Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “storage performance”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Unravelling fast-charging degradation in NMC/Gr pouch cells: Lithium plating and SEI properties

As fast-charging technology expands across the electric vehicle and emerging energy-storage applications, understanding its impact on battery performance and longevity is critical. In this study, 1.8 Ah LiNi 0.6 Mn 0.2 Co 0.2 O 2 /graphite pouch cells were charged at various charging rates (0.5C, 2C, 4C, and 6C) to investigate the degradation mechanisms. Our results showed that well-designed NMC/Gr pouch cells could reach over 1000 cycles with a 2C charging rate, while only reaching around 500 cycles with 4C and 6C charging rates. Fast-charging effects on NMC and graphite electrodes were obtained through a series of post-mortem characterizations, including electrochemical impedance spectroscopy (EIS), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDS). Although higher charging rates cause pulverization of NMC secondary particles, the dominant degradation mechanism driving the fading of fast-charging-related performance lies in the graphite anode, where lithium plating and LiF-rich solid electrolyte interphase (SEI) formation result in Li inventory loss and impedance growth. The postmortem results suggest that the formation of a LiF-rich SEI, which exacerbates anode impedance and some irreversible Li + ion loss, is likely driven by the substantial decomposition of PF 6 − during fast charging, an effect often overlooked in smaller laboratory-scale studies.

Luo, Mei [Argonne National Laboratory (ANL), Argon↗
DOE OSTI2024

Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion

In system prognostics and health management, multivariable degradation models have been widely developed to predict the life of complex systems using degradation data of multiple Performance Characteristics (PCs). Recent studies have detected a Long-Term Memory (LTM) effect among the degradation process of various PCs, implying a strong coupling phenomenon between the future degradation behavior and historical degradation trajectory. Although the LTM has been widely integrated into single-PC-based degradation modeling, it has not been considered in multi-PC-based scenarios. To capture LTM among multiple PCs, this article proposes a novel LTM-integrated Multivariate Degradation Model (MDM) for system life prediction based on multivariate fractional Brownian motion, which simultaneously incorporates the cross-correlation among different PCs. To estimate parameters of the LTM-integrated MDM, a maximum likelihood method is developed. Here, two likelihood-ratio hypothesis tests are developed to test the existence of the overall and individual LTM effect among multiple PCs. Both simulation studies and physical experiments on the performance degradation of solar energy conversion and storage devices are conducted to validate the proposed model. Results reveal that the proposed LTM-integrated MDM significantly outperforms existing MDMs in life prediction, while the lifetime uncertainty is heavily underestimated by those traditional approaches that neglect the LTM.

42 ENGINEERING↗
DOE OSTI2021

Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks

Computational high-throughput screening using molecular simulations is a powerful tool for identifying top-performing metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges are often required to model the electrostatic interactions between the MOF and the adsorbate, especially when the adsorption involves molecules with dipole or quadrupole moments such as water and CO 2 . Although ab initio methods can be used to calculate accurate partial atomic charges, these methods are impractical for screening large material databases because of the high computational cost. We developed a random forest machine learning model to predict the partial atomic charges in MOFs using a small yet meaningful set of features that represent both the elemental properties and the local environment of each atom. The model was trained and tested on a collection of about 320 000 density-derived electrostatic and chemical (DDEC) atomic charges calculated on a subset of the Computation-Ready Experimental Metal–Organic Framework (CoRE MOF-2019) database and separately on charge model 5 (CM5) charges. The model predicts accurate atomic charges for MOFs at a fraction of the computational cost of periodic density functional theory (DFT) and is found to be transferable to other porous molecular crystals and zeolites. In conclusion, a strong correlation is observed between the partial atomic charge and the average electronegativity difference between the central atom and its bonded neighbors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Nanocrystal Geometry Governs Phase Transformation Pathways in Palladium Hydride

Pathways and structural dynamics of phase transformations impact performance of materials in energy and information storage technologies. Palladium hydride (PdH x ) nanocrystals are an ideal model system for studying solute-induced phase transformations, where elastic energy from lattice mismatch between α-PdH x and β-PdH x phases is often considered a key to determining the transformation pathways. α/β-PdH x interfacial elastic energy is affected by the confined geometry of a nanocrystal. However, how nanocrystal geometry influences phase transformation pathways is largely unknown. Using in situ liquid phase transmission electron microscopy, we directly visualize hydrogenation in Pd nanocrystals with two geometries, a nanocube and a hexagonal nanoplate. Both follow similar sequences of an initially curved nucleus, interface flattening, and reverse-stage nucleation; however, their evolving α/β-PdH x interfaces exhibit geometry-dependent crystallographic alignments. In nanocubes, {100}-aligned configurations conform to static elastic energy ordering, representing a pathway that maintains a local mechanical equilibrium, whereas nanoplates display both {110}- and {211}-aligned interfaces. Theoretical simulations show that geometry determines the accessibility of alternative phase transformation pathways as the system is driven far from equilibrium during hydrogenation. In conclusion, these findings identify geometry as a fundamental parameter for directing phase transformation pathways, offering design principles for accessing atypical configurations and improving properties of intercalation-based devices.

Lee, Daewon [Lawrence Berkeley National Laboratory↗
DOE OSTI2026

High-Sensitivity DNA Aptasensors for Detecting Salivary Biomarker S100A7 in Heart Failure

Early detection of heart failure (HF) is vital for improving patient outcomes, lowering hospital readmission rates, and enabling prompt treatment. We present the first high-affinity DNA aptamer for the salivary HF biomarker S100A7 and its application in highly sensitive, noninvasive diagnostic tests. Iterative truncation of the initial 82-nt aptamer (17–82) produced a 43-nt core (17–43) with a binding affinity of 27 nM, which was further enhanced to 5.5 nM through dimerization. Biochemical and mutational studies confirmed that 17–43 adopts a G-quadruplex structure, which is essential for S100A7 recognition and resistance to enzymatic degradation in human saliva. Incorporating 17–43 into sandwich aptamer-ELISA and hybrid aptamer–antibody ELISA assays allowed detection of recombinant S100A7 in human saliva with limits of detection (LOD) of 7.4 ng mL–1 (0.6 nM) and 29 pg/mL (2.2 pM), respectively-outperforming commercial immunoassays in both sensitivity and dynamic range. The hybrid assay maintained its full performance after 2.5 months of room temperature storage. Additionally, a biolayer interferometry (BLI) sensor with 17–43 quantified S100A7 in patient saliva (n = 3), achieving a LOD of 3.2 ng mL–1 (0.3 nM) with a total assay time of less than 20 min. The aptamer’s stability, high specificity, and versatility across biosensing platforms establish it as a promising tool for noninvasive heart failure diagnostics, laying the groundwork for portable aptamer-based biosensors for multiplexed monitoring of HF biomarkers.

DNA aptamer↗
DOE OSTI2026

Identification of Solid-Electrolyte Interphase Species by Joint Characterization of Li-Ion Battery Chemistry by Mass Spectrometry and Electrochemical Reaction Networks

The formation and stability of the solid-electrolyte interphase (SEI) play central roles in determining the long-term performance and safety of modern electrochemical energy storage systems. Despite decades of research, the SEI’s heterogeneous, dynamic, and multiphase nature has defied comprehensive molecular-level characterization, creating a critical knowledge gap that limits rational battery design. In this work, we introduce a computational−experimental framework that integrates high-throughput quantum chemistry calculations, data-driven electrochemical reaction networks (eCRNs), stochastic algorithms, and laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry (LDI-FTICR-MS) to unravel SEI formation in carbonatebased electrolytes without imposing predefined mechanisms. We constructed the most comprehensive eCRN to date, spanning over 10,000 species and 209 million reactions. Through stochastic network analysis, we successfully recovered 27 species that were previously reported in the literature and predicted 28 novel SEI species nearly doubling our scientific knowledge in this area. Each new species was rigorously confirmed through advanced mass spectral analysis of its distinct molecular and isotopic signatures. We kinetically refined the formation pathways for a select set of both previously reported and novel SEI products, revealing kinetically feasible elementary reaction mechanisms with activation barriers below 1 eV. This computational−experimental approach deepens our molecular-level understanding of SEI chemistry by resolving which species form and through which decomposition mechanisms they emerge. Such knowledge provides the foundation necessary to connect electrolyte composition to the resulting SEI components, a critical step toward a more informed electrolyte development in next-generation lithium-based batteries.

25 ENERGY STORAGE↗
DOE OSTI2021

Non-equilibrium insertion of lithium ions into graphite

Graphite has been regarded as the most important anode material for currently used lithium-ion batteries due to its two-dimensional (2D) nature hosting ionic intercalations. However, the kinetic insertion of Li ions is still not well known microscopically. In this work, we investigate the real-time intercalation process of Li ions using in situ transmission electron microscopy. We observe the lithium insertion process at the atomic scale, in which the graphite layers undergo expansion, forming wrinkles and finally inhomogeneous cracks as the Li ions accumulate, different from the proposed models. Leveraging on theoretical simulations, Li-ion migration driven by an external electrical field is suggested to be induced into the irreversible wrinkled structures. This non-equilibrium behavior that occur in lithium-ion batteries can be more pronounced at a high charging rate, which will practically degrade the capacity of graphite. Furthermore, this work unveils the reaction scenario of the non-equilibrium Li-ion insertion, which benefits the understanding of the performance of graphite-based energy-storage devices.

25 ENERGY STORAGE↗
DOE OSTI2021

Pre-conditioned BFGS-based uncertainty quantification in elastic full-waveform inversion

SUMMARY Full-waveform inversion has become an essential technique for mapping geophysical subsurface structures. However, proper uncertainty quantification is often lacking in current applications. In theory, uncertainty quantification is related to the inverse Hessian (or the posterior covariance matrix). Even for common geophysical inverse problems its calculation is beyond the computational and storage capacities of the largest high-performance computing systems. In this study, we amend the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm to perform uncertainty quantification for large-scale applications. For seismic inverse problems, the limited-memory BFGS (L-BFGS) method prevails as the most efficient quasi-Newton method. We aim to augment it further to obtain an approximate inverse Hessian for uncertainty quantification in FWI. To facilitate retrieval of the inverse Hessian, we combine BFGS (essentially a full-history L-BFGS) with randomized singular value decomposition to determine a low-rank approximation of the inverse Hessian. Setting the rank number equal to the number of iterations makes this solution efficient and memory-affordable even for large-scale problems. Furthermore, based on the Gauss–Newton method, we formulate different initial, diagonal Hessian matrices as pre-conditioners for the inverse scheme and compare their performances in elastic FWI applications. We highlight our approach with the elastic Marmousi benchmark model, demonstrating the applicability of pre-conditioned BFGS for large-scale FWI and uncertainty quantification.

58 GEOSCIENCES↗
DOE OSTI2023

DLIO: A DATA-CENTRIC BENCHMARK FOR DEEP LEARNING APPLICATIONS

SF-22-136 Deep learning has been shown as a successful method for various tasks, and its popularity results in numerous open-source deep learning software tools. Deep learning has been applied to a broad spectrum of scientific domains such as cosmology, particle physics, computer vision, fusion, and astrophysics. Scientists have performed a great deal of work to optimize the computational performance of deep learning frameworks. However, the same cannot be said for I/O performance. As deep learning algorithms rely on big-data volume and variety to effectively train neural networks accurately, I/O is a significant bottleneck on large-scale distributed deep learning training. DLIO, is a novel representative benchmark suite built based on the I/O profiling of the selected workloads. DLIO can be utilized to accurately emulate the I/O behavior of modern deep learning applications. Using DLIO, application developers and system software solution architects can identify potential I/O bottlenecks in their applications and guide optimizations to boost the I/O performance leading to lower training times. The storage vendor can also use DLIO as a guide for designing and optimize the storage and filesystem targeting at deep learning application.

ZHENG, HUIHUO↗
DOE OSTI2021

Nanostructured, Targeted Layered Metal Oxides as Active and Selective Heterogeneous Electrocatalysts for Oxygen Evolution

Oxygen evolution reaction (OER) is an important chemical transformation that governs the performance of technologically relevant energy conversion and storage processes, such as electrochemical water splitting in electrolyzers and metal-air batteries. This reaction involves the evolution of oxygen gas from (i) oxygen ions formed from water splitting at high temperatures, (ii) oxygen containing species (i.e., OH, OOH) formed from water splitting at low temperatures, and (iii) metal-oxide discharge products (M x O 2 ) in metal-air batteries. One of the main challenges with OER is that it is catalyzed at low temperatures by noble metal-based electrocatalysts, which are expensive (making this process economically unviable), or at high temperatures by metal-oxide based electrocatalysts that are inefficient and lead to high overpotential losses. Our preliminary results, as well as other results in the literature, show that non-noble metal-based oxide electrocatalysts with layered structures, known as nickelate oxides, exhibit promising OER activity. Nickelate oxides (A 2 MO 4+δ ) belong to the Ruddlesden-Popper series composed of alternating perovskite-like and rocksalt-like layers. They are promising OER electrocatalysts due to their high oxygen transport/exchange properties compared to the state-of-the-art perovskite-type oxides. Unfortunately, these materials are fairly unexplored and little is known about the properties that govern their OER activity. Our preliminary studies, where we combine density functional theory (DFT) calculations, selective synthesis and characterization, show that engineering their nanocrystalline structure can significantly impact their catalytic activity.

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

Washington Clean Energy Fund Grid Modernization Projects: Economic Analysis (Final Report)

As part of CEF Round 1 funding, Pacific Northwest National Laboratory (PNNL) was engaged by the U.S. Department of Energy (DOE) and the Washington Department of Commerce to work with Puget Sound Energy (PSE), Avista, and Snohomish Public Utility District (SnoPUD) in evaluating the economic and technical performance of each of their battery energy storage systems (BESSs). This report presents the final results of the economic assessment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

NRAP-Open-IAM: Generic Aquifer Component Development and Testing

The Generic Aquifer Model calculates the concentrations of dissolved salt and dissolved CO 2 surrounding a leaking legacy well. The Generic Aquifer model can also estimate the size of an “impact plume” where concentration changes exceed user-specified thresholds. The model is a component of NRAP-Open-IAM, an open-source Integrated Assessment Model (IAM) developed by the National Risk Assessment Partnership (NRAP) to perform risk assessment for geologic CO 2 storage. The input parameters were selected to cover a wide range of groundwater aquifers and leakage rates. The generic aquifer model was developed using a generative adversarial deep learning network, trained using a large synthetic dataset of STOMP multiphase flow simulations. The deep learning model predictions of dissolved salt and dissolved CO 2 in the aquifer compare well to the original STOMP simulation results. The extent of aquifer impacted by leaking CO 2 or brine is calculated using a user-defined mass fraction threshold. The aquifer impact volumes calculated based on STOMP simulation results compare well to those calculated based on the deep learning model. In a provided python script, gridded observation results from the generic aquifer component of NRAP-Open-IAM are converted to HDF5 format files for monitoring design with the DREAM code.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Upcycling Associated Natural Gas into Liquid Chemical Intermediates

This report assesses the opportunity to utilize associated natural gas (ANG) that is currently flared or vented by conversion to value-added chemicals. In cases where ANG production is not sufficient to justify on-site capture and added investments for transportation and storage, there may be commercial opportunities to perform conversion and value-added processing of ANG. This analysis focuses on CH 4 conversion to liquid intermediates, as opposed to gaseous product, due to the opportunity to transport large volumes of liquids, by truck or rail as opposed to pipeline. This report assesses market opportunities for four liquid intermediates: benzene, toluene, xylene, and formic acid.

03 NATURAL GAS↗
DOE OSTI2021

Building Neutron Scattering Infrastructure in Louisiana for Advanced Materials (Final Report)

The main objective of the Louisiana Consortium for Neutron Scattering (LaCNS) is to build a major neutron scattering infrastructure capable of treating both soft and hard materials. The goal is to create a sustainable effort having the quality, breadth, and depth necessary to produce competitive proposals including collaborative and center type proposals. Our scientific aim is to understand the role of coupling in emergent complex materials and its impact on the structure/property relationship and to explore how to tune the key couplings to guide the design of materials with the desired properties. This naturally includes building a base of users of the Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) in Louisiana; to train highly talented graduate and post-doctoral students in synthesis, neutron scattering, and simulation and modeling techniques, thereby helping to produce the next generation of scientist who use neutron scattering techniques as a crucial part of their research. The goal of our hard matter program is to understand the interplay between spin, charge, orbital, and lattice degrees of freedom in carefully selected complex materials. We have made considerable progress on number of complex systems including the oxide Sr3(Ru1-xMnx)2O7 where short-range magnetic ordering with anisotropic spin texture is initiated at the metal-insulator transition that clearly indicates a strong spin-charge coupling. Another critical area is in quantum materials. For example, in the topological semimetal BaMnSb2, we found a 3D canted antiferromagnetic Weyl semimetal with a 2D electronic structure and a nontrivial Berry phase. In addition, in the hexagonal chiral Mn1/3NbS2 system, we found soliton/soliton and soliton/antisoliton domain walls where the application of small fields or small currents can be used to control nanoscopic magnetic domains where the control of domain walls is crucially important for information storage. In addition, chemical transformation investigations were performed on the VISION instrument at Spallation Neutron Source (SNS). The soft matter effort was focused on understanding the role of non-covalent interactions on the structure and dynamics of fluid-based soft matter. One key focus was on sequence-defined (SD) amphiphilic peptoid polymers that allow encoding of molecular interactions and thereby systematic investigations of how charge directs the solution self-assembly of amphiphilic polymers in water. Another important area was on the dynamics of lipids self-assembled into membranes for exploring the permeability and mechanical using both neutron spin echo (NSE) spectroscopy and quasielastic neutron scattering (QENS) to distinguished between viscoelasticity and permeability at the molecular scale. Overall, the LaCNS project was quite successful, generating 145 publications and 245 presentations. Our graduate and post-doctoral students were well trained resulting in positions in national laboratories (Oak Ridge National Laboratory (ORNL), Argonne National Laboratory and Los Alamos National Laboratory), major research universities, and industry. We also developed a uniaxial pressure cell along with ORNL for SNS. A critical goal of this project was to establish a foundation for competing nationally in federally funded research programs. To this end, we were quite successful in generating over twenty-two federally and non-federally funded grants including awards from NSF, DOE, and DOD, and two early career awards. Equally important, we were able to secure a key major piece of instrumentation via a large ARO grant for a state-of-the-art electron microscope.

36 MATERIALS SCIENCE↗
DOE OSTI2023

NRAP-Open-IAM Multisegmented Wellbore Reduced-Order Model: Improvement and Quality Assurance

The multisegmented wellbore model (MSW) semi-analytically estimates the amount of CO 2 and brine leakage from a leaking legacy well by segmenting it into intervals to simulate site-specific stratigraphic and hydrogeologic properties. The model is a component of the National Risk Assessment Partnership Open-Source Integrated Assessment Model (NRAP-Open-IAM), which was developed to perform risk assessment for geologic CO 2 storage. The new wellbore leakage model, which uses deep learning networks for a caprock segment, was developed to enhance the analytical MSW. The model was trained and validated using a synthetic data set of Subsurface Transport Over Multiple Phases (STOMP) multiphase flow simulations from various geological, well attribute, and operational conditions to ensure its quality. The results demonstrate that the model is more accurate than the existing model in predicting the transport of two-phase fluids (brine and injected CO 2 ) through the well. This report provides a detailed explanation of the model development and quality assurance.

58 GEOSCIENCES↗
DOE OSTI2023

AUSTENITIC STAINLESS STEEL WELD EMBRITTLEMENT BY HYDROGEN AND TRITIUM

-Commercialization of fusion power requires large scale, local re processing of D/T fuel, with throughputs ~1/6 of current global tritium supply per plant per day -Tritium is a radioactive material that has special needs in handling, processing, and storage -Tritium’s effects on the safety performance of primary pressure boundary materials (likely austenitic stainless steel) will be important for regulatory acceptance

KRENTZ, TIMOTHY M.↗
DOE OSTI2023

FY23 Status Report: SNF Interim Storage Canister Corrosion and Surface Environment Investigations

Work evaluating spent nuclear fuel (SNF) dry storage canister surface environments and canister corrosion progressed significantly in FY23, with the goal of developing a scientific understanding of the processes controlling initiation and growth of stress corrosion cracking (SCC) cracks in stainless steel canisters in relevant storage environments. The results of the work performed at Sandia National Laboratories (SNL) will guide future work and will contribute to the development of better tools for predicting potential canister penetration by SCC.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Integrated Heat Exchanger/Thermal Energy Storage System for CSP Plants

The research and development during the project focused on the design and optimization of an integrated heat exchanger/thermal energy storage (HX/TES) system, the charging and discharging performance of the developed HX/TES system, and the experimental investigation of lab-scale prototypes based on the full-scale system.

42 ENGINEERING↗