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At least 1,135 records · Page 63

Active oversight and quality control in standard Bayesian optimization for autonomous experiments

The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.

36 MATERIALS SCIENCE↗

Delayed Gamma-Ray Spectroscopy for Non-Destructive Assay of Nuclear Materials

High-­energy, beta-delayed gamma-­ray spectroscopy is a potential, non-­destructive assay techniques for the independent verification of declared quantities of special nuclear materials at key stages of the fuel cycle and for directly assaying nuclear material inventories for spent fuel handling, interim storage, reprocessing facilities, repository sites, and final disposal. Other potential applications include determination of MOX fuel composition, characterization of nuclear waste packages, and challenges in homeland security and arms control verification. Experimental measurements were performed to evaluate fission fragment yields, to test methods for determining isotopic fractions, and to benchmark the modeling code package. Experimental measurement campaigns were carried out at the IAC using a photo-­neutron source and at OSU using a thermal neutron beam from the TRIGA reactor to characterize the emission of high-­energy delayed gamma rays from 235 U, 239 Pu, and 241 Pu targets following neutron induced fission. Data were collected for pure and combined targets for several irradiation/spectroscopy cycle times ranging from 10/10 seconds to 15/30 minutes.The delayed gamma-ray signature of 241 Pu, a significant fissile constituent in spent fuel, was measured and compared to 239 Pu. The 241 Pu/ 239 Pu ratios varied between 0.5 and 1.2 for ten prominent lines in the 2700-­3600 keV energy range. Such significant differences in relative peak intensities make it possible to determine relative fractions of these isotopes in a mixed sample. A method for determining fission product yields by fitting the energy and time dependence of the delayed gamma-­ray emission was developed and demonstrated on a limited 235 U data set. De-­convolution methods for determining fissile fractions were developed and tested on the experimental data. The use of high count-­rate LaBr 3 detectors was investigated as a potential alternative to HPGe detectors. Modeling capabilities were added to an existing framework and codes were adapted as needed for analyzing experiments and assessing application-­specific assay concepts. A de-­convolution analysis of the delayed gamma-­ray response spectra modeled for spent fuel assemblies was performed using the same method that was applied to the experimental spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in Populus trichocarpa

Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906 Populus trichocarpa leaf images obtained in the field. In contrast to previous methods, our approach (a) does not require experimental or image preprocessing, (b) uses the raw RGB images at full resolution, and (c) requires very few samples for training (e.g., just 8 images for vein segmentation). Traits relating to leaf morphology and vein topology are extracted from the resulting segmentations using traditional open-source image-processing tools, validated using real-world physical measurements, and used to conduct a genome-wide association study to identify genes controlling the traits. In this way, the current work is designed to provide the plant phenotyping community with (a) methods for fast and accurate image-based feature extraction that require minimal training data and (b) a new population-scale dataset, including 68 different leaf phenotypes, for domain scientists and machine learning researchers. All of the few-shot learning code, data, and results are made publicly available.

59 BASIC BIOLOGICAL SCIENCES↗

Local structure in solid solutions of stabilised zirconia with actinide dioxides (UO{sub 2}, NpO{sub 2})

The local structure of (Zr,Lu,U)O{sub 2-x} and (Zr,Y,Np)O{sub 2-x} solid solutions has been investigated by extended X-ray absorption fine structure (EXAFS). Samples were prepared by mixing reactive (Zr,Lu)O{sub 2-x} and (Zr,Y)O{sub 2-x} precursor materials with the actinide oxide powders, respectively. Sintering at 1600 {sup o}C in Ar/H{sub 2} yields a fluorite structure with U(IV) and Np(IV). As typical for stabilised zirconia the metal-oxygen and metal-metal distances are characteristic for the different metal ions. The bond lengths increase with actinide concentration, whereas highest adaptation to the bulk stabilised zirconia structure was observed for U---O and Np---O bonds. The Zr---O bond shows only a slight increase from 2.14 A at 6 mol% actinide to 2.18 A at infinite dilution in UO{sub 2} and NpO{sub 2}. The short interatomic distance between Zr and the surrounding oxygen and metal atoms indicate a low relaxation of Zr with respect to the bulk structure, i.e. a strong Pauling behaviour. -- Graphical abstract: Metal-oxygen bond distances in (Zr,Lu,U)O{sub 2-x} solid solutions with different oxygen vacancy concentrations (Lu/Zr=1 and Lu/Zr=0.5). Display Omitted Research Highlights: {yields} EXAFS indicates high U and Np adaption to the bulk structure of stabilised zirconia. {yields} Zr---O bond length is 2.18 A at infinite Zr dilution in UO{sub 2} and NpO{sub 2}. {yields} Low relaxation (strong Pauling behaviour) of Zr explains its low solubility in UO{sub 2}.

36 MATERIALS SCIENCE↗

Path integral density matrix dynamics: A method for calculating time-dependent properties in thermal adiabatic and non-adiabatic systems

We introduce a new approach for calculating quantum time-correlation functions and time-dependent expectation values in many-body thermal systems; both electronically adiabatic and non-adiabatic cases can be treated. Our approach uses a path integral simulation to sample an initial thermal density matrix; subsequent evolution of this density matrix is equivalent to solution of the time-dependent Schrödinger equation, which we perform using a linear expansion of Gaussian wavepacket basis functions which evolve according to simple classical-like trajectories. Overall, this methodology represents a formally exact approach for calculating time-dependent quantum properties; by introducing approximations into both the imaginary-time and real-time propagations, this approach can be adapted for complex many-particle systems interacting through arbitrary potentials. We demonstrate this method for the spin Boson model, where we find good agreement with numerically exact calculations. We also discuss future directions of improvement for our approach with a view to improving accuracy and efficiency.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

STRUCTURE IN THE THREE-DIMENSIONAL GALAXY DISTRIBUTION. I. METHODS AND EXAMPLE RESULTS

Three methods for detecting and characterizing structure in point data, such as that generated by redshift surveys, are described: classification using self-organizing maps, segmentation using Bayesian blocks, and density estimation using adaptive kernels. The first two methods are new, and allow detection and characterization of structures of arbitrary shape and at a wide range of spatial scales. These methods should elucidate not only clusters, but also the more distributed, wide-ranging filaments and sheets, and further allow the possibility of detecting and characterizing an even broader class of shapes. The methods are demonstrated and compared in application to three data sets: a carefully selected volume-limited sample from the Sloan Digital Sky Survey redshift data, a similarly selected sample from the Millennium Simulation, and a set of points independently drawn from a uniform probability distribution-a so-called Poisson distribution. We demonstrate a few of the many ways in which these methods elucidate large-scale structure in the distribution of galaxies in the nearby universe.

79 ASTRONOMY AND ASTROPHYSICS↗

Preliminary Tests For Development Of A Non-Pertechnetate Analysis Method

The objective of this task was to develop a non-pertechnetate analysis method that 222-S lab could easily implement. The initial scope involved working with 222-S laboratory personnel to adapt the existing Tc analytical method to fractionate the non-pertechnetate and pertechnetate. SRNL then developed and tested a method using commercial sorbents containing Aliquat ® 336 to extract the pertechnetate (thereby separating it from non-pertechnetate), followed by oxidation, extraction, and stripping steps, and finally analysis by beta counting and Mass Spectroscopy. Several additional items were partially investigated, including impacts of a 137 Cs removal step. The method was initially tested on SRS tank waste samples to determine its viability. Although SRS tank waste does not contain non-pertechnetate, testing with it was useful to investigate the compatibility, separation efficiency, interference removal efficacy, and method sensitivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

IROCKS: spatially resolved kinematics of z ∼ 1 star-forming galaxies

We present results from the Intermediate Redshift OSIRIS Chemo-Kinematic Survey (IROCKS) for sixteen z ∼ 1 and one z ∼ 1.4 star-forming galaxies. All galaxies were observed with OSIRIS with the laser guide star adaptive optics system at Keck Observatory. We use rest-frame nebular Hα emission lines to trace morphologies and kinematics of ionized gas in star-forming galaxies on sub-kiloparsec physical scales. We observe elevated velocity dispersions (σ ≳ 50 km s{sup −1}) seen in z > 1.5 galaxies persist at z ∼ 1 in the integrated galaxies. Using an inclined disk model and the ratio of v/σ, we find that 1/3 of the z ∼ 1 sample are disk candidates while the other 2/3 of the sample are dominated by merger-like and irregular sources. We find that including extra attenuation toward H ii regions derived from stellar population synthesis modeling brings star formation rates (SFRs) using Hα and stellar population fit into a better agreement. We explore the properties of the compact Hα sub-component, or “clump,” at z ∼ 1 and find that they follow a similar size–luminosity relation as local H ii regions but are scaled-up by an order of magnitude with higher luminosities and sizes. Comparing the z ∼ 1 clumps to other high-redshift clump studies, we determine that the clump SFR surface density evolves as a function of redshift. This suggests clump formation is directly related to the gas fraction in these systems and may support disk fragmentation as their formation mechanism since gas fraction scales with redshift.

79 ASTRONOMY AND ASTROPHYSICS↗

Early Observations from Monitoring a Reintroduction Program: Return of Sockeye Salmon to a Nursery Lake of Historical Importance

Abstract The historical distribution of Sockeye Salmon Oncorhynchus nerka in the Columbia River basin has been constrained by the species’ dependence on nursery lakes for juvenile rearing. Several productive lake systems were impounded by dams during the last century leading to the extirpation of Sockeye Salmon from many nursery lakes in the region. Recent efforts to re‐establish populations in historically natal areas are exemplified by the Cle Elum Lake reintroduction program. The program is founded on outplanting adult fish from two middle Columbia River donor populations with different adaptive potentials. We used genetic stock identification methods to differentiate stock origins between Osoyoos Lake ( OSO ) and Lake Wenatchee ( WEN ) donor stocks and to evaluate the relative productivity from two brood years (2011 and 2012) of natural spawning in the novel environment. Spawning ground surveys revealed assortative mating between earlier‐spawning WEN fish that were more abundant farthest upstream and later‐spawning OSO fish that were concentrated (82%) downstream nearest the lake. Hybrids accounted for only 5% of sampled smolts and 4% of adult returns. Smolts rearing in Cle Elum Lake were significantly larger overall ( OSO , 140 mm; WEN , 129 mm) than smolts in either donor population (84 mm). However, the average size of OSO smolts varied among emigration years, and relative smolt abundances favored the WEN stock (70% overall), indicative of a rearing survival advantage. In relation, the WEN stock exhibited a better average rate of replacement (0.80) in adult‐to‐adult escapement compared with the OSO stock (0.17). Continued monitoring will focus on trends in productivity and potential demographic shifts that may arise in the Cle Elum Lake population and will provide managers with information concerning limiting factors in the environment that might affect similar approaches to reintroductions in other lake systems.

Matala, Andrew P.↗

Deep Learning Segmentation of Complex Features in Atomic-Resolution Phase-Contrast Transmission Electron Microscopy Images

Phase-contrast transmission electron microscopy (TEM) is a powerful tool for imaging the local atomic structure of materials. TEM has been used heavily in studies of defect structures of two-dimensional materials such as monolayer graphene due to its high dose efficiency. However, phase-contrast imaging can produce complex nonlinear contrast, even for weakly scattering samples. It is, therefore, difficult to develop fully automated analysis routines for phase-contrast TEM studies using conventional image processing tools. For automated analysis of large sample regions of graphene, one of the key problems is segmentation between the structure of interest and unwanted structures such as surface contaminant layers. In this study, we compare the performance of a conventional Bragg filtering method with a deep learning routine based on the U-Net architecture. We show that the deep learning method is more general, simpler to apply in practice, and produces more accurate and robust results than the conventional algorithm. We provide easily adaptable source code for all results in this paper and discuss potential applications for deep learning in fully automated TEM image analysis.

47 OTHER INSTRUMENTATION↗

ARMing the Edge: Demonstration of Edge Computing Field Campaign Report

Edge computing enables “next-to-instrument” control and intelligent data volume reduction and the potential for autonomous, adaptive measurement strategies such as for automated control of scan strategies for Doppler lidar (DL). Instruments with narrow bandwidth connections (e.g., ship and remote sites) can do scene determination and save phenomenon-appropriate data. For example, Doppler spectrum can be saved when clouds are detected by the instrument or automatic moment detection can take place in camera images and only preserve spectrum when non-monomodal spectra are detected. Automated control at the edge involves changing the sampling (temporal or scanning strategy) of an instrument to suit the phenomena both present and being studied (Jackson et al. 2020). Both data processing and instrument control introduces the possibility of a software-defined instrument.

54 ENVIRONMENTAL SCIENCES↗

Orbital Motion of the Wide Planetary-mass Companion GSC 6214-210 b: No Evidence for Dynamical Scattering

Direct-imaging exoplanet surveys have discovered a class of 5–20 M{sub Jup} substellar companions at separations >100 au from their host stars, which present a challenge to planet and star formation models. Detailed analysis of the orbital architecture of these systems can provide constraints on possible formation mechanisms, including the possibility that they were dynamically ejected onto a wide orbit. We present astrometry for the wide planetary-mass companion GSC 6214-210 b (240 au; ≈14 M{sub Jup}) obtained using NIRC2 with adaptive optics at the Keck telescope over 10 years. Our measurements achieved astrometric uncertainties of ≈1 mas per epoch. We determined a relative motion of 1.12 ± 0.15 mas yr{sup −1} (0.61 ± 0.09 km s{sup −1}), the first detection of orbital motion for this companion. We compute the minimum periastron for the companion due to our measured velocity vector and derive constraints on the orbital parameters through our modified implementation of the Orbits for the Impatient rejection sampling algorithm. We find that close periastron orbits, which could indicate that the companion was dynamically scattered, are present in our posterior but have low likelihoods. For all orbits in our posterior, we assess the detectability of close-in companions that could have scattered GSC 6214-210 b from a closer orbit, and find that most potential scatterers would have been detected in previous imaging. We conclude that formation at small orbital separation and subsequent dynamical scattering through interaction with another potential close-in object is an unlikely formation pathway for this companion. We also update stellar and substellar properties for the system using the new parallax from Gaia DR2.

79 ASTRONOMY AND ASTROPHYSICS↗

Performance and community structure dynamics of microbial electrolysis cells operated on multiple complex feedstocks

Microbial electrolysis is a promising technology for converting aqueous wastes into hydrogen. However, substrate adaptability is an important feature, seldom documented in microbial electrolysis cells (MECs). In addition, the correlation between substrate composition and community structure has not been well established. This study used an MEC capable of producing over 10 L/L-day of hydrogen from a switchgrass-derived bio-oil aqueous phase and investigated four additional substrates, tested in sequence on a mature biofilm. The additional substrates included a red oak-derived bio-oil aqueous phase, a corn stover fermentation product, a mixture of phenol and acetate, and acetate alone. The MECs fed with the corn stover fermentation product resulted in the highest performance among the complex feedstocks, producing an average current density of 7.3 ± 0.51 A/m 2 , although the acetate fed MECs outperformed complex substrates, producing 12.3 ± 0.01 A/m 2 . 16S rRNA gene sequencing showed that community structure and community diversity were not predictive of performance, and replicate community structures diverged despite identical inoculum and enrichment procedure. The trends in each replicate, however, were indicative of the influence of the substrates. Geobacter was the most dominant genus across most of the samples tested, but its abundance did not correlate strongly to current density. High-performance liquid chromatography (HPLC) showed that acetic acid accumulated during open circuit conditions when MECs were fed with complex feedstocks and was quickly degraded once closed circuit conditions were applied. The largest net acetic acid removal rate occurred when MECs were fed with red oak bio-oil aqueous phase, consuming 2.93 ± 0.00 g/L-day. Principal component analysis found that MEC performance metrics such as current density, hydrogen productivity, and chemical oxygen demand removal were closely correlated. Net acetic acid removal was also found to correlate with performance. However, no bacterial genus appeared to correlated to these performance metrics strongly, and the analysis suggested that less than 70% of the variance was accounted for by the two components. This study demonstrates the robustness of microbial communities to adapt to a range of feedstocks and conditions without relying on specific species, delivering high hydrogen productivities despite differences in community structure. The results indicate that functional adaptation may play a larger role in performance than community composition. Further investigation of the roles each microbe plays in these communities will help MECs to become integral in the 21st-century bioeconomy to produce zero-emission fuels.

09 BIOMASS FUELS↗

CO{sub 2} laser-induced micro-plume treatment of silicon: technique and application

A method for treating the surface of crystalline silicon (α-Si) covered with a fused silica (SiO{sub 2}) plate by a laser-induced plume arising from the absorption of CO{sub 2} laser radiation in glass is presented. The mechanism of micro-plume formation and the effect of this micro-plume on α-Si sample are considered. The α-Si processing regimes were determined that relate the depth of the formed relief to the radiation power density, the temperature of the micro-plume formed during SiO{sub 2} irradiation was measured and reached 3000 K. The diffraction phase gratings were designed, manufactured on silicon, and tested, they successfully split 10.6 μm laser beam at 0 and ± 1 diffraction orders. The method belongs to the group of direct writing methods and is easily adapted to different types of diffraction elements on the surface of Si and other materials transparent to 10 μm irradiation.

36 MATERIALS SCIENCE↗

Persistent Sampling: Enhancing the Efficiency of Sequential Monte Carlo

Sequential Monte Carlo (SMC) samplers are powerful tools for Bayesian inference but suffer from high computational costs due to their reliance on large particle ensembles for accurate estimates. We introduce persistent sampling (PS), an extension of SMC that systematically retains and reuses particles from all prior iterations to construct a growing, weighted ensemble. By leveraging multiple importance sampling and resampling from a mixture of historical distributions, PS mitigates the need for excessively large particle counts, directly addressing key limitations of SMC such as particle impoverishment and mode collapse. Crucially, PS achieves this without additional likelihood evaluations-weights for persistent particles are computed using cached likelihood values. This framework not only yields more accurate posterior approximations but also produces marginal likelihood estimates with significantly lower variance, enhancing reliability in model comparison. Furthermore, the persistent ensemble enables efficient adaptation of transition kernels by leveraging a larger, decorrelated particle pool. Experiments on high-dimensional Gaussian mixtures, hierarchical models, and non-convex targets demonstrate that PS consistently outperforms standard SMC and related variants, including recycled and waste-free SMC, achieving substantial reductions in mean squared error for posterior expectations and evidence estimates, all at reduced computational cost. PS thus establishes itself as a robust, scalable, and efficient alternative for complex Bayesian inference tasks.

Karamanis, Minas↗

Air-gap gating of MgZnO/ZnO heterostructures

The adaptation of “air-gap” dielectric based field-effect transistor technology to controlling the MgZnO/ZnO heterointerface confined two-dimensional electron system (2DES) is reported. We find it possible to tune the charge density of the 2DES via a gate electrode spatially separated from the heterostructure surface by a distance of 5 μm. Under static gating, the observation of the quantum Hall effect suggests that the charge carrier density remains homogeneous, with the 2DES in the 3 mm square sample the sole conductor. The availability of this technology enables the exploration of the charge carrier density degree of freedom in the pristine sample limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

A versatile setup using femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering

We report a versatile setup based on the femtosecond adaptive spectroscopic techniques for coherent anti-Stokes Raman scattering. The setup uses a femtosecond Ti:Sapphire oscillator source and a folded 4f pulse shaper, in which the pulse shaping is carried out through conventional optical elements and does not require a spatial light modulator. Our setup is simple in alignment, and can be easily switched between the collinear single-beam and the noncollinear two-beam configurations. We demonstrate the capability for investigating both transparent and highly scattering samples by detecting transmitted and reflected signals, respectively.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

THE ORIGIN AND EVOLUTION OF METALLICITY GRADIENTS: PROBING THE MODE OF MASS ASSEMBLY AT z {approx_equal} 2

We present and discuss measurements of the gas-phase metallicity gradient in four gravitationally lensed galaxies at z = 2.0-2.4 based on adaptive optics-assisted imaging spectroscopy with the Keck II telescope. Three galaxies with well-ordered rotation reveal metallicity gradients with lower gas-phase metallicities at larger galactocentric radii. Two of these display gradients much steeper than found locally, while a third has one similar to that seen in local disk galaxies. The fourth galaxy exhibits complex kinematics indicative of an ongoing merger and reveals an 'inverted' gradient with lower metallicity in the central regions. By comparing our sample to similar data in the literature for lower redshift galaxies, we determine that, on average, metallicity gradients must flatten by a factor of 2.6 {+-} 0.9 between z = 2.2 and the present epoch. This factor is in rough agreement with the size growth of massive galaxies, suggesting that inside-out growth can account for the evolution of metallicity gradients. Since the addition of our new data provides the first indication of a coherent picture of this evolution, we develop a simple model of chemical evolution to explain the collective data. We find that metallicity gradients and their evolution can be explained by the inward radial migration of gas together with a radial variation in the mass loading factor governing the ratio of outflowing gas to the local star formation rate. Average mass loading factors of {approx}< 2 are inferred from our model in good agreement with direct measurements of outflowing gas in z {approx_equal} 2 galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗