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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Are we modeling the properties of unconventional shales correctly?

Technological advancements have led to impressive growth in hydrocarbon (HC) production from unconventional shale reservoirs. However, major limiting factors in shale gas development are i) low recovery of gas in place (<20%), (ii) a rapid decline in well productivity, iii) release of contaminants, despite the use of advanced hydraulic fracturing fluids and multiple fracturing stages. This is mainly due to a lack of understanding of the nanoscale physicochemical properties of shales, especially that of kerogen, the macromolecule that is not only the source of the majority of the HC’s in shales but also holds most of the HC’s in adsorbed state. Over the last few years, a large number of studies have been published on simulating the physicochemical properties of shale utilizing molecular models of kerogen. However, the molecular models of kerogen input into these simulations are based on the kerogen “type” derived from a very limited number of shale samples. In this paper, we examine the variations that can exist in kerogen structure within a particular kerogen “type” across different shale basins and single shale basin at similar thermal maturity levels. We propose that using kerogen “type” based structural models for molecular simulations could lead to inaccurate estimation of HC reserves, HC recovery, HC production, frackability, and quantity and quality of produced water. Our study highlights the need for developing a better classification of kerogen based on its “molecular structure” instead of “type” for a more accurate prediction of physicochemical properties of shales.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2024

Optimal 1D Ly α forest power spectrum estimation – III. DESI early data

ABSTRACT The 1D power spectrum P1D of the Ly α forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of P1D with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of 54 600 quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with 2D image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly α emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and discuss the key sources of systematic error that we need to address with the upcoming DESI Year 1 analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evaluation of hydroxamate-based resins towards a more clinically viable 44 Ti/ 44 Sc radionuclide generator

For this research, several hydroxamate-based resins were synthesized and tested for use in 44 Ti/ 44 Sc generator systems in small scale experiments (740 kBq 44 Ti). The most promising resin was tested further in larger scale generator studies (37 MBq). This resin displayed impressive retention of 44 Ti over several elutions, and high quantities of 44 Sc were obtained in small volumes of dilute HCl eluents. Initial radiolabeling experiments were conducted and demonstrated the possibility of direct radiolabeling of the generator produced 44 Sc with DOTA.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Inpainting radar missing data regions with deep learning

Abstract. Missing and low-quality data regions are a frequent problem for weather radars. They stem from a variety of sources: beam blockage, instrument failure, near-ground blind zones, and many others. Filling in missing data regions is often useful for estimating local atmospheric properties and the application of high-level data processing schemes without the need for preprocessing and error-handling steps – feature detection and tracking, for instance. Interpolation schemes are typically used for this task, though they tend to produce unrealistically spatially smoothed results that are not representative of the atmospheric turbulence and variability that are usually resolved by weather radars. Recently, generative adversarial networks (GANs) have achieved impressive results in the area of photo inpainting. Here, they are demonstrated as a tool for infilling radar missing data regions. These neural networks are capable of extending large-scale cloud and precipitation features that border missing data regions into the regions while hallucinating plausible small-scale variability. In other words, they can inpaint missing data with accurate large-scale features and plausible local small-scale features. This method is demonstrated on a scanning C-band and vertically pointing Ka-band radar that were deployed as part of the Cloud Aerosol and Complex Terrain Interactions (CACTI) field campaign. Three missing data scenarios are explored: infilling low-level blind zones and short outage periods for the Ka-band radar and infilling beam blockage areas for the C-band radar. Two deep-learning-based approaches are tested, a convolutional neural network (CNN) and a GAN that optimize pixel-level error or combined pixel-level error and adversarial loss respectively. Both deep-learning approaches significantly outperform traditional inpainting schemes under several pixel-level and perceptual quality metrics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multiscale Patterning from Competing Interactions and Length Scales

We live in an era of complexity marked by impressive new tools powering the scientific method to accelerate discovery, prediction, and control of increasingly complex systems. In common with many disciplines and societal challenges and opportunities, materials and condensed matter sciences are beneficiaries. The volume and fidelity of experimental, computational, and visualization data available, and tools to rapidly interpret them, are remarkable. Conceptual frameworks, including multiscale, multiphysics modeling of this complexity, are fueled by the data and, in turn, guide directions for future experimental and computational strategies. In this spirit, we discuss the importance of competing interactions, length scales, and constraints as pervasive sources of spatio-temporal complexity. We use representative examples drawn from materials and condensed matter, including the important role of elasticity in some technologically important quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

On-Chip Batteries as Distributed Energy Sources in Heterogeneous 2.5D/3D Integrated Circuits

Energy efficiency in digital systems faces challenges due to the constraints imposed by small-scale transistors. Moreover, the growing demand for portable consumer electronics necessitates the use of compact energy sources. To address these challenges, heterogeneous 3D IC technology has emerged as a promising solution for the former. Regarding the latter, we propose the concept of distributed batteries within a heterogeneous 3D IC. This approach involves utilizing multiple smaller batteries with different specifications among different modules of 3D ICs. This approach optimizes performance and overcomes limitations associated with both 3D ICs and conventional power delivery methods. Distributed batteries play a vital role in effectively managing the heat generated by energy sources and modules within a 3D IC. Furthermore, they contribute to achieving a uniform distribution of heat throughout the entire structure, which ultimately ensures the optimal performance of the batteries and modules. The simulation results indicate a 40 percent enhancement in achieving a more even distribution of generated heat. Additionally, the proposed distributed battery techniques improve power delivery, enhance reliability, and enable optimized voltage regulation while improving efficiency. In addition to the primary benefits, alternative configurations of the proposed approach can offer extra energy storage capacity and act as efficient electromagnetic shields, resulting in an impressive reduction of external electromagnetic noises by 60 dB.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Delineating the Roles of Mn, Al, and Co by Comparing Three Layered Oxide Cathodes with the Same Nickel Content of 70% for Lithium-Ion Batteries

High-nickel layered oxides continue to prevail in the energy storage market as the frontmost cathode candidates for next-generation lithium-ion batteries. Demand and development of LiNi 1–x–y Mn x Co y O 2 (NMC) and LiNi 1–x–y Co x Al y O 2 (NCA) cathodes are rampantly increasing, particularly for the electric vehicle (EV) industry. However, the continued presence of cobalt in NMC and NCA cathodes raises global concerns due to geopolitical and ethical issues attributed to Co sourcing. We herein introduce a novel cobalt-free, high-nickel cathode LiNi 0.7 Mn 0.25 Al 0.05 O 2 (NMA70) and benchmark it against Co-containing LiNi 0.7 Mn 0.15 Co 0.15 O 2 (NMC70) as well as Co- and Al-free LiNi 0.7 Mn 0.3 O 2 (NM70) cathodes with equivalent 70% Ni contents that are all synthesized in-house. NMA70 displays a high initial C/10 capacity of 210 mA h g –1 , matching that of NMC70 in half cells with a cutoff voltage of 4.5 V. NMA70 also exhibits an impressive high-voltage full cell cycling performance with a cutoff voltage of 4.4 V with a nearly identical capacity retention of 83% compared to that of 82% for NMC70 after 300 cycles. Postmortem X-ray photoelectron spectroscopy (XPS), high-angle annular dark-field scanning transmission electron microscopy (HAADF-STEM), and electron energy loss spectroscopy (EELS) analyses indicate a thinner cathode–electrolyte interface (CEI) developed in NMA70 compared to that in NM70 and unveil a more robust solid-electrolyte interface (SEI) passivation on the graphite anode among all samples. The benefits of Al doping are additionally highlighted with enhanced high-voltage CEI and thermal stabilities in NMA70. Furthermore, this work assesses the roles of Mn, Al, and Co to demonstrate both the practicality and feasibility of synthesizing cobalt-free, high-nickel cathodes that are promising alternatives to current NMC- and NCA-based cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Efficient Electrochemical Reduction of CO 2 to Formate in Methanol Solutions by Mn‐Functionalized Electrodes in the Presence of Amines**

Carbon cloth electrode modified by covalently attaching a manganese organometallic catalyst is used as cathode for the electrochemical reduction of CO 2 in methanol solutions. Six different industrial amines are employed as co-catalyst in millimolar concentrations to deliver a series of new reactive system. While such absorbents were so far believed to provide a CO 2 reservoir and act as sacrificial proton source, we herein demonstrate that this role can be played by methanol, and that the adduct formed between CO 2 and the amine can act as an effector or inhibitor toward the catalyst, thereby enhancing or reducing the production of formate. Pentamethyldiethylentriamine (PMDETA), identified as the best effector in our series, converts CO 2 in wet methanolic solution into bisammonium bicarbonate. Computational studies revealed that this adduct is responsible for a barrierless transformation of CO 2 to formate by the reduced form of the Mn catalyst covalently bonded to the electrode surface. As a consequence, selectivity can be switched on demand from CO to formate anion, and in the case of (PMDETA) an impressive TON HCOO- of 2.8×10 4 can be reached. This new valuable knowledge on an integrated capture and utilization system paves the way toward more efficient transformation of CO 2 into liquid fuel.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Synthesis and Characterization of Silver-Modified Nanoporous Silica Materials for Enhanced Iodine Removal

In aquatic environments, the presence of iodine species, including radioactive isotopes like 129 I and I 2 , poses significant environmental and health concerns. Iodine can enter water resources from various sources, including nuclear accidents, medical procedures, and natural occurrences. To address this issue, the use of natural occurring nanoporous minerals, such as zeolitic materials, for iodine removal will be explored. This study focuses on the adsorption of iodine by silver-modified zeolites (13X-Ag, 5A-Ag, Chabazite-Ag, and Clinoptilolite-Ag) and evaluates their performance under different conditions. All materials were characterized using scanning electron microscopey (SEM), energy-dispersive X-ray spectroscopy (EDS), powdered X-ray diffraction (P-XRD), Fourier-transform infrared spectrometry (FTIR), and nitrogen adsorption studies. The results indicate that Chabazite-Ag exhibited the highest iodine adsorption capacity, with an impressive 769 mg/g, making it a viable option for iodine removal applications. 13X-Ag and 5A-Ag also demonstrated substantial adsorption capacities of 714 mg/g and 556 mg/g, respectively, though their behavior varied according to different models. In contrast, Clinoptilolite-Ag exhibited strong pH-dependent behavior, rendering it less suitable for neutral to slightly acidic conditions. Furthermore, this study explored the impact of ionic strength on iodine adsorption, revealing that Chabazite-Ag is efficient in low-salinity environments with an iodine adsorption capacity of 51.80 mg/g but less effective in saline conditions. 5A-Ag proved to be a versatile option for various water treatments, maintaining its iodine adsorption capacity across different salinity levels. In contrast, Clinoptilolite-Ag exhibited high sensitivity to ionic competition, virtually losing its iodine adsorption ability at a NaCl concentration of 0.1 M. Kinetic studies indicated that the pseudo-second-order model best describes the adsorption process, suggesting chemisorption mechanisms dominate iodine removal. Chabazite-Ag exhibited the highest initial adsorption rate with a k 2 value of 0.002 mg g -1 h -1 , emphasizing its superior adsorption capabilities. Chabazite and Clinoptilolite, naturally occurring minerals, provide eco-friendly solutions for iodine adsorption. Chabazite superior iodine removal highlights its value in critical applications and its potential for addressing pressing environmental challenges.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Uncertainty quantification of fireball features extracted from nuclear test films using computer vision

Films from the US’s historic nuclear testing era comprise the only extensive collection of imagery depicting high-yield detonations. These films offer unique insights into the characteristics of flows occurring on scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models used to describe nuclear detonations. In recent work, we implemented modern computer vision and machine learning techniques to extract features of the fireball following nuclear detonation. With a training dataset of fireball films, we fine-tuned a You Only Look Once 11 (YOLO11) model to detect and track the fireball. Applied to a video, the outer bounding box produced in each frame by YOLO11 is used as an input prompt to Meta’s Segment Anything Model 2 (SAM2), which is shown to accurately predict the boundary of the fireball over time with high resolution. These state-of-the-art computer vision foundation models exhibit impressive visual accuracy in their results but lack an output of values that robustly quantify uncertainty in scientific applications. In this paper, we develop procedures for uncertainty quantification of extracted fireball features. We outline the application of a parallel attention mechanism to calculate uncertainty ranges that complement and better pose model validation data. This higher quality fireball validation data may serve to improve prognostic models describing nuclear detonations in support of nuclear forensic and emergency response activities.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
DOE OSTI2026

CdTe Core: Final Technical Report (FTR)

CdTe is presently the cost-leading thin-film PV technology, directly competing with Si at scale, even when domestically manufactured. While an impressive technology, its efficiency remains much below the detailed balance limit with the largest cause due to its low photovoltage and fill factor. To realize gains, the carrier concentration, minority carrier lifetime, and interface recombination all need to be improved simultaneously over historic levels. Using a new defect chemistry (group V doping instead of copper) has been identified as a viable route using single crystal systems. This project focused on implementing this new defect chemistry in scalable, polycrystalline thin-film photovoltaic CdTe devices with tasks focusing improvements to the front interface, absorber, and rear interface as well as capability development & stakeholder engagement. The goal of the project was to establish a strategy using devices, test structures, detailed characterization, and modeling to quantify the sources of losses in state-of-the-art CdTe photovoltaic devices. Using this strategy and advanced synthesis, losses at the front interface, absorber, and rear interface were worked on in parallel. The final objective was to significantly improve the voltage deficit in CdTe devices to enable improvements in photovoltage and efficiency that can be implemented by industry in the near-term. Over the course of the project, the team developed new characterization techniques, analysis, and modeling which were then applied to state-of-the-art materials generated internally and collaboratively. In particular to enable rapid progress, NREL worked closely with First Solar where NREL grew complete devices as well as ones that interleaved process steps where First Solar had completed different steps such as absorber growth or absorber growth and activation using their baseline methods. Using detailed characterization and analysis including photoemission, photoluminescence, and scanning probe techniques enabled understanding of the loss pathways and area for improvements in our own and First Solar s materials. Ultimately, this contributed to the first series of new world record CdTe efficiencies since 2016, culminating in a 23.1% certified cell that was P-doped along with As-doped cells of similar performance. Internally, NREL improved the statistical variation in baseline As-doped devices and improved average photovoltage by over 100 mV. This was done through an improvement in absorber quality, changed front interface, and improved back contact. In addition to materially improving the fabrication processes at NREL, characterization, analysis, and modeling were developed and disseminated. NREL also played a pivotal role in community building over the course of this project working closely with the Cadmium Telluride Accelerator Consortium. NREL worked in a series of collaborations with academic and industry partners, leveraging knowledge and innovations from this project, as well as helped organize a series of workshops to ensure rapid progress in the field. Working closely with the academic community has led to a dissemination of knowledge; working with First Solar as increased US competitiveness First Solar expanded domestic production to ~10 GW and opened new facilities.

14 SOLAR ENERGY↗
DOE OSTI2022

Application of Convolutional and Feedforward Neural Networks for Fault Detection in Particle Accelerator Power Systems

High voltage converter modulators (HVCM) provide power to the accelerating cavities of the spallation neutron source (SNS) facility. HVCM experience catastrophic failures, which increase the downtime of the SNS and reduce beam time. The faults may occur due to different reasons including failures of the resonant capacitor, core saturation due to the magnetic flux, insulated-gate bipolar transistor (IGBT) failures, and others. We recently have setup a HVCM test stand to develop and test machine learning models for anomaly detection and fault prognostics. In this work, we propose binary classifiers and autoencoder architectures based on convolutional (CNN) and feedforward neural networks (FNN) to facilitate distinguishing normal from faulty waveforms coming from the HVCM during operation. The results indicate that the CNN binary classifier is the best model among the four showing very stable performance in the training and testing sets with impressive metrics of precision and recall reaching up to 99\% with a very small uncertainty. The FNN classifier shows the least performance with a large uncertainty in its metrics. The performances of the two autoencoders based on CNN and FNN were in between, showing very good performance nonetheless.

Radaideh, Majdi↗
DOE OSTI2025

Dielectric constant engineering of nonfullerene acceptors enables a record fill factor of 83.58% and a high efficiency of 20.80% in organic solar cells

Organic solar cells (OSCs) have achieved power conversion efficiencies (PCEs) surpassing 20%, but their development remains hindered by the inherently low dielectric constant (εr) of organic semiconductors, which limits charge transport and contributes to serious recombination losses. Herein, we present a comprehensive strategy to overcome the challenge by engineering the dielectric properties of nonfullerene acceptors (NFAs). Two Y-series NFAs of BTP-N3F and BTP-C3F have been synthesized featuring trifluoromethyl (CF 3 ) end-capped alkyl side chains. This molecular design significantly enhances the dipole moment and ε r (up to ∼5.9) when compared to the reference acceptor Y6 (∼3), reducing the exciton binding energy (E b ) and improving charge transport. Furthermore, the incorporation of a high-εr polymer additive, poly(pentafluorostyrene) (PPFS), synergistically improves the active layer morphology and dielectric properties, enabling efficient charge extraction and reduced recombination losses. Devices based on the optimized D18-Cl/BTP-C3F system have achieved a record-high fill factor (FF) of 83.58% and an impressive PCE of 20.80%, setting a new benchmark for OSCs. Finally, our results underscore the pivotal role of ε r in enhancing device performance and establish a versatile pathway for advancing OSC efficiency and stability through molecular and morphological optimization.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Constructing Highly Durable Fuel Cell Catalysts Through Integrating Graphitic Shell‐Protected Composite Carbon Support with Gaseous Co Deposition‐Driven PtCo Intermetallics

Metal dissolution, nanoparticle agglomeration, and carbon support corrosion cause significant performance degradation of current PtCo catalysts under acidic and oxidative oxygen reduction reaction. Here, in this study, an integrated strategy is presented to design high-performance Pt 3 Co intermetallic catalysts by regulating gaseous Co deposition-driven diffusion into Pt nanoparticles supported on a composite carbon. The composite carbon is derived from ZIF-8/polyaniline, consisting of a high-surface-area (HSC) core and a protective graphitic shell (GS), which is employed to design 40 wt.% Pt/HSC@GS catalyst. The corresponding membrane electrode assemblies (MEAs) can maintain 1.09 A cm −2 at 0.7 V (20. 7% loss) after 10 000 cycles (1.0–1.5 V for carbon stability) and 1.15 A cm −2 (16.1% loss) after 150 000 cycles (0.60–0.95 V for catalyst stability). A ordered Pt 3 Co intermetallic is synthesized via a gaseous Co-deposition process, which yields a homogeneous Co-rich layer onto the Pt nanoparticles on the composite support, thereby facilitating Co diffusion into Pt crystal during subsequent ordering annealing to form the ordered intermetallic structure. This gaseous deposition leads to a thin carbon layer on PtCo nanoparticles, inhibiting particle growth during the annealing and mitigating Co dissolution under dynamic electrochemical conditions. The PtCo catalyst achieves impressive MEA performance and long-term durability (1.45 A cm −2 at 0.7 V after 120 000 cycles) under heavy-duty conditions.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2025

Ferromagnetic Atomic d ‐ p Orbital Hybridization for Promoting Al‐S Batteries

Rechargeable aluminum‐sulfur batteries (Al‐S) are emerging as a promising alternative energy storage system beyond lithium‐ion batteries due to their high energy density, abundant material resources, and economic efficiency. However, their practical application remains challenged by sluggish conversion kinetics, polysulfide shuttling, and low sulfur cathode utilization. While extensive studies have focused on enhancing polysulfide adsorption through catalytic strategies, the roles of electronic structure in dictating catalytic performance remain underexplored. Here, this work unveils the critical effect of unpaired electronic structure on the catalytic performance of single atom ferromagnetic transition metals through a systematic evaluation of three typical atomically dispersed ferromagnetic single atoms—Fe, Co, and Ni—supported on porous carbon (denoted as PC‐SAFAs). Comprehensive characterizations and density functional theory (DFT) calculations reveal that the PC‐SAFe catalysts, exhibiting the highest spin polarization arising from unpaired electrons, demonstrate the strongest interactions with polysulfide, thereby facilitating rapid and reversible polysulfide conversion reactions. Consequently, Al‐S batteries incorporating the optimized PC‐SAFe cathode achieve an impressive specific capacity of 508.8 mAh g −1 at 1.0 A g −1 after 500 cycles, along with much improved rate capability. In conclusion, this work provides a deeper understanding of the role of electronic structure in catalytic chemistry, and offers new insights for developing high‐performance Al‐S batteries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing low-temperature sintering in the MgO-LiF system: Mechanistic insights

In the present article, we provide compelling evidence that minor (1 wt%) additions of micron and nanometre-sized LiF particles in MgO decompose leading to free Li diffusing into MgO surfaces enhancing vacancy production during direct current sintering. The addition of nanometre-sized LiF particles leads to a > 500 °C reduction in the sintering temperature and over 99 % theoretical density of final consolidated compacts. Correlating differential scanning calorimetry with in-situ x-ray diffraction, together with Schottky calculations, post sintering electron microscopy and laser induced breakdown spectroscopy, we uncover critical insights into this impressive reduction in sintering temperature. Our quantitative analysis reveals that MgO and LiF interact at low temperatures with the diffusion of Li into the surface of MgO particles due to the intrinsic structural disorder of the LiF and MgO crystallites. Nanometre-sized LiF particles were found to react the most at low temperatures due to their increased structural disorder. Our multimodal characterization points to a Li-promoted densification and sintering mechanism. This impressive reduction in sintering temperature can be harnessed to promote low-temperature fabrication of MgO-based composites for technological applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Spin State Modulation via Magnetic Fields in Fe Single Atom Catalysts for High-Performance Aqueous Zinc–Sulfur Batteries

Aqueous zinc–sulfur battery has garnered significant attention as a high-energy, low-cost, and safe energy storage system. However, the multielectron transfer kinetics of sulfur cathodes are relatively slow, presenting challenges such as limited sulfur utilization and lower discharge voltage, which significantly hinder their practical applications. Here, in this study, we explored a comprehensive design approach for high-performance, long-cycle aqueous zinc–sulfur batteries. The simultaneous introduction of ZnI 2 and Fe single atoms (Fe-SAs) as catalytically active agents decouples the redox reactions, effectively facilitating ZnS oxidation and S reduction separately. The application of an external magnetic field regulates the spin state of Fe-SAs, further enhancing their catalytic activity and electron transfer capability. Electrochemical tests demonstrate that the S@Fe-NC HS/ZnI 2 cathode assembled under a magnetic field exhibits excellent rate performance, achieving an impressive specific capacity of 1399 mAh g –1 at a high current density of 5 A g –1 and good cycling stability over 300 cycles, representing the highest reported high-current discharge capacity to date. This study provides a comprehensive design framework for optimizing zinc–sulfur (Zn–S) battery performance and elucidates the influence of magnetic field-induced spin state modulation on catalytic behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Exploring the relationship between deposition method, microstructure, and performance of Nb/Si-based superconducting coplanar waveguide resonators

Superconducting quantum circuits (SQC) are one of the most promising hardware platforms for quantum computing, yet their performance is currently limited by the presence of various structural defects inside the circuit's structure. Despite impressive progress in the past decade, a precise understanding of the origin of these defects from various fabrication processes and their impact on coherence is still lacking. Here, in this study, we performed a comprehensive investigation on the microstructure, superconductivity, and resonator quality factor of Nb films deposited by high-power impulse magnetron sputtering (HiPIMS) and direct current (DC) magnetron sputtering. A suite of characterization techniques, including electron microscopy with spectroscopy, secondary ion mass spectrometry, magneto-optical microscopy, and pump-probe reflectivity spectroscopy is used. We reveal that niobium (Nb) resonators fabricated using HiPIMS exhibit a smaller average grain size, thicker surface oxide with larger thickness variations (rougher surface), and a thicker amorphous Nb/Si interface layer compared to samples deposited by DC sputtering. We identified that the amorphous Nb oxides (mainly located at the Nb surface and along the grain boundaries) and Nb-Si amorphous layers (at the Nb/Si interface) are major and potential sources of two-level system (TLS), while off-stochiometric oxides and suboxides of Nb close to the surface, crystalline defects (i.e., dislocations at grain boundary, point defects introduced during deposition) are main contributors of non-TLS sources. Our findings clarify the relationship between different defects and coherence loss mechanisms, highlighting the importance of material microstructure control on performance optimization in SQC.

36 MATERIALS SCIENCE↗