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Results for “localized tuning”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Fine-tuned local coordination environment of Pt single atoms on ceria controls catalytic reactivity

Constructing single atom catalysts with fine-tuned coordination environments can be a promising strategy to achieve satisfactory catalytic performance. Herein, via a simple calcination temperature-control strategy, CeO 2 supported Pt single atom catalysts with precisely controlled coordination environments are successfully fabricated. The joint experimental and theoretical analysis reveals that the Pt single atoms on Pt 1 /CeO 2 prepared at 550 °C (Pt/CeO 2 -550) are mainly located at the edge sites of CeO 2 with a Pt–O coordination number of ca. 5, while those prepared at 800 °C (Pt/CeO 2 -800) are predominantly located at distorted Ce substitution sites on CeO 2 terrace with a Pt–O coordination number of ca. 4. Pt/CeO 2 -550 and Pt/CeO 2 -800 with different Pt 1 -CeO 2 coordination environments exhibit a reversal of activity trend in CO oxidation and NH 3 oxidation due to their different privileges in reactants activation and H 2 O desorption, suggesting that the catalytic performance of Pt single atom catalysts in different target reactions can be maximized by optimizing their local coordination structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Tuning Local Atomic Structures in MoS 2 Based Catalysts for Electrochemical Nitrate Reduction

In recent years, there has been a substantial surge in the investigation of transition-metal dichalcogenides such as MoS 2 as a promising electrochemical catalyst. Inspired by denitrification enzymes such as nitrate reductase and nitrite reductase, the electrochemical nitrate reduction catalyzed by MoS 2 with varying local atomic structures is reported. Further, it is demonstrated that the hydrothermally synthesized MoS 2 containing sulfur vacancies behaves as promising catalysts for electrochemical denitrification. With copper doping at less than 9% atomic ratio, the selectivity of denitrification to dinitrogen in the products can be effectively improved. X-ray absorption characterizations suggest that two sulfur vacancies are associated with one copper dopant in the MoS 2 skeleton. DFT calculation confirms that copper dopants replace three adjacent Mo atoms to form a trigonal defect-enriched region, introducing an exposed Mo reaction center that coordinates with Cu atom to increase N 2 selectivity. Apart from the higher activity and selectivity, the Cu-doped MoS 2 also demonstrates remarkably improved tolerance toward oxygen poisoning at high oxygen concentration. Finally, Cu-doped MoS 2 based catalysts exhibit very low specific energy consumption during the electrochemical denitrification process, paving the way for potential scale-up operations.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Tuning local chemistry of P2 layered-oxide cathode for high energy and long cycles of sodium-ion battery

Layered transition-metal oxides have attracted intensive interest for cathode materials of sodium-ion batteries. However, they are hindered by the limited capacity and inferior phase transition due to the gliding of transition-metal layers upon Na + extraction and insertion in the cathode materials. Here, we report that the large-sized K + is riveted in the prismatic Na + sites of P2-Na 0.612 K 0.056 MnO 2 to enable more thermodynamically favorable Na + vacancies. The Mn-O bonds are reinforced to reduce phase transition during charge and discharge. 0.901 Na + per formula are reversibly extracted and inserted, in which only the two-phase transition of P2 ↔ P’2 occurs at low voltages. It exhibits the highest specific capacity of 240.5 mAh g -1 and energy density of 654 Wh kg -1 based on the redox of Mn 3+ /Mn 4+ , and a capacity retention of 98.2% after 100 cycles. This investigation will shed lights on the tuneable chemical environments of transition-metal oxides for advanced cathode materials and promote the development of sodium-ion batteries.

25 ENERGY STORAGE↗
NASA NTRS2007

The Business Case for Automated Software Engineering

Adoption of advanced automated SE (ASE) tools would be more favored if a business case could be made that these tools are more valuable than alternate methods. In theory, software prediction models can be used to make that case. In practice, this is complicated by the 'local tuning' problem. Normally. predictors for software effort and defects and threat use local data to tune their predictions. Such local tuning data is often unavailable. This paper shows that assessing the relative merits of different SE methods need not require precise local tunings. STAR 1 is a simulated annealer plus a Bayesian post-processor that explores the space of possible local tunings within software prediction models. STAR 1 ranks project decisions by their effects on effort and defects and threats. In experiments with NASA systems. STARI found one project where ASE were essential for minimizing effort/ defect/ threats; and another project were ASE tools were merely optional.

model evaluation↗
DOE OSTI2026

Probing and Tuning Strain‐Localized Exciton Emission in 2D Material Bubbles at Room Temperature

In monolayer transition metal dichalcogenides bubbles-nanoscale deformations typically exhibiting a dome-like shape-Excitons are confined by the strain effect, which exhibits extraordinary emission properties, such as single photon generation, enhanced light emission, and spectrally tunable excitonic states. While the strain profiles of these bubbles are extensively studied, this work provides an approach 1) to directly visualize the associated exciton properties in bubbles formed in WSe2 monolayer, revealing an intrinsic emission wavelength shift of ≈40 nm, and 2) actively modify local strain, enabling further exciton emission tuning over a range of 50 nm. These are achieved by emission mapping and nanoindentation using a dielectric near-field probe, which enables the detection of local emission spectra and emission lifetimes within individual bubbles. Statistical analysis of 67 bubbles uncovers an emission wavelength distribution centered around 780 nm. Furthermore, saturation behavior in the power-dependent studies and the associated lifetime change reveal the localized nature of the strain-induced states. These findings provide direct insights into the strain-localized emission dynamics in bubbles and establish a robust framework for non-destructive, reversible, and predictable nanoscale emission control, presenting a potential avenue for developing next-generation tunable quantum optical sources.

2D materials bubbles↗
DOE OSTI2025

Automated workflow for non-empirical Wannier-localized optimal tuning of range-separated hybrid functionals

Here, we introduce an automated workflow for generating non-empirical Wannier-localized optimally-tuned screened range-separated hybrid (WOT-SRSH) functionals. WOT-SRSH functionals have been shown to yield highly accurate fundamental band gaps, band structures, and optical spectra for bulk and 2D semiconductors and insulators. Our workflow automatically and efficiently determines the WOT-SRSH functional parameters for a given crystal structure and composition, approximately enforcing the correct screened long-range Coulomb interaction and an ionization potential ansatz. In contrast to previous manual tuning approaches, our tuning procedure relies on a new search algorithm that only requires a few hybrid functional calculations with minimal user input. We demonstrate our workflow on 23 previously studied semiconductors and insulators, reporting the same high level of accuracy. By automating the tuning process and improving its computational efficiency, the approach outlined here enables applications of the WOT-SRSH functional to compute spectroscopic and optoelectronic properties for a wide range of materials.

Gant, Stephen E. [University of California, Berkel↗
DOE OSTI2025

Fundamental Insights into Cathode Stability: Linking Compositional Tuning and Local Coordination in Complex Metal Oxides under Aqueous Transformations

Compositional tuning of complex metal oxides in Li-ion battery materials influences their performance as well as their end-of-life behavior, in particular, the tendency to release toxic metal cations in aqueous solution. We modeled ternary variants of a parent LiCoO 2 delafossite structure by varying the metal identity and relative amounts. This yielded ten model formulations of Li(A 4/6 B 1/6 C 1/6 )O 2 , where the material is enriched with the A metal and doped with B and C, with Ni, Mn, Co, Fe, Al, V, and Ti as constituent metals. To assess their stability in aqueous conditions, metal release energetics were calculated using a combination of Density Functional Theory calculations and thermodynamics. Metal release in ternary oxides is dictated by subtle variations in the coordination environment of the leaving group. To identify governing chemical features across diverse compositions with varying local coordination environments, we leverage random forest regression and descriptor importance analysis. A key result is that metal–oxygen orbital hybridization, quantified using a projected density-of-states-derived descriptor, H d/p , provides a physically grounded measure of interaction strength that governs metal release energetics. This refined perspective goes beyond conventional oxidation state considerations and offers more robust insights for materials science. Finally, we model defect surface-bound O 2 dimer formation as a proxy for reactive oxygen species (ROS) generation. The results show that Ni-rich compositions more readily stabilize spin-polarized O 2 dimers, corroborating experimental reports of an increased ROS-driven biological response. In conclusion, our results establish a compositional and electronic basis for metal release and surface oxygen reactivity that form a rationale for complex metal oxide design principles.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Local microenvironment tuning induces switching between electrochemical CO 2 reduction pathways

Gas diffusion layers (GDL) have become a critical component in electrochemical CO 2 reduction (CO 2 R) systems because they can enable high current densities needed for industrially relevant productivity. Besides this function, it is often assumed that the choice of catalyst and electrolyte play much more important roles than the GDL in influencing the observed product selectivity. Here, we show that tuning of the GDL pore size can be used to control the local microenvironment of the catalyst and hence, effect significant changes in catalytic outcomes. This concept is demonstrated using sputtered Ag films on hydrophobic PTFE substrates with 6 different pore sizes. Although Ag is known to be a predominantly CO generating catalyst, we find that smaller pore sizes favor the generation of formate up to a faradaic efficiency of 43%. Combined experimental and simulation results show that this is due to the influence of the pore size on CO 2 mass transport, which alters the local pH at the electrode, resulting in reaction pathway switching between CO and formate. Importantly, our results highlight the importance of the local microenvironment as an experimental knob that can be rationally tuned for controlling product selectivity: a key consideration in the design of CO 2 R systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Boosting the Activity of Pd Single Atoms by Tuning Their Local Environment on Ceria for Methane Combustion

Abstract Supported Pd single atom catalysts (SACs) have triggered great research interest in methane combustion yet with contradicting views on their activity and stability. Here, we show that the Pd SAs can take different electronic structure and atomic geometry on ceria support, resulting in different catalytic properties. By a simple thermal pretreatment to ceria prior to Pd deposition, a unique anchoring site is created. The Pd SA, taking this site, can be activated to Pd δ + (0< δ <2) that has greatly enhanced activity for methane oxidation: T 50 lowered by up to 130 °C and almost 10 times higher turnover frequency compared to the untreated catalyst. The enhanced activity of Pd δ + site is related to its oxygen‐deficient local structure and elongated interacting distance with ceria, leading to enhanced capability in delivering reactive oxygen species and decomposing reaction intermediates. This work provides insights into designing highly efficient Pd SACs for oxidation reactions.

Yang, Weiwei↗
DOE OSTI2022

Boosting the Activity of Pd Single Atoms by Tuning Their Local Environment on Ceria for Methane Combustion

Supported Pd single atom catalysts (SACs) have triggered great research interest in methane combustion yet with contradicting views on their activity and stability. Here, in this paper, we show that the Pd SAs can take different electronic structure and atomic geometry on ceria support, resulting in different catalytic properties. By a simple thermal pretreatment to ceria prior to Pd deposition, a unique anchoring site is created. The Pd SA, taking this site, can be activated to Pd δ+ (0<δ<2) that has greatly enhanced activity for methane oxidation: T 50 lowered by up to 130 °C and almost 10 times higher turnover frequency compared to the untreated catalyst. The enhanced activity of Pd δ+ site is related to its oxygen-deficient local structure and elongated interacting distance with ceria, leading to enhanced capability in delivering reactive oxygen species and decomposing reaction intermediates. This work provides insights into designing highly efficient Pd SACs for oxidation reactions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Tuning of Multicell Superconducting Accelerating Cavities Using Pressurized Balloons

Plastic tuning of multicell superconducting accelerating cavities is crucial in the development cycle of cavities for particle accelerators. Cavities must meet stringent requirements regarding the operating mode frequency, field flatness, and eccentricity before lining them up in a cryomodule string. After dressing bare cavities with helium vessels, the welded vessel prevents access to individual cavity cells disallowing any further localized tuning. Currently, there is no straightforward way to tune dressed cavities other than cutting the vessel and then tuning the bare cavity and dressing it back, which would significantly impact cost and schedule. In this work, we present a novel tuning technique for already jacketed cavities that is non-invasive and cost-effective. The proposed scheme employs pressurized balloons to be temporarily deployed inside the cavity as a means to localize mechanical deformation in specific cells. The proposed tuning technique was successfully utilized to recover a 9-cell 1.3 GHz tesla-style cavity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control

This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel global-local policy search method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2015

High-Resolution Mesoscale Model Setup for the Eastern Range and Wallops Flight Facility

Mesoscale weather conditions can have an adverse effect on space launch, landing, ground processing, and weather advisories, watches, and warnings at the Eastern Range (ER) in Florida and Wallops Flight Facility (WFF) in Virginia. During summer, land-sea interactions across Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS) lead to sea breeze front formation, which can spawn deep convection that can hinder operations and endanger personnel and resources. Many other weak locally-driven low-level boundaries and their interactions with the sea breeze front and each other can also initiate deep convection in the KSC/CCAFS area. These convective processes often last 60 minutes or less and pose a significant challenge to the local forecasters. Surface winds during the transition seasons (spring and fall) pose the most difficulties for the forecasters at WFF. They also encounter problems forecasting convective activity and temperature during those seasons. Therefore, accurate mesoscale model forecasts are needed to better forecast a variety of unique weather phenomena. Global and national scale models cannot properly resolve important local-scale weather features at each location due to their horizontal resolutions being much too coarse. Therefore, a properly tuned local data assimilation (DA) and forecast model at a high resolution is needed to provide improved capability. To accomplish this, a number of sensitivity tests were performed using the Weather Research and Forecasting (WRF) model in order to determine the best DA/model configuration for operational use at each of the space launch ranges to best predict winds, precipitation, and temperature. A set of Perl scripts to run the Gridpoint Statistical Interpolation (GSI)/WRF in real-time were provided by NASA's Short-term Prediction Research and Transition Center (SPoRT). The GSI can analyze many types of observational data including satellite, radar, and conventional data. The GSI/WRF scripts use a cycled GSI system similar to the operational North American Mesoscale (NAM) model. The scripts run a 12-hour pre-cycle in which data are assimilated from 12 hours prior up to the model initialization time. A number of different model configurations were tested for both the ER and WFF by varying the horizontal resolution on which the data assimilation was done. Three different grid configurations were run for the ER and two configurations were run for WFF for archive cases from 27 Aug 2013 through 10 Nov 2013. To quantify model performance, standard model output will be compared to the Meteorological Assimilation Data Ingest System (MADIS) data. The MADIS observation data will be compared to the WRF forecasts using the Model Evaluation Tools (MET) verification package. In addition, the National Centers for Environmental Prediction's Stage IV precipitation data will be used to validate the WRF precipitation forecasts. The author will summarize the relative skill of the various WRF configurations and how each configuration behaves relative to the others, as well as determine the best model configuration for each space launch range.

Watson, Leela R.↗
DOE OSTI2026

Programming entropy production hotspots via interaction patterning

Dissipation in soft matter and living systems is highly heterogeneous, giving rise to complex local entropy production patterns. Recent work has identified the connection between local entropy production and locally extractable amount of work. This suggests that locally produced entropy can be recovered to power nanorobots and biological machines, prompting the need for efficient strategies to modulate and fine-tune local entropy production. In this work, we propose to program entropy production hotspots by leveraging local interaction patterns and test this approach in simulations of a fluid driven through a nanopore or past an obstacle. Here, the results show that patterning the surface of the pore with a repulsive patch gives rise to an entropy production hotspot around the patch, while an attractive patch leads to an entropy production cold spot. Varying the strength of the interaction and width of the patch enables modulating the hotness and extent of the hot spot. In flows past interaction patterned-obstacles, we show that the combined effects of steric hindrance and attraction allows to program a specific entropy production map, thereby opening the door to the precise powering of nanomachines.

Desgranges, Caroline [University of Massachusetts,↗
DOE OSTI2025

Laser-induced selective local patterning of vanadium oxide phases

The same elements can form different compounds with widely different physical properties. Synthesis of a single-phase material is commonly achieved by controlling experimental conditions. Synthesizing materials that incorporate multiple specific spatially distributed chemical phases is often challenging, especially if different phases must be organized into well-defined spatial patterns. Here, we present an efficient solid reaction laser annealing (SRLA) approach to directly write regions of different local chemical compositions. We demonstrate the practical utility of our approach by locally writing microscale patterns of distinct chemical phases in vanadium oxide thin films. Specifically, we achieved the controlled local recrystallization of a uniform V 2 O 3 matrix into VO 2 , V 3 O 5 , and V 4 O 7 regions exhibiting sharp 1st- and 2nd-order metal–insulator phase transitions over a wide range of critical temperatures, i.e., a characteristic feature of select vanadium oxides that is extremely sensitive to even minute structural or compositional imperfections. We utilized the local chemical phase writing to pattern spiking oscillators with distinct electrical behavior directly in the thin film sample without employing elaborate lithography fabrication. Our laser tuning local chemical composition opens a pathway to synthesize a wide range of artificially micropatterned composite materials, with precision and control unattainable in conventional material synthesis methods.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2023

Comparative Analysis of Empirical and Machine Learning Models for Chla Extraction Using Sentinel-2 and Landsat OLI Data: Opportunities, Limitations, and Challenges

Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra () from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, -fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

algal blooms↗
DOE OSTI2024

Evaluation of Autoconversion Representation in E3SMv2 Using an Ensemble of Large-Eddy Simulations of Low-Level Warm Clouds

In numerical atmospheric models that treat cloud and rain droplet populations as separate condensate categories, precipitation initiation in warm clouds is often represented by an autoconversion rate (Au), which is the rate of formation of new rain droplets through the collisions of cloud droplets. Being a function of the cloud droplet size distribution (DSD), the local Au is commonly parameterized as a function of DSD moments: cloud droplet number (n c ) and mass (q c ) concentrations. When applied in a large-scale model, the grid-mean Au must also include a correction, or enhancement factor, to account for the horizontal variability of the cloud properties across the model grid. In this study, we evaluate the Au representation in the Energy Exascale Earth System Model version 2 (E3SMv2) climate model using large-eddy simulations (LES), which explicitly resolve cloud droplet spectra, and therefore the local Au, as well as its spatial variability. The analysis of an ensemble of warm low-level cloud cases shows that the E3SMv2 formulation represents the Au reasonably well compared to the horizontally averaged explicitly computed rate from LES. The agreement, however, comes from a combination of an underestimated E3SM-tuned local Au rate and an overestimated subgrid cloud variability enhancement factor. The latter bias is traced to neglecting the horizontal variability of n c and its co-variability with q c in parameterizing the grid-mean Au.

54 ENVIRONMENTAL SCIENCES↗