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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies

Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. Methods: The tool was developed in MATLAB using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreed better with MC. Conclusions: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

An iterative bidirectional gradient boosting approach for CVR baseline estimation

Here this paper presents a novel Iterative Bidirectional Gradient Boosting Model (IBi-GBM) for estimating the baseline of Conservation Voltage Reduction (CVR) programs. In contrast to many existing methods, we treat CVR baseline estimation as a missing data retrieval problem. The approach involves dividing the load and its corresponding temperature profiles into three periods: pre-CVR, CVR, and post-CVR. To restore the missing load profile during the CVR period, the method employs a three-step process. First, a forward-pass GBM is executed using data from the pre-CVR period as inputs. Subsequently, a backward-pass GBM is applied using data from the post-CVR period. The two restored load profiles are reconciled, considering pre-calculated weights derived from forecasting accuracy, and only the leftmost and rightmost points are retained. The newly restored points are then included as inputs for the subsequent iteration. This iterative procedure continues until the original load data in the CVR period is fully restored. We develop IBi-GBM using actual smart meter and Supervisory Control and Data Acquisition (SCADA) data. Our results demonstrate that IBi-GBM exhibits robust performance across various data resolutions and in different seasons and outperforms existing methods by achieving a 1-2% reduction in normalized Root Mean Square Error (nRMSE).

42 ENGINEERING↗
DOE OSTI2021

A Systematic Way to Extend the Debye–Hückel Theory beyond Dilute Electrolyte Solutions

An extended Debye–Hückel theory with fourth order gradient term is developed for electrolyte solutions; namely, the electric potential φ(r) of the bulk electrolyte solution can be described by ∇ 2 φ(r) = κ 2 φ(r) + L Q 2 ∇ 4 φ(r), where the parameters κ and L Q are chosen to reproduce the first two roots of the dielectric response function of the bulk solution. Three boundary conditions for solving the electric potential problem are proposed based upon the continuity conditions of involving functions at the dielectric boundary, with which a boundary element method for the electric potential of a solute with a general geometrical shape and charge distribution is derived. Solutions for the electric potential of a spherical ion and a diatomic molecule are found and used to calculate their electrostatic solvation energies. In this work, the validity of the theory is successfully demonstrated when applied to binary as well as multicomponent primitive models of electrolyte solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to evaluate and constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and the radiative transfer of SIF to remote sensing sensors is an essential prerequisite for the evaluation and data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy level is usually not well-represented. In this work, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). An efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and a canopy-level model agreed well at sites except one in needle leaf forest (R2>0.91, root-mean-square error < 0.19W m -2 sr -1 um -1 ), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R 2 >0.68). At the global scale, simulated SIF generally captured the spatial and seasonal pat37 terns of satellite-observed SIF (R2 > 0.76 except for tropical forest). Factors including the fluorescence emission model, clumping, bidirectional effect, and canopy properties (leaf optical properties and leaf area index) had considerable impacts on SIF simulation, and the discrepancies between simulated and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF towards constraining and evaluating GPP simulations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Development and validation of a mathematical model of heart rate response to fluid perturbation

Physiological closed-loop controlled (PCLC) medical devices monitor and automatically adjust the patient’s condition by using physiological variables as feedback, ideally with minimal human intervention to achieve the target levels set by a clinician. PCLC devices present a challenge when it comes to evaluating their performance, where conducting large clinical trials can be expensive. Virtual physiological patients simulated by validated mathematical models can be utilized to obtain preclinical evidence of safety and assess the performance of the PCLC medical device during normal and worst-case conditions that are unlikely to happen in a limited clinical trial. A physiological variable that plays a major role during fluid resuscitation is heart rate (HR). For in silico assessment of PCLC medical devices regarding fluid perturbation, there is currently no mathematical model of HR validated in terms of its predictive capability performance. This paper develops and validates a mathematical model of HR response using data collected from sheep subjects undergoing hemorrhage and fluid infusion. The model proved to be accurate in estimating the HR response to fluid perturbation, where averaged between 21 calibration datasets, the fitting performance showed a normalized root mean square error (NRMSE) of 7.41 ± 2.8%. The model was also evaluated in terms of model predictive capability performance via a leave-one-out procedure (21 subjects) and an independent validation dataset (6 subjects). Two different virtual cohort generation tools were used in each validation analysis. The generated envelope of virtual subjects robustly met the defined acceptance criteria, in which 95% of the testing datasets presented simulated HR patterns that were within a deviation of 50% from the observed data. In addition, out of 16000 and 18522 simulated subjects for the leave one-out and independent datasets, the model was able to generate at least one virtual subject that was close to the real subject within an error margin of 9.56 ± 3.15% and 11.1 ± 1.22% NRMSE, respectively. In conclusion, the model can generate valid virtual HR physiological responses to fluid perturbation and be incorporated into future non-clinical simulated testing setups for assessing PCLC devices intended for fluid resuscitation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Response of Jointed-Structures in a Shock Tube: Simultaneous PSP and DIC with Comparison to Modeling

Experiments, modeling and simulation were used to study the nonlinear dynamics of a jointed-structure in a shock tube. The structure was a full-span square cylinder with internal bolted connections excited by fluid loading. The width-based Reynolds number was ≈10 5 . The cylinder was exposed to an impulsive force associated with the incident shock followed by transverse loading imposed by vortex shedding. In the experiment, aerodynamic loading was characterized with high-speed pressure sensitive paint (PSP). Digital image correlation (DIC) concurrently measured the structural response. The maximum displacement occurred when the vortex shedding frequency most closely matched the structural mode of the beam associated with a rocking motion at the joint. A finite element model was developed using Abaqus, where the nonlinear contact dynamics of the joint were simulated using Coulomb friction. The PSP data loaded the model and the interaction was treated as one-way coupled. The simulations well-matched the trends observed in the experiment. Overall, the root-mean-square values of the transverse displacement agreed to within 24% of the experiment. The modeling showed rocking about the joint during vortex shedding was critical to the nonlinear damping and energy dissipation in the structure. We conclude this campaign highlights the importance of jointed-connections to energy dissipation in structures under aerodynamic loading.

42 ENGINEERING↗
DOE OSTI2019

Over-Expression of a Maize N-Acetylglutamate Kinase Gene ( ZmNAGK ) Improves Drought Tolerance in Tobacco

Water deficit is a key limiting factor that affects the growth, development and productivity of crops. It is vital to understand the mechanisms by which plants respond to drought stress. Here an N -acetylglutamate kinase gene, ZmNAGK , was cloned from maize ( Zea mays ). ZmNAGK was expressed at high levels in maize leaves and at lower levels in root, stem, female flower and male flower. The expression of ZmNAGK was significantly induced by PEG, NaCl, ABA, brassinosteroid and H 2 O 2 . The ectopic expression of ZmNAGK in tobacco resulted in higher tolerance to drought compared to plants transformed with empty vector. Further physiological analysis revealed that overexpression of ZmNAGK could enhance the activities of antioxidant defense enzymes, and decrease malondialdehyde content and leakage of electrolyte in tobacco under drought stress. Moreover, the ZmNAGK transgenic tobacco accumulated more arginine and nitric oxide (NO) than control plants under drought stress. In addition, the ZmNAGK transgenic tobaccos activated drought responses faster than vector-transformed plants. Here, these results indicate that ZmNAGK can play a vital role in enhancing drought tolerance by likely affecting the arginine and NO accumulation, and ZmNAGK could be involved in different strategies in response to drought stress.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Hydrogen-Bonding Reinforced Flexible Composite Electrodes for Enhanced Energy Storage

The lack of advanced electrode materials is one of the main factors hindering the development of flexible rechargeable aqueous batteries (RABs) for high specific energy density and structural stability. It is also challenging to achieve high-capacity performance for both the positive and negative electrodes simultaneously. In this work, it is demonstrated that, by smartly designing the composite structures of positive and negative electrodes via one-step electrodeposition strategy, the energy storage performance of the RAB is largely enhanced. For positive electrode material synthesis, Co-Cu double hydroxides (Co-Cu-DH) nanosheets are skillfully rooted into electroreduced graphene oxide (eRG) via hydrogen bonding, in which graphene oxide reduction, Co-Cu-DH nucleation/growth, and formation of hydrogen bonding between Co-Cu-DH and eRG simultaneously occur. Moreover, when a RAB based on Co-Cu-DH@eRG//FeOOH@eRG using the same composite design strategy is established, a wide operating voltage window of ≈1.8 V, a high specific energy density of ≈142.8 Wh kg –1 at ≈890 W kg –1 , and long-term cyclic stability (88.5% of capacity retention after 12 000 cycles) are obtained. This study presents a general compositing strategy for the development of advanced electrode materials, and it is expected to stimulate future material synthesis/design in RABs toward the goal of high energy density storage.

25 ENERGY STORAGE↗
DOE OSTI2016

Comparing the Performance of Three Land Models in Global C Cycle Simulations: A Detailed Structural Analysis: Structural Analysis of Land Models

Land models are valuable tools to understand the dynamics of global carbon (C) cycle. Various models have been developed and used for predictions of future C dynamics but uncertainties still exist. Diagnosing the models’ behaviors in terms of structures can help to narrow down the uncertainties in prediction of C dynamics. In this study three widely used land surface models, namely CSIRO’s Atmosphere Biosphere Land Exchange (CABLE) with 9 C pools, Community Land Model (version 3.5) combined with Carnegie-Ames-Stanford Approach (CLM-CASA) with 12 C pools and Community Land Model (version 4) (CLM4) with 26 C pools were driven by the observed meteorological forcing. The simulated C storage and residence time were used for analysis. The C storage and residence time were computed globally for all individual soil and plant pools, as well as net primary productivity (NPP) and its allocation to different plant components’ based on these models. Remotely sensed NPP and statistically derived HWSD, and GLC2000 datasets were used as a reference to evaluate the performance of these models. Results showed that CABLE exhibited better agreement with referenced C storage and residence time for plant and soil pools, as compared with CLM-CASA and CLM4. CABLE had longer bulk residence time for soil C pools and stored more C in roots, whereas, CLM-CASA and CLM4 stored more C in woody pools due to differential NPP allocation. Overall, these results indicate that the differences in C storage and residence times in three models are largely due to the differences in their fundamental structures (number of C pools), NPP allocation and C transfer rates. Our results have implications in model development and provide a general framework to explain the bias/uncertainties in simulation of C storage and residence times from the perspectives of model structures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Evaluation of a high-resolution regional climate simulation for surface and hub-height wind climatology over North America

Assessing the availability of key wind resources requires augmenting observations to support the implementation of wind energy infrastructure. However, observations are limited, necessitating the development of high-resolution, long-term gridded datasets. This study presents a robust, dynamically downscaled climatological dataset, offering 20 years of hourly wind data at a 4 km spatial resolution across North America, and evaluates its performance against observations, including meteorological towers and automated surface-observing system (ASOS) stations, as well as coarse-resolution reanalysis data (the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5)). Results demonstrate that the downscaled high-resolution wind data outperform ERA5 in regions of complex terrain and coastal areas, with improved overlap coefficients for wind data distributions and reduced root mean square errors (RMSEs) for hub-height and near-surface diurnal wind patterns. The downscaled simulation also captures the synoptic drivers of seasonal wind direction patterns reasonably well, indicated by high wind rose similarity indices. This study also provides an analysis of interannual variability, utilizing the dataset's full 20-year period, and model uncertainty, generated by varying model initial conditions and physics parameterizations across 1-year ensemble members, which are key considerations for wind resource assessment in wind farm development.

17 WIND ENERGY↗
DOE OSTI2020

Genetic control of root architectural plasticity in maize

Root phenotypes regulate soil resource acquisition; however, their genetic control and phenotypic plasticity are poorly understood. We hypothesized that the responses of root architectural phenes to water deficit (stress plasticity) and different environments (environmental plasticity) are under genetic control and that these loci are distinct. Root architectural phenes were phenotyped in the field using a large maize association panel with and without water deficit stress for three seasons in Arizona and without water deficit stress for four seasons in South Africa. All root phenes were plastic and varied in their plastic response. We identified candidate genes associated with stress and environmental plasticity and candidate genes associated with phenes in well-watered conditions in South Africa and in well-watered and water-stress conditions in Arizona. Few candidate genes for plasticity overlapped with those for phenes expressed under each condition. Our results suggest that phenotypic plasticity is highly quantitative, and plasticity loci are distinct from loci that control phene expression in stress and non-stress, which poses a challenge for breeding programs. To make these loci more accessible to the wider research community, we developed a public online resource that will allow for further experimental validation towards understanding the genetic control underlying phenotypic plasticity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2016

Frontier Observatory for Research in Geothermal Energy: Phase 1 Topical Report Fallon, NV

The Department of Energy (DOE) Frontier Observatory for Research in Geothermal Energy (FORGE) is to be a dedicated site where the subsurface scientific and engineering community can develop, test, and improve technologies and techniques for the creation of cost-effective and sustainable enhanced geothermal systems (EGS) in a controlled, ideal environment. The establishment of FORGE will facilitate development of an understanding of the key mechanisms controlling a successful EGS. Execution of FORGE is occurring in three phases with five distinct sub-phases (1, 2A, 2B, 2C, and 3). This report focuses on Phase 1 activities. During Phase 1, critical technical and logistical tasks necessary to demonstrate the viability of the Fallon FORGE Project site were completed and the commitment and capability of the Fallon FORGE team to execute FORGE was demonstrated. As part of Phase 1, the Fallon FORGE Team provided an assessment of available relevant data and integrated these geologic and geophysical data to develop a conceptual 3-D geologic model of the proposed test location. Additionally, the team prepared relevant operational plans for full FORGE implementation, provided relevant site data to the science and engineering community, engaged in outreach and communications with interested stakeholders, and performed a review of the environmental and permitting activities needed to allow FORGE to progress through Phase 3. The results of these activities are provided as Appendices to this report. The Fallon FORGE Team is diverse, with deep roots in geothermal science and engineering. The institutions and key personnel that comprise the Fallon FORGE Team provide a breadth of geoscience and geoengineering capabilities, a strong and productive history in geothermal research and applications, and the capability and experience to manage projects with the complexity anticipated for FORGE. Fallon FORGE Team members include the U.S. Navy, Ormat Nevada Inc., Sandia National Laboratories (SNL), Lawrence Berkeley National Laboratory (LBNL), the United States Geological Survey (USGS), the University of Nevada, Reno (UNR), GeothermEx/Schlumberger (GeothelinEx), and Itasca Consulting Group (Itasca). The site owners (through direct land ownership or via applicable permits)—the U.S. Navy and Ormat Nevada Inc.—are deeply committed to expanding the development of geothermal resources and are fully supportive of FORGE operations taking place on their lands.

15 GEOTHERMAL ENERGY↗
DOE OSTI2019

Analytic analysis of load alignment for coning extreme‐scale rotors

Abstract Extreme‐scale wind turbines (rated powers greater than 10 MW) with large rotor diameters and conventional upwind designs must resist extreme downwind and gravity loads. This can lead to significant structural design challenges and high blade masses that can impede the reduction of levelized cost of wind energy. Herein, the theoretical basis for downwind load alignment is developed. This alignment can be addressed with active downwind coning to reduce/eliminate flapwise bending loads by balancing the transverse components of thrust, centrifugal, and gravitational force. Equations are developed herein that estimates the optimal coning angle that reduces flapwise loads by a specified amount. This analysis is then applied to a 13.2‐MW scale with 100‐m‐level wind turbine blades, where it is found that a load alignment coning schedule can substantially reduce the root flapwise bending moments. This moment reduction in this example can allow the rotor mass to be decreased significantly when compared with a conventional upwind three‐bladed rotor while maintaining structural performance and annual energy output.

Noyes, Carlos↗
DOE OSTI2026

Surrogate model evaluation and building energy benchmarking for commercial buildings

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio TM /EnergyPlus TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2018

Simulation tools for atom probe tomography: A path for diagnosis and treatment of image degradation

The ideal picture of a near-perfect 3D microscope often presented regarding Atom Probe Tomography faces several issues. These issues degrade the metrological performance of the instrument and find their roots in the phenomena acting at the atomic to the mesoscopic level in the vicinity of the surface of a field emitter. From the field evaporation process at the atomic scale, to the macroscopic scale of the instrument, the path to model the imaging process and to develop more accurate and reliable reconstruction algorithms is not a single lane road. This paper focused on the numerical methods used to understand, treat, and potentially heal imaging issues commonly affecting the data in atom probe experiments. A lot of room for improvement exists in solving accuracy problems observed in complex materials by means of purely electrostatic models describing the image formation in a classical approach. Looking at the sample at the atomic scale, the phenomena perturbing the imaging process are subtle. An examination of atomic scale modifications of the sample surface in the presence of a high surface electric field is therefore mandatory. Atomic scale molecular dynamic models integrating the influence of the high surface electric are being developed with this aim. It is also demonstrated that the complex behavior of atoms and molecules in high fields, and consequences on collected data, can be understood through the use of accurate ab-initio models modified to include the impact of the extreme surface electric field.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2018

Thermodynamic Constraints on the Size Distributions of Tropical Clouds

Tropical convective clouds evolve over a wide range of temporal and spatial scales, which makes them difficult to simulate numerically. Here we propose that cloud statistical properties can be derived within a simplified time-independent coordinate system of cloud number n, saturated static energy h ★ , and cloud perimeter λ. Under the constraint that circulations around cloud edge compete with each other for total buoyant energy and air, we show that the product of cloud number and cloud perimeter nλ is invariant with λ and that cloud number follows a negative exponential with respect to cloud-edge deviations of h &#9733 ; with respect to the mean. Overall, the summed perimeter of all clouds scales as the square root of the atmospheric static stability. These theoretical results suggest that the complexity of cloud field structures can be viewed statistically as an emergent property of atmospheric bulk thermodynamics. Comparison with a detailed tropical cloud field simulation shows general agreement to within ≤13%. For the sake of developing hypotheses about cloud temporal evolution that are testable in high resolution simulations, the shapes of tropical cloud perimeter distributions are predicted to be invariant as climate warms, although with a modest increase in total cloud amount.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Interfacial reactions in lithium batteries

The lithium-ion battery was first commercially introduced by Sony Corporation on 1991 using LiCoO 2 as the cathode material and mesocarbon microbeads as the anode material. After continuous research and development for 25 years, lithium-ion batteries have been the dominant energy storage devices for modern portable electronics, as well as for the emerging application for electric vehicles and smart grids. It has been a common sense that the success of lithium-ion technologies is rooted to the existence of a solid electrolyte interphase (SEI) that kinetically suppresses the parasitic reactions between the lithiated 2 graphitic anodes and the carbonate-based non-aqueous electrolytes. Recently, major attention has been paid to the importance of a similar passivation/protection layer on the surface of cathode materials, aiming for rational design of high-energy-density lithiumion batteries with extended cycle/calendar life. In this article, the physical model of the solid electrolyte interphase, as well as the recent research effort to under the nature and role SEI are summarized, and future perspectives on this important research field will also be presented.

25 ENERGY STORAGE↗