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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Development of a High-Rate Lithium-Air Battery Using a Gaseous CO 2 Reactant

Li-air batteries are considered a potential alternative to Li-ion batteries for transportation applications due to their high theoretical specific energy. Most works in this area focus on use of O 2 as the reactant. However, newer concepts for using gaseous reactants (such as CO 2 , which has a theoretical specific energy density of 1,876 Wh/kg) provide opportunities for further exploration. The main objective of this project was the development of a novel strategy that enables operation of Li-CO 2 batteries at high-capacity and high-rate, with a long-cycle-life. The team was able to: (1) Synthesize two novel transition metal chalcogenide (TMC) catalysts that work in synergy with ionic liquid-based electrolytes to enhance the efficiency of reactions during discharge and charge processes; (2) Fabricate high-porosity cathode electrodes with 3D printing to increase electrode surface area and gas permeability; (3) Develop a multiscale modeling framework that integrates Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties of Li-CO 2 batteries; (4) Assemble a stackable Li-CO 2 pouch-cell able to deliver a capacity of >200 mAh. These achievements were realized through an integrated approach based on materials synthesis, testing, characterization, analysis, and computation. This project produced a thorough understanding of key chemical, electronic, and kinetic parameters that govern the operation of Li- CO 2 batteries in realistic conditions. The methodologies employed, and the insight generated, will be valuable beyond advancing the field of Li-CO 2 batteries

25 ENERGY STORAGE↗
DOE OSTI2020

Post-Closure Inspection and Monitoring Report for Surface Corrective Action Unit 417 at the Central Nevada Test Area, Nevada, Site

This report documents the biennial postclosure site inspections conducted in June 2018 at the surface Corrective Action Unit (CA U) 417 at the Central Nevada Test Area, Nevada, Site. The UC-I, UC-3, and UC-4 sites are inspected every 2 years, in accordance with the Post-Closure Monitoring Plan provided in the CAU 417 Closure Report published in 2001. The requirements for postclosure monitoring have been modified over the years through negotiations with the Nevada Division of Environmental Protection (NDEP). Modifications were documented through three separate Records of Technical Change to the Closure Report, which were approved by NDEP in 2003, 2011, and 2015. The UC-1, UC-3, and UC-4 sites were all observed as being in good condition during the 2018 inspections. A few minor cracks on the UC-1 Central Mud Pit cover were repaired during the UC- I inspection. A concrete monument at the northeast corner of Mud Pit U3E at the UC-3 site had some damage near the top of the monument, but the damage has not impacted the functionality of the monument or survey pin on top of the monument. No issues were identified at the UC-4 site. No maintenance or additional repair activities are recommended at the UC-1, UC-3, and UC-4 sites.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Discrepant wear behavior of carbon nanotubes (CNTs) and dispersant in four-ball unidirectional and ball-on-flat reciprocating sliding tests

Carbon nanotubes (CNTs), basically rolled graphene sheets, have been studied lately as oil additives in the literature. However, we observed discrepant impact of CNTs on wear protection from two common tribological tests, four-ball unidirectional sliding and high frequency reciprocating rig (HFRR) ball-on-flat reciprocating sliding. To gain a stable suspension and dispersion of the CNTs in the oil, the CNT surface was functionalized with a phenyl ligand and a dispersant, polyisobutylene succinimide (PIBSI), was added. In the four-ball test, PIBSI alone failed to protect the surface but the CNTs effectively reduced the wear loss. The observations in the HFRR test however were the opposite: the PIBSI alone provided strong wear reduction but the CNTs had no positive impact. Here, such a discrepancy was hypothetically attributed to the different wear protection mechanisms by the PIBSI and CNTs which responded distinctively under different testing conditions. Additional unidirectional and reciprocating sliding tests with matching Hertzian contact pressures were able to validate the hypothesis. Worn surface morphological examination and tribofilm chemical analysis further supported the proposed wear mechanisms. Fundamental understanding gained in this study provides insights into the potential benefits and limitations of using CNTs in lubrication.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Low-iridium stabilized ruthenium oxide anode catalyst for durable proton-exchange membrane water electrolysis

While mixing iridium (Ir) with ruthenium oxide (RuO 2 ) has proven to be an effective strategy for reducing Ir loading in anode catalysts for proton-exchange membrane (PEM) water electrolysers, achieving industrially relevant long-term stability typically requires an Ir-rich, Ru-lean combination. Here, in this study, by combining density functional theory with Metropolis Monte Carlo methods, we discovered that sufficient stabilization in the RuO 2 lattice could be achieved with less than 50 at.% of Ir, and that Ir in the first subsurface layer plays a critical role. By effectively dispersing Ir dopants within the RuO 2 lattice, we demonstrated an Ir:Ru atomic ratio of only 1:6 that exhibited exceptional stability for over 1,500 h of continuous water electrolysis at 2 A cm −2 . Our Ru 6 IrO x catalyst has the potential to reduce Ir loading by 80% compared with current commercial PEM water electrolysers, and its stability was further validated under industrial testing conditions in a 25-cm 2 PEM electrolyser.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Plasma proteomic biomarkers of physical frailty in heart failure: a propensity score matched discovery-based pilot study

Background: Physical frailty is highly prevalent in heart failure (HF), but we lack an understanding of the underlying pathophysiology. Proteomics evaluation of plasma samples may elucidate potential mechanisms and biomarkers of physical frailty in HF. We aimed to identify plasma proteomic biomarkers that are differentially expressed between physically frail and non physically frail adults with HF. Methods: This was a secondary analysis of a subset of data and plasma samples from a study of frailty among patients with New York Heart Association (NYHA) Functional Classification I-IV HF. Physical frailty was measured using the Frailty Phenotype Criteria. Propensity score matching was used to match pairs of physically frail (n = 20) vs. non-physically frail (n = 20) patients on clinical characteristics. Plasma samples were processed using a sensitive liquid chromatography mass spectrometry platform, utilizing a multiplexed tandem mass tag-labeled quantitative proteomics approach. Differentially expressed proteins were quantified individually using paired t tests with associated log fold change of 0.3 and Fisher’s combined p values. Results: The sample (n = 40) was 62.8±16.9 years old, 58% female, and 55% NYHA Class III/IV. Proteomics analysis revealed 7 proteins differentially expressed using full differential criteria: matrix metalloproteinase-14 was downregulated in frailty, and copine-1, low affinity immunoglobulin gamma Fc region receptor III-A and III-B, probable non-functional immunoglobulin kappa variable 2D-24, glutathione S-transferase Mu 1, and argininosuccinate lyase were upregulated in frailty. Conclusions: Proteomic biomarkers related to the immune system, stress response, and detoxification were differentially expressed between physically frail and non-physically frail adults with HF.

Biomarkers↗
DOE OSTI2020

Assessment of Demographic, Genetic, and Imaging Variables Associated With Brain Resilience and Cognitive Resilience to Pathological Tau in Patients With Alzheimer Disease

Importance: Better understanding is needed of the degree to which individuals tolerate Alzheimer disease (AD)-like pathological tau with respect to brain structure (brain resilience) and cognition (cognitive resilience). Objective: To examine the demographic (age, sex, and educational level), genetic (APOE-ε4 status), and neuroimaging (white matter hyperintensities and cortical thickness) factors associated with interindividual differences in brain and cognitive resilience to tau positron emission tomography (PET) load and to changes in global cognition over time. Design, setting, an participants: In this cross-sectional, longitudinal study, tau PET was performed from June 1, 2014, to November 30, 2017, and global cognition monitored for a mean [SD] interval of 2.0 [1.8] years at 3 dementia centers in South Korea, Sweden, and the United States. The study included amyloid-β-positive participants with mild cognitive impairment or AD dementia. Data analysis was performed from October 26, 2018, to December 11, 2019. Exposures: Standard dementia screening, cognitive testing, brain magnetic resonance imaging, amyloid-β PET and cerebrospinal fluid analysis, and flortaucipir (tau) labeled with fluor-18 (18F) PET. Main outcomes and measures: Separate linear regression models were performed between whole cortex [18F]flortaucipir uptake and cortical thickness, and standardized residuals were used to obtain a measure of brain resilience. The same procedure was performed for whole cortex [18F]flortaucipir uptake vs Mini-Mental State Examination (MMSE) as a measure of cognitive resilience. Bivariate and multivariable linear regression models were conducted with age, sex, educational level, APOE-ε4 status, white matter hyperintensity volumes, and cortical thickness as independent variables and brain and cognitive resilience measures as dependent variables. Linear mixed models were performed to examine whether changes in MMSE scores over time differed as a function of a combined brain and cognitive resilience variable. Results: A total of 260 participants (145 [55.8%] female; mean [SD] age, 69.2 [9.5] years; mean [SD] MMSE score, 21.9 [5.5]) were included in the study. In multivariable models, women (standardized β = -0.15, P = .02) and young patients (standardized β = -0.20, P = .006) had greater brain resilience to pathological tau. Higher educational level (standardized β = 0.23, P < .001) and global cortical thickness (standardized β = 0.23, P < .001) were associated with greater cognitive resilience to pathological tau. Linear mixed models indicated a significant interaction of brain resilience × cognitive resilience × time on MMSE (β [SE] = -0.235 [0.111], P = .03), with steepest slopes for individuals with both low brain and cognitive resilience. Conclusions and relevance: Results of this study suggest that women and young patients with AD have relative preservation of brain structure when exposed to neocortical pathological tau. Interindividual differences in resilience to pathological tau may be important to disease progression because participants with both low brain and cognitive resilience had the most rapid cognitive decline over time.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Develop a new integrated macro→micro←nano (MMN) multiscale modeling framework to optimize high strength aluminum alloys and processes for vehicle light-weighting​

Bending tests provide a means to study plane strain fracture performance of 6000 series aluminum alloys. Metrics from bending tests have been correlated with self-pierce riveting (SPR) performance of a high strength AA6111 automotive aluminum alloy in previous works. Using the ORNL HPC resources, this project developed an innovative macro→micro←nano (MMN) multiscale microstructure-based finite element (FE) code to further understanding of the relationship between microstructure and fracture properties of high-strength 6000-series alloys. This work started with microstructural characterization in both mesoscale and nanoscale and bending performance characterization of AA6111 HS2-T6 alloy at Ford, the MMN framework was applied to this alloy to simulate 3-point VDA bending. From the results of the macro-modeling of 3-point VDA bending, the critical region of fracture was identified, and the region geometry was used to construct the micro-model. The fracture criterion of micron-scale precipitates and aluminum matrix which contains submicron and nano particles (AL-SMP-NP) in the micro-model was calibrated and validated by comparing simulated and measured bending results. With the AL-SMP-NP fracture strain obtained, the fracture strain of Al-matrix containing nano particles (ALNP) will similarly be determined by a submicron scale-model using an edge-constrained FE modeling approach developed by Hu et al. With the ALNP fracture strain obtained, the fracture strain of Al-matrix containing no particles will similarly be determined by a nano scale-model using an edge-constrained FE modeling approach. After the MMN framework is built and fracture criterion calibrated, nano-model FE simulations with virtual microstructures was performed to obtain a reduced order model (ROM) of the fracture criterion of the ALNP as a function of volume fraction, size, and distribution of the nanoparticle. This nano→submicron→micro modeling part allows exploration of the influence of different material nanostructures from different process conditions on the bending properties within the multiscale bending simulation framework and the ROM of material bendability as a function of nanoparticle size and shape was established. This obtained reduced order model (ROM) could help guide the design and selection of materials to improve existing SPR process models that could replace trial-and-error rivet/die selection and help to design new rivet/die combinations capable of robustly joining new higher strength 6000 5 series alloys in automotive body structures. This would enable lightweighting of Ford vehicles leading to greater fuel efficiency and reduce manufacturing time and energy.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Data-based filtered dissipation rate modelling for multi-modal turbulent combustion: evaluating a priori model generalizability

Manifold-based models offer a computationally efficient alternative to directly transporting the thermochemical state in computational simulations of turbulent reacting flows, projecting the high-dimensional thermochemical state-space onto a low-dimensional manifold. Recent efforts have yielded a manifold-based model applicable to multi-modal combustion, enabling reconstruction of the thermochemical state from solutions to two-dimensional manifold equations in mixture fraction and generalized progress variable that are parameterised by three scalar dissipation rates. In coarse-grained simulations such as Large Eddy Simulation (LES), closure of the multi-modal manifold equations and subfilter variances/covariance requires closure of three filtered scalar dissipation rates. Here, the present work adopts a data-based approach, providing closure for the three filtered scalar dissipation rates via deep neural networks (DNNs). High-fidelity datasets corresponding to an autoigniting n-dodecane jet flame and a bluff body swirl-stabilized confined lifted spray flame of two aviation fuels (Jet-A and C1) with different ignition propensities are leveraged to generate training data that spans a diverse range of thermodynamic conditions and combustion modes, including low- and high-temperature ignition regimes in addition to premixed and nonpremixed behaviour. A final DNN model is trained to enforce inherent physical constraints by learning nonlinear functional transformations of the three filtered scalar dissipation rates. The generalizability of this constrained DNN model is demonstrated a priori via conditional statistics evaluated on the lifted spray flame with C1–a configuration that had not been included in the training data. Excellent DNN agreement with conditional DNS statistics is observed, and integrated gradients are computed to identify the most sensitive input variables. The similarity of the marginal PDFs of the most informative input variables and outputs across configurations are quantified via the Wasserstein metric, demonstrating that data-based models may successfully generalize to unseen parametric conditions so long as the most informative input variables share similar distributions across training and testing datasets.

Data-based modelling↗
DOE OSTI2025

Data-Driven Tailoring Optimization of Thermoset Polymers Using Ultrasonics and Machine Learning

Thermoset polymers are highly demanded for their structural robustness, thermal stability, and chemical resistance. Tailoring the properties of these polymers for high-performance applications is often preferred to designing brand-new polymers. However, the traditional destructive techniques used to characterize their properties as a function of manufacturing parameters are expensive and time-consuming. A novel non-destructive, data-driven method leveraging ultrasonics and machine learning techniques to tailor the properties of thermosets as a function of the manufacturing parameters is demonstrated. Thermoset epoxy samples with varying curing temperatures (15–40 °C) and curing agent amounts (±40%) were manufactured and tested. Their curing kinetics were monitored by determining the sound speed in the material in real time, while the longitudinal modulus of the samples was determined post-cure. Machine learning models were developed using a k-nearest neighbors algorithm. These models were implemented to predict the curing and final elastic properties using the manufacturing parameters, i.e., stoichiometry and curing temperature, and vice versa. Understanding and modeling how these parameters affect the cure kinetics and final properties will allow for efficient and reliable optimization of thermoset tailoring and manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Can Dithiolate Ligands Report Electronic Communication in Transuranium Complexes?

The actinide elements, and in particular the transuranic elements, are some of the least studied elements on the periodic table. The degree of covalent bonding between the actinide elements and ligands in is not well understood. Dithiolene/dithiolate ligands provided a revolution in understanding the bonding in transition metals through the geometric differences the ligands exhibit when interacting with electron deficient or abundant metal centers. To test if dithiolene/dithiolate ligands can be utilized to provide an analogous understanding of transuranic element bonding, density functional theory calculations have been performed on U, Np, and Pu complexes of the formula Cp 2 AnS 2 C 2 H 2 . These calculations show that the dithiolate maintains its non-innocent redox active nature when bonding with actinides. In stark contrast with the transition metals, the direction of electron donation is reversed, in the actinide series the direction of electron flow in high fold cases is from the metal center to the ligand. In conclusion, this suggests the communication is between the π* system of the ligand interacting with the f orbitals of the metal center.

Prange, Micah P. [Pacific Northwest National Labor↗
DOE OSTI2026

Brittle-to-Ductile Transition Temperature Decreases at the Microscale in Muscovite Mica

Characterizing the structural and chemical transitions of muscovite mica across various scales and thermal profiles is essential for ensuring the reliability of high-voltage power insulation, aerospace thermal barriers, and nanoscale semiconductor substrates under extreme operational stress. Here, we investigate notch fracture toughness $K^{n}_{Ic}$ of muscovite mica as a function of feature size w and temperature T with microscale shear testing. $K^{n}_{Ic}$ decreased and then stayed constant as w increased, likely from fabrication-induced changes to the microstructure and roughness. Moreover, $K^{n}_{Ic}$ initially decreased and then increased as temperature T increased, with the reversal in trend indicative of a brittle-to-ductile transition. Complementary Raman spectroscopy and molecular dynamics showed that the observed trends were due to an increase in bond lengths and a decrease in critical stresses/strains. In comparing the microscale results to macroscale counterparts, it was surmised that the decrease in brittle-to-ductile transition temperature was due to an increase in the dehydroxylation rate.

DelRio, Frank W. [Sandia National Laboratories (SN↗
DOE OSTI2025

Proximity ferroelectricity in wurtzite heterostructures

Proximity ferroelectricity is an interface-associated phenomenon in electric-field-driven polarization reversal in a non-ferroelectric polar material induced by one or more adjacent ferroelectric materials. Here, in this paper, we report proximity ferroelectricity in wurtzite ferroelectric heterostructures. In the present case, the non-ferroelectric layers are AlN and ZnO, whereas the ferroelectric layers are Al 1−x B x N, Al 1−x Sc x N and Zn 1−x Mg x O. The layered structures include nitride–nitride, oxide–oxide and nitride–oxide stacks that feature two-layer (asymmetric) and three-layer (symmetric) configurations. Ferroelectric switching in both layers is validated by multimodal characterization methods, including polarization hysteresis, anisotropic chemical etching, second harmonic generation, piezo response force microscopy, electromechanical testing and atomic resolution polarization orientation imaging in real space by scanning transmission electron microscopy. We present a physical switching model in which antipolar nuclei originate in the ferroelectric layer and propagate towards the internal non-ferroelectric interface. The domain wall leading edge produces elastic and electric fields that extend beyond the interface at close proximity, reducing the switching barrier in the non-ferroelectric layer, and allowing complete domain propagation without breakdown. Density functional theory calculations of polymorph energies, reversal barriers and domain wall energies support this model. Proximity ferroelectricity enables polarization reversal in wurtzites without the chemical or structural disorder that accompanies elemental substitution, opening new questions and opportunities regarding interface-based ferroelectricity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Quantification of spatial and seasonal trends in the atmosphere and construction of statistical models for infrasonic propagation

SUMMARY Infrasonic waves are influenced by variations in the density, pressure and temperature as well as the ambient winds. Modelling infrasonic propagation can be challenging due to the dynamic nature of the atmosphere as well as the sparseness of measurements which result in variability and notable uncertainty. A framework is presented to quantify spatial and seasonal trends in atmospheric structure via analysis of the effective sound speed profile and identification of temporal trends in the middle atmospheric waveguide produced by the circumpolar vortex winds. Seasonal definitions identifying typical atmospheric structures during the summer, winter and spring/fall transition periods are identified using atmospheric data from 2010 through 2020. Seasonal trend analysis is conducted for a number of locations across the contiguous United States to quantify spatial variations in atmospheric structure that impact infrasonic propagation. For each season and location, empirical orthogonal function analysis is used to reduce the historical archive of atmospheric data into a smaller representative set that can be analysed using numerical tools more efficiently. Infrasonic ray tracing and finite-frequency modal propagation analyses are applied to construct propagation path geometry and transmission loss statistics which are useful in localization and yield estimation for infrasonic sources, respectively. An example application is detailed in which transmission loss statistics are combined with an explosive source model and noise statistics to quantify the capability of a network to detect nearby sources.

58 GEOSCIENCES↗
DOE OSTI2023

Systematic approaches to C-lignin engineering in Medicago truncatula

Abstract Background C-lignin is a homopolymer of caffeyl alcohol present in the seed coats of a variety of plant species including vanilla orchid, various cacti, and the ornamental plant Cleome hassleriana . Because of its unique chemical and physical properties, there is considerable interest in engineering C-lignin into the cell walls of bioenergy crops as a high-value co-product of bioprocessing. We have used information from a transcriptomic analysis of developing C. hassleriana seed coats to suggest strategies for engineering C-lignin in a heterologous system, using hairy roots of the model legume Medicago truncatula . Results We systematically tested strategies for C-lignin engineering using a combination of gene overexpression and RNAi-mediated knockdown in the caffeic acid/5-hydroxy coniferaldehyde 3/5-O-methyltransferase ( comt ) mutant background, monitoring the outcomes by analysis of lignin composition and profiling of monolignol pathway metabolites. In all cases, C-lignin accumulation required strong down-regulation of caffeoyl CoA 3- O -methyltransferase (CCoAOMT) paired with loss of function of COMT. Overexpression of the Selaginella moellendorffii ferulate 5-hydroxylase ( SmF5H ) gene in comt mutant hairy roots resulted in lines that unexpectedly accumulated high levels of S-lignin. Conclusion C-Lignin accumulation of up to 15% of total lignin in lines with the greatest reduction in CCoAOMT expression required the strong down-regulation of both COMT and CCoAOMT, but did not require expression of a heterologous laccase, cinnamyl alcohol dehydrogenase (CAD) or cinnamoyl CoA reductase (CCR) with preference for 3,4-dihydroxy-substituted substrates in M. truncatula hairy roots. Cell wall fractionation studies suggested that the engineered C-units are not present in a heteropolymer with the bulk of the G-lignin.

09 BIOMASS FUELS↗
DOE OSTI2025

Monitoring the Structural Health of the Stage-Four Gibbs Resistor In Order to Maintain a Functioning Pulse-Forming Network

The Dual-Axis Radiographic Hydrodynamic Test (DARHT) facility at Los Alamos National Laboratory (LANL) is a critical facility used for nuclear weapons research and development (Los Alamos National Laboratory). Its primary function is to provide high-resolution, real-time images of the behavior of materials under extreme conditions, specifically during the hydrodynamic testing of nuclear weapons surrogates. The facility uses advanced radiographic techniques, such as dual-axis X-ray imaging, to capture detailed snapshots of these materials as they react to high-pressure environments. DARHT plays a key role in maintaining the safety, security, and reliability of the U.S. nuclear arsenal, supporting the Stockpile Stewardship Program. The facility helps ensure that nuclear weapons perform as designed without the need for nuclear tests. Its dual-axis radiography provides more precise data than traditional single-axis imaging, offering a comprehensive view of the internal dynamics of a weapon's primary stage.

42 ENGINEERING↗
DOE OSTI2026

Predicting Li-Ion Battery Capacity Fade Using Early-Life Data and a Hybrid Data-Driven Gaussian Process-Bayesian Regression Approach

Accurately predicting Li-ion battery capacity trajectories using early-life data can dramatically improve battery-life understandings and be used to rapidly evaluate design/cost/performance trade-offs when developing new battery materials. Accurate early-life predictions enable researchers to quickly iterate over cell designs and material precursor properties without consistently cycling cells to failure. To this end, we present a toolbox that uses a combined Gaussian Process and Bayesian regression approach that capitalizes on signals other than just capacity (e.g., dQ/dV, voltage drops) to rapidly predict capacity-fade trajectories. The prediction tool uses Bayesian regression to fit functional forms, e.g., power law, sigmoids, etc., to predict capacity-fade dynamics. By fitting functional forms, the capacity fade can be interrogated at any point in the future, allowing for early cell-failure prediction. Additionally, Bayesian regression allows for accurate uncertainty estimates that account for cell-to-cell variability (aleatoric uncertainty) and the lack of observation data (epistemic uncertainty). By only using early cycle data to predict the capacity fade trajectory, uncertainty bounds at end-of-life can be extremely large. The large uncertainty bounds are further exacerbated because there is no systematic way to define the prior distribution of the functional forms' parameters. We improve our the predicted trajectory confidence interval of our predicted trajectory using two methods. First, we shows that a small amount of held-out cycling data is sufficientuse some train cells, that have been cycled to failure to derive information regarding the appropriate prior distributions for the functional forms' parameters of the functional form, effectively leading to data-driven priors.. We propose constructing the data-driven priors by first running a Bayesian regression starting with uninformed priors to generate intermediate cell-specific posterior parameter distributions. These posterior distributions are combined using a Ggaussian mixture model for each parameter to create the data-driven priors. These mixture models serve as the data-driven prior distributions for the parameters for. Second, we derive multiple features, e.g., C_dchg 0.5 DoD 0.5, log (|mean(dQ/dV_(w_3-w_0 ) (V)|), etc., from the train cellsheld-out cycling data, identify which the features are that best predicting capacity at early/mid-life cycles, and then create Ggaussian process regression models that are used for predicting capacity at early/mid-life cycles for the test cells (see blue dots with error bars in Fig 1b). Finally, these predicted data-points are used in addition to the actual early cycle data capacity fade to construct the Bayesian regression trajectory for the test cell s. Notably. We note that these two methods are complementary and can be combined with each other. We evaluate the performance of our proposed method on an testing open-source dataset from Iowa State University and Iowa Lakes Community College (ISU-ILCC). This dataset comprises of 251 nickel-manganese-cobalt/graphite Lithium-ion cells that are cycled under 63 different conditions. We compute the mean average percentage error (MAPE) and negative log predictive density (NLPD) to quantify the efficacy of our method. Our initial findings suggest that, when only few observations are available, for test cells, when using only Bayesian regression with uninformed priors, a power law functional provides the most accurate predictions. with very few data points. However, asHowever, a the number of data points increases, a twin sigmoidal function becomes more accurate as the number of observations further increases. We also find that using as little as 10% of the data set towards generating data-driven priors can lead to significant improvement in prediction accuracy when using early cycle data. Lastly, we found that augmenting early-cycle data with Gaussian process-predicted capacity data for Bayesian regression greatly improves the prediction accuracy. We will present a comprehensive comparison of our methods to other methods available in the literature and apply this method to additional battery datasets.

42 ENGINEERING↗
DOE OSTI2026

Membrane Composition Influences Expression Yield of Plant Cytochrome P450s in E. coli Lysate-Based Cell-Free Systems

Plant cytochrome P450 enzymes are central to natural product biosynthesis, but remain difficult to express in microbial hosts due to their transmembrane nature. Lysate-based, cell-free expression systems allow supplementation with artificial membranes to support the expression and translocation of transmembrane proteins. We developed a framework to systematically test liposomal membrane compositions to enhance the plant P450 expression yield. Adjustments to common phospholipid ratios or the addition of plant galactolipids had minimal impact on expression. In contrast, blended liposomes containing Egg PC, sterol-conjugated phospholipids, and PEGylated lipids produced concentration-dependent increases in expression. Expression of an Escherichia coli mechanosensitive channel and three plant P450s improved more than 2-fold, with some P450s showing up to 14-fold enhancement. Furthermore, these findings highlight membrane composition as a key determinant of the P450 expression yield in cell-free expression systems. While P450 activity was not measured, these findings provide a framework for future workflows toward achieving functional plant transmembrane enzymes for the bioproduction of natural products.

TXTL↗
DOE OSTI2025

Strong Coupling of Iridium and Boron–Carbon-Nitride Support for Enhanced Acidic Water Oxidation

Supporting iridium (Ir)-based materials on stable and suitable substrates is an efficient strategy to improve their catalytic performance for the oxygen evolution reaction (OER) in harsh acidic environments. Herein, we report a series of Ir catalysts supported on boron–carbon-nitride (BCN), denoted as Ir/BCN. Detailed experiments combined with density functional theory calculations demonstrate strong interactions between Ir nanoparticles (NPs) and the BCN support, significantly contributing to the improved OER performance. Among the synthesized catalysts, the Ir/BCN-75 catalyst outperforms commercial Ir black and IrO 2 in both half-cell and proton exchange membrane water electrolysis (PEMWE) tests. Specially, the Ir/BCN-75 catalyst achieves a high current density of 2.94 A cm –2 at 1.9 V with a low Ir loading of 0.26 mg cm –2 in PEMWE measurements, exceeding most Ir-based catalysts reported to date. The BCN support interacts strongly with Ir, wherein Ir–N bonding directs the Ir NP growth and enhances electrocatalytic activity, while Ir–B bonding contributes to the stability of the Ir/BCN catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗