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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

PV Lifetime Project (2021 NREL Annual Report)

DOE's PV Lifetime project was initiated in 2016 with the goal of accurately characterizing the early-life evolution of photovoltaic (PV) field performance. Different PV cell and module technologies result in different initial performance loss rates due to effects like light-induced degradation (LID) and light & elevated temperature-induced degradation (LeTID). To accurately characterize the initial field performance loss requires the use of high-accuracy indoor IV curve measurements at standard test conditions. Therefore, PV modules involved in this study are removed from the field once or twice per year and brought indoors for measurement. Current samples deployed and monitored in this way include Jinko Solar (2016), Trina Solar (2016), Hanwha Q-Cells (2017), Panasonic (2018), LG (2018), Canadian Solar (2018), Mission Solar (2019). More recently, modules from Sunpreme (2019), and LONGi (2020) have been deployed but not yet analyzed. Overall annual performance loss rates are as follows: our first modules to be deployed (Jinko and Trina) have annual median performance loss rate between -0.4%/yr and -0.9%/yr, mainly concentrated in the first year. The QCells mono-PERC and multi-PERC modules have an annual degradation rate of -0.76%/yr and -0.69%/yr respectively, also concentrated in the first year of operation. Panasonic and LG modules displayed negligible performance loss in the past two years, at 0.1%/yr and -0.0%/yr respectively. They also were the only modules with initial IV curve measurements consistently above the nameplate rating. Possibly relatedly, these are also the only two N-type silicon module types analyzed so far. Canadian Solar multi-PERC modules demonstrated a -1.3%/yr degradation rate which actually accelerated in the past year, so this will be a module type to monitor in future years. Mission Solar modules exhibited strong recoverable performance loss, consistent with LeTID susceptibility. (The same is true for the Jinko JKM260 module type). Annual performance loss actually showed improvement in time at +0.3%/yr after 2 years in the field, although the module initially was measured at 3% below nameplate rating. These modules could therefore be experiencing a form of post-LeTID recovery. Initial measurements have been conducted on the next two module types - Sunpreme n-HIT and LONGi bifacial mono-PERC. We will report on initial year-1 performance change for these modules in the next PV Lifetime annual report. For the remaining modules, an additional year of field exposure will provide greater certainty in annual degradation rates, particularly for those with degradation concentrated in the initial year of field deployment.

14 SOLAR ENERGY↗
DOE OSTI2024

Availability and Performance Loss Factors for U.S. PV Fleet Systems

In the PV Fleet Performance Data Initiative, we partner with photovoltaic (PV) fleet owners to collect time-series PV production data and publish aggregated, anonymized results. This report is an update of our previous publications, specifically a FY 2021 performance index publication and a FY 2022 fleet degradation analysis. In this analysis, we have increased our data participants and system totals by around 10% to 8.5 GW and 24,000 separate inverter data channels. Four major analysis topics are considered in this report: Performance Index (PI) trends, PV system availability, soiling losses, and PV system degradation. Performance Index and inverter availability are assessed on a larger set of data from our FY 2021 report: 1,128 systems compared with 200 systems from before. The increased number of systems is due to an improved data quality methodology, as well as introducing new systems to the analysis. Overall results are similar to previously published values - overall inverter availability is low in the first six months of system performance before reaching steady-state by the end of the first year. Excluding this six-month startup period, system-level aggregated data shows a median (P50) system availability of 0.99 and a lower 10th percentile (P90) value of 0.95 (Figure ES-1). A dependence on system size is also demonstrated, with worse inverter availability results for larger PV systems. Causes of this effect are under investigation, but may be impacted by inverter size, which also show lower availability for larger inverter sizes. This report also investigates PI, correcting for degradation, soiling, snow, and availability. Following these corrections, the median system PI over its entire lifetime is 0.95. PI values reported here are approximately 3% lower than what we presented in our previous FY 2021 report. Soiling loss is assessed in a comprehensive way for the first time in this report. Results are presented using the COmbined Degradation and Soiling (CODS) method, as implemented in RdTools (v3.0.0a4). Soiling values are presented for 255 systems, which indicated irradiance-weighted soiling loss greater than 1%. The values have been published in an updated NREL soiling map at nrel.gov/pv/soiling.html. Finally, we investigated system degradation using three different data analysis techniques: conventional RdTools (year-on-year (YOY)), CODS, and Performance Loss Rate (PLR) analysis. Overall degradation results are consistent with our previous publications. Rerunning conventional RdTools on our updated fleet shows that some data partners have systematically fallen below the median system degradation rate (change over time) of -0.75 %/year. A comparison with PLR analysis, which looks at change in annual PI over time, shows that median system degradation is consistent with -0.5% to -0.75% per year change. However, at the P90 value, system degradation is substantially faster. These two results are consistent and indicate that resulting degradation statistics depend to a great degree on the population of PV systems making up the analysis cohort and whether soiling impacts the systems. The use of CODS for degradation analysis provides a different method for degradation assessment, which explicitly excludes the impact of recoverable soiling on degradation analysis. Excluding soiling effects yields an annual system degradation around -0.5% per year on average. This indicates that a portion of system performance loss may be attributed to periodic soiling that is not fully recovered. This report provides PV system owners/operators with background and methods to analyze PV system performance, give guidance for expected cohort performance, and performance loss values for use in pro-forma financial models, which guide new-build system design and bankability reports.

14 SOLAR ENERGY↗
DOE OSTI2021

Evaluating temperature dependent degradation mechanisms of silicon-graphite electrodes and the effect of fluoroethylene carbonate electrolyte additive

Understanding and overcoming the relatively high rate of degradation observed with silicon (Si)-based anodes in lithium (Li)-ion batteries (LiBs) is crucial for developing cells with increased energy density. The capacity fade of Si-graphite (Gr) electrodes in electrolytes with and without 10 wt% fluoroethylene carbonate (FEC) is explored during cycling at temperatures of 25, 45, and 70 °C, focusing on the complex degradation modes arising from loss of active material, loss of Li inventory, and cell resistance growth. We first show the common half-cell testing cannot be used at elevated temperatures as the performance of Li metal overshadows the behavior of Si-Gr electrode. Symmetric cell configuration successfully eliminates the effect of foreign materials, and reveals that while the presence of FEC provides better passivation of Si, it also results in greater parasitic reactions at 70 °C and faster capacity fade. The electrolyte without FEC, on the other hand, shows higher capacity retention at 70 °C. Finally, we propose a multi-step test protocol which can successfully deconvolute the information on loss of Li inventory and of active material. By combining half-cell and symmetric cell test methods, we demonstrate that loss of active material is reduced at higher temperatures and the dominant mechanisms of capacity loss for Si-Gr electrodes at elevated temperatures is loss of Li inventory.

25 ENERGY STORAGE↗
DOE OSTI2024

Environmental Compatibility Issues for Ni-Based Alloys in Direct-Fired Supercritical CO2 Power Cycles

Direct-fired supercritical CO2 (sCO2) power cycles offer potential for high-efficiency power generation from natural gas and other fossil fuels with built-in carbon capture. Ni-based superalloys are the leading candidates for the hottest portions of these systems, where they must endure long-term exposure to high-temperature, high-pressure, high-velocity, impure CO2-rich environments. Herein we describe results of various experimental testing campaigns conduced at NETL showing that under such conditions, several simultaneously occurring degradation modes may be active including surface oxidation, alloy carburization, and oxide volatilization. It will be shown that the rates of degradation can be strongly affected by the alloy composition, impurities in the CO2, manufacturing method (wrought vs additive), and other factors. The influence of these factors in controlling alloy degradation are discussed in the context of potential compatibility issues for components in real sCO2 systems, such as compact heat exchangers. Finally, as a potential remedial measure, ongoing work involving protective coatings suitable for complex geometries is presented.

alloy oxidation↗
DOE OSTI2020

Erosion-Corrosion Detection in Carbon Steel Pipe Loops using SRNLs Thickness and Mass Loss Measurement Coupons - 20464

Structural health of waste transfer infrastructure is of utmost importance to DOE and communities surrounding their active sites. A Fitness-for-Service (FFS) program for the Waste Transfer System has been implemented by the US Department of Energy (DOE) for the purposes of evaluating and quantifying the degradation of critical components. Currently, the method of approach is to section and evaluate pipelines that have been removed at end service. In order to augment data collection methods applied to making lifespan estimates of waste transfer system components, Florida International University (FIU) has been investigating new-technology based strategies for quantifying the rate of degradation. Present research considers the option of a coupon system installed on a section of pipe at points of interest that will provide easy access to high resolution measurements of erosion/corrosion rates during operation. Researchers at Savannah River National Laboratory (SRNL) have developed this novel solution for in situ erosion and corrosion measurements in many applicable locations such as tanks, pipes, ducts, and injection points. The coupons are approximately 12.7 mm (1/2 inch) in diameter and 25.4 mm (1 inch) in length and can be placed in the pipe sections using Swagelok compression fittings. The coupon design allows for monitoring of the total degradation resulting from corrosion and erosion. High-resolution Ultrasonic Thickness (UT) testing allows in-situ monitoring of both degradation mechanisms. When removed from the system mass loss resulting from corrosion and erosion may also be evaluated. Experimental testing at FIU implemented the SRNL coupon system in a 6.10 meter (20 foot) pipe loop system comprised of 50.8 mm (2 inch) and 76.2 mm (3 inch) pipe sections in order to replicate expected use cases at the sites. Operational conditions were emulated by pumping abrasive materials through the pipeline at full capacity and collecting measurements from the SRNL coupons. Initial testing was conducted by circulating water followed by a mixture of sand and water for uniform wear rates. Thickness changes and mass loss were measured, and the surface exposed to the process fluid was observed for visual evidence of characteristic wear patterns resulting from an erosive environment. The test matrix consisted of several hours of operation and using fine, medium and coarse grain sands. Test results were analyzed to check for all three parameters (mass loss/gain, thickness change and surface patters) which could provide estimates of the remaining useful life of the pipe sections. Thus, the research presents the findings of this evaluation of the SRNL coupons as a means for conducting high accuracy, cost effective in-situ analysis of pipeline and waste transfer systems for erosion and corrosion susceptibility. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2023

Characterization of Cycle-Aged Commercial NMC and NCA Lithium-ion Cells: I. Temperature-Dependent Degradation

Lithium-ion batteries are widely used in applications from consumer electronic devices to stationary energy storage. Appropriate management of batteries is challenging due to limited data on their performance and materials degradation. Previous studies have focused on characterization of single cells under specific operating conditions. In the present work, commercial 18650 lithium-ion cells with LiNi x Mn y Co 1-x-y O 2 (NMC) and LiNi x Co y Al 1-x-y O 2 (NCA) positive electrodes were characterized by a wide range of electrochemical and materials techniques after cycling at 15, 25, or 35 °C to ∼80% capacity. The NCA cells exhibit weak temperature dependence in their cycle aging and materials degradation. The NMC cells exhibited increased capacity fade and materials degradation as ambient temperature decreased. All cells exhibited loss of lithium inventory as their primary degradation mode. However, the NCA cells only showed evidence of solid electrolyte interphase (SEI) growth whereas the NMC cells showed signs of Li plating at 15 °C, transitioning to SEI growth at 35 °C. The NMC cells displayed signs of loss of active material at the positive electrode at lower temperatures, suggesting that Li plating is correlated to additional processes that increase the rate of degradation. These results highlight the importance of avoiding broad generalizations about Li-ion battery temperature dependence.

25 ENERGY STORAGE↗
DOE OSTI2021

International collaboration framework for the calculation of performance loss rates: Data quality, benchmarks, and trends (towards a uniform methodology)

Abstract The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high‐quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

14 SOLAR ENERGY↗
DOE OSTI2025

Decoding the Desorption Mechanism of 2LiH:1Mg(NH2)2 Using Metal Borohydrides

The complex metal hydride 2LiH:1Mg­(NH2)2 has emerged as a promising material for stationary hydrogen storage applications, such as seasonal storage or energy backup systems, due to its high volumetric and gravimetric capacities and robust reversibility. However, its widespread adoption is hindered by sluggish reaction rates, performance degradation upon cycling, and improper end-use cases. To address these problems and better understand the desorption pathway, we used metal borohydrides (MBH4; M = Li–Cs) as chemical probes. A thorough analysis of the bulk behavior of all six materials, including hydrogen cycling experiments, X-ray absorption spectroscopy, FTIR, pXRD, solid-state NMR, and ab initio DFT simulations, shows that the borohydride additives decrease the activation energy of hydrogen release by about 20 kJ/mol for MBH4@2:1 materials versus pristine. Furthermore, more surface-sensitive studies show that the amide-to-imide desorption pathway in these materials, while essentially complete in the bulk, is incomplete in the near-surface region, suggesting an “inverse core–shell” desorption mechanism for amide dehydrogenation to imide. The kinetic enhancements produced by MBH4 additives (M = K, Rb, and Cs) are attributed to the destabilization of the amide N–H bond and interaction with the LiH/Mg­(NH2)2 interface to promote H–H bond formation. An inverse core-shell mechanism is also operative in the hydrogen desorption for the 2LiH:1LiNH2 system, suggesting this may be a general feature of amides. Given the fast dehydrogenation rate and large gravimetric capacity, these materials satisfy these requirements for telecom backups and seasonal microgrid storage applications.

Absorption↗
DOE OSTI2022

Photovoltaics module reliability for the terawatt age

Photovoltaics (PV), or solar electricity generation, has become the cheapest form of energy in many locations worldwide and, combined with energy storage, has the potential to satisfy most of our electricity needs. PV has grown at an annual compounded growth rate of approximately 30% in the last three decades. Solar energy systems will continue their impressive growth in distributed energy, microgrids, and utility scale, as efforts are made for dependable electricity in an age with increasing extreme weather. However, within this remarkable success lies a new challenge. The growth curve, combined with rapid product innovation and scale up, means that the majority of PV systems are new, without the three years of performance data that have been required in the past to estimate product lifetime. PV reliability has to address this challenge. In this review we present a brief summary of PV reliability starting with brief historical synopsis, detailing some of the technological challenges and present a framework required for long lifetime.

14 SOLAR ENERGY↗
DOE OSTI2026

Two-Level Sketching Alternating Anderson Acceleration for Complex Physics Applications

We present a novel two-level sketching extension of the Alternating Anderson–Picard (AAP) method for accelerating fixed-point iterations in challenging single- and multiphysics simulations governed by discretized PDEs. Our approach combines a static, physics-based projection that reduces the least-squares (LS) problem to the most informative field (e.g., via Schur-complement insight) with a dynamic, algebraic sketching stage driven by a backward stability analysis under Lipschitz continuity. We introduce inexpensive estimators for stability thresholds and cache-aware randomized selection strategies to balance computational cost against memory access overhead. The resulting algorithm solves reduced LS systems in place, minimizes memory footprints, and seamlessly alternates between low-cost Picard updates and Anderson mixing. Implemented in Julia, our two-level sketching AAP achieves up to 50% time-to-solution reductions compared to standard Anderson acceleration—without degrading convergence rates—on benchmark problems including Stokes, 𝑝-Laplacian, bidomain, and Navier–Stokes formulations at varying problem sizes. These results demonstrate the method’s robustness, scalability, and potential for integration into high-performance scientific computing frameworks. Our implementation is available open source in the AAP.jl library.

Barnafi, Nicolas [University of Chile, Santiago]↗
DOE OSTI2026

Data and scripts associated with a manuscript modeling microbial regulation of priming effects

This data package is associated with the publication “Modeling Microbial Regulatory Feedback in Organic Matter Decomposition Identifies Copiotrophic Traits as Key Drivers of Positive Priming” published as a preprint on BioRXiv by Ahamed et al. (2026); https://doi.org/10.1101/2024.08.11.607483. The package contains MATLAB scripts and saved simulation outputs used to implement a cybernetic model of microbial regulation during complex organic matter (OM) decomposition governing priming effects. It includes models of (i) single microbial functional groups (copiotrophic or oligotrophic degraders) and (ii) binary consortia composed of degraders and non-degraders with contrasting or common growth traits. Simulation results were generated using Monte Carlo analyses, with randomized key model parameters across a range of environmental mixing fractions of complex and labile OM. The dataset was created to provide a transparent and reusable computational framework for systematically exploring how microbial growth traits, metabolic regulation, and community composition influence OM decomposition dynamics and priming effects. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes the variable definitions. This package includes: (1) annotated MATLAB code implementing the system of ordinary differential equations and cybernetic control laws; (2) saved output files containing data (e.g., biomass, substrates, enzyme levels, priming metrics); and (3) scripts for processing saved outputs and regenerating figures. Specifically, the data package contains three main MATLAB scripts: runPrimingModel.m, runPlotData.m, and runPlotSuppFigS1.m, along with this readme and supporting documentation. Users should begin with runPrimingModel.m, which contains the annotated code implementing the system of ordinary differential equations and cybernetic control laws. This script runs the Monte Carlo simulations of microbial OM decomposition and allows users to modify microbial trait definitions, adjust parameter distributions, or define new community configurations. Simulation outputs are automatically saved as .mat files in the folder named SavedData, which stores all pre-generated results included in this package. The second script, runPlotData.m, reads files from the SavedData folder and processes them to regenerate the figures presented in the manuscript. The third script, runPlotSuppFigS1.m, specifically generates Figure S1 in the Supplementary Material of the manuscript. The package also includes the aforementioned files in non-proprietary .txt format. If users intend to use them, they should first save the files in their respective .m or .mat formats prior to execution in MATLAB.

Biomass concentration↗
DOE OSTI2024

Performance Modelling and Yearlong Outdoor Degradation Analysis of a GaAs//Si Tandem Module

Silicon-based tandem photovoltaic cells and modules are forecasted to enter mass production around the year 2026. Previous research efforts on tandem photovoltaic technology have focused heavily on increasing cell efficiencies. However, for successful large-scale deployment of tandem modules, reliability and long-term durability will be equally important. Outdoor performance data beyond the one-year mark enable sophisticated degradation analysis and help build confidence in the reliability and durability of this new technology.

degradation rate↗
DOE OSTI2021

Local Resistance Measurement for Degradation of c-Si Heterojunction with Intrinsic Thin Layer (HIT) Solar Modules

Silicon heterojunction with intrinsic thin layer (or HIT) modules typically degrade at a rate of less than 1% annually in solar fields with dominant degradation in open-circuit voltage and some degradation in series resistance. However, detailed mechanisms can differ from module to module. Here, we study increases in local series resistance that occur over long-term field deployment, indicated by cell areas where the photoluminescence intensity does not degrade but the electroluminescence degrades significantly. To directly measure the local series resistance, we have cored out the local electroluminescence-degraded area, and we measured the sheet resistance by 4-point-probe and local nm-scale resistance using scanning spreading resistance microscopy (SSRM). The results by 4-point-probe show scattered sheet resistance that can be caused, for example, by nonuniform current paths through the transparent conductive oxide layer, the a-Si:H emitter, or the near-junction c-Si inversion layer. In contrast, the SSRM results indicate a relatively uniform and non-degraded resistivity on smaller nanometer spatial scales. SSRM is an atomic force microscopy-based two-terminal resistance mapping technique that measures the local resistance in nm-volume beneath the probe. The consistent resistances measured on the control and degraded samples can exclude the degradation of transparent conductive oxide resistance.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2020

Auto-BLAST (AutoBLAST) [SWR-20-93]

Battery life modeling often involves a lot of manual parameter fitting and is not easy for users to adopt the model and use it. To reduce the difficulties for users to adopt battery lifetime models, an automatic battery lifetime modeling analysis and simulation tool suite, Auto-BLAST, has been developed. Auto-BLAST includes a lithium-loss-base life model, a battery electric model, and an algorithm which automatically fits all key parameters in the electric and life models using user provided data. The models and algorithm are packaged into two user-friendly GUIs, Auto-LifeMod, for easy battery life prognostic model fitting and Auto-LifeSim, for easy battery lifetime simulation. The lithium-loss-based life model adopts a similar model framework that models degradations using aging rate models, using battery cycling data to predict battery degradation and expected lifetime. The electric model is an equivalent circuit model which simulates battery voltage responses based on current/power demand profiles. The auto-fitting algorithm uses the user-input data to generate custom battery life model(s). Two GUIs, wrapping around the life model and the auto-fitting algorithm, provide friendly interfaces for users to generate a life model and use it for case study. One GUI requires summary data from accelerated battery life degradation tests as an input and produces battery life models predicting (a) capacity degradation, and (b) resistance growth of the battery. The GUI displays the electrical model response and the input experimental data against the life model predictions with fitted model parameters and fitting error. The GUI also generates an output file to save the fitted model parameters. The second GUI uses the life model from the first GUI and a user-defined battery cycling profile to predict battery lifetime degradation and expected lifetime. Predicted capacity and resistance vs. time are plotted in the GUI and saved to output file. There is a user guide for both GUIs

Mishra, Partha↗
DOE OSTI2022

Biofilm disruption enhances growth rate and carbohydrate-active enzyme production in anaerobic fungi

Anaerobic gut fungi (AGF) are lignocellulose degraders that naturally form biofilms in the rumen of large herbivores and in standard culture techniques. While biofilm formation enhances biomass degradation and carbohydrate-active enzyme (CAZyme) production in some bacteria and aerobic fungi, gene expression and metabolism in AGF biofilms have not been compared to non-biofilm cultures. Here, using the tunable morphology of the non-rhizoidal AGF, Caecomyces churrovis, the impacts of biofilm formation on AGF gene expression, metabolic flux, growth rate, and xylan degradation rate are quantified to inform future industrial scale-up efforts. Contrary to previous findings, C. churrovis upregulated catabolic CAZymes in stirred culture relative to biofilm culture. Using a de novo transcriptome, 197 new transcripts with predicted CAZyme function were identified. Stirred cultures grew and degraded xylan significantly faster than biofilm-forming cultures with negligible differences in primary metabolic flux, offering a way to accelerate AGF biomass valorization without altering the fermentation product profile. The rhizoidal AGF, Neocallimastix lanati, also grew faster with stirring on a solid plant substrate, suggesting that the advantages of stirred C. churrovis cultures may apply broadly to other AGF.

59 BASIC BIOLOGICAL SCIENCES↗