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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Recycled graphite enabled superior performance for lithium ion batteries

Recycling graphite attracts growing attention since cumulative amount of spent Li-ion batteries and the shortage of graphite supply chain. Although various recycling methods have been reported, the recycled graphite cannot reach the strict commercial standards of purity, scalability, efficiency, and capacity, preventing it from battery manufacturing. Herein, the important roles of defects and functional groups on the graphite surface are deeply studied, and a closed-loop graphite recycling process with the surface recovery and modification for the graphite from the end-of-life batteries is demonstrated. The recovered graphite delivers a purity of over 99.9 % and an average initial coulombic efficiency of 91.5 %. Compared with commercial graphite in industrial standard battery testing parameters, full cells with recovered graphite possess enhanced rate reversibility, doubled cycle life, over 10 % higher capacity along with half anode material cost. In conclusion, these impressive results not only underscore the transformative potential of surface reconstruction and modification in graphite recycling, but also present economic feasibility and sustainable pathway for significantly improving battery performance and addressing global resource challenges via integration with the hydrometallurgical recycling process.

25 ENERGY STORAGE↗
DOE OSTI2024

Amine-Assisted Ligand-Exchange Method to Enhance Photovoltaic Parameters in FAPbI3 Nanocrystal Solar Cells

Perovskite colloidal nanocrystals (PeNCs) have exceptional optoelectronic properties and phase stability, making them promising for photovoltaic applications. However, insulating ligands on PeNC surfaces limit the current density and reduce the power conversion efficiency (PCE) in PeNC solar cells (SCs). This study introduces an amine-assisted ligand-exchange (ALE) strategy using 3-phenyl-1-propylamine (3P1P) to effectively remove long ligands from PeNC films. ALE reduced long-chain ligand density without increasing the number of defect states and therefore reduced the exciton-binding energy of FAPbI3 NC films. These changes facilitated exciton dissociation and charge transport in FAPbI3 PeNC SCs. The facilitation of exciton dissociation was due to the increased magnetic dipole interaction between excitons after the ALE process. The use of ALE achieved FAPbI3 PeNC SCs that had an improved short-circuit current density of 17.98 mA/cm2 and a PCE of 15.56% with improved stability after the treatment and negligible hysteresis. This work provides new insight into engineering PeNC films.

amines↗
DOE OSTI2024

Multidimensional Rietveld refinement of high-pressure neutron diffraction data of PbNCN

High-pressure neutron powder diffraction data from PbNCN were collected on the high-pressure diffraction beamline SNAP located at the Spallation Neutron Source (SNS) of Oak Ridge National Laboratory (Tennessee, USA). The diffraction data were analyzed using the novel method of multidimensional (two dimensions for now, potentially more in the future) Rietveld refinement and, for comparison, employing the conventional Rietveld method. To achieve two-dimensional analysis, a detailed description of the SNAP instrument characteristics was created, serving as an instrument parameter file, and then yielding both cell and spatial parameters as refined under pressure for the first time for solid-state cyanamides/carbodiimides. The bulk modulus B 0 = 25.1 (15) GPa and its derivative B ′ 0 = 11.1 (8) were extracted for PbNCN following the Vinet equation of state. Surprisingly, an internal transition was observed beyond 2.0 (2) GPa, resulting from switching the bond multiplicities (and bending direction) of the NCN 2− complex anion. The results were corroborated using electronic structure calculation from first principles, highlighting both local structural and chemical bonding details.

Meinerzhagen, Yannick↗
DOE OSTI2022

Li-Ion Battery Electrode Contact Resistance Estimation by Mechanical Peel Test

Li-ion battery electrode electronic properties, including bulk conductivity and contact resistance, are critical parameters affecting cell performance and fast-charge capability. Contact resistance between the coating and current collector is often the largest electronic resistance in an electrode and is affected by chemical, microstructural, and interfacial variations. Direct measurements of contact resistance and bulk conductivity have proven to be challenging. In their absence, a mechanical electrode peel test is often used to compare adhesion and electrical contact resistance. However, using a micro-flexible-surface probe, contact resistance can be directly determined. Here, this work compares contact resistance and mechanical peel strength of multiple commercial-grade HE5050 and NCM523 cathodes and graphite and silicon anodes. It was found that peel strength correlates well with contact resistance in a carefully curated data set (p < 0.05) and in some situations may be a good metric to estimate electrical properties. However, there were distinct outliers in the data set, indicating that peel strength may not accurately reflect electrical properties when there is significant variation in electrode composition. These results illustrate the value of the micro-flexible-surface probe in quantifying contact resistance and bulk conductivity to better understand how battery composition and processing steps affect microstructure and resulting cell performance.

25 ENERGY STORAGE↗
DOE OSTI2022

Advanced Thin Film Core Technology: CIGS Final Technical Report (FTR)

Cu(In,Ga)Se 2 (CIGS) thin-film photovoltaics are a high-efficiency and reliable technology. This project completed research in two important areas and was designed to work collaboratively with industrial partners. Task 1: Alkali Science focused on alkali post-deposition treatments (PDT). PDTs have been instrumental in the dramatic voltage improvements that have moved CIGS device efficiencies from 20% to 23.35% [1]. Based on a survey of CIGS companies at the beginning of the project, the single biggest breakthrough for the CIGS community would be a mechanistic understanding of the role of alkalis in the material system. Significant accomplishments of Task 1: Alkali Science: 1) KF post-deposition treatments were shown to improve lifetime, open-circuit voltage (VOC), and efficiency of industrial partner samples, even when done as a later step, separate from the original CIGS deposition. 2) KF boosted efficiency when incorporated at the end of the third stage of NREL CIGS growth. 3) XPS characterization of CIGS surfaces with and without PDTs led to a proposed mechanism whereby K drives structural transformation at 350 degrees C that is locked in at room temperature even after K is rinsed away. 4) Published recipes for KF and RbF PDTs. Literature to date did not provide enough detail to quickly reproduce experimental results. 5) Identified most important parameters (RbF cell temperature and lamp setpoint temperature) and set boundaries for successful RbF PDTs. The purpose of Task 2: Cell-level Reliability was to overcome the largest challenges to investor confidence and long product lifetime in CIGS-based photovoltaic products: metastability, shading-induced hot spots, and potential-induced degradation (PID). Key findings were made in each of these areas by studying CIGS reliability at the cell level: 1) Published NREL's cell-level reliability testing procedures along with challenges that were encountered while developing them. These were also distributed to the community through an MRS conference presentation. 2) Decreased metastability by adding a CdS hole-injection layer between the CIGS and Zn(O,S) in the device stack. It also improved device performance. Materials other than CdS can be used for the same purpose. 3) Reduced front-glass PID by replacing soda-lime glass with low-Na borosilicate glass. 4) Found that PID depends on leakage current and light/electrical bias. This will help labs avoid test-specific degradation. 5) Discovered that CIGS can suffer from two different types of PID with different mechanisms. Front is slower and leads to shunting ZnO. Back is faster and degrades the p-n junction. 6) Holding cells at open circuit slows PID compared to short circuit. This affects testing protocols for glass/glass modules.

14 SOLAR ENERGY↗
DOE OSTI2025

Bridging the length scales in ionic separations via data-driving machine learning

We pursued a data science driven machine learning (ML) approach that blended molecular scale attributes informed from molecular dynamics (MD) simulation and materials properties to the selectivity and energy efficiency in targeted ionic separations using electric fields. The model mixtures investigated for ionic separations are pH sensitive and include organic acids, silica and boron, transition metals, such as copper and chromium. There were two major research thrusts of this project. Firstly, we investigated surrogate models and deep learning that relate material chemistries and structures to selective transport of ionic species under applied electric fields. Secondly we investigated how the bipolar junction interfacial design and water dissociation catalyst in bipolar membranes affect reverse bias polarization behavior and pH modulation in deionization platforms as a function of the platform operating parameters (e.g., cell voltage, residence time, and salt feed concentration). As a result of this work, we also were able to start a new direction, namely ML models for molecular design of surfactants.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Molten Salt Electrodeposition: Review

Molten salt electrodeposition is the process of producing impressively dense deposits of refractory metals using the electrolysis of molten salts. However, predicting which electrochemical parameters and setup will best control different kinds of deposition (density, homogeneity, etc.) is an ongoing challenge, due to our limited understanding of the properties and mechanisms that drive molten salt electrodeposition. Because these advancements have been made rapidly and in different arenas, it is worth taking the time to stop and assess the progress of the field as a whole. These advancements have increasing relevance for the energy sector, the development of space materials and engineering applications. In this review, we assess four critical facets of this field: (1) how the current understanding of process variables enhances the electrodeposition of various molten salts and the quality of the resulting product; (2) how the electrochemical setup and the process parameters (e.g., cell reactions) are known to impact the electrodeposition of different metal coatings and refractory-metal coatings; (3) the benefits and drawbacks of non-aqueous molten salt electrodeposition, and (4) promising future avenues of research. The aim of this work is to enhance our understanding of the many procedures and variables that have been developed to date. The expectation is that this review will act as a stimulant, motivating scientists to delve further into the investigation of refractory-metal alloys by utilizing molten salt electrodeposition.

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

Predicting Device Parameters for Dye-Sensitized Solar Cells from Electronic Structure Calculations to Reproduce Experiment.

Given that improvements to the power-conversion efficiency (PCE) of dye-sensitized solar cells (DSSCs) have slowed in recent years, a means to accurately predict the device parameters yielded by trial dyes in silico, without having to synthesize them, would be extremely valuable to speed up the design process. Currently, the best-performing methods of calculating device parameters rely on a set of experimentally determined kinetic coefficients. In practice, it is very difficult to measure these kinetic parameters accurately, limiting the overall accuracy of such predictive methods. This work proposes a model to obtain key parameters such as J(SC), V-OC, and PCE using only the results from density functional theory (DFT) and time-dependent DFT calculations, noting that rates of electron-transfer steps are ultimately linked to the electronic structure of the dye center dot center dot center dot TiO2 working electrode. Six organic DSSC dyes from dissimilar chemical classes (L0, L1, L2, WS-2, WS-92, and C281) were chosen to demonstrate the power of this approach. Their a priori known experimentally determined device performance metrics served to validate our predictions. The greatest absolute error in our predicted PCE values was 0.36% relative to the experiment, while the greatest fractional error was 0.042. This indicates that the proposed model offers a dramatic improvement on previous predictive methods for DSSC device parameters, both in accuracy and in consistency. Moreover, such a predictive model has great potential to be applied to other photovoltaic applications, further enabling the design of novel, highly efficient photoactive materials.

density functional theory↗
DOE OSTI2019

Memory cell comprising coupled Josephson junctions

Methods and apparatus are disclosed for operating a memory cell formed from the plurality of coupled Josephson junctions. The memory cell is configured such that applying an electrical signal to the junctions can cause at least one, but not all, of the junctions to change their respective phase states. Subsequent writes to the memory cell using substantially the same electrical pulse do not change the phase state of the plurality of junctions. The memory cell can be ready by providing another electrical pulse to one of the junctions and receiving an output electrical pulse generated in response by a different Josephson junction of the memory cell. A set of phase states are selected to represent the logic values that are stable across anticipated operating conditions for the memory cell. Methods of selecting electrical parameters and manufacturing memory cells are further disclosed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Optimizing Perovskite Thin‐Film Parameter Spaces with Machine Learning‐Guided Robotic Platform for High‐Performance Perovskite Solar Cells

Abstract Simultaneously optimizing the processing parameters of functional thin films remains a challenge. The design and utilization of a fully automated platform called SPINBOT is presented for the engineering of solution‐processed functional thin films. The SPINBOT is capable of performing experiments with high sampling variability through the unsupervised processing of hundreds of substrates with exceptional experimental control. Through the iterative optimization process enabled by the Bayesian optimization (BO) algorithm, the SPINBOT explores an intricate parameter space, continuously improving the quality and reproducibility of the produced thin films. This machine learning (ML)‐guided reliable SPINBOT platform enables the acceleration of the optimization process of perovskite solar cells via a simple photoluminescence characterization of films. As a result, this study arrives at an optimal film that, when processed into a solar cell in an ambient atmosphere, immediately yields a champion power conversion efficiency (PCE) of 21.6% with satisfactory performance reproducibility. The unsealed devices retain 90% of their initial efficiency after 1100 h of continuous operation at 60–65 °C under metal‐halide lamps. It is anticipated that the integration of robotic platforms with the intelligent algorithm will facilitate the widespread adoption of effective autonomous experimentation to address the evolving needs and constraints within the materials science research community.

14 SOLAR ENERGY↗
DOE OSTI2024

Behavioral Ensemble CLM5 Hydrological Parameter Sets

This repository contains hydrological parameter sets derived using the hybrid regionalization method for three distinct streamflow signatures: Streamflow Signatures: Q10: Represents low flow, indicating the nonexceedance probability of 0.1 for daily streamflow. Q90: Represents high flow, with a nonexceedance probability of 0.9 for daily streamflow. Qmean: Indicates the mean annual flow. Parameters for 464 CAMELS Basins: CAMELS_1000_parameters.csv: Contains 1,000 ensemble parameter sets generated using the Latin hypercube sampling method for CLM5, encompassing 15 hydrological parameters. CAMELS_q10_behavioral_parameter_num.csv: Provides the behavioral ensemble parameter sets for the Q10 streamflow signature for each basin. The associated ID number refers to entries in the CAMELS_1000_parameters.csv file. A minimum of 10 ensemble parameter sets are available for each basin. CAMELS_q90_behavioral_parameter_num.csv: Similar to the above file but for the Q90 streamflow signature. CAMELS_qmean_behavioral_parameter_num.csv: Corresponds to the Qmean streamflow signature, similar to the previous files. Parameters for 50,629 1/8° CONUS Land Grid Cells: CONUS_350_parameters.csv: Contains 350 ensemble parameter sets derived using the Latin hypercube sampling method for CLM5's 15 hydrological parameters within 1/8° CONUS land grid cells. CONUS_q10_behavioral_parameter_num.csv: Holds the behavioral ensemble parameter sets for the Q10 streamflow signature, organized for each grid cell. The ID number relates to entries in CONUS_350_parameters.csv. A minimum of 10 ensemble parameter sets are provided for each grid cell. CONUS_q90_behavioral_parameter_num.csv: Similar to the above file but focusing on the Q90 streamflow signature. CONUS_qmean_behavioral_parameter_num.csv: Corresponds to the Qmean streamflow signature, following a similar structure to the previous files.

Yan, Hongxiang↗
DOE OSTI2023

Bipolar Membrane Capacitive Deionization for pH-Assisted Ionic Separations

Selective ionic separations represent an increasingly important technical area for the strategic interests of the U.S. economy–for example, securing critical minerals and materials and circular economy aspirations that include recovering organic acids from processed biomass. This work disseminates bipolar membrane (BPM) capacitive deionization for selective ionic separations from multicomponent, ionic species mixtures. The selective separations are guided by the Pourbaix diagram and acid–base equilibria principles. BPM capacitive deionization was demonstrated to generate alkaline or acidic process streams depending upon the location of the BPM in the electrochemical cell. The role of system operating parameters, such as the cell voltage, residence time, and feed concentration on effluent stream pH was studied. It was observed that the pH adjustment in BPM-CDI/MCDI (MCDI, membrane capacitive deionization) was more sensitive to the cell voltage when compared to the process stream residence time and salt feed concentration. The BPM-MCDI gave over 6 times higher percentage of copper(II) removal when compared to sodium ion removal from brine mixtures. Lastly, BPM-MCDI demonstrated over 40% greater removal for copper ions from brine mixtures and fivefold higher removal for itaconic acid from brine mixtures when benchmarked against a traditional flow-by-MCDI setup.

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

Operating Stresses and their Effects on Degradation of LSM-Based SOFC Cathodes

The overall goals of the program were: To understand how selected operational parameters affect the performance of solid oxide fuel cells (SOFCs), with a focus on parameters affecting cathodes (air electrodes) comprised of lanthanum strontium manganite (LSM, (La 1-x Sr x ) 1-y MnO 3±δ ) and yttria-stabilized zirconia (YSZ, (Zr 1–z Y z )O 2±δ ). To relate the observed relationships between operational parameters and cell performance to the microstructural changes that can be observed in the cells after operation.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Physics-constrained deep neural network method for estimating parameters in a redox flow battery

Here, in this paper, we present a physics-constrained deep neural network (PCDNN) method for parameter estimation in the zero-dimensional (0D) model of the vanadium redox flow battery (VRFB). In this approach, we use deep neural networks to approximate the model parameters as functions of the operating conditions. This method allows the integration of VRFB computational models as the physical constraints in the parameter learning process, leading to enhanced accuracy of parameter estimation and cell voltage prediction. Using an experimental dataset, we demonstrate that the PCDNN method can estimate model parameters for a range of operating conditions and improve the 0D model prediction of voltage compared to the 0D model prediction with constant operation-condition-independent parameters estimated with traditional inverse methods. We also demonstrate that the PCDNN approach has an improved generalization ability for estimating parameter values for operating conditions not used in the training process.

25 ENERGY STORAGE↗