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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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190 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Optical versus radiographic imaging and tomography: introduction to the ROADS feature issue

Optical imaging is an ancient branch of imaging dating back to thousands of years. Radiographic imaging and tomography (RadIT), including the first use of X-rays by Wilhelm Röntgen, and then, $γ$ -rays, energetic charged particles, neutrons, etc. are about 130 years young. The synergies between optical and radiographic imaging can be cast in the framework of these building blocks: Physics, Sources, Detectors, Methods, and Data Science, as described in Appl. Opt. 61, RDS1 (2022). Optical imaging has expanded to include three-dimensional (3D) tomography (including holography), due in to part the invention of optical (including infrared) lasers. RadIT are intrinsically 3D because of the penetrating power of ionizing radiation. Both optical imaging and tomography (OIT) and RadIT are evolving into even higher dimensional regimes, such as time-resolved tomography (4D) and temporarily and spectroscopically resolved tomography (4D + ). Further advances in OIT and RadIT will continue to be driven by desires for higher information yield, higher resolutions, and higher probability models with reduced uncertainties. Synergies in quantum physics, laser-driven sources, low-cost detectors, data-driven methods, automated processing of data, and artificially intelligent data acquisition protocols will be beneficial to both branches of imaging in many applications. These topics, along with an overview of the Radiography, Applied Optics, and Data Science virtual feature issue, are discussed here.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Topological phase transition to a hidden charge density wave liquid

Charge density waves (CDWs), electronic crystals that form within a host solid, have long been speculated to melt into a spatially textured electronic liquid. Though they have not been previously detected, liquid CDWs may nonetheless be fundamental to the phase diagrams of many correlated electron systems, including high temperature superconductors and quantum Hall states. In one of the most promising candidate materials capable of hosting a liquid CDW, 1T-TaS2, a structural phase transition impedes its observation. Here, by irradiating the material with a femtosecond light pulse, we circumvent the structural phase transition to reveal how topological defect dynamics govern the otherwise invisible CDW correlations. Upon photoexcitation, the CDW diffraction peaks broaden azimuthally, initially revealing a hexatic state. At higher temperatures, photoexcitation completely destroys translational and orientational order and only a ring of diffuse scattering is observed, a key signature of a liquid CDW. Our work provides compelling evidence for a defect-unbinding transition to a CDW liquid and presents a protocol for uncovering states that are hidden by other transitions in thermal equilibrium.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Large Scale Study of Ligand–Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein–ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the “enhanced sampling” method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

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

Polymer Hole Transport Materials for Perovskite Solar Cells via Buchwald–Hartwig Amination

The development of low-cost materials for charge-selective contacts that provide good energetic alignment with perovskite active layers, favorable thermal properties, and lead to efficient photoconversion is becoming an increasingly important aspect of the perovskite solar cell (PSC) field. Presented here is a series of polymers based on a one-pot polymerization of aryl dihalides with primary aryl amines to produce solution-processable polymers in high yield, with simple purification, and promising properties for high performing P-I-N PSC devices. How these properties can be tuned by careful selection of the reactant chemical moieties is discussed. Through this strategy, a wide range of relevant properties such as glass transition temperature, highest occupied molecular orbital tuning, and polydispersity are explored. When implemented into devices using a triple-cation FAMACs perovskite active layer, the hole transport material series shows average power conversion efficiencies (PCE) in excess of 17%, which is comparable to controls using state-of-the-art poly(triarylamine). How different synthetic parameters such as the reaction time and purification protocol impact device performance is also investigated.

14 SOLAR ENERGY↗
DOE OSTI2022

Robust hierarchical three dimensional nickel cobalt tungstate-MXene nanocomposite for high performance symmetric coin cell supercapacitors

Design of rational electrode materials with hierarchically structured pseudocapacitive materials has been identified as a successful strategy for revamping the electrochemical characteristics of supercapacitors. However, the simultaneous assurance of strong electrical conductivity and higher specific capacitance via a simple and cost-effective synthesis protocol is of concern today. In this work, Ni-CoWO 4 incorporated MXene nanocomposite has been successfully prepared by a facile hydrothermal process. The structure, morphology, stoichiometry, and chemical characterization of the prepared Ni-CoWO 4 , Ti 3 C 2 T x , and Ni-CoWO4@MXene nanocomposite were explored extensively. MXene demonstrated a layered 2D nanoscale structure whereas pure Ni-CoWO 4 was composed of spherical nanoparticles. The composite Ni-CoWO 4 @MXene revealed the morphology of spherical Ni-CoWO 4 nanoparticles decorated on MXene sheets. Furthermore, a series of electrochemical characterizations illustrated the contribution of the delightful morphology of Ni-CoWO 4 @MXene composite to excellent electrochemistry. The resultant Ni-CoWO 4 @MXene electrode exhibited high specific capacitance of 582 Fg -1 at 1 A g -1 and 93.5 % capacitance retention over the 10,000 cycles., which is higher than that of the individual counterparts Ni-CoWO 4 and MXene. The excellent electrochemical performance of Ni-CoWO 4 @MXene is attributed to the synergistic interaction between Ni-CoWO4 nanoparticles and nanolayered MXene that may create new active sites that enhanced the charge transfer, charge/discharge kinetics and the electrical conductivity. Significantly, the unique Ni-CoWO 4 @MXene supercapacitor prototype delivered a higher energy density of 85.7 Wh kg -1 and power density of 0.85 kW kg -1 establishing its superiority over other Cosingle bondW based supercapacitors. A theoretical study was also performed to correlate the improved charge storage capacity of the prepared Ni-CoWO 4 @MXene composite.

25 ENERGY STORAGE↗
DOE OSTI2022

Barriers to Broader Utilization of Fault Detection Technologies for Improving Residential HVAC Equipment Efficiency

Faults in residential heating, ventilating, and air conditioning (HVAC) equipment may occur due to poor installation practices or develop over time, and these faults can negatively impact system efficiency, thermal comfort, and equipment lifespan. Automated fault detection and diagnostic (AFDD) technologies identify energy wasting HVAC faults, such as low indoor airflow and improper refrigerant charge, and guide technicians in improving system efficiency. For residential HVAC, AFDD consists of a range of fault detecting and diagnostic capabilities, sensor configurations, and target applications. AFDD technology can either be permanently installed by the original equipment manufacturer (OEM) using embedded sensors or as an add-on product either during or after installation. Additionally, several advanced installation tools and refrigerant gauge sets include AFDD features for temporary use during equipment installation and tune-ups. Some technologies can detect a fault but have limited diagnostic capabilities. For example, a single-point measurement from the home's thermostat or energy monitor can provide certain fault detection capability by analyzing the equipment runtime or energy consumption. These technologies, though limited at determining the cause of a given fault, may have significant energy savings potential due to their low cost and prevalence in the residential HVAC market. Despite the potential benefits, fault detection technologies face many technical and market barriers preventing broad adoption. Beyond the cost barriers due to the added sensor requirements and technology development, fault detection technologies face many implementation and adoption barriers such as installer training, customer awareness, standardized communication protocols, and methods of test for evaluating accuracy. The purpose of this whitepaper is to characterize market and technical barriers impeding broader utilization of fault detection technology for residential HVAC energy efficiency applications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Machine-Learning Assisted Identification of Battery Life Models

Predictive battery life models are commonly utilized to extrapolate degradation trends observed during accelerated aging tests for simulation of degradation in real-world applications. Thus, fitting accelerated aging data as accurately as possible and with low uncertainty is crucial for making believable projections of battery lifetime, but it is challenging to identify algebraic expressions that accurately fit multivariate degradation trends. A review of models published in literature reveal some common expressions for fitting calendar aging data, which is only dependent on temperature and state-of-charge, but no consistency across many models for fitting cycle aging data, indicating the need for a statistically rigorous data driven approach for developing empirical models. This talk will describe a machine-learning assisted method for identification of predictive battery life models utilizing bilevel optimization and symbolic regression. Bilevel optimization with cross-validation is used to statistically determine cell- and stress-dependent model parameters, while symbolic regression identifies both linear and multiplicative candidate expressions to predict stress-dependent degradation rates by selecting low-order subsets of features from a generated feature library. Because model expressions are identified empirically, it is crucial to ensure resulting models behave according to physical expectations, so the stability of models for interpolation or extrapolation is interrogated qualitatively through simulation and quantitatively through cross-validation and uncertainty quantification via bootstrap resampling. This model identification approach substantially improves upon models identified purely using expert judgement in terms of both accuracy and uncertainty. Model simulation and validation is then conducted by deriving a state-equation form of the predictive model, enabling simulation of battery aging under dynamic stresses. This enables validation of the predictive battery model on lab-based tests with varying conditions or on drive-cycle or application-cycle testing protocols. Parameter uncertainty can be carried forward into model simulation, giving lifetime estimates and confidence windows for cell- or system-level lifetime. The financial impact of battery model uncertainty can be estimated by incorporating uncertainty into a technoeconomic model.

battery↗
DOE OSTI2024

Innovating the next generation of commercial smart building software

Nearly 30% of commercial building energy use is wasted due to equipment faults and HVAC controls problems. The result is increased emissions, compromised comfort and productivity, and less reliable coordination of building power needs with a clean grid. The energy impact alone represents $17 billion in potential savings. Today’s smart building software provides a robust solution to address these operational deficiencies. Energy management and information systems (EMIS) are saving up to 9% on average, with two-year paybacks. They are being incorporated into energy management processes, commissioning services, and utility programs. As effective as they are, two barriers prevent even deeper benefits; limited personnel to fix problems once they are identified, and the expense and time to manually implement changes in control systems. In partnership with the research community, the EMIS industry is developing new capabilities to overcome these barriers. Moving beyond siloed products for either fault detection and diagnostics, or optimal control, these new capabilities empower users to not only automatically identify faults, but also to push corrective action, and control improvements to their buildings. In this paper, several areas for enhancements are documented: ‘one-time’ correction of faults such as setpoints, schedules, and economizer lockouts; short-term active testing for automated proportional integral derivative (PID) loop tuning and functional testing; and continuous supervisory control for demand flexibility and year-round efficiency. Results are presented from a pair of partner implementations out of a dozen providers integrating these enhancements into their products, including field tests from across the country, and insights into operator acceptance and integration into operations and maintenance practices.

Casillas, Armando↗
DOE OSTI2022

Extrapolating Contaminant Effects from Individuals to Populations: A Case Study on Nanoparticle Toxicity to Daphnia Fed Environmentally Relevant Food Levels

Ecological risk assessment (ERA) is charged with assessing the likelihood a chemical will have adverse environmental or ecological effects. When assessing the risk of a potential contaminant to biological organisms, ecologists are most concerned with the sustainability of populations of organisms, rather than protecting every individual. However, ERA most commonly relies on data on the effect of a potential contaminant on individuals because these experiments are more feasible than costly population-level exposures. In this work, we address the challenge of extrapolating these individual-level results to predict population-level effects. Previous per-capita population growth rate estimates calculated from individual-level exposures of Daphnia pulicaria to silver nanoparticles (AgNPs) at different food rations predict a critical daily food requirement for daphnid populations exposed to 200 μg/L AgNPs to avoid extinction. To test this, we exposed daphnid populations to the same AgNP concentration at three different food inputs, with the lowest ration close to the extinction threshold predicted from data on individuals. The two populations with the higher food inputs persisted, and the population with the lowest food input went extinct after 50 days but did persist through two generations. Further, we demonstrate that we can extrapolate between these levels of biological organization by parameterizing an individual-level biomass model with data on individuals’ response to AgNPs and using these parameters to predict the outcome for control and AgNP-exposed populations. Key to successful extrapolation is careful modeling of temporal changes in resource density, driven by both the experimental protocols and feedback from the consumer. The implication for ecotoxicology is that estimates of extinction thresholds based on studies of individuals may be reliable predictors of population outcomes, but only with careful treatment of resource dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

MXene-Derived Potassium-Preintercalated Bilayered Vanadium Oxide Nanostructures for Cathodes in Nonaqueous K-Ion Batteries

Bilayered vanadium oxides (BVOs) are promising cathode materials for beyond-Li-ion batteries due to their tunable chemistries and high theoretical capacities. However, the large size of beyond-Li + ions limits electrochemical cycling and rate capability of BVO electrodes. Recent reports of MXene-derived BVOs with nanoscale flower-like morphology have shown improved electrochemical stability at high rates up to 5C in nonaqueous lithium-ion batteries. Here, we report how morphological stabilization can lead to improved rate capability in potassium-ion batteries (PIBs) through the synthesis and electrochemical characterization of MXene-derived K-preintercalated BVOs (MD-KVOs), which were derived from two V 2 CT x precursor materials prepared using two different etching protocols. We show that the etching conditions affect the surface chemistry of the MXene, which plays a role in the MXene-to-oxide transformation process. MXene derived from a milder etchant transformed into a nanoflower MD-KVO with two-dimensional (2D) nanosheet petals (KVO-DMAE) while a more aggressive etchant produced a MXene that transformed into a MD-KVO with one-dimensional (1D) nanorod morphology (KVO-CMAE). Electrochemical cycling of the produced MD-KVOs after drying at 200 °C under vacuum (KVO-DMAE-200 and KVO-CMAE-200) in PIBs showed that electrochemical stability of MD-KVO at high rates improved through the morphological stabilization of 2D particles combined with the control of interlayer water and K + ion content. Structure refinement of KVO-DMAE-200 further corroborates the behavior observed during K + ion cycling, connecting structural and compositional characteristics to the improved rate capability. This work demonstrates how proper synthetic methodology can cause downstream effects in the control of structure, chemical composition, and morphology of nanostructured layered oxide materials, which is necessary for development of future materials for beyond-Li-ion battery technologies.

25 ENERGY STORAGE↗