Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Defect detection”

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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Transient Studies of CO 2 Adsorption over CeO 2 Nanostructures with In Situ DRIFTS and Modulation Excitation

Experiments of in-situ DRIFTS combined with modulation excitation (ME) spectroscopy showed a rich surface chemistry associated with the adsorption of CO 2 on nanocubes and nanospheres of ceria. The nanocubes exposed faces with a (100) orientation, with the edges and corners displaying (110) and (111) orientations, respectively. Here, the nanospheres mainly contained ceria (111) and (110) planes. DFT calculations showed that CO 2 is a multidentate adsorbate on ceria that can undergo changes in its bonding configuration depending on the chemical environment. At 250 °C, a temperature typically used for the conversion of CO 2 into oxygenates, alkanes and olefins, CO 2 reacted with O centers or OH groups present on the nanocubes and nanospheres to yield bi- and tri-dentate carbonates, hydroxycarbonates, and formates. Both nanostructures were highly reactive and a dynamic equilibrium was established: carbonate species were rapidly generated upon the injection of CO 2 and they decomposed upon the removal of CO 2 from the gas phase. In the case of the ceria nanocubes, the adsorption/desorption processes were essentially reversible, opening the door to catalytic transformations. A larger concentration of defects in the ceria nanospheres led to strongly bound carbonates and formates that may be spectators, site blockers, or surface modifiers in catalytic processes. In the ME studies, additional intermediates were detected, and it was clear that the response of surface species to the presence/absence of CO 2 was highly dependent on the morphology of the ceria nanostructures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Parallel pumping of magnons in inhomogeneous spin textures probed through NV spin relaxometry

We combine micromagnetic simulations and nitrogen-vacancy (NV) defect center spin relaxometry measurements to study magnon modes in inhomogeneous spin textures. A thin, micrometer-scale ferromagnetic disk is magnetized in a vortex state in which the magnetization curls around a central core. Micromagnetic simulations show that at zero applied field, the magnetization dynamics of the disk consist of a low frequency gyrotropic mode and higher frequency azimuthal magnon modes, all far detuned from the NV spin transition frequencies. An in-plane static magnetic field breaks the azimuthal symmetry of the vortex state, resulting in the magnon modes transforming in frequency and spatial profile as the field increases. Experimentally, we probe the dynamics of vortex magnetization as a function of applied in-plane static field and ac driving frequency by optically monitoring a nearby NV defect center spin. At certain values of the applied magnetic field, we observe enhanced spin relaxation when driving at twice the frequency of the NV ground state spin transition in optically detected magnetic resonance measurements. We attribute this effect to parallel pumping of a magnon mode in the disk producing magnons at half the excitation frequency. Micromagnetic simulations support this finding, showing spatial and spectral overlap of a confined magnon mode and an NV spin transition, with sufficient interaction strength to explain the observed signal.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Nitrogen Vacancy Centers in Diamond for Stress Sensing Applications: Theory and Experiments

Sensing of stress under high elevated environmental conditions with high resolution has critical importance for range of applications including earth’s subsurface scanning and geological CO2 storage monitoring. Using first principles density functional theory (DFT) approach combined with the theoretical modelling of low energy Hamiltonian, here we investigate a novel approach to detect unprecedented level of pressure by taking advantage of solid-state electron’s spin of Nitrogen vacancy (NV) centers in nanodiamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of diamond supercell hosting a single NV center. A low energy Hamiltonian is developed that includes effect of stress on the energy level of ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict the pressure sensing of up to 0.3 MPa/√Hz. We show superiority of the quantum sensing over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. The proposed quantum stress sensing could be useful to measure earth’s subsurface vibrations. Our results open avenues for development of sensing technology with a high sensitivity and resolution under extreme pressure conditions that potentially has a wider applicability than existing pressure sensing technologies.

Paudel, Hari P.↗
DOE OSTI2022

In Situ Ion Counting for Improved Implanted Ion Error Rate and Silicon Vacancy Yield Uncertainty

An in situ counted ion implantation experiment improving the error on the number of ions required to form a single optically active silicon vacancy (SiV) defect in diamond 7-fold compared to timed implantation is presented. Here, traditional timed implantation relies on a beam current measurement followed by implantation with a preset pulse duration. It is dominated by Poisson statistics, resulting in large errors for low ion numbers. Instead, our in situ detection, measuring the ion number arriving at the substrate, results in a 2-fold improvement of the error on the ion number required to generate a single SiV compared to timed implantation. Through postimplantation analysis, the error is improved 7-fold compared to timed implantation. SiVs are detected by photoluminescence spectroscopy, and the yield of 2.98% is calculated through the photoluminescence count rate. Hanbury–Brown–Twiss interferometry is performed on locations potentially hosting single-photon emitters, confirming that 82% of the locations exhibit single photon emission statistics.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2020

Van der Waals Nanowires with Continuously Variable Interlayer Twist and Twist Homojunctions

Abstract Moiré patterns at van der Waals interfaces between twisted 2D crystals give rise to distinct optoelectronic excitations, as well as, narrowly dispersive bands responsible for correlated electron phenomena. Contrasting with the conventional, mechanically stacked planar twist moirés, recent work shows twisted van der Waals interfaces spontaneously formed in nanowires of layered crystals, where Eshelby twist due to axial screw dislocations stabilizes a chiral structure with small interlayer rotation. Here, the realization of tunable twist in germanium(II) sulfide (GeS) van der Waals nanowires is reported. Tapered nanowires host continuously variable interlayer twist. Homojunctions between dislocated (chiral) and defect‐free (achiral) segments are obtained by triggering the emission of axial dislocations during growth. Measurements across such junctions, implemented here using local absorption and luminescence spectroscopy, provide a convenient tool for detecting twist effects. The results identify a versatile system for 3D twistronics, probing moiré physics, and for realizing moiré architectures without equivalent in planar systems.

Sutter, Peter↗
DOE OSTI2020

Effects of excess Te on flux inclusion formation in the growth of cadmium zinc telluride when forced melt convection is applied

The presence of second phase defects, particularly flux inclusions of tellurium rich composition, are of great concern for charge collection efficiency in cadmium zinc telluride (CZT) and cadmium telluride (CT) material intended for applications such as radiation detection. These inclusions can distort applied electric field lines within the detector as well as act as trapping centers for charge carriers. Reduction and/or elimination of these inclusions is required to achieve appropriate charge collection efficiencies, especially in detectors of thicknesses greater than 5 mm. These so-called flux inclusions are understood to form as a consequence of constitutional undercooling at the crystal growth interface. In this study, a forced melt convection technique was applied in Vertical Bridgman (VB) melt growth of CZT without reducing imposed growth rates of ~2 mm/hr. Several rotation profiles were tested while adjusting the melt composition from 51.62 to 61.75 atomic percent (at%) Te where the Te concentration was initially increased to improve overall material purity. With forced melt convection, the best inclusion distributions were achieved with highly Te rich melt compositions, far beyond the stoichiometric composition range for the CZT system. Average inclusion diameters were reduced to 2 µm while inclusions greater than 5 µm were essentially eliminated. Composition analyses of these ingots revealed near equilibrium concentrations of Te, even with ingots grown from Te concentrations as high as 61.75 at% Te. In this paper, a recipe for the reduction of inclusions in CZT melt growth is put forward and the implications of this method on our understanding of inclusion formation are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Scalable Parallel Measurement of Individual Nitrogen-Vacancy Centers

The nitrogen-vacancy (NV) center in diamond is a solid-state spin defect that has been widely adopted for quantum sensing and quantum information processing applications. Typically, experiments are performed either with a single isolated NV center or with an unresolved ensemble of many NV centers, resulting in a trade-off between measurement speed and spatial resolution or control over individual defects. In this work, we introduce an experimental platform that bypasses this trade-off by addressing multiple optically resolved NV centers in parallel. We perform charge- and spin-state manipulations selectively on multiple NV centers from within a larger set, and we manipulate and measure the electronic spin states of over 100 NV centers in parallel. We show that the high signal-to-noise ratio of the measurements enables the detection of shot-to-shot pairwise correlations between the spin states of 108 NV centers, corresponding to the simultaneous measurement of 5778 unique correlation coefficients. We discuss how our platform can be scaled to parallel experiments with thousands of individually resolved NV centers. These results enable parallelized high-throughput sensing experiments that retain the spatial resolution of single defects and will, thereby, help to unlock advances in applications such as single-molecule NMR and characterization of integrated circuits. In addition, our approach to multiplexing provides a natural platform for the application of recently developed correlated sensing techniques.

NV centers↗
DOE OSTI2020

Updates to Relevance Vector Machine: Multiclass Classification, Variable Selection, and Proof-of-Concept Application to Safeguards Fresh Fuel Verification using List-Mode Neutron Collar Data

To expand the capabilities of safeguards authorities to verify the integrity of fresh fuel assemblies, Oak Ridge National Laboratory has retrofit the existing electronics of the JCC-71 uranium neutron coincidence collar, which contains 18 3 He neutron detectors and an external 241 AmLi(α, n) neutron interrogation source arranged to surround a fresh nuclear fuel assembly. The new electronics system allows analysts to record list-mode neutron multiplicity data in addition to the singles and doubles rates that are currently measured. Based on previous proof-of-concept research, analysis of these new data will identify off-normal fuel configurations in an assembly and characterize or localize the specific partial fuel defects. The purpose of this report it to document the analysis algorithm development and then to demonstrate its capability for the safeguards verification of fresh fuel assemblies using list mode neutron collar data. To analyze the complex list-mode data collected with the upgraded uranium neutron collar, multivariate classification algorithms are being developed using a novel classification method, the relevance vector machine. This approach may be applied to multiclass problems to estimate the probability that test data belongs to one of many possible classes of data. In addition, our method identifies the most useful variables/channels for making predictions, which illuminates the basis for the model’s predictions, and this interpretability is largely unique among data analytics methods. Variable selection occurs during model training and parameter tuning and does not need any external hyperparameter tuning routines. Finally, we apply the modified relevance vector machine to a simulated dataset of list-mode neutron collar data generated with the radiation transport code MCNP. The method can correctly identify off-normal fuel configurations, categorize the data according to four fuel defect scenarios, and rank the channels in the data according to prediction utility. For nuclear safeguards applications, it is concluded that this method has the potential to increase the sensitivity and reliability to detect missing fuel rods from a standard 17 x 17 Pressurized Water Reactor (PWR) fresh fuel assembly. Within this analysis, “off-normal” (i.e., missing fuel rods) were correctly classified in 17 simulated test scenarios with one quarter (25%) of the fresh fuel rods missing using a training data set of 58 simulated measurements.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Impact of Oxygen Non-Stoichiometry on Near-Ambient Temperature Ionic Mobility in Polaronic Mixed-Ionic-Electronic Conducting Thin Films

Enhanced ionic mobility in mixed ionic and electronic conducting solids contributes to improved performance of memristive memory, energy storage and conversion, and catalytic devices. Ionic mobility can be significantly depressed at reduced temperatures, for example, due to defect association and therefore needs to be monitored. Measurements of ionic transport in mixed conductors, however, proves to be difficult due to dominant electronic conductivity. This study examines the impact of different levels of quenched-in oxygen deficiency on the oxygen vacancy mobility near room temperature as measured by a novel dynamic current-voltage analysis. A Pr 0.1 Ce 0.9 O 2-δ film was grown by pulsed laser deposition and subsequently annealed, from 400-600° C, in various oxygen partial pressures to modify its oxygen vacancy concentration while minimizing microstructural growth and cation segregation. To monitor changes in film non-stoichiometry, we leverage the existence of an optical absorption center, related to the oxidation state of Pr ions in Pr0.1Ce0.9O2-δ. The oxygen vacancy migration enthalpy was found to exhibit a small increase from 0.73± 0.04 to 0.79 ± 0.02 eV with increasing oxygen deficiency, while the pre-factor was found to increase by a factor of 135. Here, a nearly 13-fold increase in ionic mobility at 60 °C for increases in oxygen non-stoichiometry from 0.032 ± 0.001 to 0.042± 0.001 was thereby detected. Raman spectroscopy was employed to rule out changes in strain as the primary cause for the significant change in mobility. Several factors potentially contributing to the large pre-factor changes are examined and discussed. Insights into how ionic defect concentration can markedly impact ionic mobility should help in elucidating the origins of variations seen in nanoionic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-throughput spin-bath characterization of spin defects in semiconductors

Detailed knowledge of the local environments of spin defects in semiconductors, such as nitrogenvacancy (NV) centers in diamond or divacancies in silicon carbide, is crucial for optimizing control and entanglement protocols in quantum sensing and information applications. However, at present a direct experimental characterization of individual defect environments is not scalable, as conventional spin-bath measurements are time consuming and difficult to automate. Achieving high-throughput characterization requires short experiments to probe the spin bath. However, with fewer and noisier measurements, the inverse problem of recovering spin-bath properties from measured data becomes ill posed, with multiple spin baths having a high likelihood of yielding the same data. In this work, we present a set of computational tools to resolve the ill-posed inverse problem of recovering the atomic positions and hyperfine couplings of random nuclei surrounding spin defects from sparse, noisy experimental coherence data, which can be obtained in hours. Here, we use a trans-dimensional Bayesian approach that incorporates ab initio data to yield full posterior distributions over nuclear spin environments, enabling robust recovery from limited data. We also provide practical tools and guidelines to determine the limits of detectability for hyperfine couplings under specific dynamical decoupling sequences and sampling conditions. In addition, we demonstrate how the tools developed here, in combination with ab initio simulations of spin baths, can guide the design of efficient experimental protocols for application-specific high-throughput screening. To showcase the utility of our approach, we apply it to design fast dynamical decoupling experiments to characterize the spin baths often individual NV centers in diamond. While the primary focus is on accelerating spin-bath characterization of spin defects, this Bayesian approach also lays the foundation for digital-twin studies of spin defects, where a virtual model of the spin-defect system evolves in real time with ongoing experimental measurements. Together, the set of tools we designed and applied paves the way for scalable deployment of spin defects in semiconductors for quantum sensing and information applications.

Bayesian methods↗
DOE OSTI2021

Evaluating Safeguards Statistical Assumptions via Stochastic Simulation

Herein, the authors built and tested a stochastic simulation to estimate achieved detection probabilities (DPs) on a stratum basis, over a tailorable range of diverted amounts from 0 to 2 SQ, using typical IAEA inspection data: i.e., SQ in stratum, number of items, number of gross/partial/bias defect measurements conducted, and realistic relative standard deviation (RSD) values for typical IAEA verification measurements. For bulk strata, the model calculates achieved DP at 0.01 SQ diversion increments; for item strata, the model calculates DP using the smallest realistic diversion increment (e.g., a plate, pin, or coupon). After successfully benchmarking against IAEA deterministic models, the simulation was used to test the sensitivity of DP to certain standard assumptions and selected input parameters. First, the equal defect assumption was tested; the results suggest significant complexity in the effectiveness of partial defect measurements. Next, the authors explored the sensitivity of DP to the assumed RSD of attribute tests. Then, the authors compared non-normal models for instrument performance (e.g., logistic, step, or arbitrary functions) to the typical results from a normal distribution (characterized by RSD). This last comparison was supplemented with experimentally derived performance data for an HM-5. The HM-5 was used to make enrichment measurements on both LEU and HEU MTR fuel elements as plates were removed, and the results fit with logistic and step curves and applied in the simulation. These stochastic DP results were compared to DP estimates from a deterministic model assuming a normal curve and typical RSD, yielding insights that could improve effectiveness in the field. These early results illustrate the potential of stochastic models to better understand achieved DP and to improve safeguards effectiveness.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

Carbon nanospike coated nanoelectrodes for measurements of neurotransmitters

Here, carbon nanoelectrodes enable the detection of neurotransmitters at the level of single cells, vesicles, synapses and small brain structures. Previously, the etching of carbon fibers and 3D printing based on direct laser writing have been used to fabricate carbon nanoelectrodes, but these methods lack the ability of mass manufacturing. In this paper, we mass fabricate carbon nanoelectrodes by growing carbon nanospikes (CNSs) on metal wires. CNSs have a short, dense and defect-rich surface that produces remarkable electrochemical properties, and they can be mass fabricated on almost any substrate without using catalysts. Tungsten wires and niobium wires were electrochemically etched in batch to form sub micrometer sized tips, and a layer of CNSs was grown on the metal wires using plasma-enhanced chemical vapor deposition (PE-CVD). The thickness of the CNS layer was controlled by the deposition time, and a thin layer of CNSs can effectively cover the entire metal surface while maintaining the tip size within the sub micrometer scale. The etched tungsten wires produced tapered conical nanotips, while the etched niobium wires were long and thin. Both showed excellent sensitivity for the detection of outer sphere ruthenium hexamine and the inner sphere test compound ferricyanide. The CNS nanosensors were used for the measurement of dopamine, serotonin, ascorbic acid and DOPAC with fast-scan cyclic voltammetry. The CNS nanoelectrodes had a large surface area and numerous defect sites, which improved the sensitivity, electron transfer kinetics and adsorption. Finally, the CNS nanoelectrodes were compared with other nanoelectrode fabrication methods, including flame etching, 3D printing, and nanopipettes, which are slower to make and more difficult for mass fabrication. Thus, CNS nanoelectrodes are a promising strategy for the mass fabrication of nanoelectrode sensors for neurotransmitters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Low-Temperature Plasma-Based Metrology of Lithium-Ion Battery Electrode Materials (CRADA Final Report)

As part of the Cyclotron Road program, SirenOpt Inc. evaluated its low-temperature plasma-based metrology sensor prototype for measuring multiple critical properties of lithium-ion battery electrode materials in parallel and in real-time. Cost-effective, minimal-waste manufacturing of high-performance battery electrode materials will be vital for achieving society’s net-zero carbon emission goals. Because existing electrode metrology sensors cannot operate within most sections of manufacturing lines, manufacturers often complete hundreds of processing steps before they can test their products and detect problems. When manufacturers perform these offline tests, they typically only test a small portion of the manufactured products. Current electrode manufacturing thus often yields many low-quality products, or off-spec products that must be thrown away all together. For example, at least 6% of the total lithium-ion battery manufacturing cost (i.e., over $250 million/year for the average gigafactory) is devoted to processing defective electrodes that are not scrapped until performance tests are failed during late-stage quality control checks. Electrode variability also leads manufacturers to build extra cells into battery packs to reduce the risk of poor performance. For example, many electric vehicle (EV) manufacturers include up to 10% more cells than needed, which substantially increases the cost and weight of the final EV product. The SirenOpt sensor can potentially enable early detection of poorly manufactured electrodes and allow them to be removed earlier from manufacturing lines, which can save battery manufacturers (hundreds of) millions of dollars per year. The sensor can further be used to improve product quality by accelerating R&D and process optimization, improving quality control, and enabling real-time process control. Overall, a real-time, in-situ metrology strategy can create unprecedented opportunities for implementation of smart manufacturing practices and advanced quality and process control solutions to realize higher battery electrode throughput and performance.

25 ENERGY STORAGE↗
DOE OSTI2022

High Throughput Data-Driven Design of Laser-Crystallized 2D MoS 2 Chemical Sensors: A Demonstration for NO 2 Detection

High throughput characterization and processing techniques are becoming increasingly necessary to navigate multivariable, data-driven design challenges for sensors and electronic devices. For two-dimensional materials, device performance is highly dependent upon a vast array of material properties including the number of layers, lattice strain, carrier concentration, defect density, and grain structure. In this work, laser crystallization was used to locally pattern and transform hundreds of regions of amorphous MoS 2 thin films into 2D 2H-MoS 2 . Here a high throughput Raman spectroscopy approach was subsequently used to assess the process-dependent structural and compositional variations for each illuminated region, yielding over 6000 distinct nonresonant, resonant, and polarized Raman spectra. The rapid generation of a comprehensive library of structural and compositional data elucidated important trends between structure–property processing relationships involving laser-crystallized MoS 2 , including the relationships between grain size, grain orientation, and intrinsic strain. Moreover, extensive analysis of structure/property relationships allowed for intelligent design and evaluation of major contributions to device performance in MoS 2 chemical sensors. In particular, it is found that NO 2 sensor performance is strongly dependent on the orientation of the MoS 2 grains relative to the crystal plane.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Single-Particle Studies Reveal a Nanoscale Mechanism for Elastic, Bright, and Repeatable ZnS:Mn Mechanoluminescence in a Low-Pressure Regime

Mechanoluminescent materials, which emit light in response to elastic deformation, are demanded for use as in situ stress sensors. ZnS doped with Mn is known to exhibit one of the lowest reported thresholds for appearance of mechanoluminescence, with repeatable light emission under contact pressure <10 MPa. Here, the physical basis for such behavior remains as yet unclear. Here, reliable microscopic detection of mechanoluminescence of single ZnS:Mn microparticles, in combination with nanoscale structural characterization, provides evidence that the mechanoluminescent properties of these particles result from interplay between a non-centrosymmetric crystal lattice and its defects, viz., dislocations and stacking faults. Statistical analysis of the distributions of mechanoluminescence energy release trajectories reveals two distinct mechanisms of excitation: one attributable to a piezo-phototronic effect and the other due to dislocation motion. At pressures below 8.1 MPa, both mechanisms contribute to mechanoluminescent output, with a dominant contribution from the piezo-phototronic mechanism. In contrast, above 8.1 MPa, dislocation motion is the primary excitation source. For the piezo-phototronic mechanism, we propose a specific model that accounts for elastic ZnS:Mn mechanoluminescence under very low pressure. The charged interfaces in stacking faults lead to the presence of filled traps, which otherwise would be empty in the absence of the built-in electric field. Upon application of external stress, local enhancement of the piezoelectric field at the stacking faults’ interfaces facilitates release of the trapped carriers and subsequent luminescence. This field enhancement explains how <10 MPa pressure produces thousands of photons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Oxygen Vacancies Alter Methanol Oxidation Pathways on NiOOH

A thorough comprehension of the mechanism underlying the methanol oxidation reaction (MOR) on Ni-based catalysts is critical for future electrocatalytic design and development. However, the mechanism of MOR on these materials remains a matter of controversy. Herein, we combine in situ surface-enhanced infrared absorption spectroscopy (SEIRAS) and density functional theory (DFT) calculations to identify the active sites and determine the mechanism of MOR on monometallic Ni-based catalysts in alkaline media. The SEIRAS results show that formate and (bi)carbonate are formed after the commencement of the MOR with potential-dependent relative distributions. These spectroscopic results are in good agreement with the DFT-computed reaction profiles over an oxygen vacancy, suggesting that the MOR mainly proceeds through the formate-involving pathway, in which the early consumption of methanol yields formate as the major product, while increasing potential drives further oxidation of formate to (bi)carbonate. We also find a parallel pathway for the generation of (bi)carbonate at high potentials that bypasses the formation of formate. The two main pathways are thermodynamically more feasible than the one predominantly reported in the literature for MOR on NiOOH that involves CHO and/or CO as key intermediates. These DFT results are supported by spectroscopic evidence showing that no band associated with CHO or CO can be detected by SEIRAS, which is attributed to the nature of the oxygen vacancies as the active sites, suppressing deep dehydrogenation of CH 2 O to CHO. Furthermore, this work thus shows the promising role of defect engineering in promoting the electrocatalytic MOR activity and selectivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An integrated atom array-nanophotonic chip platform with background-free imaging

Arrays of neutral atoms trapped in optical tweezers have emerged as a leading platform for quantum information processing and quantum simulation due to their scalability, reconfigurable connectivity, and high-fidelity operations. Individual atoms are promising candidates for quantum networking due to their capability to emit indistinguishable photons that are entangled with their internal atomic states. Integrating atom arrays with photonic interfaces would enable distributed architectures in which nodes hosting many processing qubits could be efficiently linked together via the distribution of remote entanglement. However, many atom array techniques cease to work in close proximity to photonic interfaces, with atom detection via standard fluorescence imaging presenting a major challenge due to scattering from nearby photonic devices. Here, we demonstrate an architecture that combines atom arrays with up to 64 optical tweezers and a millimeter-scale photonic chip hosting more than 100 nanophotonic cavities. We achieve high-fidelity ( ~ 99.2%), background-free imaging in close proximity to nanofabricated cavities using a multichromatic excitation and detection scheme. The atoms can be imaged while trapped a few hundred nanometers above the dielectric surface, which we verify using Stark shift measurements of the modified trapping potential. Finally, we rearrange atoms into defect-free arrays and load them simultaneously onto the same or multiple devices.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2023

Localized keyhole pore prediction during laser powder bed fusion via multimodal process monitoring and X-ray radiography

Systematic fault detection and control during laser powder bed fusion (L-PBF) has been a long-standing objective for system manufacturers and researchers in the additive manufacturing (AM) industry. This manuscript investigates a data fusion approach for detection of keyhole porosity formation during laser irradiation of Ti-6Al-4V substrates by concurrent recording of thermally induced optical emission measured using both off-axis and coaxial photodiode sensors, and acoustic emission. Subsurface defect formation was monitored via high-speed synchrotron X-ray imaging at 20,000 frames per second, enabling temporal registration of keyhole pore formation events to the monitoring signals at a resolution of 50 µs. We developed data fusion machine learning (ML) models for localized prediction of keyhole pore formation at various time scales ranging from 0.5 ms to 2 ms. The signal segments were featurized using two independent approaches: (1) power spectral density (PSD) and (2) highly comparative time series analysis (HCTSA) framework. The extracted features from different sensor modalities were fused together to construct a multimodal feature space and sequential feature selection was used to determine the most informative features for training the ML models. The predictive performance was evaluated for three classifying algorithms: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Gaussian Naive Bayes (GNB). As a result, pore formation events were predicted with up to 0.95 F1-score, 1.0 recall and 0.94 accuracy. The most heavily weighted features indicate that model performance is chiefly governed by the acoustic monitoring signal, with a secondary contribution from the optical emission sensors.

36 MATERIALS SCIENCE↗