasibility.

25 ENERGY STORAGE↗

DOE OSTI2023

Enhanced thermal reliability and performance of calcium chloride hexahydrate phase change material using cellulose nanofibril and graphene nanoplatelet

In recent years, thermal energy storage (TES) has gained attention for its role in enhancing renewable energy solutions and sustainable energy consumption. The usage of strontium chloride hexahydrate (SCH), graphene nanoplatelet (GNP), and cellulose nanofibril (CNF) additives were investigated to enhance the performance of calcium chloride hexahydrate (CCH) based on the melting/solidification behavior for TES applications. In this work, we develop a promising phase-change-material (PCM) formulation by introducing these additives that reduce supercooling, improve the thermal conductivity and stabilizing the energy storage capacity of CCH. Rheological characterizations demonstrated that the addition of 1 wt% of CNF into CCH produced the required improvement in viscosity and boosted solid-like rheological behavior. Structural characterizations show a physical mixing of the materials within the PCM composites. Our observations show that the amphiphilicity of CNF enables the surface attachment to GNP via hydrophobic interactions providing effective dispersion of GNP throughout the PCM composite. The addition of a nucleating agent, SCH decreased the degree of supercooling of ~20 g of CCH from >20 °C to 3 °C at a cooling rate of 5 °C/min. Thermal characterization showed the resulting PCM composite has a latent heat of melting of 186 Jg -1 , phase change temperature of 32 °C, and stable thermal properties after being subjected to 70 melt-freeze cycles. Adding CNF and GNP to pure CCH increased its thermal conductivity by 76 %. The high thermal conductivity of GNP and its effective dispersion by CNF is responsible for this enhancement. The study highlights the use of biodegradable nanocellulose for the preparation of sustainable PCM composites with improved performance. In conclusion, these PCM composites are scalable, they have potential to increase energy efficiency and revolutionalize the heating/cooling applications in buildings and other TES systems.

25 ENERGY STORAGE↗

DOE OSTI2026

Direct regeneration of degraded LiFePO4 cathodes via a separator-enabled prelithiation strategy

A persistent challenge in lithium-ion batteries is the loss of active lithium due to the solid electrolyte interphase (SEI) formation and associated side reactions. While prelithiation employing lithium replenishment separator (LRS) has been proven effective in compensating for lithium loss, previous studies have largely been accompanied by gas evolution or solid residue formation during the prelithiation process. To surmount this challenge, we present a LRS based on 4-fluoro-1,2-dihydroxybenzene lithium salt (LiDF), capable of mitigating lithium loss while producing decomposition products that integrate directly into the electrolyte as functional additives which can assist with the stability of the SEI, free from gas or solid formation, thus establishing a sustainable and environmentally benign strategy for lithium compensation. Incorporation of the LRS enables the pristine LiFePO4||graphite (Gr) full cell to achieve 10.8% higher capacity than the cell with a polypropylene separator (PPS) after 200 cycles at 0.5C. Remarkably, the degraded LiFePO4 (D-LFP)||Gr full cell with the LRS exhibits a 135.8% capacity improvement over the PPS-based cell after 500 cycles. These findings establish the LRS as a powerful approach for both boosting high-performance lithium-ion batteries and recovering the capacity of degraded batteries.

Tao, Fujun↗

DOE OSTI2021

Ultrafast charging of energy-dense lithium-ion batteries for urban air mobility

Urban air mobility (UAM) demands batteries with high energy density, long cycle life, and fast rechargeability. In this paper, we demonstrate an energy-dense lithium-ion battery (LiB) with ultralong cycle life under ultrafast charging. By using the asymmetric temperature modulation (ATM) method, i.e., charging at an elevated temperature and discharging around the ambient temperature, it is experimentally shown that the 209 Wh/kg LiB is charged to 88% state of charge (SOC) in ~5 min under UAM cycling while retaining 97.7% capacity after 1,000 cycles. Moreover, an experimentally validated electrochemical-thermal (ECT) model is developed to elucidate the fast charging process and the degradation mode of UAM batteries, quantitatively capturing lithium plating during fast charging. We find that the LiBs for UAM applications are most prone to lithium plating due to their higher initial SOC required as the reserve for safety; nevertheless, the ATM method is effective in minimizing or preventing lithium plating in the high SOC range of 30-90%. In addition to slowing down capacity fade, the ATM method also raises the usable capacity by 10%, which boosts the battery energy density and ensures the battery to perform full UAM cycles even at the end of life.

25 ENERGY STORAGE↗

DOE OSTI2021

Assessment of sulfur trioxide formation due to enhanced interaction of nitrogen oxides and sulfur oxides in pressurized oxy-combustion

Pressurized oxy-combustion is emerging to be one of the best technologies for significantly decreasing the energy penalty for CO 2 capture in coal-fired power plants. However, the higher pressure boosts the formation of acid gases, including SO 3 and NO 2 , which could increase the risk of corrosion. The synergistic promotion of SO 3 and NO 2 formation in pressurized oxy-combustion is kinetically evaluated under representative conditions (1 ~ 30 atm, 600 ~ 1200 °C, NO/SO 2 = 0.1 ~ 5). We begin with a comprehensive mechanism (72 species and 428 reactions), covering nitrogen and sulfur chemistry, relying on GRI-Mech 3.0. This analysis shows that the interaction of SO X and NO X enhances the conversion rates of SO 2 → SO 3 , and this effect is more apparent at elevated pressures and lower temperatures. Mechanism analyses indicate that at elevated pressures, the formation pathways of SO 3 through HOSO 2 + O 2 = SO 3 + HO 2 , and NO 2 through HO 2 + NO = NO 2 + OH, are promoted due to the strong interaction between SO X and NO X . The intermediate between these two reactions is SO 2 + OH + M = HOSO 2 + M, resulting in a strong cycle, that can be expressed by the global reaction NO + SO 2 + O 2 = NO 2 + SO 3 . Finally, a nine-step reduced chemistry is developed and validated to accurately predict the formation of SO 3 in the post-flame region at elevated pressures.

42 ENGINEERING↗

DOE OSTI2021

Production, fuel properties and combustion testing of an iso-olefins blendstock for modern vehicles

With the increasing pressure to decarbonize the transportation sector, exploring strategies that can reduce emissions from light-duty vehicles (LDV) has become critical. Bioblendstocks that allow for higher engine efficiency and fuel economy could complement vehicle electrification and help reach carbon neutrality by 2050. In this context, the potential of a mixture of iso-olefins as a bioblendstock was investigated for multimode boosted spark-ignition (SI)/advanced compression ignition (ACI) engine operation designed to achieve higher overall vehicle fuel economy. By establishing the relationship between the molecular structure of iso-olefins and research octane number (RON), octane sensitivity (S) (i.e., the difference between RON and motor octane number [MON]), and phi-sensitivity a dimethyl-hexenes rich olefins mixture (DMHROM) was identified as a preferred blendstock for SI/ACI combustion engines. Here, a pathway for DMHROM production from biomass-derived ethanol was developed and scaled up. More than 1 gallon of DMHROM blendstock was produced for fuel properties assessment including engine testing. Measurements in a Cooperative Fuel Research Engine showed that the DMHROM blendstock possesses a RON of 94 and S of 13.5, and blends synergistically. Rapid compression machine tests coupled with single-cylinder gasoline direct injection engine measurements demonstrated the 20 vol.% DMHROM blend has higher phi-sensitivity than an olefin-free gasoline base fuel and a typical California Reformulated Gasoline Blendstock for Oxygenate Blending (CARBOB) gasoline fuel. These results demonstrate the potential of DMHROM for improving gasoline fuel performance and quality for operation under ACI conditions. The effectiveness of the aftertreatment system in mitigating emissions was verified and showed that the pure DMHROM blendstock and 20 vol.% blend would not increase non-methane organic gases, NO x , and carbon monoxide (CO) emissions. The DMHROM blendstock was found to slightly decrease sooting tendency when added to a gasoline-base fuel (i.e., ~6% reduction at 20 vol.% blending level). Oxidation stability and lubricant compatibility were both confirmed for the 20 vol.% blend. Overall, these results demonstrate that dimethyl-hexenes have potential for improving engine efficiency and fuel economy while meeting emissions regulations and ASTM specifications for gasoline fuel.

09 BIOMASS FUELS↗

DOE OSTI2021

The intrinsic value of bioblendstocks: Prenol as a case study

Decarbonizing the transportation sector is likely to require both electrification and increased incorporation of biofuels and/or bioblendstocks. While the social and environmental benefits of bioblendstocks are well understood, their real value for the fuel producers has not been established. As such, this work considers prenol as a bioblendstock case study to identify sources of intrinsic value to fuel blenders by studying the properties of binary mixtures with gasoline components. The considered refinery blendstocks were samples of full range naphthas from the distillation, fluidized catalytic cracking, isomerization, alkylation, and reforming units. Octane numbers, Reid vapor pressure, distillation curves, and sulfur content were evaluated. Our results indicate the need for adjusting the formulation of the base fuel, depending on the interplay among the properties of the bioblendstock and those of the base fuel. Prenol increased research octane number (RON) and octane sensitivity (OS) of the base fuel, by up to 25 and 10 octane numbers, respectively. Additionally, 10 vol% prenol reduced RVP up to 2.2 psi, for the more volatile blendstock. Thus, considering prenol as a low volatility, RON/OS boosting bioblendstock, the composition of the preferred base fuel was proposed as containing reduced olefins and aromatics, and increase light fractions. The potential impact of this new gasoline formulation on refining processes and products gives rise to direct sources of value to the refiners, such as exporting products to the chemicals market, increasing the value of intermediate refinery streams, decreasing operating severity of certain refinery units, and broadening of the product suite.

09 BIOMASS FUELS↗

DOE OSTI2022

Multi-stage heat release of multi-component fuels: Insights and implications for advanced engine operation

Multi-stage heat release (MSHR) is a unique phenomenon typically seen in lean/diluted and low- to intermediate-temperature combustion. Despite its relevance to advanced engine operation, the MSHR of multi-component fuels has barely been quantified. This study aims to characterize the MSHR of multi-component fuels in a rapid compression machine (RCM) at conditions representative of advanced combustion engines. New experimental data are first reported in the RCM at an equivalence ratio of 0.4, pressure of 60 bar and temperatures from 702 to 795 K for a research grade, multi-component gasoline surrogate, termed PACE-20. Here, experiments confirm the existence of MSHR for PACE-20 at all temperatures, and reveal the strong inhibiting effect of temperature on MSHR, where increasing temperature inhibits MSHR by suppressing first stage heat release and promoting second and third stage heat release. A detailed chemical kinetic model is also adopted to model the experiments, with good agreement observed. The response of MSHR characteristics to changes in different engine operating parameters (i.e., temperature, pressure, equivalence ratio and CO 2 dilution level) and fuel compositions (i.e., mole fraction of n-pentane, n-heptane, isooctane, cyclopentane, 1-hexene, ethanol and aromatics in PACE-20) is further evaluated via statistical analysis coupling quasi-random sampling, extensive computer experiments and high-dimensional model representation. The change in MSHR is characterized through 8 quantities of interest (QOIs), i.e., duration and extent of each heat release stage, and their standard deviations. The analysis highlights the dependence of the QOIs on the individual parameters as well as their interplays, with temperature exhibiting the strongest impact among all parameters. Furthermore, it is demonstrated that if MSHR is to be facilitated and combustion phasing is to be extended to enable engine operation at higher compression ratios, it is recommended to operate the engine at low intake temperatures, boosted intake pressures and high CO 2 dilutions (i.e., high EGR levels) with gasoline fuels containing more n-pentane and less aromatics.

42 ENGINEERING↗

DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗

DOE OSTI2024

Microwave-Assisted dry reforming of toluene as a model tar compound using low-cost iron catalyst for syngas clean-up

Gasification of waste feedstock such as biomass, waste plastics suffers from high tar yields during hydrogen-rich syngas production. The presence of tars result in lower quality syngas, lower syngas yields, reactor blockage, reactor down time, and costly maintenance. Therefore, removal of tar from syngas during gasification is essential. Catalytic reforming of tars via in-situ syngas cleanup is an effective way of mitigating tars. This work explores the possibility of microwave-assisted catalytic dry reforming of tars for syngas production. Further, due to its chemical complexity, toluene, which is one of the main tar constituents, could be used as a model tar compound. Toluene dry reforming was studied using the Fe/Al 2 O 3 catalyst under CO 2 under the temperature range of 400–700 °C. The toluene reforming reaction was conducted using microwave and conventional thermal reactors. Under microwave irradiation, CO 2 and toluene conversions are boosted to 80% at 500 °C. Hydrogen and carbon monoxide yields were approximately five times and ten times higher in the microwave reactor at 500 °C, respectively, compared to the productions obtained in the conventional fixed-bed reactor at 700 °C. Filamentous carbon was also produced as a valuable side product to improve the economy of this process and such value-added carbon was only observed in the microwave reactor. Three reaction pathways were observed during microwave reaction: the toluene decomposition produces an initial hydrogen and carbon deposit on the catalyst; the formation of methane and benzene suggests toluene hydrodemethylation as a secondary reaction; and toluene hydrogenolysis forms light alkanes such as methane, and through reforming reaction under CO 2 to syngas.

10 SYNTHETIC FUELS↗

DOE OSTI2025

"Hidden" hydrothermal technical potential & technoeconomics: Revealing permeability & fluids with more data

Historical hydrothermal estimates have largely relied on temperature or heat flow estimates ignoring the need for natural flowing fluids. More accurate hydrothermal estimates require some indication of permeability and fluids that naturally exist in the subsurface. This paper describes a novel approach that includes proxies of permeability and fluids in hydrothermal estimates by leveraging the relatively data-rich Great Basin. Specifically, nameplate capacities (megawatts) of operating geothermal plants, negative (0 megawatt) locations and 48 geophysical and geologic features are used to used in eXtreme Gradient Boosting (XGBoost) regression to make hydrothermal capacity predictions. Additionally, this work inputs the XGBoost-based hydrothermal predictions into the Renewable Energy Potential (reV) model to quantify technical capacity, its uncertainty and techno-economics. Compared to historical hydrothermal estimates, these predictions adhere to the 37 operating geothermal plants and negative locations. We present a method for subsampling the negative sites to bring the labels into balance that uses the geologic domain knowledge to proportionally represent negatives. Overall, the distributions of the hydrothermal technical capacity and the site levelized cost of energy are respectively much tighter, lower and more accurate than the previous estimates for the Great Basin, as they include geological and geophysical surrogates for permeability and fluids. Percentile (50th and 90th, median and high estimate, respectively) models provide bookends for these metrics.

13 HYDRO ENERGY↗

DOE OSTI2022

Deep learning-accelerated 3D carbon storage reservoir pressure forecasting based on data assimilation using surface displacement from InSAR

Fast forecasting of the reservoir pressure distribution during geologic carbon storage (GCS) by assimilating monitoring data is a challenging problem. Due to high drilling cost, GCS projects usually have spatially sparse measurements from few wells, leading to high uncertainties in reservoir pressure prediction. To address this challenge, we use low-cost Interferometric Synthetic-Aperture Radar (InSAR) data as monitoring data to infer reservoir pressure build up. We develop a deep learning-accelerated workflow to assimilate surface displacement maps interpreted from InSAR and to forecast dynamic reservoir pressure. Employing an Ensemble Smoother Multiple Data Assimilation (ES-MDA) framework, the workflow updates three-dimensional (3D) geologic properties and predicts reservoir pressure with quantified uncertainties. We use a synthetic commercial-scale GCS model with bimodally distributed permeability and porosity to demonstrate the efficacy of the workflow. A two-step CNN-PCA approach is employed to parameterize the bimodal fields. The computational efficiency of the workflow is boosted by two residual U-Net based surrogate models for surface displacement and reservoir pressure predictions, respectively. The workflow can complete data assimilation and reservoir pressure forecasting in half an hour on a personal computer.

25 ENERGY STORAGE↗

DOE OSTI2025

Deep-learning-enhanced assessment of wellbore barrier effectiveness in geologic storage systems with intermediate aquifers

For geologic systems where carbon dioxide (CO 2 ) is injected underground, existing wells represent potential pathways for fluid migration. Here, this study introduces a novel deep learning model to quantify the likelihood and potential magnitude of fluid migration through wellbores at sites with intermediate aquifers or thief zones between the injection units and underground drinking water sources. Synthetic datasets, generated using reservoir simulations, captured a wide range of subsurface conditions, well attributes, operational parameters, and fluid migration scenarios. Among the regression models developed to predict brine and CO 2 leakage rates and CO 2 saturations along leaky wellbores, convolutional neural network (CNN) outperformed both Light Gradient Boosting Machine and deep neural network. Additionally, a CNN-based classification model was created to predict whether brine and CO 2 would leak along a wellbore, further improving performance over regression alone. The best models were integrated into the National Risk Assessment Partnership Open-source Integrated Assessment Model for rapid, stochastic assessment of storage system containment and leakage risks. A case study demonstrated the model’s ability to simulate fluid migration through existing wells with multiple intermediate aquifers. This computationally efficient wellbore model offers value in support of site performance evaluation and risk-informed decision making by stakeholders.

CO2 leakage↗

DOE OSTI2025

Machine-learning and first-principles investigation of lightweight medium-entropy alloys for hydrogen-storage applications

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of medium-entripy alloys (easily extendable to multi-principal-element alloys), such as Ti–Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co–Ni-X (X = Al, Mg, V). Ti–Nb–Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40–50 %. We attributed results in molybdenum-rich alloys to slow hydrogen kinetics, as validated by our pressure-composition-temperature (PCT) isotherm experiments on pure Ti and Ti 5 Mo 95 alloys. Density functional theory (DFT) and molecular dynamics (MD) simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. Here, to aid material selection, we present two periodic tables illustrating elemental effects on (a) H 2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

08 HYDROGEN↗

DOE OSTI2026

Facile synthesis of Co(OH)2 nanoneedle arrays grown on stainless steel for industrial electrochemical oxygen evolution reaction

The electrochemical oxygen evolution reaction (OER) is a critical half-reaction in a variety of energy conversion and storage applications, however, OER suffers from sluggish kinetics due to the four proton-electron transfer processes. To remedy this, interfacial engineering proves an effective strategy to design high active OER catalysts. Herein, we report a facile synthesis of Co(OH)2 nanoneedle (Co-NN) arrays grown on stainless steel (SS) via a one-step hydrothermal reaction, and the resultant impressive OER performance. Particularly, the Co-NN/SS needs 267, 307, and 576 mV overpotentials to reach 10, 100, and 1000 mA cm−2 in 1 M KOH & room temperature, and the overpotentials drop to 199, 257, and 283 mV to achieve the same current densities in 30 wt% KOH & 80 °C. Additionally, an alkaline water electrolysis (AWE) cell coupled with Co-NN/SS anode and PtRu/NiMo cathode only requires 1.65 and 1.73 V iR-free cell voltage to reach 1.0 and 2.0 A cm−2, respectively. The sterling OER performance could be attributed to four possible reasons, the Fe in SS surface tailoring the electronic properties of Co(OH)2, the mixed metal oxides on SS surface accelerating surface reconstruction of Co(OH)2 nanoneedles into CoOOH active species, a Ni-rich surface layer formation cooperatively boosting the OER activity, and a local electric field effect concentrating reactants on the tip surface and accelerating the mass transfer. This work provides a facile approach for synthesizing highly efficient OER catalysts for industrial applications by interfacial engineering.

Lyu, Xiang [ORNL] (ORCID:0000000208673248)↗

DOE OSTI2021

Five-Year Longitudinal Analysis of Patient-Reported Outcomes and Cosmesis in a Randomized Trial of Conventionally Fractionated Versus Hypofractionated Whole-Breast Irradiation

There are limited prospective data on predictors of patient-reported outcomes (PROs) after whole-breast irradiation (WBI) plus a boost. We sought to characterize longitudinal PROs and cosmesis in a randomized trial comparing conventionally fractionated (CF) versus hypofractionated (HF) WBI.

62 RADIOLOGY AND NUCLEAR MEDICINE↗