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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Reaching the Excitonic Limit in 2D Janus Monolayers by In Situ Deterministic Growth

Abstract Named after the two‐faced Roman god of transitions, transition metal dichalcogenide (TMD) Janus monolayers have two different chalcogen surfaces, inherently breaking the out‐of‐plane mirror symmetry. The broken mirror symmetry and the resulting potential gradient lead to the emergence of quantum properties such as the Rashba effect and the formation of dipolar excitons. Experimental access to these quantum properties, however, hinges on the ability to produce high‐quality 2D Janus monolayers. Here, these results introduce a holistic 2D Janus synthesis technique that allows real‐time monitoring of the growth process. This prototype chamber integrates in situ spectroscopy, offering fundamental insights into the structural evolution and growth kinetics, that allow the evaluation and optimization of the quality of Janus monolayers. The versatility of this method is demonstrated by synthesizing and monitoring the conversion of SWSe, SNbSe, and SMoSe Janus monolayers. Deterministic conversion and real‐time data collection further aid in conversion of exfoliated TMDs to Janus monolayers and unparalleled exciton linewidth values are reached, compared to the current best standard. The results offer an insight into the process kinetics and aid in the development of new Janus monolayers with high optical quality, which is much needed to access their exotic properties.

2D materials↗
DOE OSTI2022

A graph signal processing‐based multiple model Kalman filter ( GSP‐MMKF ) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.

Ghosh, Sambit↗
DOE OSTI2013

A retrospective comparison of smart prep and test bolus multi-detector CT pulmonary angiography protocols

Optimal arterial opacification is crucial in imaging the pulmonary arteries using computed tomography (CT). This poses the challenge of precisely timing data acquisition to coincide with the transit of the contrast bolus through the pulmonary vasculature. The aim of this quality assurance exercise was to investigate if a change in CT pulmonary angiography (CTPA) scanning protocol resulted in improved opacification of the pulmonary arteries. Comparison was made between the smart prep protocol (SPP) and the test bolus protocol (TBP) for opacification in the pulmonary trunk. A total of 160 CTPA examinations (80 using each protocol) performed between January 2010 and February 2011 were assessed retrospectively. CT attenuation coefficients were measured in Hounsfield Units (HU) using regions of interest at the level of the pulmonary trunk. The average pixel value, standard deviation (SD), maximum, and minimum were recorded. For each of these variables a mean value was then calculated and compared for these two CTPA protocols. Minimum opacification of 200 HU was achieved in 98% of the TBP sample but only 90% of the SPP sample. The average CT attenuation over the pulmonary trunk for the SPP was 329 (SD = ±21) HU, whereas for the TBP it was 396 (SD = ±22) HU (P = 0.0017). The TBP also recorded higher maximum (P = 0.0024) and minimum (P = 0.0039) levels of opacification. This study has found that a TBP resulted in significantly better opacification of the pulmonary trunk than the SPP.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

A dynamic model of wind turbine yaw for active farm control

This paper presents a graph-based dynamic yaw model to predict the dynamic response of the hub-height velocities and the power of a wind farm to a change in yaw. The model builds on previous work where the turbines define the nodes of the graph and the edges represent the interactions between turbines. Advances associated with the dynamic yaw model include a novel analytical description of the deformation of wind turbine wakes under yaw to represent the velocity deficits and a more accurate representation of the interturbine travel time of wakes. The accuracy of the model is improved by coupling it with time- and space-dependent estimates of the wind farm inflow based on real-time data from the wind farm. The model is validated both statically and dynamically using large-eddy simulations. An application of the model is presented that incorporates the model into an optimal control loop to control the farm power output.

17 WIND ENERGY↗
DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗
DOE OSTI2020

Hyperspectral X-ray Imaging with TES Detectors for Nanoscale Chemical Speciation Mapping

We are developing an imaging capability (“Hyperspectral X-ray Imaging”) for mapping chemical information (molecular formula, phase, oxidation state, hydration) that is based on ultra-high-resolution X-ray emission spectroscopy with large transition-edge sensor microcalorimeter arrays in the scanning electron microscope. By combining microcalorimeter arrays with hundreds of pixels, high-bandwidth microwave frequency-division multiplexing, and fast digital electronics for near real-time data processing, our goal is to enable measurements using laboratory-scale instrumentation rather than synchrotron beamlines. Our application focus here is on mapping the chemical form of uranium compounds on the nanoscale. Furthermore, we will present our approach to developing the Hyperspectral X-ray Imaging capability, progress toward a 128-pixel microwave multiplexed X-ray fluorescence instrument at LANL, and the path to high-throughput nanoscale chemical mapping.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

A precise non-invasive blood glucose measurement system using NIR spectroscopy and Huber’s regression model

Diabetes is one of the prominent diseases around the world. Presently, invasive techniques need a finger prick blood sample . A repetitively painful procedure that produces the chance of infection. To resolve this issue, non-invasive measurement approach is proposed. In this paper, an efficient NIR wave based optical detection system is proposed with optimized post-processing regression model. After real-time data analysis, it has been found that the coefficient of determination (R{sup 2}) is improved with the value of 0.9084 using proposed regression model. Mean absolute derivative is also increased with 3.87 mg/dl corresponding to predicted blood glucose concentration. Mean absolute relative difference has exceeded to 3.25%, and average error is improved with 3.77% using proposed regression model. Average accuaracy has been analyzed 94–95% for predicted blood glucose concentration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Ten questions concerning Large Language Models (LLMs) for building applications

Large Language Models (LLMs) are emerging as powerful AI tools capable of transforming how building information is collected, processed, analyzed, and applied across diverse research areas. Their capabilities can help building operators, facility managers and other stakeholders such as designers, architects and engineers by providing actionable insights for decision-making across planning, construction, operations, and maintenance of buildings and facilities. This paper explores ten key questions concerning the role of LLMs in shaping sustainable, intelligent, and human-centric buildings. From fundamental definitions to advanced applications, we examine how LLMs facilitate decision-making across the life cycle of buildings and energy systems. LLMs can enhance life cycle assessments (LCA), building energy simulations, and real-time data integration, empowering more efficient and adaptive human-AI environments. They can also contribute to streamlining regulatory compliance, improving post-occupancy evaluations, and fostering more inclusive and participatory design processes. Additionally, this paper addresses the ethical challenges posed by LLMs, such as bias, data privacy, and environmental impacts, and explores their potentials in advancing intelligent digital twins (DT) for ongoing building operations and maintenance. Built upon our applied research using LLMs and the review of tools, datasets, and research gaps, we provide a forward-looking perspective on how LLMs can drive innovation, collaboration, and productivity in the built environment while supporting ethical and effective implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Human tear-production rate from closed-eye Schirmer-strip capillary dynamics

A Schirmer tear test (STT) is commonly used to gauge human tear production, especially when dry-eye symptoms present. In an STT, the rounded tip of a standardized paper strip is inserted into the lower fornix of the eye, and the wetted length extending out from the lower lid is recorded after 5 min of eye closure. Longer wetted lengths suggest higher tear production rates. To date, however, there is no methodology to transform STT transient wetting lengths into basal tear- production rates. We develop a physical model to elucidate wetting kinetics in a Schirmer strip. Tear evaporation from the exposed portion of the strip and gravity are accounted for. Careful consideration of the initial depletion of tear in the closed-eye tear prism reveals an initial fast increase in wetted length followed by slower growth. Excellent agreement of the proposed model is achieved with experimental observation. When evaporation is negligible, the slow-growth regime exhibits a linear increase of wetted length in time. The linear-length-growth time regime permits simple calculation of quantitative tear-production rates. In this paper, we suggest measuring several dynamic wetting lengths along a sheathed Schirmer strip and near the 5-min insertion duration followed by fitting to a straight line. The slope of the length-versus-time data gives the basal lacrimal-supply rate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Impact of Pretreatment Tumor Growth Rate on Outcome of Early-Stage Lung Cancer Treated With Stereotactic Body Radiation Therapy

Purpose: To determine the influence of pretreatment tumor growth rate on outcomes in patients with early-stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). Methods and Materials: A review was conducted on 160 patients with T1-T2N0M0 NSCLC treated with SBRT at single institution. The patient's demographic and clinical data, time interval (t) between diagnostic and planning computed tomography (CT), vital status, disease status, and cause of death were extracted from a prospectively kept database. Differences in gross tumor volume between diagnostic CT (GTV1) and planning CT (GTV2) were recorded, and growth rate was calculated by use of specific growth rate (SGR). Kaplan-Meier curves were constructed for overall survival (OS). Differences between groups were compared with a log-rank test. Multivariate analyses were performed by use of the Cox proportional hazard model with SGR and other relevant clinical factors. Cumulative incidence was calculated for local, regional, and distant failures by use of the competing risk approach and was compared with Gray's test. Results: The median time interval between diagnostic and planning CT was 82 days. The patients were divided into 2 groups, and the median SGR was used as a cut-off. The median survival times were 38.6 and 27.7 months for the low and high SGR groups, respectively (P=.03). Eastern Cooperative Oncology Group performance status (P=.01), sex (P=.04), SGR (P=.03), and GTV2 (P=.002) were predictive for OS in multivariable Cox regression analysis and, except sex, were similarly predictive for failure-free survival (FFS). The 3-year cumulative incidences of regional failure were 19.2% and 6.0% for the high and low SGR groups, respectively (P=.047). Conclusion: High SGR was correlated with both poorer OS and FFS in patients with early-stage NSCLC treated with SBRT. If validated, this measurement may be useful in identifying patients most likely to benefit from adjuvant therapy after SBRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Characterization, antioxidant and cytotoxicity evaluation of green synthesized silver nanoparticles using Cleistanthus collinus extract as surface modifier

Graphical abstract: The figure is the TEM image of green synthesized silver nanoparticles from Cleistanthus collinus. In this investigation we have used the poisonous plant as a reducing and capping agent. This is a first time data to synthesis the metal nanoparticles using poisonous plant. - Highlights: • A hitherto unreported venomous plant mediated AgNPs synthesis. • The particle size is observed in the range of 20–40 nm. • Surface morphology of the well-dispersed silver nanoparticles is studied using SEM and TEM. • Crystalline nature of AgNPs is confirmed by X-ray diffraction analysis. • Antioxidant activities of green synthesized AgNPs are tested in vitro. - Abstract: We report, here a simple green method for the preparation of silver nanoparticles (AgNPs) using the plant extract of Cleistanthus collinus as potential phyto reducer. The synthesized AgNPs were characterized by UV–vis spectra, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The obtained results confirmed that the AgNPs were crystalline in nature and the morphological studies reveal the spherical shape of AgNPs with size ranging from 20 to 40 nm. The in vitro antioxidant activity of AgNPs showed a significant effect on scavenging of free radicals. The cytotoxicity study exhibited a dose-dependent effect against human lung cancer cells (A549) and normal cells (HBL-100), the inhibitory concentration (IC{sub 50}) were found to be 30 μg/mL and 60 μg/mL respectively. The in vivo histopathology of mouse organs proved that AgNPs does not possess toxic effect and can be extensively applied in biomedical sciences.

36 MATERIALS SCIENCE↗
DOE OSTI2018

KALYPSO: Linear array detector for high-repetition rate and real-time beam diagnostics

Synchrotrons and modern FEL light sources operate with bunch repetition rates in the MHz range. The profile of the electron beam inside the accelerator can be characterized with indirect experimental techniques where linear array detectors are employed to measure the emitted synchrotron radiation or the spectrum of a near-IR laser. To improve the performance of modern beam diagnostics we have developed KALYPSO, a detector system operating with a continuous frame rate of up to 2.7 MHz. To facilitate the integration in different experiments, a modular architecture has been adopted. Different semiconductor micro-strip sensors can be connected to front-end ASICs to optimize the quantum efficiency at different photon energies, ranging from visible light up to near-IR. The front-end electronics are integrated within an heterogeneous DAQ consisting of FPGAs and GPUs, which allows scientists to implement real-time data processing algorithms. The current version of the detector is in operation at the KARA synchrotron light source and at the European XFEL. Finally, in this contribution we present the detector architecture, the performance results and the on-going technical developments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions

Pixel tracking detectors at upcoming collider experiments will see unprecedented charged-particle densities. Real-time data reduction on the detector will enable higher granularity and faster readout, possibly enabling the use of the pixel detector in high-rate online event selection, such as the ATLAS or CMS first-level trigger systems. This data reduction can be accomplished with a neural network (NN) in the readout chip bonded with the sensor that recognizes and rejects tracks with low transverse momentum (p T ) based on the geometrical shape of the charge deposition (“cluster”). To design viable detectors for deployment, the dependence of the NN as a function of the sensor geometry, external magnetic field, irradiation, and noise must be understood. In this paper, we present first studies of the efficiency and data reduction for planar pixel sensors exploring these parameters. For the CMS HL-LHC sensor geometry, we obtain a signal efficiency of (91.9 ± 0.7)% and a data reduction of (29.7 ± 1.0)%. A smaller sensor pitch in the bending direction improves the p T discrimination, but a larger pitch can be partially compensated with detector thickness. Any accumulated radiation damage also changes the cluster shape, reducing the signal efficiency compared to the baseline by approximately 30–60% in absolute terms, but nearly all of the performance can be recovered through retraining of the network and updating the weights. Finally, the impact of noise was investigated, and retraining the network on noise-injected datasets was found to maintain performance within 6% of the baseline network trained and evaluated on noiseless data. •ASIC-compatible track-momentum classifier is robust in realistic detector conditions.•About 90% signal efficiency and 30% data reduction per layer for CMS HL-LHC geometry.•Single-layer signal efficiency increases for smaller pixel pitch or thicker sensors.•Performance with noise or after radiation damage mostly recovered by retraining.

Shekar, Danush [Illinois U., Chicago] (ORCID:00000↗
DOE OSTI2026

Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling as Catalysts for Next-Generation Breakthroughs

The Presidential Symposium (PRES) at the 2025 Fall Meeting, hosted by the President’s Office and Energy and Fuels Division, American Chemical Society (ACS) in Washington, DC, brought together a diverse group of chemists, engineers, and materials scientists working in battery materials & systems, automation and artificial intelligence from academia, industry, and national laboratories. The accelerating demand for high-performance, scalable, and sustainable energy storage has catalyzed a paradigm shift in how materials are dis-covered, devices are engineered, and systems are optimized. This Presidential Symposium, entitled “Revolutionizing Energy Storage: AI, Automation, and Advanced Modeling Driving Next-Gen Breakthroughs”, brings together global leaders to unveil transformative strategies anchored in the AAA framework: Artificial Intelligence, Automation, and Advanced Modeling. Artificial Intelligence is redefining the frontiers of energy storage by enabling predictive design, real-time optimization, and intelligent control across diverse chemistries and architectures. Automation is streamlining the synthesis, characterization, and testing of battery materials, dramatically accelerating innovation cycles and unlocking scalable solutions for grid and mobility applications. Advanced Modeling, spanning atomic to system-level scales, provides unprecedented insight into electrochemical dynamics, degradation pathways, and thermal behavior, particularly when coupled with physics-informed machine learning and digital twin technologies. Digital twins, in turn, leverage the AAA framework by integrating real-time data, physics-based models, and AI predictions into dynamic virtual replicas, enabling proactive diagnostics, optimization, and system resilience. Together, these synergistic pillars are not only re-shaping the scientific landscape but also forging a new era of reproducible, data-driven, and resilient energy storage innovation. In conclusion, this symposium marks a pivotal moment in the convergence of computational intelligence and experimental rigor, charting the course for next-generation breakthroughs in lithium-ion, solid-state, and flow battery technologies.

Artificial Intelligence (AI)↗
DOE OSTI2021

Estimating and Forecasting Time-Varying Groundwater Recharge in Fractured Rock: A State-Space Formulation with Preferential and Diffuse Flow to the Water Table

Rapid infiltration following precipitation may result in groundwater contamination from surface contaminants or pathogens. In fractured rock, contaminants can migrate rapidly to points of groundwater withdrawals. In contrast to the temporal availability of groundwater quality chemical indicators, meteorological and groundwater level observations are available in real-time to estimate time-varying recharge, which can act as a surrogate to identify periods of rapid infiltration that may indicate contamination susceptibility. Estimating recharge using methods, such as base-flow recession, unsaturated infiltration models, or Water-Table Fluctuations (WTF), cannot capitalize on currently available technologies and telecommunication infrastructure to conduct real-time recharge estimation at scales relevant to characterizing rapid infiltration. We present a linear, physics-based State-Space (SS) model of one-dimensional infiltration to estimate recharge, which includes preferential and diffuse-flow to the water table. The model can take advantage of real-time data for water-table altitude, precipitation, and evapotranspiration. Model parameters are calibrated over an observation period, and the Kalman Filter (KF) is subsequently applied to continuously update the observed (water-table altitude) and unobserved (groundwater recharge) system states and predict future states as new data become available. The SS/KF algorithm is demonstrated at the Masser Groundwater Recharge Site in Pennsylvania, USA and comparisons are made with recharge estimates from WTF methods. Model results indicate that the frequency of observations (daily versus sub-daily) dictates the allocation between preferential and diffuse flow. Additionally, because infiltration processes encompass many nonlinearities, model parameters estimated from observation periods need to be updated at least seasonally to account for changing recharge conditions.

groundwater, Recharge, infiltration, recursive est↗
DOE OSTI2020

Evaluation of Low Dose Silicon Carbide Temperature Monitors

Thermocouples are generally used to provide real-time temperature indications in instrumented tests performed at materials and test reactors. Melt wires or paint spots are often included in such tests as an independent technique of detecting peak temperatures incurred during irradiation. In addition, less expensive static capsule tests, which have no leads attached for real-time data transmission, often rely on melt wires and paint spots as a post-irradiation technique for peak temperature indication. Unfortunately, these techniques are limited in that they can only detect whether a single temperature is or is not exceeded. Silicon carbide (SiC) monitors are advantageous because a single monitor can be used to determine the peak temperature reached within a relatively broad range (200 – 800°C). Although the use of SiC monitors was proposed more than five decades ago, the ultimate performance limits of this technique are not fully understood. The Nuclear Science User Facilities (NSUF) is the United States Department of Energy Office of Nuclear Energy's only designated nuclear energy user facility. Its mission is to provide nuclear energy researchers access to world-class capabilities and to facilitate the advancement of nuclear science and technology. This mission is supported by providing access to state-of-the-art experimental irradiation testing, post irradiation examination facilities, and high performance computing capabilities as well as technical and scientific assistance for the design and execution of projects. As part of an NSUF project, low dose silicon carbide monitors were irradiated in the Belgian Reactor 2 and were then evaluated both at the SCK•CEN and at Idaho National Laboratory's High Temperature Test Laboratory to determine their peak temperature achieved during irradiation. The technical significance of this work was that the monitors were irradiated to a dose that was significantly less than recommended in published literature. This paper will discuss the evaluation process, the irradiation test, and the performance of the low dose silicon carbide temperature monitors.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Passive seismic tomography application for cave monitoring in DOZ underground mine PT. Freeport Indonesia

It is already known that tomography has a great impact for analyzing and mapping unknown objects based on inversion, travel time as well as waveform inversion. Therefore, tomography has used in wide area, not only in medical but also in petroleum as well as mining. Recently, tomography method is being applied in several mining industries. A case study of tomography imaging has been carried out in DOZ ( Deep Ore Zone ) block caving mine, Tembagapura, Papua. Many researchers are undergoing to investigate the properties of DOZ cave not only outside but also inside which is unknown. Tomography takes a part for determining this objective.The sources are natural from the seismic events that caused by mining induced seismicity and rocks deformation activity, therefore it is called as passive seismic. These microseismic travel time data are processed by Simultaneous Iterative Reconstruction Technique (SIRT). The result of the inversion can be used for DOZ cave monitoring. These information must be used for identifying weak zone inside the cave. In addition, these results of tomography can be used to determine DOZ and cave information to support mine activity in PT. Freeport Indonesia.

58 GEOSCIENCES↗
DOE OSTI2020

TURBOMOLE: Modular program suite for ab initio quantum-chemical and condensed-matter simulations

TURBOMOLE is a collaborative, multi-national software development project aiming to provide highly efficient and stable computational tools for quantum chemical simulations of molecules, clusters, periodic systems, and solutions. The TURBOMOLE software suite is optimized for widely available, inexpensive, and resource-efficient hardware such as multi-core workstations and small computer clusters. TURBOMOLE specializes in electronic structure methods with outstanding accuracy–cost ratio, such as density functional theory including local hybrids and the random phase approximation (RPA), GW-Bethe–Salpeter methods, second-order Møller–Plesset theory, and explicitly correlated coupled-cluster methods. TURBOMOLE is based on Gaussian basis sets and has been pivotal for the development of many fast and low-scaling algorithms in the past three decades, such as integral-direct methods, fast multipole methods, the resolution-of-the-identity approximation, imaginary frequency integration, Laplace transform, and pair natural orbital methods. This review focuses on recent additions to TURBOMOLE’s functionality, including excited-state methods, RPA and Green’s function methods, relativistic approaches, high-order molecular properties, solvation effects, and periodic systems. A variety of illustrative applications along with accuracy and timing data are discussed. Moreover, available interfaces to users as well as other software are summarized. TURBOMOLE’s current licensing, distribution, and support model are discussed, and an overview of TURBOMOLE’s development workflow is provided. Challenges such as communication and outreach, software infrastructure, and funding are highlighted.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗