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At least 199 records · Page 11

Measurement of the $\bar ν_μ-$Hydrogen Charged-Current Quasi-Elastic Cross Section using the NOvA Near Detector

We report a measurement of the total cross section for muon antineutrino charged-current quasi-elastic scattering on hydrogen, $\bar ν_μ{\rm H} \to μ^+ n$, in the NOvA near detector using a $1.2\times10^{21}$ proton-on-target exposure in the NuMI beam. A selection based on topological and kinematic constraints yields 35,509 signal events in the hydrogen-rich ($10.8\%$) detector, providing the highest statistics of (anti)neutrino--hydrogen interactions measured to date. Backgrounds from (anti)neutrino interactions on heavier nuclei are constrained using dedicated data control samples, significantly reducing the related systematic uncertainties. We obtain a value $σ(\bar ν_μ{\rm H} \to μ^+ n) = 0.538 \pm 0.009 ({\rm stat}) \pm 0.010 ({\rm syst}) \pm0.055 ({\rm flux}) \times 10^{-38}$ cm$^2$ for the total cross section at an average energy of 1.9 GeV, the most precise total cross-section measurement of this process to date. The combined statistical and non-flux systematic uncertainty is more than four times smaller than the flux uncertainty, allowing a future use of this measurement to constrain the absolute $\bar ν_μ$ flux.

Abubakar, S. [Erciyes U.]↗

Measurement of the $\bar ν_μ-$Hydrogen Charged-Current Quasi-Elastic Cross Section using the NOvA Near Detector

We report a measurement of the total cross section for muon antineutrino charged-current quasi-elastic scattering on hydrogen, $\bar ν_μ{\rm H} \to μ^+ n$, in the NOvA near detector using a $1.2\times10^{21}$ proton-on-target exposure in the NuMI beam. A selection based on topological and kinematic constraints yields 35,509 signal events in the hydrogen-rich ($10.8\%$) detector, providing the highest statistics of (anti)neutrino--hydrogen interactions measured to date. Backgrounds from (anti)neutrino interactions on heavier nuclei are constrained using dedicated data control samples, significantly reducing the related systematic uncertainties. We obtain a value $σ(\bar ν_μ{\rm H} \to μ^+ n) = 0.538 \pm 0.009 ({\rm stat}) \pm 0.010 ({\rm syst}) \pm0.055 ({\rm flux}) \times 10^{-38}$ cm$^2$ for the total cross section at an average energy of 1.9 GeV, the most precise total cross-section measurement of this process to date. The combined statistical and non-flux systematic uncertainty is more than four times smaller than the flux uncertainty, allowing a future use of this measurement to constrain the absolute $\bar ν_μ$ flux.

Abubakar, S. [Erciyes U.]↗

Measurement of correlations among net-charge, net-proton, and net-kaon multiplicity distributions in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV

Correlations among conserved quantum numbers, such as the net-electric charge, the net-baryon, and the net-strangeness in heavy-ion collisions, are crucial for exploring the QCD phase diagram. In this paper, these correlations are investigated using net-proton number (as a proxy for the net-baryon), net-kaon number (for the net-strangeness), and net-charged particle number in Pb-Pb collisions at $\sqrt{s_{NN}}$ = 5.02 TeV with the ALICE detector. The observed correlations deviate from the Poissonian baseline, with a more pronounced deviation at LHC energies than at RHIC. Theoretical calculations of the Thermal-FIST hadron resonance gas model, HIJING, and EPOS LHC event generators are compared with experimental results, where a significant impact of resonance decays is observed. Thermal-FIST calculations under the grand canonical and canonical ensembles highlight significant differences, underscoring the role of local charge conservation in explaining the data. Recent lattice QCD studies have demonstrated that the magnetic field generated by spectator protons in heavy-ion collisions affects susceptibility ratios, in particular those related to the net-electric charge and the net-baryon numbers. The experimental findings are in qualitative agreement with the expectations of lattice QCD.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Space-time evolution of particle emission in p–Pb collisions at $\sqrt{{s}_{\text{NN}}} = 5.02$ TeV with 3D kaon femtoscopy

The measurement of three-dimensional femtoscopic correlations between identical charged kaons (K ± K ± ) produced in p–Pb collisions at center-of-mass energy per nucleon pair $\sqrt{{s}_{\text{NN}}}=5.02$ TeV with ALICE at the LHC is presented for the first time. This measurement, supplementary to those in pp and Pb–Pb collisions, allows understanding the particle-production mechanisms at different charged-particle multiplicities and provides information on the dynamics of the source of particles created in p–Pb collisions, for which a general consensus does not yet exist. It is shown that the measured source sizes increase with charged-particle multiplicity and decrease with increasing pair transverse momentum. These trends for K ± K ± are similar to the ones observed earlier in identical charged-pion and ${\text{K}}_{\text{s}}^{0}{\text{K}}_{\text{s}}^{0}$ correlations in Pb–Pb collisions at various energies and in π ± π ± correlations in p–Pb collisions at $\sqrt{{s}_{\text{NN}}}=5.02$ TeV. At comparable multiplicity, the source sizes measured in p–Pb collisions agree within uncertainties with those observed in pp collisions, and there is an indication that they are smaller than those observed in Pb–Pb collisions. The obtained results are also compared with predictions from the hadronic interaction model EPOS 3, which tends to underestimate the source size for the most central collisions and agrees with the data for semicentral and peripheral events. Furthermore, the time of maximal emission for kaons is extracted. It turns out to be comparable with the value obtained in highly peripheral Pb–Pb collisions at the same energy, indicating that the kaon emission evolution is similar to that in p–Pb collisions.

Heavy Ion Experiments↗

Medium-induced modification of azimuthal correlations of electrons from heavy-flavor hadron decays with charged particles in Pb–Pb collisions at $\mathbf {\sqrt{s_{\textrm{NN}}} = 5.02}$ TeV

The azimuthal-correlation distributions between electrons from the decays of heavy-flavor hadrons and associated charged particles in Pb–Pb collisions at $\sqrt{s_{\textrm{NN}}} = 5.02$ TeV are reported for the 0–10% and 30–50% centrality classes. This measurement provides access to the jet-like correlation observables in the heavy-flavor sector in Pb–Pb collisions. The analysis is performed for trigger electrons from heavy-flavor hadron decays with transverse momentum $4< p_\textrm{T}^\textrm{e} < 16~\textrm{GeV}/c$, considering associated particles within the transverse-momentum range $1< p_\textrm{T}^\textrm{assoc} < 7$ GeV/c, and a pseudorapidity difference of $|\Delta \eta |<1$ between the trigger electron and associated particles. The per-trigger nuclear modification factor ( I AA ) is calculated to compare the near- and away-side peak yields to those in pp collisions at $\sqrt{s} = 5.02$ TeV. In 0–10% central collisions, the indicates a hint of enhancement of associated-particle yields with $p_\textrm{T}<3$ GeV/c on the near side, and a suppression of yields with $p_\textrm{T}>4$ GeV/c on the away side. The I AA for electron triggers from heavy-flavor hadron decays is compared with that for light-flavor and strange-particle triggers to investigate the dependence on different fragmentation processes and parton-medium dynamics, and is found to be the same within uncertainties.

Abualrob, I. J. [University of Houston] (ORCID:000↗

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗

Methanol at Water–TiO 2 Interfaces: Free Energies of Water and Methanol Dissociation

Methanol adsorption on TiO 2 surfaces has long been studied due to its role in enhancing photocatalytic hydrogen evolution, yet how it modulates surface chemistry under aqueous conditions remains little understood. Using molecular dynamics with an ab initio-based deep neural network potential, we find that methanol adsorption induces markedly different effects on the aqueous surfaces of anatase and rutile, the two common phases of TiO 2 . In anatase, methanol adsorption significantly enhances water dissociation, which is otherwise rare at the neat water interface. This enhancement arises from an alternative dissociation pathway mediated by surface-bound methoxyl groups. In contrast, methanol adsorption tends to suppress water dissociation on rutile, replacing it with thermodynamically favored methanol dissociation. Overall, methanol adsorption in an aqueous environment alters not only the availability of key reactive intermediates involved in hydrogen evolution but also the hydrogen source, which turns out to be primarily methanol on rutile, whereas both water and methanol are consumed on anatase. These results provide mechanistic insights into the coupled roles of organic adsorbates and water at photocatalytic interfaces, with implications on how methanol enhances the activity of H 2 evolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Massively parallel phase-field simulations targeting exascale

The interface thickness in the phase-field (PF) method limits its simulation scales. Consequently, large-scale PF simulations become prohibitively expensive for resolving the extremely fine microstructures that typically form during rapid solidification processing. This challenge is significant in predicting microstructure evolution in metal additive manufacturing and has been identified by the United States Department of Energy’s Exascale Computing Project. Here, to address this, we develop a multi-GPU and MPI-based massively parallel simulation code, utilizing state-of-the-art algorithms, software, and libraries, for large-scale three-dimensional (3D) PF simulations. We report the first GPU-parallel PF simulations on Frontier (currently the second TOP500 exascale cluster) and Summit machines, taking dendritic growth as an example problem. We evaluate the parallel performance of our implementation using scaling studies with more than 24 000 GPUs (among the largest known computations to date) and the acceleration performance using large-scale simulations of dendritic growth in 3D. Finally, massively parallel GPUs in these supercomputers enabled the first coupled multiscale simulations of laser melting and subsequent dendritic solidification on the scale of a full melt-pool, demonstrating the feasibility of performing PF simulations with a point total over 2 billion grid points within an acceptable time.

Exascale↗

Water-In-Glass: A Self-Supporting Inorganic Aqueous Electrolyte

Aqueous rechargeable sodium-ion batteries (ARNIBs) are emerging as cost-effective and safe candidates for large-scale energy storage applications. However, their advancement has been constrained by the narrow electrochemical stability window (ESW) of conventional aqueous electrolytes (1.23 V). Here, in this study, we present a transformative approach using an inexpensive and rapidly dissolvable inorganic glass material, water glass (W-glass), to significantly enhance the ESW and enable the development of solid-state, self-supporting aqueous film (SSA film) electrolytes. These SSA film electrolytes exhibit an extended ESW of up to 3.5 V and a conductivity of ∼10 –4 S/cm at room temperature. Structural analysis using magic-angle spinning nuclear magnetic resonance (NMR) and solution-state NMR reveals that the dissolution of W-glass in water is driven by the interdependent hydrolysis of P–O–P linkages and Na + –H + ion exchange. This work offers a cost-effective and scalable solution for advancing high-performance ARNIB technology, addressing critical barriers to commercial adoption.

Electrochemical stability window↗

Effect of Ambient Organic Acids on the Water Structure at ${\rm TiO}_{2}$ Interfaces

A molecular-level understanding of the effects of ambient organic compounds on the wettability of titanium dioxide ( ${\rm TiO}_{2}$ ) surfaces is relevant to many of its energy-related and environmental applications. Herein, we focus on two common atmospheric carboxylic acids, formic and acetic acid, and characterize their adsorption/ desorption at the aqueous interfaces of anatase and rutile ${\rm TiO}_{2}$ using molecular dynamics with an ab initio deep neural network potential. Our simulations show that these acids prefer to be localized in the interfacial water layers close to the ${\rm TiO}_{2}$ surface where they are stabilized by the interaction/exchange of their acid proton with a surface oxygen, rather than chemisorb at the surface Ti sites by displacing the adsorbed water. Notably, these acids make the surface of anatase hydrophobic, whereas the larger fraction of adsorbed water dissociation can offset their effect on rutile. Furthermore, these results provide a picture where carboxylic acids control the wettability of ${\rm TiO}_{2}$ largely through acid-base chemistry at the interface rather than chemisorption on the oxide surface, a finding that can help improve the design of self-cleaning surfaces and photocatalytic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Towards a numu CC exclusive cross section measurement with one proton and one muon in the final state at the NOvA near detector

NOvA is a long-baseline neutrino experiment at Fermilab that studies neutrino oscillations via electron neutrino appearance and muon neutrino disappearance. The NOvA Near Detector, located 1 km from the NuMI target, records a high rate of neutrino interactions in the energy range of 1-5 GeV. One of the limitations in precise extraction of oscillations is the large systematic uncertainty on neutrino-nucleus interactions. The high rate of neutrino interactions in the NOvA Near Detector provides the opportunity to probe various neutrino interaction processes and nuclear models, via the extraction of cross sections of particular final states. Nuclear models can be improved by measuring neutrino-nucleus cross sections with respect to kinematic variables which are known to be more sensitive to nuclear effects, such as transverse kinematic imbalance (TKI) variables. Usage of TKI variables allows for more direct constraints on models of neutrino scattering, as these variables can more directly investigate, for instance, effects of Fermi motion and final state interactions. In this work, we describe progress towards a method of identifying and reconstructing protons in the NOvA near detector to enable the cross section measurement of charged current neutrino interactions with a visible muon and a proton in the final state, reported in TKI variables.

Weber, Colin [Minnesota U.] (ORCID:000000031117067↗

Autonomous platform for solution processing of electronic polymers

The manipulation of electronic polymers’ solid-state properties through processing is crucial in electronics and energy research. Yet, efficiently processing electronic polymer solutions into thin films with specific properties remains a formidable challenge. We introduce Polybot, an artificial intelligence (AI) driven automated material laboratory designed to autonomously explore processing pathways for achieving high-conductivity, low-defect electronic polymers films. Leveraging importance-guided Bayesian optimization, Polybot efficiently navigates a complex 7-dimensional processing space. In particular, the automated workflow and algorithms effectively explore the search space, mitigate biases, employ statistical methods to ensure data repeatability, and concurrently optimize multiple objectives with precision. The experimental campaign yields scale-up fabrication recipes, producing transparent conductive thin films with averaged conductivity exceeding 4500 S/cm. Feature importance analysis and morphological characterizations reveal key design factors. This work signifies a significant step towards transforming the manufacturing of electronic polymers, highlighting the potential of AI-driven automation in material science.

Wang, Chengshi [Argonne National Laboratory (ANL),↗

Magnetocaloric effect in Mn-rich Heusler-derived alloys for room temperature-based applications

Magnetic refrigeration-based technologies rely on the magnetocaloric properties of materials and are crucial for improving energy efficiency and for supporting clean environments. However, materials possessing significant magnetocaloric properties that can be used for room temperature-based applications are still lacking. Heusler alloys (such as Mn–Ni–Ga) show giant magnetocaloric effects, which can be further improved by tuning their composition. Herein, we optimized the ratio of Ni to Ga to achieve an alloy with unique magnetocaloric properties. Here, the alloy demonstrated a substantial magnetocaloric effect, attributed to its narrow temperature range for structural and magnetic phase transitions (near room temperature with minimal hysteresis). Structural phase changes were also observed through high-resolution transmission electron microscopy. Optimum entropy changes, calculated from isothermal magnetization curves (4 Tesla magnetic field), were found to be 24.50 J kg −1 K −1 , which is higher than that of state-of-the-art alloys. Experimental demonstration of the magnetocaloric effect (refrigeration cycle) was also carried out using a thermal imaging camera. Furthermore, first-principles calculations were conducted to validate the experimental findings, specifically focusing on the compositional effects on the structural transitions and magnetic properties of the investigated MNG alloys.

36 MATERIALS SCIENCE↗

Joint Analysis of Small-scale Galaxy Clustering and Galaxy–Galaxy Lensing from BOSS Galaxies

We present a joint analysis of galaxy clustering and galaxy–galaxy lensing measurements from BOSS galaxies using a simulation-based emulation method combined with a halo occupation distribution model. Our emulators are constructed with the Aemulus ν simulations, a suite of wνCDM N-body simulations with massive neutrinos as independent particle species. We combine small-scale analysis of clustering from 0.1 to 60.2 h −1 Mpc and lensing from 1.7 to 60.2 h −1 Mpc to perform cosmological constraints. We split the BOSS galaxies into three redshift bins to measure their clustering and employ galaxies from Dark Energy Camera Legacy Survey and Hyper Suprime-Cam as source galaxies to measure lensing separately. We find that the addition of lensing significantly improves the constraining power on $S_8 = σ_8(Ω_m/0.3)^{0.5}$, with a weak improvement for fσ 8 . Our results of fσ 8 indicate tensions of around 1σ−4σ below the results of the cosmic microwave background observations of Planck. For S 8 , our results are also lower than Planck, and the tension can be mitigated when considering possible systematics in lensing measurement. As a by-product, our analysis prefers a nonzero neutrino mass but without strong significance, with the constraining power dominated by the clustering. Given the accuracy and precision of our model and the observational data, it is anticipated that larger and higher-quality spectroscopic data sets will improve the constraints on this fundamental property in the near future.

Gao, Wenhao [Shanghai Jiao Tong University (China)↗

Atomic dynamics in 𝑀⁢Cr⁢𝑋 2 (𝑀=Ag, Cu; 𝑋 = S, Se) across magnetic and superionic transitions

Here, a systematic study of atomic dynamics and thermal properties of the family of layered chalcogenide compounds 𝑀⁢Cr⁢𝑋 2 (𝑀= Ag, Cu; 𝑋 = S, Se) was performed, including neutron and x-ray scattering, thermal characterization, and first-principles simulations. In all compounds, we observe a breakdown of specific phonon modes across the superionic phase transition, for phonons whose eigenvectors exhibit large contributions of mobile ions. In particular, the nondispersive portions of transverse acoustic (TA) branches at short-wavelengths and the low-energy optical phonons with large contributions from Ag + or Cu + become severely damped in the superionic phase. However, well-defined quasiparticles persist in the superionic state for long-wavelength TA phonons. In the case of AgCrS 2 , the coupling of lattice dynamics with its antiferromagnetic transition was also investigated. The magnetic ordering couples with the monoclinic–rhombohedral structural transition, and the Cr 3+ spin arrangement strongly affects the phonon dispersions. We qualitatively reproduce the magnetic and nuclear components of the INS measurement for antiferromagnetic AgCrS 2 by combining models of spin-waves and spin-polarized first-principles phonon simulations. Quasielastic magnetic fluctuations persist in the paramagnetic phase up to high temperature, but are clearly distinguished from the nuclear component through their momentum dependence. Finally, we report measurements of the thermal properties of the selenide compounds and find good agreement with our DFT simulations.

36 MATERIALS SCIENCE↗

Runaway electron-induced plasma facing component damagein tokamaks

This Roadmap article addresses the critical and multifaceted challenge of plasma facing component (PFC) damage caused by runaway electrons (REs) in tokamaks, a phenomenon that poses a significant threat to the viability and longevity of future fusion reactors such as ITER and DEMO. The dramatically increased RE production expected in future high-current tokamaks makes it very difficult to avoid or mitigate REs in such devices when a plasma discharge terminates abnormally. Preventing damage from the intense localised heat loads they can cause requires a holistic approach that considers plasma, REs and PFC damage. Despite decades of progress in understanding the physics of REs and the thermomechanical response of PFCs separately, their complex interplay remains poorly understood. This document aims to initiate a coordinated, interdisciplinary approach to bridge this gap by reviewing experimental evidence, advancing diagnostic capabilities, and improving modelling tools across different scales, dimensionalities, and fidelities. Key topics include RE beam formation and transport, damage mechanisms in both brittle and metallic PFCs, and observed effects in major facilities such as JET, DIII-D, WEST and EAST. The Roadmap emphasises the urgency of predictive, high-fidelity modelling validated against well-diagnosed controlled experiments, particularly in the light of recent changes in ITER’s wall material strategy and the growing importance of private sector fusion initiatives. Each section of the Roadmap article is written to provide a concise overview of one area of this multidisciplinary subject, with an assessment of the status, a look at current and future challenges, and a brief summary. The ultimate goal of this initiative is to guide future mitigation strategies and design resilient components that can withstand the intense localised loads imposed by REs, thus ensuring the safe and sustainable operation of the next generation of fusion power plants.

Ratynskaia, Svetlana [KTH Royal Inst. of Technolog↗

Locating Undocumented Wells Using Historical Oil and Gas Exploration Maps: A Case Study in Osage County, Oklahoma

Undocumented oil and gas wells lack reliable information about their locations and characteristics, making them difficult to identify. These wells can result in unanticipated delays and costs in the development of nearby surface and subsurface resources, and, if improperly plugged, can cause contamination. This study leverages historical petroleum exploration maps to locate such wells, focusing on Osage County, Oklahoma. Two sets of early 20th century oil and gas exploration maps by the United States Geological Survey were georeferenced and analyzed using a computer vision model to detect well symbols. The locations of detected wells were compared to the location of known wells in the database from the Bureau of Indian Affairs Osage Agency to identify potential undocumented wells. The analysis yielded over 500 potential undocumented wells, with dry holes constituting the largest fraction. Field verification confirmed the presence of some undocumented wells. Comparison with prior work revealed limited overlap, underscoring the complementary value of historical oil and gas maps for locating undocumented wells. This approach demonstrates the utility of integrating historical cartographic resources with modern geospatial and machine learning techniques to improve the identification and management of undocumented wells.

Energy - Petroleum↗