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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↗

NASA NTRS1967

Liquid crystals detect voids in fiber glass laminates

Liquid crystal solution nondestructively detects voids or poor bond lines in fiber glass laminates. A thin coating of the solution is applied by spray or brush to the test article surface, and when heated indicates the exact location of defects by differences in color.

Hollar, W. T.↗

NASA NTRS1991

New optical approaches to the quantitative characterization of crystal growth, segregation and defect formation

Elemental and compound semiconductors were characterized using new optical approach based on NIR microscopy in conjunction with computational image analysis and contrast enhancement. The approach made it possible to perform a quantitative microsegregation analysis of GaAs and InP. NIR dark file illumination in transmission mode makes it possible to detect submicron precipitates in semiinsulating GaAs.

Carlson, D. J.↗

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↗

DOE OSTI2025

High-Sensitivity NO 2 Gas Sensor: Exploiting UV-Enhanced Recovery in a Hexadecafluorinated Iron Phthalocyanine-Reduced Graphene Oxide

Monitoring ultralow nitrogen dioxide (NO 2 ) concentrations is crucial for air quality management and public health. However, the existing NO 2 gas sensors have several defects, like high cost and power consumption, and exhibit poor selectivity. This study addresses these challenges by presenting a novel hexadecafluorinated iron phthalocyanine-reduced graphene oxide (FePcF 16 -rGO) covalent hybrid sensor for NO 2 detection. This innovative approach, which overcomes the limitations of fabrication cost, energy efficiency, and gas selectivity, is a significant step forward in gas sensor technology. The sensor demonstrates exceptional sensitivity toward ultralow NO 2 concentrations (15.14% response for 100 ppb) with a rapid 60 s UV light-induced recovery. Additionally, the sensor exhibits high selectivity for NO 2 , achieving a limit of detection (LOD) of 8.59 ppb. This approach paves the way for developing cost-effective, energy-efficient, and miniature NO 2 monitoring devices for improved environmental monitoring and enhanced safety in workplaces where NO 2 exposure is a concern.

36 MATERIALS SCIENCE↗

NASA NTRS2009

Fiber Optic Thermal Health Monitoring of Aerospace Structures and Materials

A new technique is presented for thermographic detection of flaws in materials and structures by performing temperature measurements with fiber Bragg gratings. Individual optical fibers with multiple Bragg gratings employed as surface temperature sensors were bonded to the surfaces of structures with subsurface defects or thickness variations. Both during and following the application of a thermal heat flux to the surface, the individual Bragg grating sensors measured the temporal and spatial temperature variations. The investigated structures included a 10-ply composite specimen with subsurface delaminations of various sizes and depths. The data obtained from grating sensors were further analyzed with thermal modeling to reveal particular characteristics of the interested areas. These results were found to be consistent with those from conventional thermography techniques. Limitations of the technique were investigated using both experimental and numerical simulation techniques. Methods for performing in-situ structural health monitoring are discussed.

Wu, Meng-Chou↗

NASA NTRS2007

Roller Bearing Health Monitoring Using CPLE Frequency Analysis Method

This paper describes a unique vibration signature analysis technique Coherence Phase Line Enhancer (CPLE) Frequency Analysis - for roller bearing health monitoring. Defects of roller bearing (e.g. wear, foreign debris, crack in bearing supporting structure, etc.) can cause small bearing characteristic frequency shifts due to minor changes in bearing geometry. Such frequency shifts are often too small to detect by the conventional Power Spectral Density (PSD) due to its frequency bandwidth limitation. This Coherent Phase Line Enhancer technology has been evolving over the last few years and has culminated in the introduction of a new and novel frequency spectrum which is fully described in this paper. This CPLE technology uses a "key phasor" or speed probe as a preprocessor for this analysis. With the aid of this key phasor, this CPLE technology can develop a two dimensional frequency spectrum that preserves both amplitude and phase that is not normally obtained using conventional frequency analysis. This two-dimensional frequency transformation results in several newly defined spectral functions; i. e. CPLE-PSD, CPLE-Coherence and the CPLE-Frequency. This paper uses this CPLE frequency analysis to detect subtle, low level bearing related signals in the High Pressure Fuel Pump (HPFP) of the Space Shuttle Main Engine (SSME). For many rotating machinery applications, a key phasor is an essential measurement that is used in the detection of bearing related signatures. There are times however, when a key phasor is not available; i. e. during flight of any of the SSME turbopumps or on the SSME High Pressure Oxygen Turbopump (HPOTP) where no speed probe is present. In this case, the CPLE analysis approach can still be achieved using a novel Pseudo Key Phasor (PKP) technique to reconstruct a 1/Rev PKP signal directly from external vibration measurements. This paper develops this Pseudo Key Phasor technique and applies it to the SSME vibration data.

Jong, Jen-Yi↗

NASA NTRS2010

Fiber Optic Thermal Health Monitoring of Composites

A recently developed technique is presented for thermographic detection of flaws in composite materials by performing temperature measurements with fiber optic Bragg gratings. Individual optical fibers with multiple Bragg gratings employed as surface temperature sensors were bonded to the surfaces of composites with subsurface defects. The investigated structures included a 10-ply composite specimen with subsurface delaminations of various sizes and depths. Both during and following the application of a thermal heat flux to the surface, the individual Bragg grating sensors measured the temporal and spatial temperature variations. The data obtained from grating sensors were analyzed with thermal modeling techniques of conventional thermography to reveal particular characteristics of the interested areas. Results were compared with the calculations using numerical simulation techniques. Methods and limitations for performing in-situ structural health monitoring are discussed.

Wu, Meng-Chou↗

NASA NTRS2002

Fatigue Crack and Porosity Measurement in Composite Materials by Thermographic and Ultrasonic Methods

Many nondestructive methods exist for the detection of localized material anomalies in an otherwise good composite structure. The problem arises when the material system as a whole has degraded during service or was improperly manufactured. Porosity and intra-ply microcracking are two such conditions that in unlined composite pressure vessels can be very troublesome to detect and when linked through the thickness can be critical to mission success. These leak paths may lead to loss of pressure/propellant, increased risk of explosion and possible cryo-pumping. Research sought nondestructive methods for quantifying porosity and microcracking in composite tankage. Both thermographic and resonance ultrasound methods have been utilized with artificial neural network and statistical approaches to analyze the data. Resonant ultrasound spectroscopy provides measurements, which are sensitive to fine details in the materials character, such as micro-cracking and porosity. Here, the higher frequency (shorter wavelength) components of the signal train provide more significant interaction with the defects causing the spectral characteristics to shift toward lower amplitudes at the higher frequencies. As the density of the defects increases more interactions occur and more drastic amplitude changes are observed. From a thermal perspective, the higher the defect density the lower the through thickness thermal diffusivity will be. Utilizing a point heat source, and thermographically recording the heat profile with time, diffusivity calculations can be made which in turn can be related to the relative quality of the material. Preliminary experiments to verify the measurable effect on the resonance spectrum of the ultrasonic data to detect microcracking and for porosity detection thermographically are presented. Methods involving supervised and unsupervised artificial neural networks as well as other clustering algorithms are developed for signal identification.

Walker, James L.↗

DOE OSTI2021

A simplified framework for prediction of sensor network coverage in real-time structural health monitoring of plate-like structures

In this article, the coverage area prediction of piezoelectric sensor network for detecting a specific type of under-surface crack in plate-like structures is addressed. In particular, this article proposes a simplified framework to estimate the coverage of any given sensor network arrangement when a critical defect is known. Based on numerical results from finite element methods (FEM), a simplified framework to estimate coverage area of any given network arrangement is developed. Using such a simplified framework, one can avoid time-consuming procedure of evaluating numerous FEM models in estimating sensor network coverage. Back-scatter fields of partial cracks are estimated using a proposed function, whose parameters are estimated from the results of a limited number of FEM simulations. The proposed function efficiently predicts the back-scattered field of any combination of transmitters and receivers for a given crack geometry. Superposition is used to estimate the coverage area of an arbitrary piezoelectric (e.g., PZT) sensor network. It is shown that the coverage area of a sensor network depends on both sensor network geometry and defect properties (e.g., crack inclination) and it is not necessarily a linear function of the number of sensors. Furthermore, it is shown that the network arrangement has an important effect on the geometry of the coverage area. Experimental results of a network of 14 PZTs in two clusters confirm the accuracy of the method.

Engineering↗

NASA NTRS2011

Fiber Optic Thermal Detection of Composite Delaminations

A recently developed technique is presented for thermographic detection of delaminations in composites by performing temperature measurements with fiber optic Bragg gratings. A single optical fiber with multiple Bragg gratings employed as surface temperature sensors was bonded to the surface of a composite with subsurface defects. The investigated structure was a 10-ply composite specimen with prefabricated delaminations of various sizes and depths. Both during and following the application of a thermal heat flux to the surface, the individual Bragg grating sensors measured the temporal and spatial temperature variations. The data obtained from grating sensors were analyzed with thermal modeling techniques of conventional thermography to reveal particular characteristics of the interested areas. Results were compared and found to be consistent with the calculations using numerical simulation techniques. Also discussed are methods including various heating sources and patterns, and their limitations for performing in-situ structural health monitoring.

Wu, Meng-Chou↗