Search NASA⌕ Search

SEARCH · Search NASA

Results for “proximity”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural basis for antibody recognition of the proximal MUC16 ectodomain

Mucin 16 (MUC16) overexpression is linked with cancer progression, metastasis, and therapy resistance in high grade serous ovarian cancer and other malignancies. The cleavage of MUC16 forms independent bimodular fragments, the shed tandem repeat sequence which circulates as a protein bearing the ovarian cancer biomarker (CA125) and a proximal membrane-bound component which is critical in MUC16 oncogenic behavior. A humanized, high affinity antibody targeting the proximal ectodomain represents a potential therapeutic agent against MUC16 with lower antigenic potential and restricted human tissue expression. Here, we demonstrate the potential therapeutic versatility of the humanized antibody as a monoclonal antibody, antibody drug conjugate, and chimeric antigen receptor. We report the crystal structures of 4H11-scFv, derived from an antibody specifically targeting the MUC16 C-terminal region, alone and in complex with a 26-amino acid MUC16 segment resolved at 2.36 Å and 2.47 Å resolution, respectively. The scFv forms a robust interaction with an epitope consisting of two consecutive β-turns and a β-hairpin stabilized by 2 hydrogen bonds. The V H -V L interface within the 4H11-scFv is stabilized through an intricate network of 11 hydrogen bonds and a cation-π interaction. Together, our studies offer insight into antibody-MUC16 ectodomain interaction and advance our ability to design agents with potentially improved therapeutic properties over anti-CA125 moiety antibodies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Proximal Observations of Epicentral Infrasound Generated by Shallow Low-Magnitude Earthquakes in the Permian Basin, West Texas

Infrasound generated by earthquakes and explosions is generally detected at receivers at epicentral distances of hundreds to thousands of kilometers. However, proximal (<50 km) observations are especially important for low-magnitude earthquakes and low-yield explosions that may not generate signals capable of being detected at great ranges. Here, in this study, we present on the signals detected on an infrasound array 3 km away from two M L 2.9 earthquakes in the Permian Basin of west Texas. Local infrasound (LIS), generated at receivers during the passage of seismic waves, was detected following each earthquake. Epicentral infrasound (EIS), created at or near the epicenter and propagating away as a sound wave, was also detected. Array processing methods show that the EIS signals arrive from the same direction as the earthquake epicenters and at acoustic speeds. To our knowledge, these are the first observations of laterally propagating EIS at proximal ranges following an earthquake of any magnitude.

58 GEOSCIENCES↗
DOE OSTI2022

Photocrosslinking Probes Proximity of Thymine Modifiers Tethering Excitonically Coupled Dye Aggregates to DNA Holliday Junction

A DNA Holliday junction (HJ) has been used as a versatile scaffold to create a variety of covalently templated molecular dye aggregates exhibiting strong excitonic coupling. In these dye-DNA constructs, one way to attach dyes to DNA is to tether them via single long linkers to thymine modifiers incorporated in the core of the HJ. Here, using photoinduced [2 + 2] cycloaddition (photocrosslinking) between thymines, we investigated the relative positions of squaraine-labeled thymine modifiers in the core of the HJ, and whether the proximity of thymine modifiers correlated with the excitonic coupling strength in squaraine dimers. Photocrosslinking between squaraine-labeled thymine modifiers was carried out in two distinct types of configurations: adjacent dimer and transverse dimer. The outcomes of the reactions in terms of relative photocrosslinking yields were evaluated by denaturing polyacrylamide electrophoresis. We found that for photocrosslinking to occur at a high yield, a synergetic combination of three parameters was necessary: adjacent dimer configuration, strong attractive dye–dye interactions that led to excitonic coupling, and an A-T neighboring base pair. The insight into the proximity of dye-labeled thymines in adjacent and transverse configurations correlated with the strength of excitonic coupling in the corresponding dimers. To demonstrate a utility of photocrosslinking, we created a squaraine tetramer templated by a doubly crosslinked HJ with increased thermal stability. These findings provide guidance for the design of HJ-templated dye aggregates exhibiting strong excitonic coupling for exciton-based applications such as organic optoelectronics and quantum computing.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems

Resource allocation in High Performance Computing (HPC) environments presents a complex and multifaceted challenge for job scheduling algorithms. Beyond the efficient allocation of system resources, schedulers must account for and optimize multiple performance metrics, including job wait time and system throughput. Traditional heuristic-based scheduling algorithms increasingly struggle and lack the efficiency needed to meet the demands and address the complexity and scale of modern HPC systems. Consequently, recent research efforts have focused on leveraging advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly Reinforcement Learning (RL), to develop more adaptable and intelligent scheduling strategies. Previous RL-based scheduling approaches have explored a range of algorithms, from Deep Q-Networks (DQN) to Proximal Policy Optimization (PPO), and more recently, hybrid methods that integrate Graph Neural Networks (GNNs) with RL techniques. However, a common limitation across these methods is their reliance on relatively small datasets, with few methods being evaluated using large-scale, multi-million-job trace datasets representative of real-world HPC workloads. Moreover, existing RL schedulers face scalability issues due to centralized policy updates, which hinder training efficiency and performance when applied to large datasets. This study introduces a novel RL-based scheduler utilizing Decentralized Distributed Proximal Policy Optimization (DD-PPO) algorithm, which supports large-scale distributed training across multiple workers without requiring parameter synchronization at every step. By eliminating reliance on centralized updates to a shared policy, the DD-PPO scheduler enhances scalability, training efficiency, and sample utilization. Experimental validation using a large real-world dataset containing over 11.5 million job traces collected from petascale HPC systems over six years assesses the influence of dataset scale on training effectiveness and compares DD-PPO performance to traditional and advanced scheduling approaches. The experimental results demonstrate improved scheduling performance in comparison to both heuristic-based schedulers and existing RL-based scheduling algorithms.

AI↗
DOE OSTI2021

Proximity proteomics in a marine diatom reveals a putative cell surface-to-chloroplast iron trafficking pathway

Iron is a biochemically critical metal cofactor in enzymes involved in photosynthesis, cellular respiration, nitrate assimilation, nitrogen fixation, and reactive oxygen species defense. Marine microeukaryotes have evolved a phytotransferrin-based iron uptake system to cope with iron scarcity, a major factor limiting primary productivity in the global ocean. Diatom phytotransferrin is endocytosed; however, proteins downstream of this environmentally ubiquitous iron receptor are unknown. We applied engineered ascorbate peroxidase APEX2-based subcellular proteomics to catalog proximal proteins of phytotransferrin in the model marine diatom Phaeodactylum tricornutum . Proteins encoded by poorly characterized iron-sensitive genes were identified including three that are expressed from a chromosomal gene cluster. Two of them showed unambiguous colocalization with phytotransferrin adjacent to the chloroplast. Further phylogenetic, domain, and biochemical analyses suggest their involvement in intracellular iron processing. Proximity proteomics holds enormous potential to glean new insights into iron acquisition pathways and beyond in these evolutionarily, ecologically, and biotechnologically important microalgae.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Controlling T c of iridium films using the proximity effect

A superconducting Transition-Edge Sensor (TES) with low-$T_c$ is essential in a high resolution calorimetric detection. With a motivation of developing sensitive calorimeters for applications in cryogenic neutrinoless double beta decay searches, we have been investigating methods to reduce the $T_c$ of an Ir film down to 20 mK. Utilizing the proximity effect between a superconductor and a normal metal, we found two room temperature fabrication recipes of making Ir-based low-$T_c$ films. In the first approach, an Ir film sandwiched between two Au films, a Au/Ir/Au trilayer, has a tunable $T_c$ in the range of 20-100 mK depending on the relative thicknesses. In the second approach, a paramagnetic Pt thin film is used to create Ir/Pt bilayer with a tunable $T_c$ in the same range. We present detailed study of fabrication and characterization of Ir-based low-$T_c$ films, and compare the experimental results to theoretical models. We show that Ir-based films with predictable and reproducible critical temperature can be consistently fabricated for use in large scale detector applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Integrated path planning and control through proximal policy optimization for a marine current turbine

This paper presents an integrated path planning and tracking control framework for a marine current turbine (MCT), where the MCT is treated as an energy-harvesting autonomous underwater vehicle (AUV). Considering the ocean (space of action) is continuous, the proposed framework employs two modules to address path planning and path tracking enabled by the proximal policy optimization (PPO) algorithm, which is a policy gradient deep reinforcement learning (RL) method. Further, to enable fully autonomous operation in a stochastic oceanic environment, the proposed path planning seeks a primary objective of maximizing the harvested energy; then, the path tracking module is designed to minimize the tracking error and avoid collisions with static and dynamic obstacles. Using field-collected acoustic Doppler current profiler (ADCP) data, the performance of the proposed framework is evaluated. Comparative studies with baseline algorithms in three different scenarios of path planning, path tracking without an obstacle, and path tracking with collision avoidance verify the effectiveness of our proposed approach.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

A Hybrid Biophysical‐Machine Learning Framework for Diurnal Surface Energy Flux Estimation Using Proximal Sensing

Thermal infrared-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy ( LE ) and sensible heat ( H ) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal infrared data sets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of a ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R 2 = 0.81–0.94) and H (R 2 = 0.46–0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical-machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.

evapotranspiration↗
DOE OSTI2022

Proximity enhanced magnetism at NiFe 2 O 4 /Graphene interface

Here, we explore the change in effective magnetic anisotropy of the ferrimagnetic (FM) insulator nickel ferrite (NFO) thin film due to the inclusion of monolayer graphene (MLG) grown on top of the NFO layer. This was done by performing radio frequency (RF) transverse susceptibility (TS) measurements on bare NFO and NFO/MLG bilayer samples for both in-plane (IP) and out-of-plane (OOP) configurations utilizing a tunnel diode oscillator technique. Our magnetometry measurements indicated an enhancement in the overall saturation magnetization of the NFO/MLG bilayer with respect to the bare NFO film. The TS measurements reveal that the inclusion of MLG reduces the effective magnetic anisotropy for both IP and OOP configurations drastically, by up to a factor of 2 over the temperature range 40 K ≤ T ≤ 280 K. Since NFO is a magnetic substrate, it is possible that NFO could induce magnetic ordering in MLG at the NFO/MLG interface via the magnetic proximity effect. Furthermore, since NFO is insulating and MLG is a semimetal, there likely exists a large conductivity difference at the interface, making charge transfer plausible. These two effects could modify the interfacial magnetism leading to a change in the effective magnetic anisotropy. These results highlight the importance of understanding the interfacial magnetism of FM/MLG heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Interfacial alloying contributions to the magnetic proximity effect in Pt/dysprosium iron garnet

The magnetic proximity effect (MPE), a magnetic response from a heavy metal layer induced by an adjacent magnetic layer, plays an important role in the behavior of heavy-metal/ferro- or ferrimagnet thin film heterostructures. However, in the case of heavy-metal/rare earth iron garnet bilayers the presence and magnitude of the MPE has been debated. We previously reported an MPE in a Pt/dysprosium iron garnet (DyIG) heterostructure where the two layers were grown without breaking vacuum, finding that the magnetic moment present in the Pt layer scales with the magnitude of the DyIG moment and varies nonmonotonically through the DyIG compensation temperature. However, the origin of the magnetic signal from the Pt layer was not identified. In contrast, Pt/DyIG grown with a vacuum break did not exhibit an MPE. Here we investigate the origin of the magnetic moment in the Pt layer using x-ray absorption spectroscopy, x-ray circular dichroism, composition mapping and simulations of ion bombardment. In conclusion, the results are consistent with the presence of an intermixed Fe-Dy-Pt interfacial alloy layer formed during the growth of the Pt on the DyIG and resulting in a measurable MPE.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Proximity-induced collective modes in an unconventional superconductor heterostructure

Unconventional superconductors have been long sought for their potential applications in quantum technologies and devices. A key challenge impeding this effort is the difficulty associated with probing and characterizing candidate materials and establishing their order parameter. Here we present a platform that allows one to spectroscopically probe unconventional superconductivity in thin-layer materials via the proximity effect. Here we show that inducing an s-wave gap in a sample with an intrinsic d-wave instability leads to the formation of bound states of quasiparticle pairs, which manifest as a collective mode in the d-wave channel. This finding provides a way to study the underlying pairing interactions vicariously through the collective mode spectrum of the system. Upon further cooling of the system we observe that this mode softens considerably and may even condense, signaling the onset of time-reversal symmetry-breaking superconductivity. Therefore, our proposal also allows for the creation and study of these elusive unconventional states.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Proximity-induced equilibrium supercurrent and perfect superconducting diode effect due to band asymmetry

Herein we theoretically investigate the consequences of proximity-induced conventional superconductivity in metals that break time-reversal and inversion symmetries through their energy dispersion. We discover behaviors impossible in an isolated superconductor such as an equilibrium supercurrent that apparently violates a no-go theorem and, at suitable topological defects, nonconservation of electric charge reminiscent of the chiral anomaly. The equilibrium supercurrent is expected to be trainable by a helical electromagnetic field in the normal state. Remarkably, if the band asymmetry exceeds the critical current of the parent superconductor in appropriate units, we predict a perfect superconducting diode effect with diode coefficient unity. We propose toroidal metals such as UNi 4 ⁢B and metals with directional scalar spin chiral order as potential platforms.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Microwave Meissner screening properties of proximity-coupled topological-insulator / superconductor bilayers

The proximity-coupled topological-insulator / superconductor (TI/SC) bilayer system is a representative system to realize topological superconductivity. In order to better understand this unique state and enable future applications of the TI/SC bilayer, a comprehensive characterization and understanding of the microscopic properties of the bilayer are required. In this work, a microwave Meissner screening study, which exploits a high-precision microwave resonator technique, is conducted on the SmB 6 /YB 6 thin-film bilayers as an example TI/SC system. The study reveals spatially dependent electrodynamic screening response of the TI/SC system that is not accessible to other techniques, from which the corresponding microscopic properties of a TI/SC bilayer can be obtained. The TI thickness dependence of the effective penetration depth suggests the existence of a bulk insulating region in the TI layer. The spatially dependent electrodynamic screening model analysis provides an estimate for the characteristic lengths of the TI/SC bilayer: normal penetration depth, normal coherence length, and the thickness of the surface states.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Proximity Portability and in Transit , M-to-N Data Partitioning and Movement in SENSEI [Book Chapter]

In high-performance parallel in situ processing, the term in transit processing refers to those configurations where data must move from a producer to a consumer that runs on separate resources. In the context of parallel and distributed computing on an HPC platform one of the central challenges is to determine a mapping of data from producer ranks to consumer ranks. This problem is complicated by the heterogeneity that arises in producer-consumer pairs, such as when producer and consumer codes have different levels of concurrency, different scaling characteristics, or different data models. The resulting mapping and movement of data from M producer to N consumer ranks can have a significant impact on aggregate application performance, particularly when the data consumer requires only a subset of the overall data for its task. This chapter focuses on the design considerations that underlie SENSEI’s implementation to this challenging problem. These design considerations extend the core SENSEI architecture and include ideas like the need to accommodate flexibility in the choice of different partitioning methods, the ability for a data consumer to request and receive only the subset of data needed for its particular operation, and the ability to leverage any of several different data transport tools. The idea of proximity portability, being able to use different data transport methods as part of an in transit workflow, is illustrated through the use of three different transport layers where switching from one transport tool to another is accomplished with only a configuration file change. Here, the chapter also includes a performance analysis summary showing the performance gains that are possible in terms of multiple metrics, such as memory footprint, time to solution, and amount of data moved, when using optimized partitioners in an in transit setting, gains that are made possible by the implementation shaped by specific design considerations.

Bethel, E. Wes↗
DOE OSTI2022

A proximal trust-region method for nonsmooth optimization with inexact function and gradient evaluations

Many applications require minimizing the sum of smooth and nonsmooth functions. For example, basis pursuit denoising problems in data science require minimizing a measure of data misfit plus an $\ell^1$-regularizer. Similar problems arise in the optimal control of partial differential equations (PDEs) when sparsity of the control is desired. Here, we develop a novel trust-region method to minimize the sum of a smooth nonconvex function and a nonsmooth convex function. Our method is unique in that it permits and systematically controls the use of inexact objective function and derivative evaluations. When using a quadratic Taylor model for the trust-region subproblem, our algorithm is an inexact, matrix-free proximal Newton-type method that permits indefinite Hessians. We prove global convergence of our method in Hilbert space and demonstrate its efficacy on three examples from data science and PDE-constrained optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Disorder-induced proximate quantum spin ice phase in Pr2Sn2O7

We report a comprehensive bulk characterization and neutron scattering investigation of single-crystalline Pr2Sn2O7, a magnetic pyrochlore synthesized via a flux-growth method. Unpolarized neutron diffuse scattering reveals the emergence of spin-ice correlations below T ∼ 1 K, evidenced by the development of anisotropic pinch-point features that are consistent with quantum-spin-ice (QSI) behavior. A.C. susceptibility measurements indicate a progressive slowing of spin dynamics in this regime, culminating in complete spin freezing below Tf ≈ 0.15 K. Inelastic neutron scattering at T = 0.5 K reveals a broad spectrum of quasi-elastic magnetic excitations, with intensity in the low-energy range [0, 0.2] meV significantly suppressed below Tf. Meanwhile, an incipient (100)-type magnetic order begins to nucleate, and a gapped excitation centered at ℏω = 0.23 meV persists. We further identify two distinct dynamical timescales above Tf , a slow component τ_slow ∼ 10^−5 s and a fast component τ_fast ∼ 10^−10 s, in quantitative agreement with theoretical predictions for QSI systems. Taken together, these results indicate that Pr2Sn2O7 enters a disorder-induced spin-frozen phase below Tf , lying in close proximity to a U(1) quantum spin liquid

Luo, Yi [ORNL] (ORCID:000900048710428X)↗
DOE OSTI2026

A proximity-focusing RICH detector for the ePIC Experiment at the EIC

The Electron-Proton/Ion Collider Experiment (ePIC) will be a large, multi-purpose detector to be installed at the Electron-Ion Collider (EIC) being built at Brookhaven National Laboratory. As robust particle identification (PID) capabilities are essential for fully realizing the EIC science program, ePIC contains several PID subsystems spanning different angular ranges. PID capability in the electron-going endcap is provided by a proximity-focusing Ring Imaging Cherenkov detector (pfRICH) designed to deliver at least 3σ separation between pions and kaons for momenta up to 7 GeV/ c . It will also aid with electron-hadron discrimination at low momentum and assist in the determination of the collision time (t 0 ). This contribution will summarize the design of the pfRICH as well as ongoing fabrication and component testing efforts. geant-based performance simulations will also be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Proximate Quantum Spin Liquid on Designer Lattice

Complementary to bulk synthesis, here we propose a designer lattice with extremely high magnetic frustration and demonstrate the possible realization of a quantum spin liquid state from both experiments and theoretical calculations. In an ultrathin (111) CoCr 2 O 4 slice composed of three triangular and one kagome cation planes, the absence of a spin ordering or freezing transition is demonstrated down to 0.03 K, in the presence of strong antiferromagnetic correlations in the energy scale of 30 K between Co and Cr sublattices, leading to the frustration factor of similar to 1000. Persisting spin fluctuations are observed at low temperatures via low-energy muon spin relaxation. Our calculations further demonstrate the emergence of highly degenerate magnetic ground states at the 0 K limit, due to the competition among multiply altered exchange interactions. Furthermore, these results collectively indicate the realization of a proximate quantum spin liquid state on the synthetic lattice.

77 NANOSCIENCE AND NANOTECHNOLOGY↗