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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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Temperature and gamma radiation effects on electric breakdown of polycrystalline alpha phase aluminum oxide ceramic insulators used in nuclear thermionic reactors
Gas pressure effect on electrical breakdown and field emission, discussing ion bombardment and whisker formation
Destructive breakdown conduction in aluminum- silicon oxide-aluminum capacitors, discussing voltage threshold, etc
Electrical breakdown association with field emission observed through electron microscopy of whiskerlike projections on cathode surface
An investigation of the high voltage breakdown thresholds for molybdenum and carbon-based electrodes used in ion thruster accelerator grids has been undertaken.
This viewgraph presentation reviews exploration activities at JPL into various potting materials. Since high power space-borne microwave transmitters invariably use a vacuum tube as a final power amplifier, and this tube requires high electrode voltages for operation. The associated high voltage insulation typically represents a significant fraction of the mass of the transmitter. Since mass is always a premium resource on board spacecraft, we have been investigating materials with the potential to reduce the mass required for our applications here at JPL. This paper describes electrical breakdown results obtained with various potting materials. Conathane EN-11 (polyurethane) is the traditional HVPS encapsulant at JPL, but due to temperature limitations and durability issues it was deemed inappropriate for the particular application (i.e., CloudSat radar). The choices for the best available materials were epoxies, or silicones. Epoxies are too rigid, and were deemed inadvisable. Two silicones were further investigated (i.e.,ASTM E595- 93e2: GE RTV566(R) and Dow Corning 93-500X(R), another compound was considered (i.e., DC material, Sylgard 184(R)). "Loading" (adding filler materials) the potting compound will frequently alter the final material properties. Powdered alumina and borosilicate glass known as "microballoons" were investigated as possible loading materials. The testing of the materials is described. Each of the two loading materials offers advantages and disadvantages. The advantages and disadvantages are described.
Application of thin dielectric, base metal electrode (BME) ceramic capacitors for high-reliability applications requires development of testing procedures that can assure high quality and reliability of the parts. In this work, distributions of breakdown voltages (VBR) in variety of low-voltage BME multilayer ceramic capacitors (MLCCs) have been measured and analyzed. It has been shown that analysis of the distributions can indicate the proportion of defective parts in the lot and significance of the defects. Variations of the distributions after solder dip testing allow for an assessment of the robustness of capacitors to soldering-related stresses. The drawbacks of the existing screening and qualification methods to reveal defects in high-value, low-voltage MLCCs and the importance of VBR measurements are discussed. Analysis has shown that due to a larger concentration of oxygen vacancies, defect-related degradation of the insulation resistance (IR) and failures are more likely in BME compared to the precious metal electrode (PME) capacitors.
Performance breakdown (PB) has been anecdotally described as a state where the human operator "loses control of context" and "cannot maintain required task performance." Preventing such a decline in performance is critical to assure the safety and reliability of human-integrated systems, and therefore PB could be useful as a point at which automation can be applied to support human performance. However, PB has never been scientifically defined or empirically demonstrated. Moreover, there is no validated objective way of detecting such a state or the transition to that state. The purpose of this work is: 1) to empirically demonstrate a PB state, and 2) to develop an objective way of detecting such a state. This paper defines PB and proposes an objective method for its detection. A human-in-the-loop study was conducted: 1) to demonstrate PB by increasing workload until the subject reported being in a state of PB, and 2) to identify possible parameters of a detection method for objectively identifying the subjectively-reported PB point, and 3) to determine if the parameters are idiosyncratic to an individual/context or are more generally applicable. In the experiment, fifteen participants were asked to manage three concurrent tasks (one primary and two secondary) for 18 minutes. The difficulty of the primary task was manipulated over time to induce PB while the difficulty of the secondary tasks remained static. The participants' task performance data was collected. Three hypotheses were constructed: 1) increasing workload will induce subjectively-identified PB, 2) there exists criteria that identifies the threshold parameters that best matches the subjectively-identified PB point, and 3) the criteria for choosing the threshold parameters is consistent across individuals. The results show that increasing workload can induce subjectively-identified PB, although it might not be generalizable-only 12 out of 15 participants declared PB. The PB detection method based on signal detection analysis was applied to the performance data and the results showed that PB can be identified using the method, particularly when the values of the parameters for the detection method were calibrated individually.
This contribution explores a low-temperature route to forming dielectric pinholes to form poly-Si/dielectric/c-Si passivated contacts. The method utilizes controlled dielectric breakdown to form nanoscale pinholes in a thick (non-tunnelling) dielectric which, when annealed, allows dopant atoms to pass from doped poly-Si through the dielectric and into the c-Si wafer forming conductive pathways. We show that the pinholes can be repassivated with dopants and H diffusion which results in increased PL signals after processing. The method can be expanded to optimized dielectric passivation stacks (not just a single layer) and can be formed in parallel over the faces of the wafer in selected areas (only under the grid).
Quantum anomalous Hall (QAH) insulators transport charge without resistance along topologically protected chiral 1D edge states. Yet, in magnetic topological insulators to date, topological protection is far from robust, with zero-magnetic field QAH effect only realized at temperatures an order of magnitude below the Néel temperature T N , though small magnetic fields can stabilize QAH effect. Understanding why topological protection breaks down is therefore essential to realizing QAH effect at higher temperatures. Here a scanning tunneling microscope is used to directly map the size of exchange gap (E g,ex ) and its spatial fluctuation in the QAH insulator 5-layer MnBi 2 Te 4 . Long-range fluctuations of E g,ex are observed, with values ranging between 0 (gapless) and 70 meV, appearing to be uncorrelated to individual surface point defects. The breakdown of topological protection is directly imaged, showing that the gapless edge state, the hallmark signature of a QAH insulator, hybridizes with extended gapless regions in the bulk. Finally, it is unambiguously demonstrated that the gapless regions originate from magnetic disorder, by demonstrating that a small magnetic field restores E g,ex in these regions, explaining the recovery of topological protection in magnetic fields. The results indicate that overcoming magnetic disorder is the key to exploiting the unique properties of QAH insulators.
Solid-state nanopores are powerful platforms for single-molecule sensing, yet their performance is often constrained by fabrication complexity, noise, and limited control over surface properties. Here we report a direct method to fabricate heterogeneous multilayer nanopores using chemically tuned controlled dielectric breakdown (CT-CDB). We integrate hBN, MoS 2 , or graphene atop a silicon nitride membrane to form five distinct bilayer and tri-layer architectures, with bare SiN x nanopore as a control. CT-CDB achieves pore formation reproducibly through material-stacks with high efficiency, good pore size control, and strong yield, validated by various characterizations. Transferrin protein translocation experiments, supported by simulations, reveal that multilayer configurations modulate protein conformations, ionic current blockade and dwell time distributions, reflecting combined effects of membrane type, interfacial chemistry, and local electric field gradients. A supervised machine learning framework is implemented to assist identifying multilayer structure effects embedded in signal signatures, with over 96% accuracy. This work presents a modular and scalable framework for functional nanopore engineering with complex structural integration, thereby expanding the potential of 2D materials in single-molecule sensing applications.
The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.
Abstract We discuss back-reaction in the semiclassical treatment of quantum fields near a black hole. When the state deviates significantly from Hartle-Hawking, simple energetic considerations of back-reaction give rise to a characteristic radial distance scale ∼$$ {\left({r}_s^2{G}_N\right)}^{1/D} $$ r s 2 G N 1 / D , below which some breakdown of effective field theory may occur.
The work presented herein employs two laser-based analytical techniques (laser-induced breakdown spectroscopy (LIBS) and laser ablation – inductively coupled plasma – mass spectrometry (LA-ICP-MS)) to spatially determine the elemental and isotopic composition of uranium bearing minerals. Uniquely, this work leverages the high-speed applicability of LIBS to “screen” the sample(s) for their elemental constituents. After determining the location of the uranium inclusions (via LIBS), high-resolution LA-ICP-MS was employed to further characterize the inclusions. The high-resolution (sub-µm) capabilities of LA-ICP-MS were able to extract important information from the uranium minerals including discerning its chemical form (e.g., finchite from carnotite, Sr- and K-bearing uranyl vanadates, respectively) as well as their 235 U/ 238 U isotopic composition. This approach, LIBS followed by LA-ICP-MS, significantly reduces the analysis time (~95 %) in comparison to employing a LA-ICP-MS only approach. Furthermore, this work presented a novel approach to analyzing inclusions via a particle/inclusion analysis tool which is commercially available within the iolite 4 software. This tool allowed for a more accurate characterization of the isotopic distribution of the inclusions, as well as allowing for rapid sizing of the inclusions. Finally, this analytical approach could readily be applied to other sample types in which the target species (e.g., µm-sized inclusions) are embedded in complex matrices (e.g., cm-sized samples).
This work proposes a spectral convolutional neural network (CNN) operating on laser induced breakdown spectroscopy (LIBS) signals to learn to (1) disentangle spectral signals from the sources of sensor uncertainty (i.e., pre-process) and (2) get qualitative and quantitative measures of chemical content of a sample given a spectral signal (i.e., calibrate). Once the spectral CNN is trained, it can accomplish either task through a single feed-forward pass, with real-time benefits and without any additional side information requirements including dark current, system response, temperature and detector-to-target range. Our experiments demonstrate that the proposed method outperforms the existing approaches used by the Mars Science Lab for pre-processing and calibration for remote sensing observations from the Mars rover, ‘Curiosity’.
The inorganic characterization of 74 samples of switchgrass using laser-induced breakdown spectroscopy (LIBS) was undertaken. Determination of ash and inorganic elements content in biomass materials is vital for feedstock screening for bioconversion processes. Hierarchical models using principal component analysis (PCA) and partial least square analysis (PLS) were used to determine the presence of specific elemental micronutrients that are important in determining plant health for robust biomass production. LIBS uses a 532 nm laser with 45 mJ of laser power to excite the samples of switchgrass plant material and the emission of all the elements present in the plant samples were recorded in single spectra with a wide wavelength range of 200–800 nm. The results were compared to the laboratory standard technique, e.g., ICP-OES technique, to determine the true values for major micronutrients such as, silicon (Si), potassium (K), calcium (Ca), magnesium (Mg), phosphorus (P), and sulfur (S). Overall, our objectives were: 1) To determine the spectral features of switchgrass containing different amounts of these elements and 2) To examine the viability of this technique for determining the quality of the feedstock in terms of its inorganic composition. Cross-validation results showed that the broad-based model developed is promising for inorganics prediction in switchgrass. The LIBS validation prediction for the micronutrient elements mentioned here have been obtained. The regression coefficients for Si, were obtained to be 0.995, 0.994 for calibration and validation respectively, in case of Ca the regression coefficients were, 0.994 and 0.992 for calibration and validation. Similarly, in the case of Mg and K these were calculated to be 0.992 and 0.985, and 0.994 and 0.993 respectively. The regression coefficients are not as good as those for the elements mentioned, in case of the two elements S and P. They are 0.957, and 0.878, and 0.952 and 0.894 respectively for calibration, validation for the two elements. This demonstrates that LIBS-based techniques are inherently well suited for diverse environmental applications. Furthermore, LIBS along with PLS model can show capability in determining the viability of switchgrass as a biomass in the production of biofuels and survivability of switchgrass in processes associated with climate change. LIBS can help determining which switchgrass would be appropriate for a specific conversion process that favors low ash content overall or low value of specific inorganics.
In order to enable area-selective atomic layer deposition (AS-ALD), self-assembled monolayers (SAMs) have been used as the surface inhibitor to block a variety of ALD processes. The integrity of the SAM throughout the ALD process is critical to AS-ALD. Despite the demonstrated effectiveness of inhibition by SAMs, nucleation during ALD eventually occurs on SAM-protected surfaces, but its impact on SAM structures is still not fully understood. In this study, we chose the octadecyltrichlorosilane (ODTS) SAM as a model system to investigate the evolution of crystallinity and structure of SAMs before and after ALD. The breakdown behavior of SAMs when exposed to ZnO and Al 2 O 3 ALD was systematically studied by combining synchrotron X-ray techniques and electron microscopy. We show that the crystallinity and structure of ODTS SAMs grown on Si substrates remain intact until a significant amount of material deposition takes place. In addition, the undesired ALD materials that grow on ODTS SAMs present contrasting morphologies: dispersed nanoparticles for ZnO while relatively continuous film for Al 2 O 3 . Lastly, substrate dependency was explored by comparing a Si substrate to single-crystal sapphire. Similar results in the evolution of SAM crystallinity and formation of ALD nuclei on top of SAM are observed in the ODTS–sapphire system. This study provides an in-depth view of the influence of ALD processes on the SAM structure and the nucleation behavior of ALD on SAM-protected surfaces.
A method was developed to sample molten salts by sparging to generate and transport aerosols to an isolated instrument for compositional analysis by laser-induced breakdown spectroscopy (LIBS). Real-time monitoring of molten salt composition is critical to developing molten salt nuclear reactors, which offer enhanced safety and efficiency. In this article, the sparge sampling method is described and compared with sampling using a Collison nebulizer. The size distribution and transport of aerosols produced from molten eutectic NaNO 3 –KNO 3 salt were compared for multiple gas flow rates (75–1200 mL min –1 ) and transport distances (0.68–2.61 m). Both methods produced aerosols ranging from 0.5 to 5.0 μm determined using a cascade impactor. Aerosols were effectively transported without pre- or trace-heating of gas lines, but transport efficiency was reduced by the formation of agglomerates. Sparge sampling was found to use less sample and less gas than a Collison nebulizer while producing a more concentrated aerosol stream (up to 5 μg L –1 ). The effects of laser energy and delay time on the signal quality of LIBS measurements of these aerosols were also studied. High energy and short delay times were found to enhance signal and repeatability, whereas signal-to-background and signal-to-noise ratios were highest at low energy and longer delay times. The capabilities of this system for online monitoring of molten salts were demonstrated with calibrations for Sr and Li with relative standard deviations of 2.6% and 1.5% and limits of detection of 380 and 180 μg g –1 , respectively.