Material Transition Detection in Drilling Using Data Analytics.
Abstract not provided.
McBrayer, Kepra↗
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Material Transition Detection in Drilling Using Data Analytics.
Abstract not provided.
McBrayer, K. L.↗
Material Transition Detection in Drilling Using Data Analytics.
Abstract not provided.
McBrayer, Kepra↗
Material Transition Detection in Drilling Using Data Analytics.
Abstract not provided.
McBrayer, K. L.↗
Effects of cluster expansion on the locations of phase transition boundary as a first step to quantify uncertainty in first principles statistical mechanics framework
Predicting phase diagrams from first principle calculations eliminates the need of tedious experimental trials and errors. Fully automating first principle phase diagram calculations without any sort of human intervention has been a long daunting task and troubling scientific communities for decades. This grand problem remains not fully resolved, largely due to the vastly high-dimensional parameter space associated with density functional theory, cluster expansion, lattice Monte Carlo, and the substantial uncertainty propagating through a set of complex simulations. As a first step to tackle this grand problem, we reported a first demonstration of how sensitive phase boundary locations can be to various cluster expansion fittings and input DFT training data. To the best knowledge of the authors, this study reported the first ever attempt to quantify uncertainty in first principles statistical mechanics framework. In addition, a semi-automated phase transition detection algorithm has been devised in this paper to deal with the associated statistical errors and uncertainties from Monte Carlo method and its predecessor DFT calculations and cluster expansions. This algorithm has been applied in a classical cluster expansion Mg-Cd binary alloy system to detect phase transitions at various locations of phase diagram using different cluster expansions to demonstrate its predictive power and quantify uncertainties. The results suggested that using Chebyshev basis function shifted the transition locations toward the dilute solid solution phase and expanded the phase stability of concentrated ordered phases to wider composition range. The addition of perturbed defect configurations into training data set lowered the order-disorder transition temperature to be away from true transition temperature, suggesting the transition nature is indeed configurational disorder dominated rather than defect assisted. Finally, we found that the weighting has negligible effect on the transition locations, except for the case of using Chebyshev basis function to fit all non-weighted configurations that can be susceptible to Monte Carlo sampling hysteresis.
36 MATERIALS SCIENCE↗
In-Situ FTIR Detection of Transition Metal (TM)-Ion Dissolution From Cathodes in Li-Ion Batteries
Transition metal (TM) ions, commonly Ni and Mn, play a crucial role in Li-ion battery cathodes as the reaction centers for rapid redox reactions. A major challenge with TM-based cathodes is capacity degradation, particularly at higher operating voltages. This degradation is closely linked to the dissolution of TMs from the cathode materials and their subsequent deposition on the anode. This process not only modifies the surface structure of the cathode but, more significantly, alters the SEI composition on the anode [1-2]. The dissolution of TMs cations into a liquid electrolyte from cathode materials, such as Mn-ion dissolution from Mn-rich cathode (LMR), is detrimental to the cycling performance of Li-ion batteries [3-4]. Much attention has been paid to this issue but there remains a lack of characterization techniques which can detect the TM-ion dissolution from the cathode during electrochemical measurements. In our study, we use in-situ ATR-FTIR as an effective technique to probe the TM-ion dissolution from the cathode. We have first demonstrated the detrimental effects of TM ions on the electrochemical performance of Li-ion batteries by adding a small amount of TM salt (50 mM Mn(PF6)) to the electrolyte of a Li-ion coin cell with LFP and graphite electrode. We observed a rapid capacity fade after the first delithiation cycle. To investigate TM ion dissolution, we established a baseline IR spectrum for various TM solvation states (such as Mn and Ni) by measuring concentration-dependent IR spectra. This baseline spectrum helps us detect TM ion dissolution during battery cycling. In this work, we discuss in detail the effect of TM ions on the electrochemical performance of Li-ion batteries and the detection of TM ions during battery cycling using in-situ FTIR spectroscopy. We will compare TM dissolution between coated and uncoated cathodes to examine the effect of cathode coatings to mitigate degradation due to TM dissolution and cross-over from cathode to anode. References: (1) Zhan, C.; Wu, T.; Lu, J.; Amine, K. Dissolution, migration, anddeposition of transition metal ions in Li-ion batteries exemplified byMn-based cathodes - a critical review. Energy Environ. Sci. 2018, 11,243-257. (2) Jung, R.; Linsenmann, F.; Thomas, R.; Wandt, J.; Solchenbach,S.; Maglia, F.; Stinner, C.; Tromp, M.; Gasteiger, H. A. Nickel,Manganese, and Cobalt Dissolution from Ni-Rich NMC and TheirEffects on NMC622-Graphite Cells. J. Electrochem. Soc. 2019, 166,A378-A389. (3) Zhao, L.; Chenard, E.; Capraz, O. O.; Sottos, N. R.; White, S.R. Direct Detection of Manganese Ions in Organic Electrolyte by UV-Vis Spectroscopy. J. Electrochem. Soc. 2018, 165, A345-A348 (4) Zhang, Y.; Hu, A.; Xia, D.; Hwang, S.; Sainio, S.; Nordlund, D.;Michel, F. M.; Moore, R. B.; Li, L.; Lin, F. Operando characterization and regulation of metal dissolution and redeposition dynamics nearbattery electrode surface. Nat. Nanotechnol. 2023, 18, 790.
25 ENERGY STORAGE↗
SSAPy in the Loop: Catalog-Scale TLE Anomaly Detection with Transition Residuals, Autoencoders, and Isolation Forests
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Astronomy and AstroPhysics↗
Higher Compact Multiple Occurrence around Metal-poor M-dwarfs and Late-K-dwarfs
The planet–metallicity correlation serves as a potential link between exoplanet systems as we observe them today and the effects of bulk composition on the planet formation process. Many observers have noted a tendency for Jovian planets to form around stars with higher metallicities; however, there is no consensus on a trend for smaller planets. Here, we investigate the planet–metallicity correlation for rocky planets in single- and multi-planet systems around Kepler M-dwarf and late-K-dwarf stars. Due to molecular blanketing and the dim nature of these low-mass stars, it is difficult to make direct elemental abundance measurements via spectroscopy. We instead use a combination of accurate and uniformly measured parallaxes and photometry to obtain relative metallicities and validate this method with a subsample of spectroscopically determined metallicities. We use the Kolmogorov–Smirnov (K-S) test, Mann–Whitney U-test, and Anderson–Darling (AD) test to compare the compact multiple planetary systems with single-transiting planet systems and systems with no detected transiting planets. We find that the compact multiple planetary systems are derived from a statistically more metal-poor population, with a p-value of 0.015 in the K-S test, a p-value of 0.005 in the Mann–Whitney U-test, and a value of 2.574 in the AD test statistic, which exceeds the derived threshold for significance by a factor of 25. We conclude that metallicity plays a significant role in determining the architecture of rocky planet systems. Compact multiples either form more readily, or are more likely to survive on gigayear timescales, around metal-poor stars.
47 OTHER INSTRUMENTATION↗
Orbital Refinement and Stellar Properties for the HD 9446, HD 43691, and HD 179079 Planetary Systems
The Transit Ephemeris Refinement and Monitoring Survey is a project that aims to detect transits of intermediate-long period planets by refining orbital parameters of the known radial velocity planets using additional data from ground-based telescopes, calculating a revised transit ephemeris for the planet, then monitoring the planet host star during the predicted transit window. Here we present the results from three systems that had high probabilities of transiting planets: HD 9446 b and c, HD 43691 b, and HD 179079 b. We provide new radial velocity (RV) measurements that are then used to improve the orbital solution for the known planets. We search the RV data for indications of additional planets in orbit and find that HD 9446 shows a strong linear trend of 4.8σ. Using the newly refined planet orbital solutions, which include a new best-fit solution for the orbital period of HD 9446 c, and an improved transit ephemerides, we found no evidence of transiting planets in the photometry for each system. Transits of HD 9446 b can be ruled out completely and transits HD 9446 c and HD 43691 b can be ruled out for impact parameters up to b = 0.5778 and b = 0.898, respectively, due to gaps in the photometry. A transit of HD 179079 b cannot be ruled out, however, due to the relatively small size of this planet compared to the large star and thus low signal to noise. We determine properties of the three host stars through spectroscopic analysis and find through photometric analysis that HD 9446 exhibits periodic variability.
79 ASTRONOMY AND ASTROPHYSICS↗
Direct WIMP detection rates for transitions in isomeric nuclei
The direct detection of dark matter constituents, in particular the weakly interacting massive particles (WIMPs), is central to particle physics and cosmology. In this paper we study WIMP induced transitions from isomeric nuclear states for two possible isomeric candidates: 180 Ta and 166 Ho. The experimental setup, which can measure the possible decay of 180 Ta induced by WIMPs, was proposed. The corresponding estimates of the half-life of 180 Ta are given in the sense that the WIMP-nucleon interaction can be interpreted as ordinary radioactive decay.
47 OTHER INSTRUMENTATION↗
Magnetic properties of equiatomic CrMnFeCoNi
Magnetic, specific heat, and structural properties of the equiatomic Cantor alloy system are reported for temperatures between 5 and 300 K, and up to fields of 70 kOe. Here magnetization measurements performed on as-cast, annealed, and cold-worked samples reveal a strong processing history dependence and that high-temperature annealing after cold working does not restore the alloy to a “pristine” state. Measurements on known precipitates show that the two transitions, detected at 43 and 85 K, are intrinsic to the Cantor alloy and not the result of an impurity phase. Experimental and ab initio density functional theory computational results suggest that these transitions are a weak ferrimagnetic transition and a spin-glass-like transition, respectively, and magnetic and specific heat measurements provide evidence of significant Stoner enhancement and electron-electron interactions within the material.
36 MATERIALS SCIENCE↗
β − decay of exotic P and S isotopes with neutron number near 28
β - decay of very neutron-rich isotopes of P and S, studied at the National Superconducting Cyclotron Laboratory (NSCL) using the Beta Counting Station (BCS) consisting of a Double Sided Strip Detector surrounded by clovers detectors for observing delayed γ transitions is reported here. β-decay half-lifes and delayed neutron emission probabilities were extracted for 42,43,44 P and 44,46 S by analyzing spatial and temporal correlations between implants and decay events in the Si detector with further coincidence with γ transitions. Detection of delayed γ rays allowed for the identification of negative parity 1p1h states in 42 S for the first time, also constraining the parent ( 42 P) spin/parity to 2 - or 3 - . For the most exotic isotope studied, 46 S, no strong γ transition was observed unlike lighter even-even S isotopes, thus implying the shift of Gamow Teller (GT) strength distribution to higher energies. In conclusion, comparison of experimental observations to detailed shell-model calculations using the SDPFSDG-MU interaction allowed us to infer the importance and role of First Forbidden (FF) β transitions as the neutron number approaches and then exceeds N = 28.
39 ≤ A ≤ 58↗
Characterizing skyrmion flow phases with principal component analysis
Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.
36 MATERIALS SCIENCE↗
A semi–automatic analytical methodology for characterizing the energy consumption of MRI systems using load duration curves
Background and purpose: Magnetic resonance imaging (MRI) scanners are a major contributor to greenhouse gas emissions from the healthcare sector, and efforts to improve energy efficiency and reduce energy consumption rely on quantification of the characteristics of energy consumption. The purpose of this work was to develop a semi-automatic analytical methodology for the characterization of the energy consumption of MRI systems using only the load duration curve (LDC). LDCs are a fundamental tool used across various fields to analyze and understand the behavior of loads over time. Methods: An electric current transformer sensor and data logger were installed on two 3T MRI scanners from two vendors, termed M1 (outpatient scanner) and M2 (inpatient/emergency scanner). Data was collected for 1 month (7/11/2023 to 8/11/2023). Active power was calculated, assuming a balanced three-phase system, using the average current measured across all three phases, a 480 V reference voltage for both machines, and vendor-provided power factors. An LDC was constructed for each system by sorting the active power values in descending order and computing the cumulative time (in units of percentage) for each data point. The first derivative of the LDC was then computed (LDC’), smoothed by convolution with a window function (sLDC’), and used to detect transitions between different system modes including (in descending power levels): scan, prepared-to-scan, idle, low-power, and off. The final, segmented LDC was used to measure time (% total time), total energy (kWh), and mean power (kW) for each system mode on both scanners. The method was validated by comparing mean power values, computed using the segmented 1-month LDC, for each nonproductive system mode (i.e., prepared-to-scan, idle, lower-power, and off) against power levels measured after a deliberate system shutdown was performed for each scanner (1 day worth of data). Results: The validation revealed differences in mean power values <1.4% for all nonproductive modes and both scanners. In the scan system mode, the mean power values ranged from 29.8 to 37.2 kW and the total energy consumed for 1 month ranged from 11 106 to 14 466 kWh depending on the scanner. Over the course of 1 month, the portion of time the scanners were in nonproductive modes ranged from 76% to 80% across scanners and the nonproductive energy consumption ranged from 8010 to 6722 kWh depending on the scanner. The M1 (outpatient) scanner consumed 99.9 and 183.9 kWh/day in idle mode for weekdays and weekends, respectively, because the scanner spent 23% more time proportionally in idle mode on the weekends. Conclusions: A semi-automatic method for quantifying energy consumption characteristics of MRI scanners was introduced and validated. This method is relatively simple to implement as it requires only power data from the scanners and avoids the technical challenges associated with extracting and processing scanner log files. Finally, the methodology enables quantitative evaluation of the power, time, and energy characteristics of MRI scanners in scan and nonproductive system modes, providing baseline data and the capability of identifying potential opportunities for enhancing the energy efficiency of MRI scanners.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Field-tunable BKT and quantum phase transitions in spin-$\frac{1}{2}$ triangular lattice antiferromagnet
Quantum magnetism is one of the most active fields for exploring exotic phases and phase transitions. The recently synthesized Na 2 BaCo(PO 4 ) 2 (NBCP) is an ideal material incarnation of the spin-$\frac{1}{2}$ easy-axis triangular lattice antiferromagnet (TLAF). Experimental evidence shows that NBCP hosts the spin supersolid state with a giant magnetocaloric effect. Theory further predicts that magnetic fields can drive NBCP through Berezinskii-Kosterlitz-Thouless (BKT) and other richer quantum phase transitions. However, detecting these transitions is challenging, as they onset at ultralow temperatures near 60 mK and require high magnetization sensitivity. Using a newly developed gradient force magnetometer in a dilution refrigerator, we mapped the magnetic susceptibility phase diagram down to 30 mK. Our results provide a more comprehensive and accurate understanding of BKT melting of spin supersolidity and several field-tunable quantum phase transitions, which establish NBCP as a model platform for frustrated magnetism and highlight potential applications of its giant magnetocaloric effects.
BKT transition↗
Spatially inhomogeneous superconductivity in UTe 2
The newly discovered superconductor UTe 2 is a strong contender for a topological spin-triplet state wherein a multicomponent order parameter arises from two nearly degenerate superconducting states. A key issue is whether both of these states intrinsically exist at ambient pressure. Through thermal expansion and calorimetry, we show that UTe 2 at ambient conditions exhibits two detectable transitions only in some samples, and the size of the thermal expansion jump at each transition varies when the measurement is performed in different regions of the sample. This result indicates that the two transitions arise from two spatially separated regions that are inhomogeneously mixed throughout the volume of the sample, each with a discrete superconducting transition temperature (T c ). Notably, samples with higher T c only show a single transition at ambient pressure. Above 0.3 GPa, however, two transitions are invariably observed in ac calorimetry. Our results not only point to a nearly vertical line (constant pressure) in the pressure-temperature phase diagram but also provide a consistent scenario for the sample dependence of UTe 2 .
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
An Analysis of the Statistics and Systematics of Limb Anomaly Detections in HST/STIS Transit Images of Europa
Several recent studies derived the existence of plumes on Jupiter’s moon Europa. The only technique that provided multiple detections is the far-ultraviolet imaging observations of Europa in transit of Jupiter taken by the Space Telescope Imaging Spectrograph (STIS) on the Hubble Space Telescope (HST). In this study, we reanalyze the three HST/STIS transit images in which Sparks et al. identified limb anomalies as evidence for Europa’s plume activity. After reproducing the results of Sparks et al., we find that positive outliers are similarly present in the images as the negative outliers that were attributed to plume absorption. A physical explanation for the positive outliers is missing. We then investigate the systematic uncertainties and statistics in the images and identify two factors that are crucial when searching for anomalies around the limb. One factor is the alignment between the actual and assumed locations of Europa on the detector. A misalignment introduces distorted statistics, most strongly affecting the limb above the darker trailing hemisphere where the plumes were detected. The second factor is a discrepancy between the observation and the model used for comparison, adding uncertainty in the statistics. When accounting for these two factors, the limb minima (and maxima) are consistent with random statistical occurrence in a sample size given by the number of pixels in the analyzed limb region. The plume candidate features in the three analyzed images can be explained by purely statistical fluctuations and do not provide evidence for absorption by plumes.
79 ASTRONOMY AND ASTROPHYSICS↗
Detecting thermodynamic phase transition via explainable machine learning of photoemission spectroscopy
Identifying thermodynamic signatures of electronic phases, such as superconductivity, is challenging in low-dimensional materials due to strong fluctuations and low probing volume. Spectroscopic methods are often used to identify new bulk phases, but their main measurable quantity—electronic energy gaps—is no longer an effective order parameter in low-dimensional and fluctuating systems. Combining angle-resolved photoemission with a domain-adversarial neural network, we report a data-driven method to identify thermodynamic phase transitions solely based on single-particle spectra. We demonstrate 97.6% accuracy in cuprate superconductor Bi 2 Sr 2 CaCu 2 O 8+δ with strong superconducting fluctuations. This model notably compensates for the scarcity of experimental data by leveraging virtually inexhaustible simulated data. Further, its explainability reveals the crucial role of in-gap spectral weight in detecting phase fluctuations and thermodynamic transitions. Our work pinpoints the spectroscopic signatures of fluctuating orders and enables using spectroscopy for machine-learning-assisted material discovery for low-dimensional and strong coupling systems.
2D materials↗