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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.

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DOE OSTI2022

Blanco DECam Bulge Survey (BDBS)

The Blanco DECam Bulge Survey (BDBS) has imaged more than 200 square degrees of the southern Galactic bulge, providing photometry in the ugrizy filters for ~250 million unique stars. The presence of a strong foreground disk population, along with complex reddening and extreme image crowding, has made it difficult to constrain the presence of young and intermediate age stars in the bulge population. We employed an accurate cross-match of BDBS with the latest data release (EDR3) from the Gaia mission, matching more than 140 million sources with BDBS photometry and Gaia EDR3 photometry and astrometry. We relied on Gaia EDR3 astrometry, without any photometric selection, to produce clean BDBS bulge colour-magnitude diagrams (CMDs). Gaia parallaxes were used to filter out bright foreground sources, and a Gaussian mixture model fit to Galactic proper motions could identify stars kinematically consistent with bulge membership. We applied this method to 127 different bulge fields of 1 deg 2 each, with |$\ell$| ≤ 9.5 ° and –9.5 ° ≤ b ≤ –2.5 °. The astrometric cleaning procedure removes the majority of blue stars in each field, especially near the Galactic plane, where the ratio of blue to red stars is .10%, increasing to values ~20% at higher Galactic latitudes. We rule out the presence of a widespread population of stars younger than 2 Gyr. The vast majority of blue stars brighter than the turnoff belong to the foreground population, according to their measured astrometry. We introduce the distance between the observed red giant branch bump and the red clump as a simple age proxy for the dominant population in the field, and we confirm the picture of a predominantly old bulge. Further work is needed to apply the method to estimate ages to fields at higher latitudes, and to model the complex morphology of the Galactic bulge. We also produce transverse kinematic maps, recovering expected patterns related to the presence of the bar and of the X-shaped nature of the bulge.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Water dissociation at the water–rutile TiO 2 (110) interface from ab initio-based deep neural network simulations

The interaction of water with TiO 2 surfaces is of crucial importance in various scientific fields and applications, from photocatalysis for hydrogen production and the photooxidation of organic pollutants to self-cleaning surfaces and bio-medical devices. In particular, the equilibrium fraction of water dissociation at the TiO 2 –water interface has a critical role in the surface chemistry of TiO 2 , but is difficult to determine both experimentally and computationally. Among TiO 2 surfaces, rutile TiO 2 (110) is of special interest as the most abundant surface of TiO 2 ’s stable rutile phase. While surface-science studies have provided detailed information on the interaction of rutile TiO 2 (110) with gas-phase water, much less is known about the TiO 2 (110)–water interface, which is more relevant to many applications. In this work, we characterize the structure of the aqueous TiO 2 (110) interface using nanosecond timescale molecular dynamics simulations with ab initio-based deep neural network potentials that accurately describe water/TiO 2 (110) interactions over a wide range of water coverages. Simulations on TiO 2 (110) slab models of increasing thickness provide insight into the dynamic equilibrium between molecular and dissociated adsorbed water at the interface and allow us to obtain a reliable estimate of the equilibrium fraction of water dissociation. We find a dissociation fraction of 22 ± 6% with an associated average hydroxyl lifetime of 7.6 ± 1.8 ns. These quantities are both much larger than corresponding estimates for the aqueous anatase TiO 2 (101) interface, consistent with the higher water photooxidation activity that is observed for rutile relative to anatase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Distributed Wind and Impacts of FERC Order No. 2222 Implementation

In September of 2020, FERC issued Order No. 2222, directing ISOs to adjust their long-standing tariffs and participation models to enable the operation of distributed energy resource (DER) aggregators in wholesale energy markets. The rule sought to bring wholesale markets under its jurisdiction up to speed with existing expansion of DERs across the United States and to capture the potential benefits that these technologies can provide. This report describes the implementation of FERC Order No. 2222 and the compliance plans that have been submitted so far, attempt to understand the potential impact the rule may have on distributed wind, and provide opportunities for future work to analyze and encourage deployment under these policy conditions. There is an information gap for the type of market interactions distributed wind may have or how it could be best deployed in DER aggregations under future market conditions. There is significant potential for profitable deployment of distributed wind in states that are served by ISOs and covered under Order No. 2222. Distributed wind and other DERs provide local energy that does not need to travel those distances and avoids the losses typically associated with long-distance energy transmission. Deployment of distributed wind can benefit communities that exist away from large load centers by providing local, clean, and affordable energy. Aggregating DERs that include distributed wind could provide these benefits across multiple far-ranging communities if they have access to participate in wholesale markets. A new baseline valuation of distributed wind in areas covered by Order No. 2222 is required to accurately gauge where it is profitable and how it can compete or complement existing or future DER deployment, including as part of an aggregate.

17 WIND ENERGY↗

DOE OSTI2022

Electron doping of NdNiO 3 thin films using dual chamber CaH 2 annealing

Hydrogen donor doping has been exploited as a strategy to manipulate the electronic structure and electrical properties of functional oxide systems. Especially, the development of synthetic methods to achieve electron doping of perovskite rare-earth nickelate thin films utilizing interstitial hydrogen is highly desirable considering the rich electronic phase diagram hosting several functional properties. Here, in this work, we present the hydrogenation of NdNiO 3 (NNO) thin films using CaH 2 annealing and the resulting giant modulation of electrical resistivity in hydrogenated NNO (H–NNO) thin films. Magnetron sputtering was employed to deposit epitaxial ~60 nm-thin NNO films on single crystal LaAlO 3 (LAO) substrates. The formation of the pristine perovskite NNO phase was realized after annealing the films at 500 °C for 24 h. CaH 2 annealing of NNO thin films for time durations ranging from 1 to 6 h was performed in a vacuumed ampule with two interconnected chambers at 280 °C. The two-chamber design enables a simple and clean approach for hydrogen doping without physical contact between the powder and sample of interest. X-ray diffraction and Raman spectroscopy revealed the formation of NNO in pristine samples, and the subsequent hydrogen incorporation upon CaH 2 annealing without forming any impurity phases. The conversion of the oxidation state of Ni towards +2 upon CaH 2 annealing was probed using X-ray photoelectron spectroscopy and X-ray absorption spectroscopy. Consequently, a substantial increase in the room-temperature resistivity was observed upon CaH 2 annealing indicating the formation of a strongly correlated electronic configuration of Ni in H–NNO due to electron doping. Overall, the findings of this study highlight the versatility of CaH 2 annealing as an electron doping method to tune the electrical properties of correlated oxides at low temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Piezo-electrocatalytic oxidation of methanol with UV-ozone treated wurtzite zinc oxide nanostructures

Leveraging mechanically-induced piezoelectric polarization, piezocatalysis emerges as a viable mechanism for enhancing the efficiency of catalytic processes. Nanostructured, catalytically active, rationally designed piezoelectric semiconductors can achieve high-performance catalysts for various applications using cost-effective electrocatalytic pathways, such as mechanical stimuli. We design and demonstrate for the first time a cost-effective, high-performance piezo-electrocatalyst for anodic methanol oxidation, which is crucial for the practical application and deployment of direct methanol fuel cells in a variety of emerging clean energy technologies. We synthesized wurtzite ZnO nanorods and nanosheets treated with UV-O 3 to characterize and compare their efficacy for piezo-electrocatalytic methanol oxidation. The generation of piezoelectric polarization charges in nanostructured semiconducting ZnO catalysts significantly increased their electrocatalytic performance. By elucidating the charge transfer between mechanically-deformed ZnO nanostructures and methanol molecules, we identified the underlying mechanism for the piezo-electrocatalytic process for methanol oxidation. The facile synthesis of high-quality ZnO nanostructures enables low-cost, scalable manufacture and direct integration into electrocatalysts whose performance could be enhanced by harvesting mechanical energy that would otherwise be wasted in the working environment.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Characterizing the effects of drive asymmetries, component offsets, and joint gaps in double shell capsule implosions

This work provides a numerical study of how double shell capsule deformations caused by drive asymmetries and fabrication imperfections affect implosion symmetry and neutron yield. Hydrodynamics simulations are performed in two dimensions and focus on low-mode deformations that are caused by corresponding asymmetries in the Hohlraum drive, component offsets, and ablator joint gaps. By providing a parameter study of these features, our goal is to understand the dominant sources for inner shell deformation and yield degradation. The discussed capsules are composed of an aluminum ablator with a chromium inner shell. The latter encloses a carbon-deuterium foam ball that serves as fuel. We find that for clean capsules, even-numbered low-mode asymmetries in the drive are imprinted on the ablator and smoothly transferred to the inner shell during shell collision. The resulting deformation of the inner shell is more pronounced with larger fuel radius, while the yield is inversely proportional to the amplitude of the drive asymmetry and varies by factors ≤4 in comparison with clean simulations. Capsule component offsets in the vertical direction and ablator thickness nonuniformity result in p1-type deformations of the imploding inner shell. Finally, joint gaps have the largest effect in deforming the ablator and inner shell and degrading yield. While small gap widths (1 μm) result in prolate inner shells, larger gap widths (4 μm) cause an oblate deformation. More importantly, capsules with a small outer gap (1 μm) experience a dramatic drop in yield, typically <3% of a clean simulation.

Goodarzi, S. (ORCID:000000018556489X)↗

DOE OSTI2025

Fully gauge-fixed SU(2) Hamiltonian for quantum simulations

Here, we demonstrate how to construct a fully gauge-fixed lattice Hamiltonian for a pure SU(2) gauge theory. Our work extends upon previous work, where a formulation of an SU(2) lattice gauge theory was developed that is efficient to simulate at all values of the gauge coupling. That formulation utilized maximal-tree gauge, where all local gauge symmetries are fixed and a residual global gauge symmetry remains. By using the geometric picture of an SU(2) lattice gauge theory as a system of rotating rods, we demonstrate how to fix the remaining global gauge symmetry. In particular, the quantum numbers associated with total charge can be isolated by rotating between the lab and body frames using the three Euler angles. The Hilbert space in this new “sequestered” basis partitions cleanly into sectors with differing total angular momentum, which makes gauge-fixing to a particular total charge sector trivial, particularly for the charge-zero sector. In addition to this sequestered basis inheriting the property of being efficient at all values of the coupling, we show that, despite the global nature of the final gauge-fixing procedure, this Hamiltonian can be simulated using quantum resources scaling only polynomially with the lattice volume.

Lattice gauge theory↗

DOE OSTI2024

Developing and Evaluating Energy Justice Metrics for Early-Stage Materials Research

Materials science is a central component of early-stage research and development of virtually all clean energy technologies. But as much as material breakthroughs often hold the key to high efficiencies, long lifetimes, and high stability in eventual devices, early-stage choices about material types, structures, and processing can also serve to lock in long-term social and equity impacts of deployed energy technologies. Thus, to achieve a just and sustainable energy transition, tools to assess the energy justice impacts of early-stage materials research are critical. Here, we discuss development of the Justice Underpinning Science and Technology Research (JUST-R) metrics framework - a suite of metrics targeted at early-stage researchers to assess energy justice considerations in their work. The framework is evaluated for its appeal to researchers and effectiveness at promoting integration of energy justice into research through case studies, which reveal its ability to broaden researcher perspectives and key avenues for future improvement.

energy justice↗

DOE OSTI2024

Advancing Understanding of Geothermal Representation in the Power Sector to Accelerate Deployment

Driven mostly by decarbonization goals, geothermal interest in the US power sector has grown considerably, gradually evolving from being considered a niche technology, to being recognized as a viable source of clean, baseload, grid-balancing power, and renewable electric power generation. Furthermore, recent technical advances that could greatly accelerate deployment in the near future, and US federal incentives for low-carbon generation technologies, including geothermal, can enable opportunities for integrating geothermal into utilities resource planning portfolios. However, in general, utilities do not have in-house expertise to evaluate geothermal technologies and its potential role in helping decarbonize the grid as well as help them achieve their individual decarbonization goals. To date, geothermal is rarely included in Capacity Expansion Models (CEM) which utilities use in their planning activities and resource/technologies prioritization. To this end, EPRI and NREL are working together in a DOE-GTO funded research project to improve geothermal understanding (opportunities, value, risks) among the power industry to help accelerate geothermal deployment. In the present paper we describe the approach and preliminary findings of this work, focused on two topics: 1) Expand the degree of understanding on the value, opportunity, and risk of geothermal technologies among utilities and related companies/groups, specifically around geothermal for power generation. 2) Improve representation of geothermal power technologies in capacity expansion models (CEM).

capacity expansion models↗

DOE OSTI2026

Triso Analysis Tool For Predictive Source Terms

Source term modeling for TRi-structural ISOtropic (TRISO) fuel has been performed for previous reactor designs, but few are available in the open literature. Thus, there is a need to develop a simple, versatile, and mechanistic model of fission product release and transport in gas reactor cores that can be applied to a variety of reactors through user inputs and reactor-specific radionuclide inventories. To meet this need, the TRISO Analysis Tool for Predictive Source terms (TRISO-ATOPS) was developed. This model calculates the release of the key safety-important fission products by diffusion through the kernel, silicon carbide and graphite based on fuel and graphite temperatures in the reactor under normal operation. These releases from the fuel enter the coolant where they can plate-out on cooler surfaces. A clean-up model is included for designs with a coolant purification system for removing fission gases. This initial distribution of fission products in the reactor serves as an initial condition for potential releases under postulated accident conditions. From this initial condition, the model calculates the fission product release for any transient temperature profile, and the fission product releases can then be used to assess radiological dose to the workers and the public using conventional radiological dose tools. Data on the diffusion of fission products is based on historic German TRISO experiments and the more current Department of Energy Advanced Gas Reactor TRISO fuel development program. The example cases in this work demonstrate the flexibility of the model

Stoyer, Benjamin [Idaho National Laboratory (INL),↗

DOE OSTI2020

FLC Notable Technology Award Oleo-Furan Surfactant

It’s a well-kept secret that laundry detergents are harmful to the environment. The important chemical in laundry detergent, called a surfactant. Surfactants are the workhorses of laundry detergent and soaps, and they're attracted to things like grease on your hands and dirt in your clothes to wash them away. Surfactants are used in everything from shampoo, hand soap, laundry detergent, and cosmetics. Many current surfactants are made from petroleum feedstock. These petroleum-derived building blocks come with a big carbon footprint. As the need for greener feedstock alternatives for synthesizing surfactants became obvious, manufacturers turned to natural oils, such as palm kernel and coconut oil. However, these oils are costly, particularly in areas where they are not domestically produced, and using them for surfactant synthesis competes with the food supply chain. Surfactants deactivate and do not work in hard water. Hard water is common - 85% of the U.S. water supply is considered hard because of the presence of small metal ions. These metal ions cover the surfactants making them useless. To solve this problem, many manufacturers add chemicals called chelators to bind the metal ions and make the water less hard, enhancing the effectiveness of the laundry detergent. However, this solution leads to more problems, including increased cost of detergent production and a large output of environmentally harmful chemicals. There remains a strong need for effective laundry detergents that do not negatively impact the environment and are available to consumers at an affordable cost. The American Cleaning Institute (ACI), a surfactant manufacturer organizing body, is emphasizing commitment to developing bio-renewable alternatives to current surfactants in laundry detergents. That’s why chemists at SIRONIX Renewables partnered with Los Alamos National Laboratory to further develop Oleo-furan surfactants (OFS). OFS are a new class of non-toxic, non-irritating surfactants for laundry detergent. It is the only surfactant that performs effectively in cold and hard water without chelators to bind the metal ions in hard water. OFS can be produced readily from sustainable, bio-derived molecules. They are multi-functional molecules - performing the role of a surfactant and a chelator in one - and are completely bio-degradable. OFS does not rely on petroleum feedstock and is synthesized from non-food, bio-renewable cellulosic feedstock. OFS solves each of the pertinent problems facing detergents and our environment today.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

GeoTGo: AI/ML software for development of community geothermal resources

For effective and equitable outcomes in achieving the national goal of net-zero carbon emissions, communities must be not only included, but even lead the implementation of innovative green-energy technologies. Collaborations with communities should happen through informed decision-making, community-centered research and engagement of stakeholders at the local, state, and regional levels. Community-led research and implementation are fundamental to achieving success. These collaborations include rule makers, environmental regulators, clean energy industries, and technology researchers and developers. Unfortunately, many green infrastructure initiatives still adhere to a top-down and expert-driven process of site selection and design without awareness and acknowledgment of public engagement needs. This can lead to costly delays, including lawsuits, and ultimately less than desired or lacking outcomes as well as missed opportunities1. Geothermal, like many new technologies whose social and economic impacts are not fully understood, often cause disproportionately high adverse effects on disadvantaged communities. These effects can be related to human health, environmental, climate, and other cumulative impacts, as well as the accompanying economic challenges of these impacts. We are focusing our work on the needs of the New Mexico Native American Pueblos and Tribes (NMP&T). To address these needs, we are developing a novel web-based interactive software and user friendly interface called GeoTGO (https://geotgo.com) that provides everything that is needed for communities to better understand and develop their geothermal resources. We will bridge the gap between technology advancements and community needs by facilitating the interactions between the geothermal industry, regulators, stakeholders, and end-users. GeoTGO will merge data, software (including data analysis, text mining, artificial intelligence, and modeling tools), knowledge, expertise, and experience to provide fast processing and dissemination of the latest information about cutting-edge geothermal technologies to users and communities. More information about the project is available at https://envitrace.com/projects/geotgo.html.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis

HPC datacenters offer a backbone to the modern digital society. Increasingly, they run Machine Learning (ML) jobs next to generic, compute-intensive workloads, supporting science, business, and other decision-making processes. However, understanding how ML jobs impact the operation of HPC datacenters, relative to generic jobs, remains desirable but understudied. In this work, we leverage long-term operational data, collected from a national-scale production HPC datacenter, and statistically compare how ML and generic jobs can impact the performance, failures, resource utilization, and energy consumption of HPC datacenters. Our study provides key insights, e.g., ML-related power usage causes GPU nodes to run into temperature limitations, median/mean runtime and failure rates are higher for ML jobs than for generic jobs, both ML and generic jobs exhibit highly variable arrival processes and resource demands, significant amounts of energy are spent on unsuccessfully terminating jobs, and concurrent jobs tend to terminate in the same state. We open-source our cleaned-up data traces on Zenodo (https://doi. org/10.5281/zenodo.13685426), and provide our analysis toolkit as software hosted on GitHub (https://github.com/atlarge-research/2024-icpads-hpc-workload-characterization). This study offers multiple benefits for data center administrators, who can improve operational efficiency, and for researchers, who can further improve system designs, scheduling techniques, etc.

crossanalysis↗

DOE OSTI2025

Characterization of wind conditions and impact on wind loading at an operational parabolic trough concentrating solar power plant using LiDAR observations

Wind loading is a major factor influencing the structural design costs of Concentrating Solar Power (CSP) collector systems, including heliostats and parabolic troughs. Traditionally, these designs have been based on wind-tunnel data, which often fail to accurately represent the dynamic effects experienced at full scale. This study presents a first-of-its-kind experimental characterization of wind conditions within an operational parabolic-trough CSP power plant focusing specifically on using lidar observations. The lidar observations give a unique opportunity to provide insights into wind flow conditions deep within the trough arrays. Our results suggest that (1) after being blocked by the first few rows, the wind speed above the troughs recovers to 73% of its inflow magnitude as it flows further over the trough field due to enhanced turbulent mixing and (2) due to the wind speed recovery, troughs in the interior field will likely experience higher shear-induced turning moments compared those at the front. The conclusions from this work stress the importance of better understanding the wind patterns and interior wind loads when designing solar collectors and highlights the need for more interior load measurements in the future field campaigns.

17 WIND ENERGY↗

DOE OSTI2022

Disruption prediction with artificial intelligence techniques in tokamak plasmas

In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. In this work, to prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

14 SOLAR ENERGY↗

DOE OSTI2022

Investigation of Ni catalyst activation during plasma-assisted methane oxidation

Atmospheric pressure plasma has shown promise in improving thermally activated catalytic reactions through a process termed plasma-catalysis synergy. In this work, we investigated atmospheric pressure plasma jet (APPJ)-assisted CH 4 oxidation over a Ni/SiO 2 .Al 2 O 3 catalyst. Downstream gas-phase products from CH 4 conversion were quantified by Fourier transform infrared spectroscopy. The catalyst near-surface region was characterized by in-situ diffuse reflectance infrared Fourier transform spectroscopy. The catalyst was observed to be activated at elevated temperature (500 °C) if it was exposed to the APPJ operated at large plasma power. 'Catalyst activation' signifies that the purely thermal conversion of CH 4 using catalysts which had been pre-exposed to plasma became more intense and produced consistently CO product, even if the plasma was extinguished. Without the application of the APPJ to the Ni catalyst surface this was not observed at 500 °C. The study of different exposure conditions of the activated catalyst indicates that the reduction of the catalyst by the APPJ is likely the cause of the catalyst activation. We also observed a systematic shift of the vibrational frequency of adsorbed CO on Ni catalyst when plasma operating conditions and catalyst temperatures were varied and discussed possible explanations for the observed changes. Furthermore, this work provides insights into the plasma-catalyst interaction, especially catalyst modification in the plasma catalysis process, and potentially demonstrates the possibility of utilizing the surface CO as a local probe to understand the plasma-catalyst interaction and shed light on the complexity of plasma catalysis.

methane oxidation↗

DOE OSTI2024

Bilayer Crystals of Trapped Ions for Quantum Information Processing

Trapped-ion systems are a leading platform for quantum information processing, but they are currently limited to 1D and 2D arrays, which imposes restrictions on both their scalability and their range of applications. Here, we propose a path to overcome this limitation by demonstrating that Penning traps can be used to realize remarkably clean bilayer crystals, wherein hundreds of ions self-organize into two well-defined layers. These bilayer crystals are made possible by the inclusion of an anharmonic trapping potential, which is readily implementable with current technology. We study the normal modes of this system and discover salient differences compared to the modes of single-plane crystals. The bilayer geometry and the unique properties of the normal modes open new opportunities—in particular, in quantum sensing and quantum simulation—that are not straightforward in single-plane crystals. Furthermore, we illustrate that it may be possible to extend the ideas presented here to realize multilayer crystals with more than two layers. Our work increases the dimensionality of trapped-ion systems by efficiently utilizing all three spatial dimensions, and it lays the foundation for a new generation of quantum information processing experiments with multilayer 3D crystals of trapped ions. Published by the American Physical Society 2024

Physics↗