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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Ferromagnetic Insulator to Metal Transition in Noncentrosymmetric Graphene Nanoribbons

Engineering sublattice imbalance within the unit cell of bottom-up synthesized graphene nanoribbons (GNRs) represents a versatile tool for realizing custom-tailored quantum nanomaterials. The interaction between low-energy zero-modes (ZMs) not only contributes to frontier bands but can form the basis for magnetically ordered phases. Here, we present the bottom-up synthesis of a noncentrosymmetric GNR that places all ZMs on the majority sublattice sites. Scanning tunneling microscopy and spectroscopy reveal that strong electron–electron correlations, leading to the Stoner magnetic instability, drive the system into a ferromagnetically ordered insulating ground state featuring a sizable band gap of E g ∼1.2 eV. At higher temperatures, a chemical transformation induces an insulator-to-metal transition that quenches the ferromagnetic order. Tight-binding (TB), density functional theory, and GW calculations corroborate our experimental observations. Furthermore, this work showcases how control over molecular symmetry, sublattice polarization, and ZM hybridization in bottom-up synthesized nanographenes can open a path to the exploration of many-body physics in rationally designed quantum materials.

Band structure↗
NASA NTRS2013

Copper-Multiwall Carbon Nanotubes and Copper-Diamond Composites for Advanced Rocket Engines

This paper reports on the research effort to improve the thermal conductivity of the copper-based alloy NARloy-Z (Cu-3 wt.%Ag-0.5 wt.% Zr), the state-of-the-art alloy used to make combustion chamber liners in regeneratively-cooled liquid rocket engines, using nanotechnology. The approach was to embed high thermal conductivity multiwall carbon nanotubes (MWCNTs) and diamond (D) particles in the NARloy-Z matrix using powder metallurgy techniques. The thermal conductivity of MWCNTs and D have been reported to be 5 to 10 times that of NARloy-Z. Hence, 10 to 20 vol. % MWCNT finely dispersed in NARloy-Z matrix could nearly double the thermal conductivity, provided there is a good thermal bond between MWCNTs and copper matrix. Quantum mechanics-based modeling showed that zirconium (Zr) in NARloy-Z should form ZrC at the MWCNT-Cu interface and provide a good thermal bond. In this study, NARloy-Z powder was blended with MWCNTs in a ball mill, and the resulting mixture was consolidated under high pressure and temperature using Field Assisted Sintering Technology (FAST). Microstructural analysis showed that the MWCNTs, which were provided as tangles of MWCNTs by the manufacturer, did not detangle well during blending and formed clumps at the prior particle boundaries. The composites made form these powders showed lower thermal conductivity than the base NARloy-Z. To eliminate the observed physical agglomeration, tangled multiwall MWCNTs were separated by acid treatment and electroless plated with a thin layer of chromium to keep them separated during further processing. Separately, the thermal conductivities of MWCNTs used in this work were measured, and the results showed very low values, a major factor in the low thermal conductivity of the composite. On the other hand, D particles embedded in NARloy-Z matrix showed much improved thermal conductivity. Elemental analysis showed migration of Zr to the NARloy-Z-D interface to form ZrC, which appeared to provide a low contact thermal resistance. These results are consistent with the quantum mechanics-based model predictions. NARloy-Z-D composites have relatively high thermal conductivities and are promising for further development.

Bhat, Biliyar N.↗
NASA NTRS2013

Copper Multiwall Carbon Nanotubes and Copper-Diamond Composites for Advanced Rocket Engines

This paper reports on the research effort to improve the thermal conductivity of the copper-based alloy NARloy-Z (Cu-3 wt.%Ag-0.5 wt.% Zr), the state-of-the-art alloy used to make combustion chamber liners in regeneratively-cooled liquid rocket engines, using nanotechnology. The approach was to embed high thermal conductivity multiwall carbon nanotubes (MWCNTs) and diamond (D) particles in the NARloy-Z matrix using powder metallurgy techniques. The thermal conductivity of MWCNTs and D have been reported to be 5 to 10 times that of NARloy-Z. Hence, 10 to 20 vol. % MWCNT finely dispersed in NARloy-Z matrix could nearly double the thermal conductivity, provided there is a good thermal bond between MWCNTs and copper matrix. Quantum mechanics-based modeling showed that zirconium (Zr) in NARloy-Z should form ZrC at the MWCNT-Cu interface and provide a good thermal bond. In this study, NARloy-Z powder was blended with MWCNTs in a ball mill, and the resulting mixture was consolidated under high pressure and temperature using Field Assisted Sintering Technology (FAST). Microstructural analysis showed that the MWCNTs, which were provided as tangles of MWCNTs by the manufacturer, did not detangle well during blending and formed clumps at the prior particle boundaries. The composites made form these powders showed lower thermal conductivity than the base NARloy-Z. To eliminate the observed physical agglomeration, tangled multiwall MWCNTs were separated by acid treatment and electroless plated with a thin layer of chromium to keep them separated during further processing. Separately, the thermal conductivities of MWCNTs used in this work were measured, and the results showed very low values, a major factor in the low thermal conductivity of the composite. On the other hand, D particles embedded in NARloy-Z matrix showed much improved thermal conductivity. Elemental analysis showed migration of Zr to the NARloy-Z-D interface to form ZrC, which appeared to provide a low contact thermal resistance. These results are consistent with the quantum mechanics-based model predictions. NARloy-Z-D composites have relatively high thermal conductivities and are promising for further development.

Bhat, Biliyar N.↗
DOE OSTI2022

Artificial spin ice phase-change memory resistors

Abstract We present a proposal for realization of an electrical memory reminiscent of a memristor in connected Kagome artificial spin ice. We show that current flowing through the system alters the magnetic ensemble, which in turns controls the overall resistance thus leaving memory of current passage in the system. This introduces a current-dependent effect for a dynamic resistive state. We simulate a spin-induced thermal phase-change mechanism, and an athermal domain-wall spin inversion. In both cases we observe electrical memory behavior with an I – V hysteretic pinched loop, typical of memristors. These results can be extended to the more complex geometries in which artificial spin ice can be designed to engineer the hysteresis curve.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Quantum approximate multi-objective optimization

The goal of multi-objective optimization is to understand optimal trade-offs between competing objective functions by finding the Pareto front, that is, the set of all Pareto-optimal solutions, where no objective can be improved without degrading another one. Multi-objective optimization can be challenging classically, even if the corresponding single-objective optimization problems are efficiently solvable. Thus, multi-objective optimization represents a compelling problem class to analyze with quantum computers. Here we use a low-depth quantum approximate optimization algorithm to approximate the optimal Pareto front of certain multi-objective weighted maximum-cut problems. We demonstrate its performance on an IBM Quantum computer, as well as with matrix product state numerical simulation, and show its potential to outperform classical approaches.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Anti-site defect-induced disorder in compensated topological magnet MnBi2-xSbxTe4

Abstract The gapped Dirac-like surface states of compensated magnetic topological insulator MnBi 2-x Sb x Te 4 (MBST) are a promising host for exotic quantum phenomena such as the quantum anomalous Hall effect and axion insulating state. However, it has become clear that atomic defects undermine the stabilization of such quantum phases as they lead to spatial variations in the surface state gap and doping levels. The large number of possible defect configurations in MBST make studying the influence of individual defects virtually impossible. Here, we present a statistical analysis of the nanoscale effect of defects in MBST with x =0.64, by scanning tunnelling microscopy/spectroscopy. We identify (Bi,Sb) Mn anti-site defects to be the main source of the observed doping fluctuations, leading towards the formation of nanoscale charge puddles and effectively closing the transport gap. Our findings will guide further optimization of this material system via defect engineering, to enable exploitation of its promising properties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the Role of InGaAs and InAlAs Buffers on Carrier Dynamics of Tensile-Strained Ge Double Heterostructures

Extensive research efforts of strained germanium (Ge) are currently underway due to its unique properties, namely, (i) possibility of band gap and strain engineering to achieve a direct band gap, thus exhibiting superior radiative properties, and (ii) higher electron and hole mobilities than Si for upcoming technology nodes. Realizing lasing structures is vital to leveraging the benefits of tensile-strained Ge (ε-Ge). Here, we use a combination of different analytical tools to elucidate the effect of the underlying InGaAs/InAlAs and InGaAs overlaying heterostructures on the material quality and strain state of ε-Ge grown by molecular beam epitaxy. Using X-ray analysis, we show the constancy of tensile strain in sub-50 nm ε-Ge in a quantum-well (QW) heterostructure. Further, effective carrier lifetime using photoconductive decay as a function of buffer type exhibited a high (low) defect-limited carrier lifetime of ~68 ns (~13 ns) in 0.61% (0.66%) ε-Ge grown on an InGaAs (InAlAs) buffer. These results correspond well with the measured surface roughness of 1.289 nm (6.303 nm), consistent with the surface effect of the ε-Ge/III–V heterointerface. Furthermore, a reasonably high effective lifetime of ~78 ns is demonstrated in a QW of ~30 nm 1.6% ε-Ge, a moderate reduction from ~99 ns in uncapped ε-Ge, alluding to the surface effect of the overlying heterointerface. Thus, the above results highlight the prime quality of ε-Ge that can be achieved via III–V heteroepitaxy and paves a path for integrated Ge photonics.

36 MATERIALS SCIENCE↗
DOE OSTI2021

2020 LDRD Annual Report

The largest component of Argonne’s program is LDRD Prime, which emphasizes R&D explicitly aligned with Laboratory major initiatives in support of Argonne’s strategic plan. The choice of Focus Areas under the LDRD Prime component reflects the major initiatives; the state of development of relevant technical fields; the potential value of advancing those fields to DOE/NNSA and the nation; and the compatibility of the fields with existing facilities, capabilities, and staff expertise at Argonne.

25 ENERGY STORAGE↗
DOE OSTI2023

Symposium: Quantum Theory, Quantum Materials, Quantum Computing (Final Report, DE-SC0022551)

Quantum mechanics is fundamental to understanding, predicting, and controlling many physical processes in a vast range of scientific and technological areas. In molecules and materials, the quantum mechanics of Coulombic many-body systems determines far more than the ground state and bulk properties. Quantum mechanics governs magnetism, electron and spin transport, chemical reactions and catalysis, superconductivity, photosynthesis, etc. Each plays a vital role in modern society. As digital technologies push both temporal and length-scale limits, fundamental quantum principles become the current focus for a new class of technologies: quantum computing, quantum sensing, quantum encryption, etc. This oncoming quantum information sciences wave is manifest recently in frontier foci of all six Department of Energy Office of Science programs. The Sanibel Symposium offers a proven, influential platform for facilitating and advancing cross-fertilization among theoretical and computational chemists and physicists, computer scientists, and materials engineers. The Symposia have a long, successful record of driving progress on theory and computation of materials, nanostructures, and their molecular constituents. The Symposia thus are well-suited for advancing the quantum information science thrust in the Office of Basic Energy Sciences (BES). With that QIS thrust as the focus, we propose five sessions of the 61st Sanibel Symposium on the theme Quantum Theory, Quantum Materials, Quantum Computing. From a longer perspective, this thematic program for 2022 is the second year of a five year plan of program segments addressed at challenges in quantum information sciences and quantum materials. The rest of the Symposium program will be of considerable relevance, with, for example, sessions on quantum molecular and material dynamics, and new directions in coupled cluster theory. New ideas for methods and applications can be expected to emerge from the stimulus of the formal presentations and informal discussions. The proposed award is targeted at students, post-docs, and junior scientists. Conscious effort, characteristic of the Sanibel Symposia, to stimulate the participation of women and under-represented minorities will continue.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Photoluminescence of Cis-Polyacetylene Semiconductor Material

Photoluminescence (PL) is one of the key experimental characterizations of optoelectronic materials, including conjugated polymers (CPs). In this study, a simplified model of an undoped cis-polyacetylene (cis-PA) oligomer was selected and used to explain the mechanism of photoluminescence (PL) of the CPs. Using a combination of the ab initio electronic structure and a time-dependent density matrix methodology, the photo-induced time-dependent excited state dynamics were computed. We explored the phonon-induced relaxation of the photoexcited state for a single oligomer of cis-PA. Here, the dissipative Redfield equation of the motion was used to compute the dissipative excited state dynamics of electronic degrees of freedom. This equation used the nonadiabatic couplings as parameters. The computed excited state dynamics showed that the relaxation rate of the electron is faster than the relaxation rate of the hole. The dissipative excited-state dynamics were combined with radiative recombination channels to predict the PL spectrum. The simulated results showed that the absorption and emission spectra both have a similar transition. The main result is that the computed PL spectrum demonstrates two mechanisms of light emission originating from (i) the inter-band transitions, corresponding to the same range of transition energies as the absorption spectrum and (ii) intra-band transitions not available in the absorption spectra. However, the dissipative Redfield equation of the motion was used to compute the electronic degrees of freedom of the nonadiabatic couplings, which helped to process the time propagation of the excited dynamic state. This excited dynamic state shows that the relaxation rate of the electron is faster than the relaxation rate of the hole, which can be used for improving organic semiconductor materials for photovoltaic and LED applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Layer-Dependent Magnetism and Spin Fluctuations in Atomically Thin van der Waals Magnet CrPS 4

van der Waals (vdW) magnets, an emerging family of two-dimensional (2D) materials, have received tremendous attention due to their rich fundamental physics and significant potential for cutting-edge technological applications. In contrast to the conventional bulk counterparts, vdW magnets exhibit significant tunability of local material properties, such as stacking engineered interlayer coupling and layer-number dependent magnetic and electronic interactions, which promise to deliver previously unavailable merits to develop multifunctional microelectronic devices. As a further ingredient of this emerging topic, here we report nanoscale quantum sensing and imaging of atomically thin vdW magnet chromium thiophosphate CrPS 4 , revealing its characteristic layer-dependent 2D static magnetism and dynamic spin fluctuations. We also show a large tunneling magnetoresistance in CrPS 4 -based spin filter vdW heterostructures. Here, the excellent material stability, robust strategy against environmental degradation, in combination with tailored magnetic properties highlight the potential of CrPS 4 in developing state-of-the-art 2D spintronic devices for next-generation information technologies.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Nanoparticle self-assemblies with modern complexity

Thanks to decades of tireless efforts, nanoparticle assemblies have reached at an extremely high level of controllability, sophistication, and complexity, with new insights provided by integration with graph theory, cutting-edge characterization, and machine learning (ML)-based computation and modeling, as well as with ever-diversifying applications in energy, catalysis, biomedicine, optics, electronics, magnetics, organic biosynthesis, and quantum technology. Nanoparticle assemblies can be crystalline, known as superlattices or supracrystals. Their assembly entails a transition from disorder—dispersed nanoparticles—to order, which can be achieved through classical nucleation pathways or nonclassical pathways via prenucleation precursors or particle aggregation. Further, the periodic lattices allow facile manipulations of electrons, phonons, photons, and even spins, leading to advanced device components and metamaterials. Meanwhile, aperiodic assemblies out of nanoparticles, such as gels, networks, and amorphous solids, also start to attract attentions. Despite the loss of periodicity, symmetry-lowering or symmetry-breaking three-dimensional (3D) structures emerge with unique properties, such as chiroptical activity, topological mechanical strength, and quantum entanglement. Real-space imaging such as electron microscopy and X-ray based tomography methods are utilized to characterize these complex structures, while mathematical tools such as graph theories are in need to describe such complex structures. This issue aims to provide a timely review of the efforts in this greatly broadened materials design space including experiment, simulation, theory, and applications. Nine top experts (and their teams) from four countries deliver six review papers, summarizing fundamental mechanistic understandings of nanoparticle assemblies, highlighted with the developments of state-of-the-art in situ characterization tools and ML-assisted reverse engineering, and newly emergent applications of nanoarchitectures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning

Quantum error mitigation (QEM) is critical in reducing the impact of noise in the pre-fault-tolerant era, and is expected to complement error correction in fault-tolerant quantum computing (FTQC). In this work, we propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution. Our approach starts with a simplified bit-flip noise model. It first performs clustering on noisy measurement results, i.e., bit-strings, based on the Hamming distance. The centroid of each cluster is calculated using a qubit-wise majority vote. Next, the noisy distribution is adjusted with the clustering outcomes and the bitflip error rates using Bayesian inference. Our simulation results show that Q-Cluster can mitigate high noise rates (up to 40% per qubit) with the simple bit-flip noise model. However, real quantum computers do not fit such a simple noise model. To address the problem, we (a) apply Pauli twirling to tailor the complex noise channels to Pauli errors, and (b) employ a machine learning model, ExtraTrees regressor, to estimate an effective bit-flip error rate using a feature vector consisting of machine calibration data (gate & measurement error rates), circuit features (number of qubits, numbers of different types of gates, etc.) and the shape of the noisy distribution (entropy). Our experimental results show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution, for a set of low-entropy benchmarks on five different IBM quantum machines. Our approach outperforms the state-of-art QEM approaches RZNE [28], M3 [24], Hammer [35], and QBEEP [33] by 1.26x,1.29x,1.47x, and 2.65 x, respectively.

42 ENGINEERING↗
DOE OSTI2025

Data for "Integrated Green Biorefinery for the Production of Anthocyanins, Fermentable Sugars, and High Pure Lignin from Miscanthus × giganteus "

Miscanthus x giganteus (Mxg) is a promising perennial crop for producing natural colorants, renewable fuels, and bioproducts. However, natural recalcitrance and high pretreatment cost are major barriers to their complete conversion. In this study, a green processing method has been investigated for efficient recovery of natural pigments (anthocyanins), fermentable sugars, and pure lignin from Mxg genotypes using choline chloride-based natural deep eutectic solvents (NADES) systems. Interestingly, choline chloride: lactic acid (ChCl: LA) NADES-processed biomass resulted in 67.8 ± 2.1 μg g−1 of anthocyanins from dry biomass. A maximum of 87.4%–94.1% glucose yield was achieved after enzymatic saccharification. The effective extraction of lignin with high purity with higher β-aryl ether (βO4) bonds from advanced crops is crucial for lignin valorization. Notably, highly pure lignin (≈93.4% ± 1.4%) is achieved after low-temperature NADES pretreatment while retaining lignin’s native structure. 31P nuclear magnetic resonance demonstrated that total phenolics for ChCl: LA-lignin resulted in 1.20 mmol g−1 hydroxyls. The relative monolignol composition of syringyl (S), guaiacyl (G), and p-hydroxyphenyl (H) is 19.0, 65.7, and 14.3%, respectively, as evidenced by heteronuclear single quantum coherence analysis. This study provides a novel approach for obtaining high-purity lignin for catalytic depolymerization for oligomers and bifunctional monoaromatics production and leverages current cellulosic biorefinery technologies.

biomass analytics↗
DOE OSTI2021

Highly Efficient Pure‐Blue Light‐Emitting Diodes Based on Rubidium and Chlorine Alloyed Metal Halide Perovskite

Abstract Perovskite light‐emitting diodes (PeLEDs) are promising candidates for display and solid‐state lighting, due to their tunable colors, high conversion efficiencies, and low cost. However, the performance of blue PeLEDs is far inferior to that of the near‐infrared, red, and green counterparts. Here, the fabrication of pure‐blue PeLEDs with an emission peak at 475 nm, a peak external quantum efficiency of 10.1%, and a maximum luminance of 14 000 cd m −2 is demonstrated by tailoring the compositions of perovskites. The pure‐blue electroluminescence is achieved by simultaneous addition of rubidium and chlorine ions into CsPbBr 3 and incorporation of phenylethylammonium chloride forms quasi‐2D hybrid perovskites. The combination of these composition engineering results in blueshifted emissions without reducing the quantum yield. The judicious alloying is shown to be critical to result in the better morphology with suppressed current leakage and enhanced light outcoupling.

Yang, Yang↗
DOE OSTI2023

Foundations of machine learning for low-temperature plasmas: methods and case studies

Abstract Machine learning (ML) and artificial intelligence have proven to be an invaluable tool in tackling a vast array of scientific, engineering, and societal problems. The main drivers behind the recent proliferation of ML in practically all aspects of science and technology can be attributed to: (a) improved data acquisition and inexpensive data storage; (b) exponential growth in computing power; and (c) availability of open-source software and resources that have made the use of state-of-the-art ML algorithms widely accessible. The impact of ML on the field of low-temperature plasmas (LTPs) could be particularly significant in the emerging applications that involve plasma treatment of complex interfaces in areas ranging from the manufacture of microelectronics and processing of quantum materials, to the LTP-driven electrification of the chemical industry, and to medicine and biotechnology. This is primarily due to the complex and poorly-understood nature of the plasma-surface interactions in these applications that pose unique challenges to the modeling, diagnostics, and predictive control of LTPs. As the use of ML is becoming more prevalent, it is increasingly paramount for the LTP community to be able to critically analyze and assess the concepts and techniques behind data-driven approaches. To this end, the goal of this paper is to provide a tutorial overview of some of the widely-used ML methods that can be useful, amongst others, for discovering and correlating patterns in the data that may be otherwise impractical to decipher by human intuition alone, for learning multivariable nonlinear data-driven prediction models that are capable of describing the complex behavior of plasma interacting with interfaces, and for guiding the design of experiments to explore the parameter space of plasma-assisted processes in a systematic and resource-efficient manner. We illustrate the utility of various supervised, unsupervised and active learning methods using LTP datasets consisting of commonly-available, information-rich measurements (e.g. optical emission spectra, current–voltage characteristics, scanning electron microscope images, infrared surface temperature measurements, Fourier transform infrared spectra). All the ML demonstrations presented in this paper are carried out using open-source software; the datasets and codes are made publicly available. The FAIR guiding principles for scientific data management and stewardship can accelerate the adoption and development of ML in the LTP community.

Physics↗
DOE OSTI2020

Light-Induced Renormalization of the Dirac Quasiparticles in the Nodal-Line Semimetal ZrSiSe

In nodal-line semimetals, linearly dispersing states form Dirac loops in the reciprocal space with a high degree of electron-hole symmetry and a reduced density of states near the Fermi level. The result is reduced electronic screening and enhanced correlations between Dirac quasiparticles. In this study we investigate the electronic structure of ZrSiSe, by combining time- and angle-resolved photoelectron spectroscopy with ab initio density functional theory (DFT) complemented by an extended Hubbard model ( DFT + U + V ) and by time-dependent DFT + U + V . We show that electronic correlations are reduced on an ultrashort timescale by optical excitation of high-energy electrons-hole pairs, which transiently screen the Coulomb interaction. Our findings demonstrate an all-optical method for engineering the band structure of a quantum material.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗