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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The sources and diurnal variations of submicron aerosols in a coastal–rural environment near Houston, US

Aerosol properties were characterized at a rural site southwest of Houston from May to September 2022 during the intensive operation periods (IOPs) of the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). Backward trajectory analysis reveals three major air mass types: marine air mass from the Gulf, urban air mass influenced by urban emissions, and regional air mass. Marine aerosols typically show a bimodal size distribution and have the lowest particle number and mass concentrations of PM 1 (particulate matter with an aerodynamic diameter of less than 1 µm), while aerosols from air masses strongly influenced by urban emissions exhibit the highest concentrations. Organic aerosol (OA) accounts for more than 50 % of PM 1 for urban and regional air masses, whereas sulfate is comparable to OA in marine air masses. Positive matrix factorization (PMF) analysis of aerosol mass spectra identifies 6 OA factors: hydrocarbon-like OA (HOA), OA from the oxidation of monoterpenes (MT-SOA), OA from the reactive uptake of isoprene epoxydiols by acidic sulfate particles (isoprene-SOA), oxygenated OA arising from shipping emissions (shipping-OOA), and two oxygenated OA factors with high O : C ratios (OOA1 and OOA2). OOA2 has the highest O : C ratio and exhibits elevated mass concentration in the afternoon. Similar diurnal variation of highly oxidized OA factors was commonly observed in the Houston area during previous studies and attributed to the SOA formation by photochemistry and mixing from aloft. Here, using air mass backward trajectories and a 1-D box model, we show the diurnal trend of OOA2 mass concentration is instead driven by changes in air mass arriving at the rural site. The air mass changes are likely caused by the shift between land breezes and sea/bay breezes. Within the same air mass type (e.g., either urban or marine air mass), OOA2 mass concentration is largely independent of wind direction and shows essentially no diurnal variation, suggesting OOA2 is related to aged OA with minimal influence by local emissions. This study helps identify the major sources of OA in the Houston region and highlights the impacts of both atmospheric chemistry and meteorology on aerosol properties in the coastal–rural environment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Meteorological environments associated with California wildfires and their potential roles in wildfire changes during 1984-2017

California has been seeing more wildfires in recent years, resulting in huge economic losses and threatening human health. Clarifying the meteorological environments of wildfires is foundational to improving the understanding and prediction of wildfires and their impacts. Here, 1535 California wildfires during 1984-2017 are systematically investigated. Based on two key meteorological factors - temperature and moisture anomalies - all wildfires are classified into four groups: hot-dry, hot-wet, cold-dry, cold-wet. Most (~60%) wildfires occurred on hot-dry days. Compositing the large-scale environments of the four groups shows that persistent high pressure and strong northeasterly wind descending from inland favor hot-dry conditions for wildfires. This analysis also reveals an important role of anomalies in southerly onshore flow that supports stronger convection, accompanied by more lightning flashes that provide a triggering mechanism for wildfires under hot-wet conditions. Self-organizing map analysis lends confidence in the large-scale meteorological pattern for dominating hot-dry wildfires in California. Besides wildfire occurrence, wildfire size is also influenced by meteorological anomalies through their magnitudes. Among them, moisture anomaly explains the largest fraction (~69%) of variability in wildfire sizes. Large-scale meteorological anomalies are found to play an important role in the devastating 2018 wildfire season in California. During 1984-2017, wildfire burned area has significantly increased by ~3.6% per year, indicating a doubling of burned area in 2017 relative to 1984, with the trend dominated by hot-dry wildfires in summer. Drying and warming in conjunction with strengthening of the high pressure in summer support more frequent and larger wildfires in California.

58 GEOSCIENCES↗
DOE OSTI2021

Hydrogen-Bonding Reinforced Flexible Composite Electrodes for Enhanced Energy Storage

The lack of advanced electrode materials is one of the main factors hindering the development of flexible rechargeable aqueous batteries (RABs) for high specific energy density and structural stability. It is also challenging to achieve high-capacity performance for both the positive and negative electrodes simultaneously. In this work, it is demonstrated that, by smartly designing the composite structures of positive and negative electrodes via one-step electrodeposition strategy, the energy storage performance of the RAB is largely enhanced. For positive electrode material synthesis, Co-Cu double hydroxides (Co-Cu-DH) nanosheets are skillfully rooted into electroreduced graphene oxide (eRG) via hydrogen bonding, in which graphene oxide reduction, Co-Cu-DH nucleation/growth, and formation of hydrogen bonding between Co-Cu-DH and eRG simultaneously occur. Moreover, when a RAB based on Co-Cu-DH@eRG//FeOOH@eRG using the same composite design strategy is established, a wide operating voltage window of ≈1.8 V, a high specific energy density of ≈142.8 Wh kg –1 at ≈890 W kg –1 , and long-term cyclic stability (88.5% of capacity retention after 12 000 cycles) are obtained. This study presents a general compositing strategy for the development of advanced electrode materials, and it is expected to stimulate future material synthesis/design in RABs toward the goal of high energy density storage.

25 ENERGY STORAGE↗
DOE OSTI2022

A Top-Down Strategy to Engineer ActiveLayer Morphology for Highly Efficient and Stable All-Polymer Solar Cells

A major challenge hindering the further development of all-polymer solar cells (all-PSCs) employing polymerized small-molecule acceptors is the relatively low fill factor (FF) due to the difficulty in controlling the active-layer morphology. The issues typically arise from oversized phase separation resulting from the thermodynamically unfavorable mixing between two macromolecular species, and disordered molecular orientation/packing of highly anisotropic polymer chains. Herein, a facile top-down controlling strategy to engineer the morphology of all-polymer blends is developed by leveraging the layer-by-layer (LBL) deposition. Optimal intermixing of polymer components can be achieved in the two-step process by tuning the bottom-layer polymer swelling during top-layer deposition. Consequently, both the molecular orientation/packing of the bottom layer and the molecular ordering of the top layer can be optimized with a suitable top-layer processing solvent. A favorable morphology with gradient vertical composition distribution for efficient charge transport and extraction is therefore realized, affording a high all-PSC efficiency of 17.0% with a FF of 76.1%. The derived devices also possess excellent long-term thermal stability and can retain >90% of their initial efficiencies after being annealed at 65 °C for 1300 h. In conclusion, these results validate the distinct advantages of employing an LBL processing protocol to fabricate high-performance all-PSCs.

14 SOLAR ENERGY↗
DOE OSTI2026

Manganese Oxidation during Vegetation Burning

Redox recycling of manganese (Mn) plays a key role in organic matter decomposition and nutrient cycling in terrestrial vegetated ecosystems, and it is expected to be changed by fires. This study revealed how Mn is oxidized during vegetation burning, by characterizing the chemical speciation of Mn in fire ash from wildland fires and laboratory burning and evaluating the factors governing its average oxidation state (AOS) and speciation. Manganese in wildland fire ash from different ecosystems showed variable AOS that ranges from 2.5 to 3.3. Laboratory burning experiments showed that Mn oxidation was primarily controlled by fire thermal intensity (temperature × duration) and burning completeness. As heating time increased from 5 min to 5 h at 550 and 700 °C, Mn AOS in the lab-burned vegetation ash increased from 2.7 to 4.0 and the oxidation rate was faster at higher temperature. Diverse Mn species can present in wildland fire ash and differ structurally from biogenic Mn oxides. The oxidized Mn species enable fire ash to mediate oxidative degradation of catechol, demonstrating its potential in mediating organic matter decomposition. This study revealed a new paradigm of Mn redox recycling, as compared to the microbe-mediated Mn redox cycling in the absence of fires.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Interfacial Reactivity-Triggered Oscillatory Lattice Strains of Nanoalloys

Understanding the structure evolution of nanoalloys under reaction conditions is vital to the design of active and durable catalysts. Herein, we report an operando measurement of the dynamic lattice strains of dual-noble-metal alloyed with an earth-abundant metal as a model electrocatalyst in a working proton-exchange membrane fuel cell using synchrotron high-energy X-ray diffraction coupled with pair distribution function analysis. Additionally, the results reveal an interfacial reaction-triggered oscillatory lattice strain in the alloy nanoparticles upon surface dealloying. Analysis of the lattice strains with an apparent oscillatory irregularity in terms of frequency and amplitude using time-frequency domain transformation and theoretical calculation reveals its origin from a metal atom vacancy diffusion pathway to facilitate realloying upon dealloying. This process, coupled with surface metal partial oxidation, constitutes a key factor for the nanoalloy's durability under the electrocatalytic oxygen reduction reaction condition, which serves as a new guiding principle for engineering durable or self-healable electrocatalysts for sustainable fuel cell energy conversion.

Wu, Zhi-Peng [State University of New York (SUNY),↗
DOE OSTI2024

Measuring Impact: Evaluating Thermal Zoning Simplification on Energy Efficiency Measures Analysis

Building Energy Modeling (BEM) is a versatile tool for designing, retrofitting, ensuring code compliance, obtaining certifications, qualifying for incentives, and enabling real-time building control. However, capturing all the details of building geometry for thermal zoning can be time-consuming, costly, and sometimes computationally challenging. As a result, modelers have been applying zoning simplification based on factors such as space functions and internal loads, as well as relying on their experience and judgment while adhering to zoning rules outlined in industry standards. Despite the prevalence of this common practice, a notable gap exists in the literature regarding studies quantifying the influence of simplified thermal zoning on the evaluation of Energy Efficiency Measures (EEMs). Recognizing this gap, this paper seeks to contribute to the field by enhancing the understanding of how the simplification of thermal zoning influences the evaluation of EEMs against a baseline design. The study utilized a medium office prototype model with a detailed floor plan featuring over 20 zones per floor covering diverse functional spaces with varying internal loads and occupancy schedules. A standard thermal zoning strategy outlined in ASHRAE Standard 90.1 Appendix G was employed as the simplified zoning method. This strategy condenses the zoning into a core zone and four perimeter zones per floor. It was compared with the detailed zoning approach, which involves one zone per space. Common Energy EEMs, such as enhanced envelope, high-efficiency appliances and equipment, and HVAC controls, were individually implemented and evaluated. The results indicate that the performance comparison between the two zoning methods varies depending on the type of measures considered. Basic measures, such as adding wall insulation, demonstrate similar energy impacts, while advanced HVAC control measures, such as static pressure reset, exhibit a more substantial difference that cannot be overlooked.

Xie, Jiarong↗
DOE OSTI2025

DOME: Directional medical embedding vectors from Electronic Health Records

Motivation: The increasing availability of Electronic Health Record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data. On the other hand, scalable approaches that only require summary-level data do not incorporate temporal dependencies between concepts. Methods: We introduce a DirectiOnal Medical Embedding (DOME) algorithm to encode temporally directional relationships between medical concepts, using summary-level EHR data. Specifically, DOME first aggregates patient-level EHR data into an asymmetric co-occurrence matrix. Then it computes two Positive Pointwise Mutual Information (PPMI) matrices to correspondingly encode the pairwise prior and posterior dependencies between medical concepts. Following that, a joint matrix factorization is performed on the two PPMI matrices, which results in three vectors for each concept: a semantic embedding and two directional context embeddings. They collectively provide a comprehensive depiction of the temporal relationship between EHR concepts. Results: We highlight the advantages and translational potential of DOME through three sets of validation studies. First, DOME consistently improves existing direction-agnostic embedding vectors for disease risk prediction in several diseases, for example achieving a relative gain of 5.5% in the area under the receiver operating characteristic (AUROC) for lung cancer. Second, DOME excels in directional drug-disease relationship inference by successfully differentiating between drug side effects and indications, correspondingly achieving relative AUROC gain over the state-of-the-art methods by 10.8% and 6.6%. Finally, DOME effectively constructs directional knowledge graphs, which distinguish disease risk factors from comorbidities, thereby revealing disease progression trajectories. The source codes are provided at https://github.com/celehs/Directional-EHRembedding.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning

High-fidelity modeling is an essential design tool for many engineering applications. However, for complex systems, computational cost can be a limiting factor. Analyzing parameter sensitivity, uncertainty quantification, and design optimization require many model evaluations. Surrogate models are often used to develop the relationship between model parameters and quantities of interest. However, in the case of complex systems, surrogate models require several degrees of freedom and, thus, a large number of data points to determine the correct dependencies. For many applications, this may be prohibitively expensive. The reduction of computational requirements can be achieved by leveraging low-fidelity models. Low-fidelity models represent the system at a coarser resolution with the advantage of computational efficiency. Therefore, a bi-fidelity modeling paradigm, which augments the accuracy of a low-fidelity model in a computationally efficient manner by invoking limited runs of a high-fidelity model, can be leveraged to sufficiently balance the accuracy and computational requirements. In this work, a bi-fidelity weighted transfer learning method using neural networks was applied to a computational fluid dynamics heat transfer modeling problem. The transfer learning advantage was investigated as a function of hyperparameters. Our main finding is that the use of a bi-fidelity modeling paradigm achieves accuracy close to that of a high-fidelity Gaussian process model while significantly reducing computational cost. The bi-fidelity model achieves comparable performance with 90 high-fidelity samples-that is, 60% less than the samples needed to achieve similar accuracy without the use of bi-fidelity modeling,

Borowiec, Katarzyna↗
DOE OSTI2024

Large-scale simulation-based parametric analysis of an optimal precooling strategy for demand flexibility in a commercial office building

Achieving success with grid-interactive efficient buildings (GEBs) is closely tied to the utilization of flexible loads. A valuable strategy involves the implementation of precooling techniques before high-demand events, such as peak hours, by adjusting zone air temperature setpoints. This leads to a reduction in thermal loads and peak electricity demand during these times, as the building’s thermal mass stores and subsequently releases thermal energy. However, the effectiveness of the pre-cooling optimization is highly contingent on specific conditions such as building thermal properties, weather conditions, utility rate structure, HVAC equipment sizing, etc. Therefore, investigating the impacts of these condition-specific factors is crucial, especially when considering precooling strategies that utilize thermal mass in commercial buildings. In this paper, we first devised a novel heuristic control approach that incorporates parameterized optimal precooling thermostat schedules to enhance demand flexibility in a commercial office building. Subsequently, we conducted a thorough performance evaluation of this control strategy. Here, the optimal thermostat schedule was parameterized using three optimization variables: the precooling start time, the precooling end time, and the precooling temperature setpoint. Utilizing the DOE medium-sized office building as the virtual testbed, we showed that the parameterized schedule effectively approximates model predictive control and requires drastically reduced computational overhead. In addition, we investigated the impact of different influencing factors on the optimal precooling strategy. These factors include building thermal mass, outdoor air conditions, and energy price profiles. Using high-performance computing, we simulated a total of 225 scenarios, consisting of three levels of thermal mass, five typical outdoor air temperature profiles, and fifteen time-of-use price plans. The results demonstrate that optimal thermostat scheduling could save substantial energy cost in medium-sized office buildings with heavy thermal mass but with some energy penalty. Although the potential for cost savings is lower in buildings with low and medium thermal mass, the energy penalty remains consistent in all three thermal mass scenarios. The study also highlights the need to account for zone diversity and recognize that a one-size-fits-all-zone setpoint schedule may not be suitable for all zones and can lead to unnecessary energy wastage. Furthermore, the results highlight that while outdoor air conditions play a role in cost and energy performance, the cooling load exerts a more immediate and substantial influence on cost savings in precooling strategies. Although cost savings are comparable under certain conditions with the same cooling load, observed deviations in energy penalty indicate potential disparities in the efficiency of the HVAC system during the load-shifting process. In addition, the duration of peak pricing and the ratio between peak and off-peak times exhibit clear correlations with cost savings and energy consumption, aligning with intuitive expectations. These findings offer valuable insights for optimizing precooling strategies in office buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Aerosol hygroscopicity over the South-East Atlantic Ocean during the biomass burning season – Part 2: Influence of sea salt and burning conditions on CCN hygroscopicity

Biomass burning (BB) significantly influences cloud condensation nuclei (CCN) concentrations over the southeastern Atlantic; however, aerosol hygroscopicity (κ) – a key factor for CCN activation – remains poorly constrained during the BB season. This study investigates κ variability using in situ measurements from Ascension Island during the 2016 and 2017 BB seasons. Results show substantial monthly variability, with κ values lowest in August and increasing through October. On average, κ was significantly higher in 2017 (∼ 0.55) than in 2016 (∼ 0.33), suggesting that the aerosols in 2017 were more hygroscopic and more easily activated as CCN. Sulfate and sea salt were the two dominant contributors to κ and the primary drivers of its interannual variability. During the 2017 BB season, sulfate – the major inorganic component – accounted for ∼ 34 % of the submicron aerosol mass, while sea salt, estimated via κ-closure analysis, contributed ∼ 17 %. The higher κ in 2017 was largely attributed to increased sea salt, likely driven by stronger marine winds. Approximately 67 % of sulfate was linked to BB emissions. Variations in BB combustion efficiency, modulated by regional meteorology, influenced sulfate fraction and thus κ values. Specifically, higher relative humidity and lower wind speeds over BB source regions in 2017 favored smoldering combustion, explaining the higher sulfate fraction. Overall, the observed interannual differences in aerosol hygroscopicity reflect the combined impacts of BB combustion characteristics and sea salt emissions, underscoring the critical roles of both BB and marine aerosol sources in regulating aerosol-cloud interactions over the southeastern Atlantic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quantifying trapped magnetic vortex losses in niobium resonators at mK temperatures

Trapped magnetic vortices in niobium introduce microwave losses that degrade the performance of superconducting resonators. While such losses have been extensively studied above 1 K, we report here their direct quantification in the millikelvin and low-photon regime relevant to quantum devices. Using a high-quality factor 3D niobium cavity cooled through its superconducting transition in controlled magnetic fields, we isolate vortex-induced losses and find the resistive component of the sensitivity to trapped flux S to be approximately 2 n Ω/mG at 10 mK and 6 GHz. The decay rate is initially dominated by two-level system (TLS) losses from the native niobium pentoxide, with vortex-induced degradation of T 1 occurring above B trap ∼ 50 mG. In the absence of the oxide, even 10 mG of trapped flux limits performance, Q 0 ∼ 10 10 , or T 1 ∼ 350 ms, underscoring the need for stringent magnetic shielding. The resistive sensitivity, S, decreases with temperature and remains largely field-independent, whereas the reactive component, S′, exhibits a maximum near 0.8 K. These behaviors are well modeled within the Coffey–Clem framework in the zero-creep limit, under the assumption that vortex pinning is enhanced by thermally activated processes. Our results suggest that niobium-based transmon qubits can tolerate vortex-induced dissipation at trapped field levels up to several hundred mG, but achieving long coherence times still requires careful magnetic shielding to suppress lower-field losses from other mechanisms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Equally high efficiencies of organic solar cells processed from different solvents reveal key factors for morphology control

The power conversion efficiency of organic solar cells (OSCs) is exceeding 20%, an advance in which morphology optimization has played a significant role. It is generally accepted that the processing solvent (or solvent mixture) can help optimize morphology, impacting the OSC efficiency. Here we develop OSCs that show strong tolerance to a range of processing solvents, with all devices delivering high power conversion efficiencies around 19%. By investigating the solution states, the film formation dynamics and the characteristics of the processed films both experimentally and computationally, we identify the key factors that control morphology, that is, the interactions between the side chains of the acceptor materials and the solvent as well as the interactions between the donor and acceptor materials. Our work provides new understanding on the long-standing question of morphology control and effective guides to design OSC materials towards practical applications, where green solvents are required for large-scale processing.

14 SOLAR ENERGY↗
DOE OSTI2023

A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel

Abstract U-10 wt.% Zr (U-10Zr) metallic fuel is the leading candidate for next-generation sodium-cooled fast reactors. Porosity is one of the most important factors that impacts the performance of U-10Zr metallic fuel. The pores generated by the fission gas accumulation can lead to changes in thermal conductivity, fuel swelling, Fuel-Cladding Chemical Interaction (FCCI) and Fuel-Cladding Mechanical Interaction (FCMI). Therefore, it is crucial to accurately segment and analyze porosity to understand the U-10Zr fuel system to design future fast reactors. To address the above issues, we introduce a workflow to process and analyze multi-source Scanning Electron Microscope (SEM) image data. Moreover, an encoder-decoder-based, deep fully convolutional network is proposed to segment pores accurately by integrating the residual unit and the densely-connected units. Two SEM 250 × field of view image datasets with different formats are utilized to evaluate the new proposed model’s performance. Sufficient comparison results demonstrate that our method quantitatively outperforms two popular deep fully convolutional networks. Furthermore, we conducted experiments on the third SEM 2500 × field of view image dataset, and the transfer learning results show the potential capability to transfer the knowledge from low-magnification images to high-magnification images. Finally, we use a pre-trained network to predict the pores of SEM images in the whole cross-sectional image and obtain quantitative porosity analysis. Our findings will guide the SEM microscopy data collection efficiently, provide a mechanistic understanding of the U-10Zr fuel system and bridge the gap between advanced characterization to fuel system design.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optimization of operational strategies for rich gas enhanced oil recovery based on a pilot test in the Bakken tight oil reservoir

Horizontal well drilling and multistage hydraulic fracturing have been demonstrated as effective approaches for stimulating oil production in the Bakken tight oil reservoir. However, after multiple years of production, primary oil recovery in the Bakken is generally less than 10% of the estimated original oil in place. Gas huff ‘n’ puff (HnP) has been tested in the Bakken Formation as an enhanced oil recovery (EOR) method; however, most field pilot test results showed no significant incremental oil production. One of the factors affecting HnP EOR performance is premature gas breakthrough, which is one of the most critical issues observed in the field because of the presence of interwell fractures. Consequently, injected gas rapidly reaches adjacent production wells without contacting reservoir rock and increasing oil recovery. Proper conformance control is therefore needed to avoid early gas breakthrough and improve EOR performance. In this study, a rich gas EOR pilot in the Bakken was carefully analyzed to collect the essential reservoir and operational data. A simulation model with 16 wells was then developed to reproduce the production history and predict the EOR performance with and without conformance control. EOR operational strategies, including single- and multiple-well HnP, with different gas injection constraints were investigated. The simulation results of single-well HnP without conformance control showed that a rich gas injection rate of at least 10 MMscfd was needed to yield meaningful incremental oil production. The strategy of conformance control via water injection could significantly improve oil production in the HnP well, but injecting an excessive amount of water also leads to water breakthrough and loss of oil production in the offset wells. By analyzing the production performance of the wells individually, the arrangement of wells was optimized for multiple-well HnP EOR. The multiwell results showed that rich gas EOR could improve oil production up to 7.4% by employing conformance control strategies. Furthermore, replacing rich gas with propane as the injection gas could result in 14% of incremental oil production.

02 PETROLEUM↗
DOE OSTI2022

Exponential Water Uptake in Ionomer Membranes Results from Polymer Plasticization

Water content is the most influential factor in the performance of ion-exchange membranes (IEMs), controlling their mechanical rigidity, ionic conductivity, and degradation rates. Membranes absorb water exponentially at high water activity (a w ), making that region of the sorption isotherm the most influential on membrane properties. Plasticization of the polymer by water has been proposed to cause this exponential uptake at high a w . However, an integrated microscopic picture of the structure, thermodynamic, and mechanical properties of IEMs as a function of a w has remained elusive. In this work, we use large-scale molecular simulations validated with experimental measurements to compute the sorption isotherms, Young’s modulus, polymer dynamics, and structure of IEMs. The simulations unveil that the exponential increase in water uptake (WU) coincides in all cases with the glass to rubber transition of the membrane, as measured through its Young’s modulus and segmental polymer dynamics. Functionalization of the polymer with alkyl groups further contributes to its plasticization, increasing the WU at a given a w and ion-exchange capacity (IEC). The alkyl chains act synergistically with water to plasticize the polymer matrix and allow water penetration in the membrane. The simulations reveal that the width of the water channels depends on the ratio λ of water to ions in the polymer but is independent of its IEC. We conclude that differences in the polymer matrix–not the water channels–are responsible for the distinct uptake response of IEMs to the thermodynamic driving force of water activity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Multidimensional measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic charged-kaon electroproduction off protons in the valence region

Measurements of beam single-spin asymmetries in semi-inclusive deep-inelastic electron scattering (SIDIS) with positively charged kaons off protons have been performed with 10.6 and 10.2 GeV incident electron beams using the CLAS12 spectrometer at Jefferson Lab. We report an analysis of the electroproduction of positively charged kaons over a large kinematic range of fractional energy, Bjorken 𝑥, transverse momentum, and photon virtualities 𝑄 2 ranging from 1 GeV 2 up to 6 GeV 2 . This is the first published multidimensionally binned CLAS12 measurement of a kaon SIDIS single-spin asymmetry in the valence quark regime. The data provide constraints on the structure function ratio $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$⁡/$𝐹_{𝑈⁢𝑈}$, where $𝐹^{sin \space𝜙}_{𝐿⁢𝑈}$ is a quantity with a leading twist of twist-3 that can reveal novel aspects of the quark-gluon correlations within the nucleon. The impact of the data on understanding the underlying reaction mechanisms and their kinematic variation is explored using theoretical models for the different contributing twist-3 parton-distribution functions (PDFs) and fragmentation functions (FFs).

form factors↗
DOE OSTI2025

Floquet-engineered fast SNAP gates in weakly coupled circuit-QED systems

Superconducting cavities with high quality factors, coupled to a fixed-frequency transmon, provide a state-of-the-art platform for quantum information storage and manipulation. The commonly used selective number-dependent arbitrary phase ( SNAP ) gate faces significant challenges in ultrahigh-coherence cavities, where the weak dispersive shifts necessary for preserving high coherence typically result in prolonged gate times. Here, in this work, we propose a protocol to achieve high-fidelity SNAP gates that are orders of magnitude faster than the standard implementation, surpassing the speed limit set by the bare dispersive shift. We achieve this enhancement by dynamically amplifying the dispersive coupling via sideband interactions, followed by quantum optimal control on the Floquet-engineered system. We also present a unified perturbation theory that explains both the gate acceleration and the associated benign drive-induced decoherence, corroborated by Floquet-Markov simulations. These results pave the way for the experimental realization of high-fidelity, selective control of weakly coupled, high-coherence cavities, and expanding the scope of optimal control techniques to a broader class of Floquet quantum systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗