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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data Agnostic Feature-Target Analysis & Ranking Machine Learning Pipeline (DAFTAR-ML) v0.1.0

DAFTAR-ML is a specialized machine-learning pipeline that identifies relevant features based on their relationship to a target variable. Many ML pipelines focus solely on prediction, and feature ranking is often absent or lacks robust statistical methods. DAFTAR-ML performs its tasks with this outcome in mind. Model training is robust, using nested cross-validation and hyperparameter tuning. Instead of relying on native feature-importance scores, it employs SHAP (SHapley Additive exPlanations) to quantify feature importance. The pipeline also produces comprehensive results, including publication-quality visualizations.

Melie, Tina [Lawrence Berkeley National Laboratory↗
DOE OSTI2020

Online Modelling and Optimization of Nonlinear Integrable Systems

Nonlinear integrable optics was recently proposed as a design approach to increase the limits on beam brightness and intensity imposed by fast collective instabilities. To study these systems experimentally, a new research electron and proton storage ring, the Integrable Optics Test Accelerator, was constructed and recently commissioned at Fermilab. Beam-based diagnostics and online modelling of nonlinear systems presents unique challenges - in this paper, we report on our efforts to develop optimization methods suited for such lattices. We explore the effectiveness of neural networks as fast online surrogate estimators, and integrate them into a beam-based tuning algorithm. We also develop a method of knob dimensionality reduction and subsequent robust multivariate optimization for maximizing key performance metrics under complicated lattice optics constraints.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Observation of a Synchro-Betatron Instability in Fermilab Booster

In preparation for PIP-II, there has been interest in running the Fermilab Booster at a higher current more indicative of the PIP-II era operation. In July 2023, an experiment was performed to study collective instabilities over the transition crossing at the Fermilab Booster. Over the transition crossing, the synchrotron tune becomes small and synchro-betatron instabilities become possible. During the experiment, an intensity threshold was observed, above which a dipole instability with losses concentrated in the tail of the bunch. These losses are consistent with the Convective Instability

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Modular Processing of Flare Gas for Carbon Nanoproducts

This project demonstrated the technical viability and economic promise of a modular system for converting flared natural gas into valuable carbon nanoproducts (CNPs) through catalytic chemical vapor deposition (CVD). All major project milestones were successfully completed, including reactor design and commissioning, catalyst development, process optimization, technoeconomic analysis, and application testing in concrete systems. The overarching goal was to create a scalable, field-deployable process that valorizes stranded methane by producing high-value carbon materials for use in cementitious composites. At the lab scale, the team designed and built a fluidized bed reactor optimized for use with silica fume-supported nickel catalysts synthesized via atomic layer deposition (ALD). A statistically designed sintering study enabled precise tuning of nickel nanoparticle size, identifying the influence of oxygen partial pressure, time, and temperature on catalyst morphology and performance. These insights allowed the team to target catalyst conditions that maximize carbon nanofilament growth. Subsequent CVD experiments achieved up to 31.8 wt% carbon deposition under optimized conditions, with TEM confirming the presence of nanofilament structures and sustained hydrogen evolution during reaction. Reactor upgrades and empirical fluidization studies supported the development of reliable, repeatable experimental protocols. The modular pilot-scale skid reactor was fully constructed, instrumented, and commissioned. Capable of operating at 675–800°C and pressures up to 290 psig, the system was designed for continuous operation at a carbon production rate of 1 kg/hr. Initial demonstration runs confirmed solids handling, thermal control, and system leak-tightness, although a critical reactor component (the downfeed tube) was inadvertently omitted during final assembly. This omission limited gas–solid contact and prevented meaningful carbon deposition during pilot-scale CVD runs. Nonetheless, the system operated safely under design conditions, and the root cause of performance limitations was clearly identified. Complementary work on UHPC formulations demonstrated that small additions of carbon nanoproducts, including those derived from flare gas, can significantly enhance mechanical performance while preserving workability. A comprehensive study of CNF dispersion techniques and mix design optimization led to a clear protocol for integrating these nanomaterials into concrete. Incorporation of CNPs improved flexural toughness and reduced porosity, supporting their use in high-performance infrastructure applications. A technoeconomic analysis (TEA) confirmed that this process can produce CNP-loaded catalyst material at a levelized cost below $\$$7/kg across a range of catalyst loadings and reaction yields. With estimated market values for the carbon composite product ranging from $\$$14 to over $\$$60/kg, and the ability to blend CNPs into concrete at sub-percent levels with less than 10% added cost, the system presents a compelling economic case. While additional engineering work is needed to optimize fluidization and heat transfer at scale, this project establishes a strong foundation for commercial development. The process is not only technically sound but also economically promising, representing a viable pathway for flare gas mitigation through modular carbon nanomaterial production.

03 NATURAL GAS↗
DOE OSTI2024

Observation of a Synchro-betatron Instability in Fermilab Booster

In preparation for PIP-II, there has been interest in running the Fermilab Booster at a higher current more indicative of the PIP-II era operation. In July 2023, an experiment was performed to study collective instabilities over the transition crossing at the Fermilab Booster. Over the transition crossing, the synchrotron tune becomes small and synchro-betatron instabilities become possible. During the experiment, an intensity threshold was observed, above which a dipole instability with losses concentrated in the tail of the bunch. These losses are consistent with the Convective Instability.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Tuning the Mechanical Properties of a Polymer Semiconductor by Modulating Hydrogen Bonding Interactions

Conjugation breakers (CBs) with different H-bonding chemistry and linker flexibility are designed and incorporated into a Diketopyrrolopyrrole (DPP)-based conjugated polymer backbone. The effects of H-bonding interactions on polymer semiconductor morphology, mechanical properties and electrical performance are systematically investigated. We observe that CBs with H-bonding self-association constant >0.7 or denser packing tendency are able to induce higher polymer chain aggregation and crystallinity in as-casted thin film, resulting in higher modulus and crack on-set strain. Additionally, the rDoC (relative degree of crystallinity) of the stretched thin film with the highest crack on-set strain only suffers a small decrease, suggesting the main energy dissipation mechanism is the breakage of H-bonding interactions. By contrast, other less stretchable polymer films dissipate strain energy through the breakage of crystalline domains, indicated by drastic decrease in rDoC. Furthermore, we evaluate their electrical performance under mechanical strain in fully stretchable field-effect transistors. The polymer with the highest crack on-set strain has the least degradation in mobility as a function of strain. Overall, these observations suggest that we can aptly tune the mechanical properties in polymer semiconductors by modulating intermolecular interactions, such as H-bonding chemistry and linker flexibility. Such understanding provides molecular design guidelines for future stretchable semiconductors.

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

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Tuning effect of vanadium substitution on the structural and electronic properties of potassium hollandite surfaces

Metal oxide surfaces possess unique properties that are crucial for a wide variety of applications. Herein, density functional theory calculations are performed to study surfaces of potassium hollandite, KMn 8 O 16 , a promising cathode material for electrochemical energy storage, and the vanadium-substituted analog KMn 7 VO 16 . The results show that there is a clear increase in the stability of KMn 8 O 16 with (001) < (110) < (100) or (010), apt to adopt an elongated rod-like morphology. The vanadium (V)-substitution lowers the crystal symmetry and prefers to occupy the surface sites, resulting in electron redistribution and selective tuning of surface energy depending on the surface structures. In particular, the higher stability of substituted V 4+ compared with Mn 4+ ions leads to stabilization of the (001) surface due to the direct interaction of reduced Mn δ+ ions on the surface, while such tuning effect decreases with the increase in surface stability, (110) > (100) and (010). As a result, the KMnO 16 rod is shortened upon V-substitution as observed experimentally, effectively facilitating the ion transport during discharge. The V substituents also introduce stabilization to the defect surfaces resulting from Mn 2+ dissolution during cycling, thereby hindering further structural decay. In conclusion, our study demonstrates the potential tuning effect of V-substitution to promote the ion transport and mitigate the capacity degradation of α-MnO 2 -based materials.

25 ENERGY STORAGE↗
DOE OSTI2023

HCPerf: Driving Performance-Directed Hierarchical Coordination for Autonomous Vehicles

The rapid development of autonomous driving poses new research challenges to the on-vehicle computing system. In particular, the execution time of autonomous driving tasks highly depends on the specific driving environment. For instance, the execution time of configurable sensor fusion increases significantly as the scene becomes complex, which leads to end-to-end deadline misses from sensing to control and may cause accidents. Thus, a framework that can effectively utilize the system resources to guarantee the end-to-end deadlines of autonomous driving tasks as well as effectively prioritize the responsiveness and throughput of the control commands is crucial for autonomous driving. In this paper, we propose HCPerf, a performance-directed hierarchical coordination framework that intelligently coordinates the autonomous driving tasks with high execution time variation and complex dependencies according to the driving performance in real-time. Specifically, HCPerf mainly consists of two coordinators. The internal coordinator intelligently schedules the tasks according to the driving performance of the vehicle in order to help them meet the end-to-end deadlines while well prioritizing the responsiveness and throughput of the control commands. At the same time, the external coordinator dynamically tunes the rates of tasks according to the schedulability in order to efficiently utilize the system resource. We conduct extensive experiments on both simulation and hardware testbeds with the representative autonomous driving application. The results show that HCPerf can effectively improve the driving performance by 7.69%-45.94% in different driving scenarios.

Ma, Jialiang↗
DOE OSTI2024

Nanoscale Probing of Electrical Memory Effects in van der Waals Layered PdSe 2

Tunable electronic materials that can be switched between different impedance states are fundamental to the hardware elements for neuromorphic computing architectures. This “brain-like” computing paradigm uses highly paralleled and colocated data processing, leading to greatly improved energy efficiency and performance compared to traditional architectures in which data have to be frequently transferred between processor and memory. In this work, we use scanning microwave impedance microscopy for nanoscale electrical and electronic characterization of two-dimensional layered semiconductor PdSe 2 to probe neuromorphic properties. The local resolution of tens of nanometers reveals significant differences in electronic behavior between and within PdSe 2 nanosheets (NSs). In particular, we detected both n-type and p-type behaviors, although previous reports only point to ambipolar n-type dominating characteristics. Nanoscale capacitance–voltage curves and subsequent calculation of characteristic maps revealed a hysteretic behavior originating from the creation and erasure of Se vacancies as well as the switching of defect charge states. In addition, stacks consisting of two NSs show enhanced resistive and capacitive switching, which is attributed to trapped charge carriers at the interfaces between the stacked NSs. Stacking n- and p-type NSs results in a combined behavior that allows one to tune electrical characteristics. In conclusion, as local inhomogeneities of electrical and electronic behavior can have a significant impact on the overall device performance, the demonstrated nanoscale characterization and analysis will be applicable to a wide range of semiconducting materials.

2D material↗
DOE OSTI2022

Electronic modulation of metal-support interactions improves polypropylene hydrogenolysis over ruthenium catalysts

Abstract Ruthenium (Ru) is the one of the most promising catalysts for polyolefin hydrogenolysis. Its performance varies widely with the support, but the reasons remain unknown. Here, we introduce a simple synthetic strategy (using ammonia as a modulator) to tune metal-support interactions and apply it to Ru deposited on titania (TiO 2 ). We demonstrate that combining deuterium nuclear magnetic resonance spectroscopy with temperature variation and density functional theory can reveal the complex nature, binding strength, and H amount. H 2 activation occurs heterolytically, leading to a hydride on Ru, an H + on the nearest oxygen, and a partially positively charged Ru. This leads to partial reduction of TiO 2 and high coverages of H for spillover, showcasing a threefold increase in hydrogenolysis rates. This result points to the key role of the surface hydrogen coverage in improving hydrogenolysis catalyst performance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Effects of organized convection parameterization on the MJO and precipitation in E3SMv1. Part I: Mesoscale heating

Mesoscale organization of convection is typically not represented in global circulation models, and hence its influence on the global circulation is not accounted for. The heating component of a parameterization that represents the dynamical and physical effects of circulations associated with organized convection, referred to as the multiscale coherent structure parameterization (MCSP), is implemented in the Energy Exascale Earth System Model version 1 (E3SMv1). Numerical simulations are conducted to assess its impact on the simulated climate. Besides E3SMv1 simulations, we performed high-resolution (1 km) simulations using the Weather Research and Forecasting (WRF) Model to determine the temperature tendencies induced by mesoscale convective systems embedded in deep convection. We tuned the free parameters of the MCSP based on the WRF simulations. MCSP heating enhances Kevin wave spectra in E3SMv1, improves the representation of the Madden-Julian Oscillation, and impacts on mean precipitation that vary with locations and seasons, including a reduction of biases over the tropical Pacific.

Climate Modeling↗
DOE OSTI2023

Investigation on Tuning Power–Frequency Droop for Improved Grid–Forming Inverter and Synchronous Generator Transient Load Sharing

Poor transient load sharing has been observed during the parallel operation of synchronous generators (SGs) and droop-controlled grid-forming (GFM) inverter-based resources (IBRs) in islanded microgrids. This can result in overcurrent protections, causing the inverters to trip, which leads to the collapse of voltage and frequency. In this article, small-signal analysis supported by electromagnetic transients (EMTs) simulation is performed on a detailed model of a microgrid containing IBRs and an SG to identify underdamped modes and their sensitivities to the power–frequency (P-F) droop parameter. Further EMT simulation of a more complex microgrid model with multiple GFM IBRs and SGs is performed to determine whether trends identified from the modal analysis extend to cases where the number of SGs in operation may vary. The objective is to investigate the effectiveness of tuning this parameter to reduce the likelihood of overcurrents in response to load and generation disturbances. The work uncovers both fast and slow modes of concern. Primarily, the findings show that increasing the P-F droop value improves the damping of the slower mode while degrading the damping of the faster mode, leading to an upper limit on the damping improvement of the GFM IBR output current. In EMT simulations of the more complex microgrid, the greatest damping improvement still exhibits significant maximum overshoot. Furthermore, the optimal value of the P-F droop parameter is sensitive to the number of SGs in operation. The conclusion is that tuning the P-F droop parameter is not an effective means to avoid the overcurrent tripping of GFM IBRs during response to large load or generation disturbances when operating in parallel to SGs in islanded microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

SQMS science advances impact on Rigetti commercial processors

The collaboration between the Superconducting Quantum Materials and Systems Center (SQMS) and Rigetti Computing produced several advancements in our understanding of the role of materials characteristics in quantum processor performance. This partnership leverages SQMS's extensive characterization infrastructure and cutting-edge research in materials, and Rigetti's expertise in quantum hardware and robust nanofabrication to improve precision and performance of Rigetti's test QPUs. Qubit frequency is determined in large part by the properties of Josephson junctions (JJs) made of amorphous oxide tunnel barriers; the Alternating-Bias Assisted Annealing (ABAA) process allows us to tune JJs to their desired frequency [1]. Work by SQMS researchers in characterizing high-precision JJs post-processed (using ABAA) have yielded crucial information on the nature of the structure and chemical bonding uniformity of the ABAA processed amorphous oxides. Performance has also been improved through a comprehensive series of experiments that tested encapsulation and surface treatment. Encapsulation of the niobium metal layer with tantalum resulted in an T1 improvement of 80%, experimentally confirming the role of Nb surface losses in qubit performance [2]. Pre-treatment of the underlying silicon surface prior to JJ fabrication by replacing a buffered oxide etch (BOE) with hydrofluoric acid (HF) followed by aqueous ammonium fluoride (NH4F) has shown a statistically significant improvement of T1 by 22%, and reduction in the number of strongly-coupled TLS [3]. These examples, as well as many other published and ongoing investigations, demonstrate the mutual benefits that come from Rigetti's involvement in the SQMS collaboration. [1] - Pappas, D.P., et al. (2024). https://doi.org/10.1038/s43246-024-00596-z [2] - Bal, M., et al. (2024). https://doi.org/10.1038/s41534-024-00840-x [3] Kopas, C. J. et al. Preprint at https://doi.org/10.48550/arXiv.2408.02863 (2024).

Lachman, Ella↗
DOE OSTI2021

A Proposed Beam-Beam Test Facility COMBINE

The COmpact Machine for Beam-beam Interactions in Non-Equilibrium systems (COMBINE) is a proposed, dedicated, beam-beam test facility. This is currently a joint effort between Jefferson Lab and Old Dominion University to create a small, low energy pair of collider rings that would be used to study beam-beam interactions, as well as act as a teaching tool for students. The base design would make use of a pair of identical rings (2.5 meters per side) one rotated 180 degrees from the other, meeting at their common interaction point. These would be fed by an electron gun producing up to 125 keV electrons. The low energy will allow for beam-beam tune shifts commensurate with existing colliders, and will also allow for an exploration of the predicted effects of gear-changing, which would be performed using a variable pathlength scheme. Finally, this machine can also test linac-ring type systems.

Nissen, Edith↗
DOE OSTI2025

Pathway to tune sputter coated Nb$_3$Sn with Zr

Superconducting radio frequency (SRF) cavities are essential elements of many modern-day particle accelerators such as, European XFEL (DESY), LCLS II-HE (SLAC) and PIP II (Fermilab). Nb$_3$Sn superconducting material promises significant potential to exceed the performance of conventional niobium based SRF cavities. We aim to advance the ongoing R$&$D efforts to achieve Nb$_3$Sn coated cavities with high material quality. In this work, we have developed to achieve a route for effectively reducing the intrinsic defects in magnetron sputter coated Nb$_3$Sn SRF cavities and eventually improve the overall RF performance. We have observed that Zr is mainly being incorporated as ZrO$_2$ precipitates of average dimensions ranging from 20-100nm. By increasing the concentration of Zr, we have three major effects in the physical properties. Firstly, we noted that the density of the surface and bulk voids as well as their average dimensions are dramatically reduced on increasing the Zr content in Nb$_3$Sn. These surface voids are believed to be detrimental to cavity applications due to increase the local dissipation of electric and magnetic fields. These results indicate that inclusion of Zr in Nb$_3$Sn sputtered coating will improve the material quality and might help to reduce the overall RF power dissipation. Secondly, we observed that increasing Zr concentration from 0 to 2.4% substantially improves both superconducting transition temperature (Tc) and upper critical magnetic field (Hc2). However, on further increasing the Zr concentration to 24%, we noticed a small decrement in the Hc2 value. These results infer that the increase in Zr concentration up to an optimal concentration will be beneficial to enhance the superconducting properties. Increase in Tc promises lesser RF losses and improvement in accelerating gradient, whereas the increase in Hc2 suggests more efficient flux pinning which could give rise to high quality factors in presence of multi-Tesla magnetic field. Additionally, we also observed that increasing Zr concentration possibly prevents the oxygen diffusion and leads to thinner formation of primary surface oxide (Nb$_2$O$_5$ and SnO). This study gives a route to tune Zr fraction in Nb3Sn to modify the physical properties that control the high field performance of Nb3Sn RF cavities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Tuning Discharge Behavior of Hollandite α-MnO 2 in Hydrated Zinc Ion Battery by Transition Metal Substitution

The tunnel-type hollandite α-MnO 2 is a promising cathode material for rechargeable aqueous zinc-ion batteries (ZIBs) due to its low cost in synthesis and high energy density. However, irreversible structural degradation upon continuous cycling prevents the cathode from being utilized commercially. Herein, density functional theory (DFT) was utilized to conduct a systematic study on tuning the behavior of α-MnO 2 by substituting the Mn ions on the tunnel wall with a transition metal (V or Cr) during the H + -/Zn 2+ -intercalation in hydrated ZIB. Our study revealed that both substituents aid cyclability and capacity retention with Cr outperforming V. In term of discharge voltage, only the Cr-substitution displays clear promotion at the early stage of discharge. The superior performance of substituted Cr 4+ comes from its unique atomic and electronic structures. Upon discharge, it can be reduced to Cr 3+ more readily than Mn 4+ and thereby limits the formation of unstable Mn 3+ or Mn 2+ centers; the formed Cr 3+ is more stable than Mn 3+ and Mn 2+ from the reduction of Mn 4+ ; and Cr 3+ can also greatly stabilize the neighboring Mn ions. Here this study highlights the significant tuning effect of transition metal substitution on the electrochemical and physical performance of α-MnO 2 as a cathode in hydrated ZIBs.

25 ENERGY STORAGE↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗