Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “breakthrough”

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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Knocksideways in plants: an inducible system for in planta visualization of protein interactions

Understanding protein-protein interactions (PPIs) is key to unlocking the secrets of various cellular processes. While many tools have been developed to detect PPIs, such as the yeast two-hybrid assay, bimolecular fluorescence complementation, and affinity purification, there is great demand for additional methods to complement existing ones. As all approaches have limitations, evidence from at least two independent assays is required to validate PPIs. In a Breakthrough Report, Joanna Winkler and colleagues present here a promising method, named “knocksideways in plants” (KSP), for inducible visualization of PPIs in planta.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Enzymes in 3D: Synthesis, remodelling, and hydrolysis of cell wall (1,3;1,4)-β-glucans

Abstract Recent breakthroughs in structural biology have provided valuable new insights into enzymes involved in plant cell wall metabolism. More specifically, the molecular mechanism of synthesis of (1,3;1,4)-β-glucans, which are widespread in cell walls of commercially important cereals and grasses, has been the topic of debate and intense research activity for decades. However, an inability to purify these integral membrane enzymes or apply transgenic approaches without interpretative problems associated with pleiotropic effects has presented barriers to attempts to define their synthetic mechanisms. Following the demonstration that some members of the CslF sub-family of GT2 family enzymes mediate (1,3;1,4)-β-glucan synthesis, the expression of the corresponding genes in a heterologous system that is free of background complications has now been achieved. Biochemical analyses of the (1,3;1,4)-β-glucan synthesized in vitro, combined with 3-dimensional (3D) cryogenic-electron microscopy and AlphaFold protein structure predictions, have demonstrated how a single CslF6 enzyme, without exogenous primers, can incorporate both (1,3)- and (1,4)-β-linkages into the nascent polysaccharide chain. Similarly, 3D structures of xyloglucan endo-transglycosylases and (1,3;1,4)-β-glucan endo- and exohydrolases have allowed the mechanisms of (1,3;1,4)-β-glucan modification and degradation to be defined. X-ray crystallography and multi-scale modeling of a broad specificity GH3 β-glucan exohydrolase recently revealed a previously unknown and remarkable molecular mechanism with reactant trajectories through which a polysaccharide exohydrolase can act with a processive action pattern. The availability of high-quality protein 3D structural predictions should prove invaluable for defining structures, dynamics, and functions of other enzymes involved in plant cell wall metabolism in the immediate future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Compromise of Personal Protective Clothing from Liquid Exposure

Following critiques of multiple personal contamination events from entries into the Oak Ridge National Laboratory’s Spallation Neutron Source transfer bay, it was considered that the most likely causes for contamination were personal protective clothing doffing errors or that moisture (sweat) allowed contamination to wick through the protective clothing. To pursue this issue, radiological protection staff looked more closely, however, at the specific area of the clothing where contamination was highest; under enhanced lighting and photochromic manipulation, there appeared to have been some type of moisture in the area. Recognizing the possibility that moisture may have allowed for migration of contamination through the clothing, further experiments were undertaken to determine under which conditions this transport might have occurred. The objective for this work was to identify the susceptibility of different types of personal protective clothing to various liquids encountered in the workplace.Method: Several tests were performed to determine if perspiration had enabled migration of contamination, and to identify what other liquids might have affected contamination transport. Two layers of personal protective clothing were subjected to static conditions and dynamic conditions, to include active rubbing of the materials while wet. Food dye added to each of the liquids tested enabled visual indications of liquid breakthrough. Additional tests were conducted to see if solid contamination could be transported through the materials along with the liquids. As a result, it was found that all but one type of non-rubberized personal protective clothing in use at Oak Ridge National Laboratory were permanently compromised to some extent by the solvents used for decontamination. It was determined that most common cleaning agents immediately and permanently destroyed the hydrophobic nature of several of the splash-resistant protective clothing materials, allowing for radioactive contamination to penetrate through the material to the worker. Work around wet surfaces or performing wet decontamination will only be performed in protective clothing known to prevent transport of the wetting agent.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2020

Exascale applications: skin in the game

As noted in Wikipedia, skin in the game refers to having ‘incurred risk by being involved in achieving a goal’, where ‘ skin is a synecdoche for the person involved, and game is the metaphor for actions on the field of play under discussion’. For exascale applications under development in the US Department of Energy Exascale Computing Project, nothing could be more apt, with the skin being exascale applications and the game being delivering comprehensive science-based computational applications that effectively exploit exascale high-performance computing technologies to provide breakthrough modelling and simulation and data science solutions. These solutions will yield high-confidence insights and answers to the most critical problems and challenges for the USA in scientific discovery, national security, energy assurance, economic competitiveness and advanced healthcare. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Stabilization of magnetic bubbles in [Ni/Co]n multilayers on an oxygen-reconstructed Nb(110) surface via an ultra-thin Cu interlayer

Magnetic thin films hosting topological spin textures, such as magnetic skyrmions, hold high potential for breakthroughs in the field of spintronics, due to good scalability and energy efficiency. Novel computational architectures such as memory-in-logic devices rely on material platforms able to host those topological spin textures. Furthermore, recently proposed designs of novel quantum information technologies are based on heterostructures where topological spin textures are in direct proximity to a superconducting layer. Here, we demonstrate the stabilization of out-of-plane magnetic bubbles in highly ordered [Ni/Co]n multilayers on a Nb(110) single crystal. This is achieved without the need for the removal of the well-known Nb(110)-oxide surface reconstruction, due to the introduction of an ultra-thin Cu interlayer in between the Nb substrate and the magnetic multilayer. The Cu interlayer generates a well-ordered hexagonal surface, which is key for the epitaxial growth of the [Ni/Co]n multilayers hosting the desired out-of-plane anisotropy. The magnetic ground state of the prepared material stacks is directly imaged via spin-polarized low-energy electron microscopy, revealing the presence of magnetic bubble domains with lateral sizes as small as 450 nm.

Dibajeh, Ahmad↗
DOE OSTI2021

Probabilistic simulation of quantum circuits using a deep-learning architecture

The fundamental question of how to best simulate quantum systems using conventional computational resources lies at the forefront of condensed matter and quantum computation. It impacts both our understanding of quantum materials and our ability to emulate quantum circuits. Here we present an exact formulation of quantum dynamics via factorized generalized measurements which maps quantum states to probability distributions with the advantage that local unitary dynamics and quantum channels map to local quasistochastic matrices. This representation provides a general framework for using state-of-the-art probabilistic models in machine learning for the simulation of quantum many-body dynamics. Using this framework, we have developed a practical algorithm to simulate quantum circuits using an attention network based on a powerful neural network ansatz responsible for the most recent breakthroughs in natural language processing. We demonstrate our approach by simulating circuits that build Greenberger-Horne-Zeilinger and linear graph states of up to 60 qubits, as well as a variational quantum eigensolver circuit for preparing the ground state of the transverse field Ising model on several system sizes. Our methodology constitutes a modern machine learning approach to the simulation of quantum physics with applicability both to quantum circuits as well as other quantum many-body systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Possibilistic simulation of quantum circuits by classical circuits

In a breakthrough work, Bravyi, Gosset, and König (BGK) [Science 362, 308 (2018)] unconditionally proved that constant-depth quantum circuits are more powerful than their classical counterparts. Here, their result is equivalent to saying that a particular family of constant-depth quantum circuits takes classical circuits at least Ω⁡(log⁡n) depth to “simulate,” in a certain sense. In our paper, we formalize their sense of simulation, which we call “possibilistic simulation” or “p-simulation,” and construct explicit classical circuits that can p-simulate any depth-d quantum circuit with Clifford and t T-gates in depth O(d+t). Our classical circuits use {NOT,AND,OR} gates of fan-in ≤ 2.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Theoretical investigation of superconductivity in trilayer square-planar nickelates

The discovery of superconductivity in Sr-doped NdNiO 2 is a crucial breakthrough in the long pursuit for nickel oxide materials with electronic and magnetic properties similar to those of the cuprates. NdNiO 2 is the infinite-layer member of a family of square-planar nickelates with general chemical formula R n +1 Ni n O 2 n +2 (R = La, Pr, Nd, n = 2, 3,...∞). In this Rapid Communication, we investigate superconductivity in the trilayer member of this series (R4Ni 3 O 8 ) using a combination of first-principles and $\textit{t–J}$ model calculations. R 4 Ni 3 O 8 compounds resemble cuprates more than RNiO 2 materials in that only Ni-d n 2 – y 2 bands cross the Fermi level, they exhibit a largely reduced charge transfer energy, and as a consequence superexchange interactions are significantly enhanced. In this work, we find that the superconducting instability in doped R 4 Ni 3 O 8 compounds is considerably stronger with a maximum gap about four times larger than that in Sr 0.2 Nd 0.8 NiO 2 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Multiresonant metasurfaces for arbitrarily broad bandwidth pulse chirping and dispersion compensation

Here, we show that ultrathin metasurfaces with a specific multiresonant response can enable simultaneously arbitrarily strong and arbitrarily broadband dispersion compensation, pulse (de)chirping, and compression or broadening. This breakthrough overcomes the fundamental limitations of both conventional nonresonant approaches (bulky) and modern singly resonant metasurfaces (narrowband) for quadratic phase manipulations of electromagnetic signals. The required nonuniform trains of resonances in the electric and magnetic sheet conductivities that completely control phase delay, group delay, and chirp are rigorously derived and the limitations imposed by fundamental physical constraints are thoroughly discussed. Subsequently, a practical, truncated approximation by finite sequences of physically realizable linear resonances is constructed and the associated error is quantified. By appropriate spectral ordering of the resonances, operation can be achieved either in transmission or reflection mode, enabling full space coverage. The proposed concept is not limited to dispersion compensation, but introduces a generic and powerful ultrathin platform for the spatiotemporal control of broadband real-world signals with a myriad of applications in modern optics, microwave photonics, radar, and communication systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Solid-state platform for cooperative quantum dynamics driven by correlated emission

While traditionally regarded as an obstacle to quantum coherence, recent breakthroughs in quantum optics have shown that the dissipative interaction of a qubit with its environment can be leveraged to protect quantum states and synthesize many-body entanglement. Inspired by this progress, here we set the stage for the—as yet uncharted—exploration of analogous cooperative phenomena in hybrid solid-state platforms. We develop a comprehensive formalism for the quantum many-body dynamics of an ensemble of solid-state spin defects interacting with the magnetic field fluctuations of a common solid-state reservoir. Our framework applies to any solid-state reservoir whose fluctuating spin, pseudospin, or charge degrees of freedom generate magnetic fields. To understand whether correlations induced by dissipative processes can play a relevant role in a realistic experimental setup, we apply our model to a qubit array interacting via the spin fluctuations of a ferromagnetic bath. Our results show that the low-temperature collective relaxation rates of the qubit ensemble can display clear signatures of super- and subradiance, i.e., forms of cooperative dynamics traditionally achieved in atomic ensembles. We find that the solid-state analog of these cooperative phenomena is robust against spatial disorder in the qubit ensemble and thermal fluctuations of the magnetic reservoir, providing a route for their feasibility in near-term experiments. Finally, our work lays the foundation for a multiqubit approach to quantum sensing of solid-state systems and the direct generation of many-body entanglement in spin-defect ensembles. Furthermore, we discuss how the tunability of solid-state reservoirs opens up novel pathways for exploring cooperative phenomena in regimes beyond the reach of conventional quantum optics setups.

NV centers↗
DOE OSTI2021

Windowed multipole representation of R -matrix cross sections

Nuclear cross sections are basic inputs to any nuclear computation. Campaigns of experiments are fitted with the parametric R-matrix model of quantum nuclear interactions, and the resulting cross sections are documented—both pointwise and as resonance parameters (with uncertainties)—in standard evaluated nuclear data libraries (ENDF, JEFF, BROND, JENDL, CENDL, TENDL): these constitute our common knowledge of fundamental low-energy nuclear cross sections. In the past decade, a collaborative effort has been deployed to establish a new nuclear cross-section library format—the Windowed Multipole Library—with the goal of considerably reducing the computational cost of cross-section calculations in nuclear transport simulations. This work lays the theoretical foundations underpinning these efforts. From general R-matrix scattering theory, we derive the windowed multipole representation of nuclear cross sections. Though physically and mathematically equivalent to R-matrix cross sections, the windowed multipole representation is particularly well suited for subsequent temperature treatment of angle-integrated cross sections, in particular Doppler broadening, which is the averaging of cross sections over the thermal motion of the target atoms. Doppler broadening is of critical importance in neutron transport applications, as it ensures the stability of many nuclear reactors (negative thermal reactivity). Yet, Doppler broadening of nuclear cross sections has been a considerable bottleneck for nuclear transport computations, often requiring memory-costly pretabulations. We show that the windowed multipole representation can perform accurate Doppler broadening analytically (up to the first reaction threshold), from which we derive cross-section temperature derivatives to any order—all computable on the fly (without precalculations stored in memory). Furthermore, we here establish a way of converting the R-matrix resonance parameters uncertainty (covariance matrices) into windowed multipole parameters uncertainty. We show that generating stochastic nuclear cross sections by sampling from the resulting windowed multipole covariance matrix can reproduce the cross-section uncertainty in the original nuclear data file. The windowed multipole representation is therefore a novel nuclear physics formalism able to generate Doppler broadened stochastic nuclear cross sections on the fly, unlocking breakthrough computational gains for nuclear computations. Through this foundational paper, we hope to make the windowed multipole representation accessible, reproducible, and usable for the nuclear physics community, as well as provide the theoretical basis for future research on expanding its capabilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

KamNet: An integrated spatiotemporal deep neural network for rare event searches in KamLAND-Zen

Rare event searches allow us to search for new physics at energy scales inaccessible with other means by leveraging specialized large-mass detectors. Machine learning provides a new tool to maximize the information provided by these detectors. The information is sparse, which forces these algorithms to start from the lowest level data and exploit all symmetries in the detector to produce results. In this work we present KamNet, which harnesses breakthroughs in geometric deep learning and spatiotemporal data analysis to maximize the physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator detector searching for 0⁢νβ⁢β. Using a simplified background model for KamLAND, we show that KamNet outperforms a conventional convolutional neural network (CNN) on benchmarking Monte Carlo simulations with an increasing level of robustness. Using simulated data, we then demonstrate KamNet's ability to increase KamLAND-Zen's sensitivity to 0ν⁢ββ and 2⁢ν⁢β⁢β decay to excited states. A key component of this work is the addition of an attention mechanism to elucidate the underlying physics KamNet is using for the background rejection.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Correlated States of 2D Electrons near the Landau Level Filling ν = 1 / 7

The ground state of two-dimensional electron systems (2DESs) at low Landau level filling factors ( ν ≲ 1 / 6 ) has long been a topic of interest and controversy in condensed matter. Following the recent breakthrough in the quality of ultrahigh-mobility GaAs 2DESs, we revisit this problem experimentally and investigate the impact of reduced disorder. In a GaAs 2DES sample with density n = 6.1 × 10 10 / cm 2 and mobility μ = 25 × 10 6 cm 2 / V s , we find a deep minimum in the longitudinal magnetoresistance ( R x x ) at ν = 1 / 7 when T ≃ 104 mK . There is also a clear sign of a developing minimum in R x x at ν = 2 / 13 . While insulating phases are still predominant when ν ≲ 1 / 6 , these minima strongly suggest the existence of fractional quantum Hall states at filling factors that comply with the Jain sequence ν = p / ( 2 m p ± 1 ) even in the very low Landau level filling limit. The magnetic-field-dependent activation energies deduced from the relation R x x ∝ e E A / 2 k T corroborate this view and imply the presence of pinned Wigner solid states when ν ≠ p / ( 2 m p ± 1 ) . Similar results are seen in another sample with a lower density, further generalizing our observations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

First Measurement of Drift-Alfvén Wave Polarization in Magnetically Confined Fusion Plasmas

Polarization of drift-Alfvén waves, defined as the ratio of electrostatic to electromagnetic fluctuations, has remained unmeasurable in fusion plasmas for decades, despite its pivotal role in understanding wave dynamics and their impact on plasmas. We report the first measurements of drift-Alfvén wave polarization in a hot, magnetically-confined plasma. Here, the breakthrough is enabled by a novel methodology developed from gyrokinetic theory, utilizing fluctuations of electron temperature and density. Analysis of data from the DIII-D tokamak reveals that the waves above the geodesic acoustic mode frequency exhibit dominant electromagnetic polarization, whereas lower frequency waves show a mix of electromagnetic and electrostatic polarization, indicating a strong coupling between shear Alfvén waves and drift-acoustic waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Scattering mechanisms in state-of-the-art GaAs/AlGaAs quantum wells

Motivated by recent breakthroughs in molecular beam epitaxy of GaAs/AlGaAs quantum wells, we examine contributions to mobility and quantum mobility from various scattering mechanisms and their dependencies on the electron density. Here we find that at lower electron densities, n e ≲ 1 x 10 11 cm -2 , both transport and quantum mobility are limited by unintentional background impurities and follow a power-law dependence, ∝ n$^{α}_{e}$, with α ≈ 0.85. Our predictions for quantum mobility are in reasonable agreement with an estimate obtained from the resistivity at filling factor ν = 1/2 in a sample of Y. J. Chung et al. with n e = 1 x 10 11 cm -2 . Consideration of other scattering mechanisms indicates that interface roughness (remote donors) is likely a limiting factor of transport (quantum) mobility at higher electron densities. Future measurements of quantum mobility should yield information on the distribution of background impurities in GaAs and AlGaAs.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Quantum Nature of Dielectric Laser Accelerators

Dielectric laser accelerators (DLAs) hold great promise for producing economic and compact on-chip radiation sources. On-chip DLAs benefit from fabrication capabilities of the silicon industry and from breakthroughs in silicon-photonic nanostructures to enhance the interaction between particles and laser fields. Seemingly unrelated recent advances in the quantum interactions of electrons and light have raised interest in the underlying classical-quantum correspondence principle at the foundations of electron acceleration. Here, we present the observation of the underlying quantum nature of DLAs: observing quantized peaks in the electron-energy spectra. Our findings demonstrate quasi-phase-matching between an electron wave function and a light wave, which also demonstrates the role of the quantum wave function in the inverse Smith-Purcell effect. We harness the capabilities of an ultrafast transmission electron microscope (UTEM) to maintain a long electron-light interaction length extending over hundreds of periods of the laser pulse, mediated by a silicon-photonic nanograting DLA. The UTEM is shown as a new platform for characterization of future DLA concepts. The results raise fundamental questions regarding the role of quantum mechanics in DLA design, and more generally about the prospects of manipulating particles’ quantum wave functions in accelerator physics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Theory for Equivariant Quantum Neural Networks

Quantum neural network architectures that have little to no inductive biases are known to face trainability and generalization issues. Inspired by a similar problem, recent breakthroughs in machine learning address this challenge by creating models encoding the symmetries of the learning task. This is materialized through the usage of equivariant neural networks the action of which commutes with that of the symmetry. In this work, we import these ideas to the quantum realm by presenting a comprehensive theoretical framework to design equivariant quantum neural networks (EQNNs) for essentially any relevant symmetry group. We develop multiple methods to construct equivariant layers for EQNNs and analyze their advantages and drawbacks. Our methods can find unitary or general equivariant quantum channels efficiently even when the symmetry group is exponentially large or continuous. As a special implementation, we show how standard quantum convolutional neural networks (QCNNs) can be generalized to group-equivariant QCNNs where both the convolution and pooling layers are equivariant to the symmetry group. We then numerically demonstrate the effectiveness of a S U ( 2 ) -equivariant QCNN over symmetry-agnostic QCNN on a classification task of phases of matter in the bond-alternating Heisenberg model. Our framework can be readily applied to virtually all areas of quantum machine learning. Lastly, we discuss about how symmetry-informed models such as EQNNs provide hopes to alleviate central challenges such as barren plateaus, poor local minima, and sample complexity. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Practical Introduction to Benchmarking and Characterization of Quantum Computers

Rapid progress in quantum technology has transformed quantum computing and quantum information science from theoretical possibilities into tangible engineering challenges. Breakthroughs in quantum algorithms, quantum simulations, and quantum error correction are bringing useful quantum computation closer to fruition. These remarkable achievements have been facilitated by advances in quantum characterization, verification, and validation (QCVV). QCVV methods and protocols enable scientists and engineers to scrutinize, understand, and enhance the performance of quantum information-processing devices. In this tutorial, we review the fundamental principles underpinning QCVV, and introduce a diverse array of QCVV tools used by quantum researchers. We define and explain QCVV’s core models and concepts—quantum states, measurements, and processes—and illustrate how these building blocks are leveraged to examine a target system or operation. We survey and introduce protocols ranging from simple qubit characterization to advanced benchmarking methods. Along the way, we provide illustrated examples and detailed descriptions of the protocols, highlight the advantages and disadvantages of each, and discuss their potential scalability to future large-scale quantum computers. This tutorial serves as a guidebook for researchers unfamiliar with the benchmarking and characterization of quantum computers, and also as a detailed reference for experienced practitioners.

open quantum systems & decoherence↗