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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Deployment of neural-network-based neutron microscopic cross sections in the Griffin reactor physics application

The capability to utilize neural networks to predict macroscopic and microscopic cross section parametric spaces has been developed for the Griffin reactor physics application. The LibTorch interface enables Griffin's MOOSE-based materials to interact with LibTorch-trained models, allowing for the evaluation of complex macroscopic or microscopic cross section spaces, which are then used to evaluate the neutronic properties of the Griffin finite element model. This study benchmarks traditional ISOXML-formatted tabulation libraries against neural network-based models for 279 nuclides on 20,160 grid points for zero-dimensional and two-dimensional reactor models. Benchmark metrics include the fundamental mode eigenvalue, fission and absorption rates, and various temperature coefficients of reactivity (isothermal, fuel, and moderator). From the perspective of storage space, the complete set of LibTorch models uses 11 MB on disk, compared to the 10 GB for the ISOXML multigroup library that covers the same grid space. For the two-dimensional performance case considered in Griffin, the Torch model uses 97% less RAM than the reference ISOXML dataset while runtime increases by a factor of 3 when using the LibTorch model compared to the ISOXML dataset with multi-linear interpolation. The LibTorch model consistently yields errors within 0.01% for most analyzed quantities except for the temperature coefficients of reactivity where the maximum discrepancies are up to 0.3 $\frac{pcm}{K}$. Due to the neural network attempting to best predict quantities with no regard for a positive or negative bias for any given quantity, predictions may experience random fluctuations, resulting in both positive and negative errors. Future work will entail both depletion and coupled transient analysis to determine the predictive capabilities of Griffin with neural network-based cross sections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Weighted Mobility

Engineering semiconductor devices requires an understanding of charge carrier mobility. Typically, mobilities are estimated using Hall effect and electrical resistivity meausrements, which are are routinely performed at room temperature and below, in materials with mobilities greater than 1 cm 2 V -1 s -1 . With the availability of combined Seebeck coefficient and electrical resistivity measurement systems, it is now easy to measure the weighted mobility (electron mobility weighted by the density of electronic states). A simple method to calculate the weighted mobility from Seebeck coefficient and electrical resistivity measurements is introduced, which gives good results at room temperature and above, and for mobilities as low as 10 -3 cm 2 V -1 s -1 , <!--FIGURE--> μ w = 331 c m 2 V s ( m Ω c m ρ ) ( T 300 K ) - 3 / 2 [ e x p [ | S | k B / e - 2 ] 1 + exp [ - 5 ( | S | k B / e - 1 ) ] + 3 π 2 | S | k B / e 1 + exp [ 5 ( | S | k B / e - 1 ) ] ] In this paper, μ w is the weighted mobility, ρ is the electrical resistivity measured in mΩ cm, T is the absolute temperature in K, S is the Seebeck coefficient, and k B /e = 86.3 µV K -1 . Weighted mobility analysis can elucidate the electronic structure and scattering mechanisms in materials and is particularly helpful in understanding and optimizing thermoelectric systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Benchmarking Cylindrical Blast Wave Theory Against the OSIRIS-REx Sample Return Capsule Reentry

Weak shock theory based on cylindrical blast waves has been used to interpret meteor infrasound, but it has not been systematically benchmarked against a non-ablating hypersonic source with independently known parameters. The objective of this study is not to propose a new theoretical framework, but to evaluate the operational validity of the existing suite of blast radius formulations against a high-fidelity ground truth dataset. The OSIRIS-REx Sample Return Capsule reentry on 24 September 2023 provides such a benchmark because the capsule geometry, trajectory, and infrasound emission points are constrained from mission data and ray tracing, reducing source-side uncertainty associated with ablation. Using observations from 39 infrasound stations, this benchmarking study evaluates six published blast radius (${R}_{0}$ ) formulations and three weak-shock transition coefficients ( C ) within a stratified atmospheric propagation model to predict signal period and peak overpressure. The benchmarking identifies the Sakurai formulation as the best-performing formulation for non-ablating bodies, with the Jones/Plooster formulation performing comparably when a physically appropriate C is adopted. Sakurai and Jones/Plooster yield linear-period median absolute percentage residuals of 9% and 11%, respectively. The period predictions show only weak sensitivity to C at these propagation distances. The Mach-diameter approximation commonly used in meteor studies overestimates ${R}_{0}$ by more than a factor of 3 in the absence of ablation. Finally, these results establish a performance baseline for applying cylindrical blast wave theory to effectively non-ablating hypersonic bodies and demonstrate that the signal period is a robust observable for constraining ${R}_{0}$.

atmospheric entry↗
DOE OSTI2023

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. In conclusion, these results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Screening green solvents for multilayer plastic film recycling processes

Multilayer (ML) plastic films are essential packaging materials that help protect products from diverse external factors; however, only 5% of all ML films are recycled in the United States. Solvent-based technologies are a promising alternative for recycling ML films because they enable recovery of constituent polymer resins. For example, the Solvent Targeted Recovery and Precipitation (STRAPTM) process sequentially dissolves and separates polymer components using a series of targeted solvent washes. A crucial design aspect of this process is the impact of selected solvents on human health and on the environment. Here, this work introduces a computational framework that integrates molecular modeling, process modeling, techno-economic analysis (TEA), and life-cycle analysis (LCA) to quickly screen green solvents for solvent-based ML recycling processes. Initial screening for solvents based on selectivity is performed by estimating temperature-dependent solubilities using molecular-scale models. Subsequent screening uses basic estimates of energy use and octanol-water partition coefficients (logP) as key measures of health, safety, and environmental hazards. Detailed process modeling, TEA, and LCA are used on a reduced set of promising solvents identified in early screening steps to more accurately determine how solvent selection and associated operating conditions impact overall economics and environmental impacts. The framework is used for the identification of green solvents (from a database of 1,000 solvents) that separate an industrial ML film composed of polyethylene (PE), ethylene vinyl alcohol (EVOH), and polyethylene terephthalate (PET). Our analysis shows the effectiveness of the framework and reveals fundamental trade-offs between solvent greenness, solubility, and economics. Our work emphasizes the importance of taking a holistic systems view during solvent design and aims to inform the development of new processes for ML film recycling and the identification of new ML films that are easier to recycle.

economics↗
DOE OSTI2022

Effects of Supercritical CO 2 Injection on the Shale Pore Structures and Mass Transport Rates

Characterizing the pore structures and transport properties of low-permeability shales is critical for evaluating these formations as potential seals or storage sites for geological CO 2 sequestration. Here, we use low-pressure gas adsorption in conjunction with nuclear magnetic resonance (NMR) to characterize the pore-size distribution of shales before and after injection of supercritical CO 2 . Nitrogen gas was used as the detecting phase for the adsorption experiments and pentane liquid was used for the NMR experiments. We also performed time-resolved NMR and gravimetric microbalance measurements to observe mass transport during desaturation. We use these data to estimate the self-diffusion coefficient of pentane and changes in the saturation state of the pore network. We analyzed samples with a range of compositions from the Wolfcamp shale before and after exposure to supercritical CO 2 for 3 days. Integrating the gas adsorption and NMR data shows how supercritical CO 2 injection alters the pore-size distribution for pore sizes <1 nm to 1 mm. Finally, our results provide insights on how the pore structure and mass transport properties of different shale lithologies may evolve during storage of supercritical CO 2 .

58 GEOSCIENCES↗
DOE OSTI2021

Reducing sputter induced stress and damage for efficient perovskite/silicon tandem solar cells

Reducing damages caused by sputtering of transparent conductive oxide (TCO) electrodes is critical in achieving highly efficient and stable perovskite/silicon tandem solar cells. Here we study the sputter caused damage to bathocuproine (BCP), which is widely used in highly efficient p-i-n structure single junction perovskite solar cells. While BCP buffer layer protects the underneath layers from damage, itself can be damaged by sputtering of TCOs at a wide range of target-substrate distances, supported by molecular dynamic simulation. More intriguingly, it is observed that TCO easily peeled off after sputtering when the sputtering target is close to substrate. This is ascribed to formation of stress during cooling down process after sputtering due to different thermal expansion coefficients of the layers. Our studies explain why tin oxide (SnO2) made by atomic layer deposition can replace BCP for a much better tandem device performance. SnO2 has high affinity with sputtered TCO electrode to suppress peeling-off issue and has higher bond energy to resist sputter induced damage, thus it allows a wider window of target-substrate distances than BCP during TCO sputtering. Ultimately, we demonstrate an efficient perovskite/silicon monolithic tandem solar cell with efficiency of 26.0% to illustrate the beneficial effects of reduced stress and damage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Does Bayesian model averaging improve polynomial extrapolations? Two toy problems as tests

We assess the accuracy of Bayesian polynomial extrapolations from small parameter values, x, to large values of x. We consider a set of polynomials of fixed order, intended as a proxy for a fixed-order effective field theory (EFT) description of data. We employ Bayesian model averaging (BMA) to combine results from different order polynomials (EFT orders). Our study considers two 'toy problems' where the underlying function used to generate data sets is known. We use Bayesian parameter estimation to extract the polynomial coefficients that describe these data at low x. A 'naturalness' prior is imposed on the coefficients, so that they are $\mathcal{O}(1)$. We BMA different polynomial degrees by weighting each according to its Bayesian evidence and compare the predictive performance of this BMA with that of the individual polynomials. In conclusion, the credibility intervals on the BMA forecast have the stated coverage properties more consistently than does the highest evidence polynomial, though BMA does not necessarily outperform every polynomial.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Comparative analysis of the sensitivity of nanometallic thin film thermometers

Thin film platinum resistive thermometers are conventionally applied for resistance thermometry techniques due to their stability and proven measurement accuracy. Depending upon the required thermometer thickness and temperature measurement, however, performance benefits can be realized through the application of alternative nanometallic thin films. Herein, a comparative experimental analysis is provided on the performance of nanometallic thin film thermometers most relevant to microelectronics and thermal sensing applications: Al, Au, Cu, and Pt. Sensitivity is assessed through the temperature coefficient of resistance, measured over a range of 10–300 K for thicknesses nominally spanning 25–200 nm. In this work, the interplay of electron scattering sources, which give rise to the temperature-dependent TCR properties for each metal, are analyzed in the framework of a Mayadas–Shatzkes based model. Despite the prevalence of evaporated Pt thin film thermometers, Au and Cu films fabricated in a similar manner may provide enhanced sensitivity depending upon thickness. These results may serve as a guide as the movement toward smaller measurement platforms necessitates the use of smaller, thinner metallic resistance thermometers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Beryllium Oxide (BeO) Handbook

Beryllium oxide (BeO) is an oxide ceramic material. BeO ceramics provide thermal conductivity second only to diamond among electrically insulating materials, dissipating nearly 300 W/mK at room temperature. This property leads to electronic applications for BeO. BeO has a thermal expansion coefficient intermediate between gallium arsenide and refractory metal composites. Its low dielectric constant (6.7) and low loss index (0.0012 at 1 MHz) permits electrical circuit performance at high frequencies. It is employed as a window material for high frequency microwave applications. BeO is very stable under oxidizing and reducing high temperature and high humidity environments, unlike nitrides which will decompose to their oxide equivalent. Similar to beryllium metal, BeO acts as a scatterer of neutrons and is used as a neutron moderator material in nuclear reactor applications. The largest use of BeO is as the raw material for the manufacture of beryllium-copper alloys, followed closely as the raw material for conversion to beryllium metal. Most of the research performed on BeO was conducted in the 1960’s and 1970’s. 75% of the references cited in this Handbook are from the time period 1960-1980. Up to 1995, there were three commercial sources of BeO: Materion Corporation, Eagle-Picher, and Ceradyne Corporation. However, since 1995 Eagle-Picher and Ceradyne no longer supply BeO commercial products. Thus, the only current commercial source of BeO materials is Materion Corporation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Predictions of Transition Probabilities in Atomic Spectra

Forward modeling of optical spectra with absolute radiometric intensities requires knowledge of the individual transition probabilities for every transition in the spectrum. In many cases, these transition probabilities, or Einstein A-coefficients, quickly become practically impossible to obtain through either theoretical or experimental methods. Complicated electronic orbitals with higher order effects will reduce the accuracy of theoretical models. Experimental measurements can be prohibitively expensive and are rarely comprehensive due to physical constraints and sheer volume of required measurements. Due to these limitations, spectral predictions for many element transitions are not attainable. In this work, we investigate the efficacy of using machine learning models, specifically fully connected neural networks (FCNN), to predict Einstein A-coefficients using data from the NIST Atomic Spectra Database. For simple elements where closed form quantum calculations are possible, the data-driven modeling workflow performs well but can still have lower precision than theoretical calculations. For more complicated nuclei, deep learning emerged more comparable to theoretical predictions, such as Hartree–Fock. Unlike experiment or theory, the deep learning approach scales favorably with the number of transitions in a spectrum, especially if the transition probabilities are distributed across a wide range of values. It is also capable of being trained on both theoretical and experimental values simultaneously. In addition, the model performance improves when training on multiple elements prior to testing. The scalability of the machine learning approach makes it a potentially promising technique for estimating transition probabilities in previously inaccessible regions of the spectral and thermal domains on a significantly reduced timeline.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Resonant Ultrasonic Spectroscopy: A Modal-Analysis Approach for Additive Manufacturing

Resonant ultrasonic spectroscopy is a methodology capable of measuring a change in modal resonant frequencies of material structures. It encompasses an inversion technique based on the eigen-frequencies of a simple regular sample geometry to estimate the elastic tensor of a solid. Obtaining an accurate and complete set of resonant frequencies is a critical first step in the RUS process. In this paper a variety of techniques to extract and validate material resonances from complicated RUS measurement spectra are developed. Various processing methodologies employing modal analysis techniques are applied to estimate the underlying resonances that lead to the extraction of the elastic coefficients characterizing the specimen under test. A case study of a simple isotropic material (304 SS) is investigated to analyze the algorithms and evaluate their performance

Coal, lignite, and peat↗
DOE OSTI2025

Optimizing Insulation Design for Transformers in Medium Voltage Power Conversion Systems

Medium-frequency transformers (MFTs) play a crucial role in medium-voltage (MV) solidstate transformer (SST) systems, particularly in extreme fast charging applications. Achieving partial discharge (PD)-free operation while maintaining high power density is a significant challenge due to the high electric field (E-field) stresses inherent in MV applications. This dissertation focuses on the insulation design and optimization of MFTs used in both the main power electronics circuits and auxiliary power supplies. The study begins with an overview of insulation testing methodologies, including high potential tests, basic insulation level tests, and PD tests, which are critical for evaluating MFT insulation reliability. Given the importance of PD-free operation for long-term reliability, particular emphasis is placed on understanding PD mechanisms, including void, corona, and surface discharge, and their mitigation strategies. A high voltage isolated auxiliary power supply is then introduced, utilizing a gapped transformer encapsulated in silicone gel. This design achieves PD-free insulation up to 18 kV RMS while maintaining low coupling capacitance to minimize common-mode current. The proposed solution ensures reliable operation in MV environments and offers a scalable approach for auxiliary power in cascaded SST architectures. To improve MFT insulation in main power conversion circuits, a novel structure is developed using polypropylene sheets and potting compounds to create a void-free air gap, effectively mitigating E-field intensity. A prototype transformer with this insulation structure is built and achieves PD-free operation up to 30 kV RMS. This design is experimentally validated in a resonant converter operating at 46 kW, demonstrating its feasibility for MV SST applications. Further optimization is implemented to enhance MFT performance for dual-active-bridge(DAB) converters by integrating a semiconductive shielding layer within the insulation structure. This shielding layer improves the magnetic coupling coefficient while effectively confining the E-field within high insulation materials, thereby reducing eddy current losses. The optimized MFT achieves PD-free operation at 12.6 kV RMS and is successfully tested in a DAB converter operating at 43 kW, which meets the insulation requirements for a 13.2 kV SST system. This dissertation advances MFT insulation design by introducing and experimentally validating novel approaches that improve high voltage insulation while optimizing magnetic coupling and manufacturability. The proposed insulation structures enable PD-free operation while minimizing insulation material usage and simplifying assembly, making them ideal for high power, high voltage applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Initial study on cross-section generation requirements for a PBR equilibrium core

A Serpent model of the equilibrium core HTR-PM small modular nuclear reactor in China, was developed for use in cross-section preparation studies in order to guide methods development for the Griffin reactor multiphysics application. The model includes detailed isotopics for 10 distinct pebble burnup groups in 126 core zones with unique fuel and moderator temperatures obtained from a coupled neutronics-thermal-fluids equilibrium core calculation using Griffin-Pronghorn. A sensitivity study of the fuel and moderator temperatures for various core regions was performed with the MOOSE stochastic tools. The results show that the uncertainties are, not unexpectedly, dominated by the value of the fluid temperature and that the power level, heat transfer coefficient and effective conduction to neighboring pebbles and fluid constitute, at best, second order effects. The temperature uncertainty range varies from 28 K to 57 K between the core entry and exit planes, respectively, but these values are probably higher. We still have to quantify the significance of these uncertainties in the preparation of cross-sections in future work. In addition, we verified that the effective pebble approximation used in the PEBBED and V.S.O.P. computer codes works well for the preparation of region averaged cross-sections. Nevertheless, there are some discrepancies in the cross-sections when compared to the multi-pebble model, which could affect the prediction of peak values and the depletion calculation. We conclude that is highly desirable for future studies with Griffin to be able to handle both the 'effective' pebble approximation and the multi-pebble approach for various pebble burnup groups. This enables Griffin users with the flexibility to perform higher-fidelity studies. Finally, we initiated the preparation of cross-sections for various core regions from the full core Serpent reference model. We quantified the differences in 26 group cross-sections from infinite domain models versus the full core approach. These reference cross-sections will serve to verify the double heterogeneity, self-shielding, and spectrum-correction methods in Griffin. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

On the validity and limitations of 1D model for heat and mass transfer performance evaluation in a multilayer binder-free desiccant dehumidifier: isothermal dehumidification with internal cooling

Efficient humidity control is essential for maintaining indoor thermal comfort, yet conventional vapor-compression-based dehumidifiers are energy-intensive. Employing separate sensible and latent cooling through desiccant-coated heat exchangers (DCHEs) combined with evaporative coolers offers energy savings of up to 80 % compared to conventional systems. However, the dehumidification performance of DCHEs remains limited due to the use of polymer binders for coating desiccant materials onto heat exchange surfaces. In our previous study, we developed a multilayer fixed-bed binder-free desiccant dehumidifier (MFBDD) that demonstrated high dehumidification capacity and low pressure drop compared to rotary desiccant wheels. Nevertheless, its potential for further enhancement through internal cooling and the use of step-shaped adsorption isotherms has not been explored. In this study, a physics-based one-dimensional (1D) transient model is developed and validated to capture the coupled heat and mass transfer processes in the MFBDD and extended to simulate internal cooling using a high-capacity composite metal–organic framework, MIL-101/GO-6 (water uptake ≈1.6 g/g within 35–47 % RH). The model enables detailed analysis of local air and bed temperature dynamics and quantifies how internal cooling affects the dehumidification performance under a wide range of operating conditions. Results show that integrating internal cooling and using MIL-101/GO-6 enhance mass adsorbed, moisture removal capacity, and dehumidification effectiveness by 50 %–99 % compared with the M.S. Gel baseline. The study further reveals that achieving near-isothermal operation requires simultaneous enhancement of the convective heat transfer coefficient and heat exchange surface area. In conclusion, this work provides the first detailed physical insight into the interplay between internal cooling and step-shaped isotherms in a binder-free desiccant device and establishes a validated modeling framework for scaling up and system-level performance evaluation of next-generation energy-efficient dehumidification systems.

Heat and mass transfer↗
DOE OSTI2019

Low-Cost, Efficient and Durable High Temperature Wireless Sensors by Direct Write Additive Manufacturing for Application in Fossil Energy Systems

In-situ monitoring of physical parameters in fossil energy (FE) systems is challenging because of their high operating temperatures and oxidizing environments. This research aims utilize Aerosol Jet (AJ) nanoparticle printing, an advanced additive manufacturing method, to fabricate high temperature sensors for FE applications. Sensor films were fabricated by the AJ printing method with an excellent control over film microstructure and properties, in addition to creating less waste. Printing of six different materials, namely, silver (Ag), nickel (Ni), nickel-chromium (NiCr) alloy, copper (Cu), copper-nickel (CuNi), and carbon nanotubes (CNTs) was carried out for their evaluation of as sensor films. The films were then characterized using impedance spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and x-ray photoelectron spectroscopy (XPS). Film microstructure was correlated with the sensor electrical and mechanical behavior at different temperatures (24-500 °C) and frequencies (0.02 - 300 kHz). This knowledge was then used to fabricate, test, and characterize strain and temperature sensors for high temperature applications. The strain sensors were shown to have a very low thermal strain up to 500 °C and a 57% higher gauge factor compared to the commercially available counterparts. This enhance performance was attributed to the inherent porosity in the sensor films that allowed the films to exhibit a metamaterial-like behavior when deformed. The AJ printed temperature sensors were shown to have the highest Seebeck coefficient amongst all the film-based temperature sensors. In addition, fundamental aspects of crystal structure and phase transformations in Ba(Fe0.7Ta0.3)O3-δ (BFTO), a high temperature sensor material, was investigated along with 3D printed antennas that can transmit sensor signals at high temperatures. Two PhD students, one from first generation college-going underrepresented minority community (URM), two postdoctoral fellows, and several masters and undergraduate students were supported by this award. This work opens up the possibility of realizing low-cost high-performance sensor devices using advanced manufacturing techniques for high temperature FE applications.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Revealing the Electrochemical Kinetics of Electrolytes in Nanosized LiFePO 4 Electrodes

Lithium-ion battery rate performance is ultimately limited by the electrolyte, yet the behaviors of electrolytes during high-rate (dis)charge remain elusive to electrochemical measurement. Herein, we develop and study a nanosized LiFePO 4 model system in which the electrolyte completely controls the electrochemical kinetics of the porous electrode. Impedance spectroscopy, cyclic voltammetry, and rate performance testing prove that ion transport in the electrolyte is the sole rate-limiting process, even in thin electrodes. A novel pseudo-steady-state extrapolation (S3E) method for Tafel analysis shows that LiFePO 4 obeys Butler-Volmer kinetics with a transfer coefficient of 3. The combination of these unexpectedly rapid interfacial kinetics and an activation barrier for phase transformation causes extreme reaction heterogeneity, which manifests as a moving reaction zone. Resistance versus capacity analysis enables direct measurement of electrolyte resistance growth during high-rate (dis)charge, revealing how the interaction between concentration polarization and a moving reaction zone controls electrolyte rate performance in LiFePO 4 electrodes. This work elucidates the profound impacts of the electrolyte on electrochemical measurements in porous battery electrodes: when the active material is not rate limiting, it is impossible to directly measure the intrinsic kinetics of the active material, but conversely, it becomes possible to directly measure the kinetics of the electrolyte.

Electrochemistry↗
DOE OSTI2021

Pozzolanic reactivity of natural pozzolans, ground glasses and coal bottom ashes and implication of their incorporation on the chloride permeability of concrete

It is well understood that the partial replacement of portland cement with pozzolans in sufficient proportions results in improved long-term performance of concrete. However, dwindling availability of the most widely used pozzolan in the construction industry, fly ash, is forecast due to the termination of coal-fired electricity power stations around the world, necessitating the exploration of alternative pozzolans. Experimental investigations are conducted to study the performance of traditional pozzolans such as fly ash, silica fume and natural pozzolans (metakaolin, pumice, perlite and lassenite) together with novel pozzolans, including ground glasses (high-alkali and low-alkali), and ground bottom ash. Twenty-one concrete mixtures are examined for compressive strength, electrical conductivity, electrical resistivity, chloride permeability, and chloride migration coefficient. The reactivity of pozzolans studied is also determined in terms of compressive strength in lime-pozzolan mortar using a modified lime-reactivity test method. Many of the materials tested demonstrate the potential to be used in place of fly ash. However, a wide variation in the performance of these materials is evident which highlights the need for a reliable test to determine the level of reactivity of a pozzolan. The results indicate that the 7-day strength in the modified lime-reactivity test provides a good indication of the pozzolanic reactivity of the material and of how the material can be expected to contribute to the strength and permeability of concrete. The use of electrical resistivity as an indicator of the performance of pozzolans in terms of strength development and chloride penetration resistance when used to partially replace portland cement in concrete is also discussed.

36 MATERIALS SCIENCE↗