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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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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Feedback on Forty-year Long Clean-up Operations of a Contaminated Soil for Environmental Purpose - 20026

One of the CEA facilities in France is the place of miscellaneous mapping and clean-up operations since the 1980's. The final purpose of the conducted work deals with the environmental remediation of the building. In France, the absence of a regulatory framework for the management of sites polluted by radioactive substances, especially as the lack of release thresholds, has led to develop different approaches of soil radiological characterization. The geostatistical approach is part of them. Preliminary investigations are an unavoidable step prior to clearance and remediation. First, historical analysis enables to define site perimeters to investigate as well as to precisely locate expected contaminated parts. For radioactive pollutants, the media (water, soil, air) and transfer routes participate in the definition of the investigation perimeter and thus also need to be identified through a geological study. At the same time, functional analysis leads to choose the best measurement means which could provide useful information when combined with visual inspection. An equipment well adapted to easy-to-measure radionuclides as gamma emitters, such as gamma probe or on-site gamma spectrometry, is usually selected as a non-destructive assay. Coupled with spatial positions, processed on-site radiological data can be mapped. Data processing consists in mapping and kriging before a geostatistical analysis in order to identify the zones of interest to be targeted. Selected zones are not only linked to high level count rates but also to low level ones so that a pollution-free reference is known in the area. This data processing also enables to categorize waste types according to their origin and contamination levels. An expected volume of different waste then follows as well as the waste characterization equipments. In-depth investigations, e.g. core drillings and samplings, naturally ensue to aim at remediation optimization through the assessment of environmental impact. After clean-up operations, non-destructive assays as well as destructive samplings enable to check the effective clearance. Following this methodology in the present remediation work, results of the first non-destructive assay campaign highlighted a long-lived transuranic radionuclides contamination in the concrete flagstone. Successive non-destructive assays were then performed in the defined zones of interest. Count rates measured with a surface probe were first mapped. The best localization of destructive assays, i.e. core drillings at a 1 meter depth, came from this surface mapping followed by a geostatistical analysis. The concrete flagstone was totally removed. Then, the depth of soil to remove under the flagstone was optimized from the results of radiological activities measured in core drillings samples. A new surface mapping was then drawn after this partial soil excavation. The sand and demolition rubble samples, homogeneously constituted from the flagstone fragments, were analyzed by gamma spectrometry. Meanwhile, waste drums and other containers were also measured by gamma spectrometry and passive neutron measurement devices. All these results have brought miscellaneous pieces of information. In this context, major issues appear in terms of physical constraints such as the premises tininess as well as of soil sampling techniques and in terms of measurement performances assessment and the representativeness of homogeneous samples. The choice of radiological soil and waste characterization devices is described in the light of enhanced performances by keeping in mind radiation protection requirements and a willing to always optimize waste categories (very low level waste) and volume. The improvement of the characterization methods appears through this forty-year long work while sharing the feedback of encountered difficulties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

A hybrid CNN-LSTM surrogate model for hyper-resolution spatiotemporal flood forecasting in Norfolk, Virginia

Study region: Norfolk, Virginia, United States Study focus: Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. New hydrologic insights for the region: The hybrid CNN-LSTM model was trained using the physics-based hydrodynamic model simulations obtained from the Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. The reduced computational time from four to six hours using TUFLOW to 3.2 min per event using CNN-LSTM enables rapid flood inundation mapping and early warning applications. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5-m spatial resolution and 15-min temporal resolution and a one-hour-ahead prediction horizon. While challenges remain in terms of transferability to new regions and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.

Coastal urban flooding↗
DOE OSTI2022

ESS-DIVE Reporting Format for Amplicon Abundance Table

While standardized sequencing data is available in public repositories and efforts such as MIxS for common sample collection and processing metadata are well established, the lack of common bioinformatic processing metadata has hindered the ability to do large-scale metaanalyses and the potential for data re-use by non-experts such as ecosystem, watershed, or earth system modelers. To address this need for Department of Energy researchers, we have developed an amplicon reporting format which captures both sample preparation and bioinformatic processing metadata and stores processed amplicon data as a paired abundance table and sequencing file to maximize the potential for re-use of these data. To aid in the adoption of accessible and reproducible analysis workflows, this reporting format was developed in concert with amplicon functionality within the Department of Energy’s Systems Biology Knowledgebase (KBase) to ensure common data and metadata requirements and facilitate seamless transfer between these platforms.This dataset contains support documentation for the amplicon reporting format (README.md and instructions.md), templates for both bioinformatic and sequencing metadata (amplicon_bioinformatic_metadata_template_2021_10_03.csv and amplicon_sequencing_metadata_template_2021_10_03.csv), a crosswalk indicating how this reporting format relates to the current MIxS format (ESSDIVE-MIxS_crosswalk.csv), a list of available instrument terms (amplicon_seq_instrument_terms_2021_10_03.csv), a map between QIIME2 parameter settings and metadata fields (amplicon_qiime2_plugin_metadata_map.csv), a data dictionary (amplicon_CSV_dd.csv), and file-level metadata (amplicon_FLMD.csv).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Transfer learning-based soybean LAI estimations by integrating PROSAIL, UAV, and PlanetScope imagery

Accurate Leaf Area Index (LAI) estimations at the soybean plot scale is achievable using high-resolution Unmanned Aerial Vehicle (UAV) imagery and field measurement samples. However, the limited coverage of UAV flights restricts large-scale remote sensing monitoring in expansive soybean fields. This study leverages the broad coverage and 3-m resolution of PlanetScope satellite imagery to extend LAI prediction from UAV to satellite scales through transfer learning, using UAV-scale LAI estimates as a benchmark to validate cross-scale consistency. To address this challenge, this study proposed the LAI-TransNet, a two-stage transfer learning framework designed for precise and scalable soybean LAI prediction across large areas, demonstrating its effectiveness in cross-scale monitoring. In Stage 1, a UAV-scale benchmark is established using PROSAIL-simulated UAV reflectance data (UAV-Sim) and field-measured soybean LAI. Traditional machine learning, deep learning, and transfer learning models are trained on a hybrid UAV-Sim and field-measured dataset (UAV-Sim_Measured), with the transfer learning model CNN-TL, fine-tuned using pre-trained weights derived from UAV-Sim, achieving the highest accuracy (R 2 = 0.81, RMSE = 0.64 m 2 /m 2 , rRMSE = 11.5 %). In Stage 2, LAI-TransNet is developed by fine-tuning the CNN-TL model on PlanetScope simulated data (PS-Sim), preprocessed via cross-domain mapping to align UAV and satellite spectral features. Real PlanetScope imagery is corrected for reflectance consistency with reference to UAV imagery spectral profiles. LAI-TransNet outperforms other deep learning models trained directly on PS-Sim (R 2 = 0.69 vs. 0.60–0.63), ensuring robust cross-scale consistency. In conclusion, by bridging UAV and satellite scales, LAI-TransNet enables large-scale soybean LAI monitoring, enhancing precision agriculture management through improved monitoring with the PlanetScope imagery.

Leaf area index (LAI)↗
DOE OSTI2020

Solvent Mediated Excited State Proton Transfer in Indigo Carmine

Excited state proton transfer (ESPT) is thought to be responsible for the photostability of biological molecules, including DNA and proteins, and natural dyes such as indigo. However, the mechanistic role of the solvent interaction in driving ESPT is not well understood. Here, the electronic excited state deactivation dynamics of indigo carmine (InC) is mapped by visible pump-infrared probe and two-dimensional electronic-vibrational (2DEV) spectroscopy and complemented by electronic structure calculations. The observed dynamics reveal notable differences between InC in a protic solvent, D$_2$O , and an aprotic solvent, deuterated dimethyl sulfoxide (dDMSO). Notably, an acceleration in the excited state decay is observed in D$_2$O (<10 ps) compared to dDMSO (130 ps). Our data reveals clear evidence for ESPT in D$_2$O accompanied by a significant change in dipole moment, which is found not to occur in dDMSO. We conclude that the ability of protic solvents to form intermolecular H-bonds with InC enables ESPT, which facilitates a rapid nonradiative S$_1$ → S$_0$ transition via the monoenol intermediate.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Machine learning predictions of near-surface permafrost extent at Teller 27, Teller 47, and the Kougarok 64 Hillslope sites on the Seward Peninsula, Alaska: Supporting Data

Geophysical surveys were conducted at the NGEE Arctic Teller mile marker 27 site, Teller mile marker 47 site, and Kougarok mile marker 64 site during the summers of 2018, 2019, and 2021. Additional data was collected at Teller mile marker 47 during September 2021 and August 2022. These surveys were used to identify locations of near-surface permafrost during the period of maximum seasonal thaw depth for ground truth data used in machine learning predictions of near-surface permafrost extent at each site. This dataset contains CSV files of ground truth observations of near-permafrost presence or absence for each site, where PF = 1 indicates permafrost presence and PF = 0 indicates permafrost absence. The dataset also includes 2 sets of binary rasters (WGS84 UTM zone 3) of permafrost extent for each site using 1) all of the training data and 2) the transferred model. For both sets of rasters, 0 = non-permafrost and 1 = permafrost. Included are 6 *.tif files and 5 *.csv files that include a data dictionary (dd.csv) and file-level metadata (flmd.csv). This dataset is in support of the paper "Machine learning-derived high-resolution maps of near-surface permafrost for three watersheds on the Seward Peninsula, Alaska" that is in review (May 2023). The Next-Generation Experiments: Arctic (NGEE Arctic), a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Remote Radiation Sensing Using Aerial and Ground Platforms

Remote sensing of ionizing radiation has a significant role in waste management, nuclear material management and nonproliferation, and radiation safety. Robotic platforms can surpass the number of tasks that are achieved by humans. With this technique, the operator's radiation exposure can be decreased. Remote sensing allows for the evaluation and monitoring of radiological contamination. Gamma-ray and neutron sensors were integrated onto the robotic platforms. This approach allows for the radiation sensor data to be dynamically tracked and mapped thus enabling further analysis of the radiation flux in temporal and spatial domains. The goal is to complete scheduled tasks while the robot is being irradiated. To achieve this, electronic components must be shielded and radiation hardened. CZT Detector: Cadmium Zinc Telluride (CZT) detector technology has been a promising solution for gamma-ray and x-ray measurements. Detector data is transferred to the Odroid minicomputer that controls and powers the module via the USB. Robot Operating System (ROS) was utilized for data acquisition and data fusion. The Mariscotti method was employed for the spectrum analysis. A function was programmed in ROS for the automatic identification of photopeaks. CLYC Detector: A Cs{sub 2}LiYCl{sub 6}:Ce{sup 3+} (CLYC) detector was used for simultaneous medium-resolution gamma-ray measurements and neutron counting. A 2.54 cm diameter photomultiplier tube (PMT) was equipped with a high voltage supply and a miniature digitizer. Gamma-ray excitation: fast core-to-valence luminescence (CVL) with 1 ns decay constant, and prompt Ce{sup 3+} emission with 50 ns decay constant. Neutron excitation: slow cerium self-trapped excitation (Ce{sup 3+} STE), 1000 ns decay constant. Radiation Source Localization: Maximum Likelihood Estimation (MLE) and gradient-based methods were used to locate the position of a radiation source based on measured radiation intensities. Multi-Particle Transport Code FLUKA: Estimation of radiation damage of the electronic components is important in order to optimize the robot's operational time while it is irradiated. Displacement per atom (DPA) represents the radiation damage in materials exposed to the ionizing radiation. Various shielding layers of different thickness t were analyzed (< 5% statistical error). The model of the controller of the UAS was designed in FLUKA. Conclusion: CZT and CLYC detectors were integrated onto the robotic platforms. Radiation source localization and contour mapping using robotic platforms were studied. Functions for data analysis and fusion were developed in ROS. FLUKA code was utilized to analyze DPA values. Layers of low-density and high-density materials were used to shield the UAS electronics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Surface Charge Distribution Mapping Using an Electrostatic Probe

Undesired accumulation of charge on dielectric materials causing electrostatic discharges can be an issue in pulsed power systems and electrical systems in general. As such, an understanding of surface charge distribution, charge accumulation, and decay is required. An in-house no-contact electrostatic probe designed with a 2 mm resolution was fabricated to measure and map surface charge distribution. Designed as a contactless instrument, the probe consisting of a metal rod and low leakage amplifier circuit probes the electric potential near the surface. A scan of the surface then provides the raw data, a potential distribution. The actual probe response - i.e., the surface charge to voltage transfer function, is accounted for in post-processing. This is accomplished via an Inverse Wien Filter - a technique often applied in image processing - to deconvolve the probe response from the measured data. A commercially available electrostatic probe, the Trek 341B meter with a 3455ET probe, capable of measuring +/- 20 kV was compared to the in- house probe that is designed to cover a wider range from +/- 35 kV. A resolution better than the simple probe resolution is achieved through the distribution’s scanning voltage method and deconvolution. Applying repeated scans, surface charge decay was tracked on various polymer materials to determine the material and environment dependence; materials included were PA6, PTFE, and others. As an example of material dependence, samples charged to 20 kV at 65% humidity experience full charge decay in approximately 45 seconds for PA5 (152 mm dia.), while it took about 100 times longer for PTFE (51 mm dia.) to observe the same decay/redistribution of charge.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Tuned Reactivity at the Lithium Metal–Argyrodite Solid State Electrolyte Interphase

Thin intermetallic Li 2 Te–LiTe 3 bilayer (0.75 µm) derived from 2D tellurene stabilizes the solid electrolyte interphase (SEI) of lithium metal and argyrodite (LPSCl, Li 6 PS 5 Cl) solid-state electrolyte (SSE). Tellurene is loaded onto a standard battery separator and reacted with lithium through single-pass mechanical rolling or transferred directly to SSE surface by pressing. State-of-the-art electrochemical performance is achieved, e.g., symmetric cell stable for 300 cycles (1800 h) at 1 mA cm -2 and 3 mAh cm -2 (25% DOD, 60 µm foil). Cryo-stage focused ion beam (Cryo-FIB) sectioning and Raman mapping demonstrate that the Li 2 Te–LiTe 3 bilayer impedes SSE decomposition. The unmodified Li–LPSCl interphase is electrochemically unstable with a geometrically heterogeneous reduction decomposition reaction front that extends deep into the SSE. Decomposition drives voiding in Li metal due to its high flux to the reaction front, as well as voiding in the SSE due to the associated volume changes. Analysis of cycled SSE found no evidence for pristine (unreacted) lithium metal filaments/dendrites, implying failure driven by decomposition phases with sufficient electrical conductivity that span electrolyte thickness. In conclusion, DFT calculations clarify thermodynamic stability, interfacial adhesion, and electronic transport properties of interphases, while mesoscale modeling examines interrelations between reaction front heterogeneity (SEI heterogeneity), current distribution, and localized chemo-mechanical stresses.

25 ENERGY STORAGE↗
DOE OSTI2024

Unraveling Interdiffusion Phenomena and the Role of Nanoscale Diffusion Barriers in the Copper–Gold System

Diffusion is one of the most fundamental concepts in materials science, playing a pivotal role in materials synthesis, forming, and degradation. Of particular importance is solid state interdiffusion of metals which defines the usable parameter space for material combinations in the form of alloys. This parameter space can be explored on the macroscopic scale by using diffusion couples. However, this method reaches its limit when going to low temperatures, small scales, and when testing ultrathin diffusion barriers. Therefore, this work transfers the principle of the diffusion couples to small scales by using core–shell nanowires and in situ heating. This allows us to delve into the interdiffusion dynamics of copper and gold, revealing the interplay between diffusion and the disorder–order phase transition. Our in situ TEM experiments in combination with chemical mapping reveal the interdiffusion coefficients of Cu and Au at low temperatures and highlight the impact of ordering processes on the diffusion behavior. The formation of ordered domains within the solid-solution is examined using high-resolution imaging and nanodiffraction including strain mapping. In addition, we examine the effectiveness of ultrathin Al 2 O 3 barrier layers to control interdiffusion of the diffusion couple. Our findings indicate that a 5 nm thick layer serves as an efficient diffusion barrier. Furthermore, this research provides valuable insights into the interdiffusion behavior of Cu and Au on the nanoscale, offering potential applications in the development of miniaturized integrated circuits and nanodevices.

alloys↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On Alfvénic turbulence of solar wind streams observed by Solar Orbiter during March 2022 perihelion and their source regions

It has been recently accepted that the standard classification of the solar wind solely according to flow speed is outdated, and particular interest has been devoted to the study of the origin and evolution of so-called Alfvénic slow solar wind streams and to what extent such streams resemble or differ from fast wind. In March 2022, Solar Orbiter completed its first nominal phase perihelion passage. During this interval, it observed several Alfvénic streams, allowing for characterization of fluctuations in three slow wind intervals (AS1-AS3) and comparison with a fast wind stream (F) at almost the same heliocentric distance. This work makes use of Solar Orbiter plasma parameters from the Solar Wind Analyzer (SWA) and magnetic field measurements from the magnetometer (MAG). The magnetic connectivity to the solar sources of selected solar wind intervals was reconstructed using a ballistic extrapolation based on measured solar wind speed down to the (spherical) source surface at 2.5 R s below which a potential field extrapolation was used to map back to the Sun. The source regions were identified using SDO/AIA observations. A spectral analysis of in situ measured magnetic field and velocity fluctuations was performed to characterize correlations, Alfvénicity, normalized cross-helicity, and residual energy in the frequency domain as well as intermittency of the fluctuations and spectral energy transfer rate estimated via mixed third-order moments. A machine learning technique was used to separate proton core, proton beam, and alpha particles and to study v − b correlations for the different ion populations in order to evaluate the role played by each population in determining the Alfvénic content of solar wind fluctuations. The comparison between fast wind and Alfvénic slow wind intervals highlights the differences between the two solar wind regimes: The fast wind is characterized by larger amplitude fluctuations, and magnetic and velocity fluctuations are closer to equipartition of energy. In fact the Alfvénic slow wind streams appear to be on a spectrum of wind types, with AS1, originating from open field lines neighboring active regions and displaying similarities with the fast wind in terms of fluctuation amplitude and turbulence characteristics, but not with respect to the alpha particles and proton beams. The other two slow streams differed both in their sources as well as plasma characteristics, with AS2 coming from the expansion of a narrow coronal hole corridor and AS3 from a region straddling a pseudostreamer. The latter displayed the coldest and highest density but the slowest stream with the smallest fluctuation amplitude and greatest magnetic energy excess. It also showed the largest scatter in proton beam speeds and the greatest difference in speed between proton beam and alpha particles. This study shows how the old fast–slow solar wind dichotomy, already called into question by the observations of slower Alfvénic solar wind streams, should further be refined, as the Alfvénic slow wind, originating in different solar wind regions, show significant differences in density, temperature, and proton and alpha-particle properties in the inner heliosphere. The observations presented here provide the starting point for a better understanding of the origin and evolution of different solar wind streams as well as the evolving turbulence contained within.

magnetohydrodynamics (MHD)↗
DOE OSTI2025

Distributed Sapphire Fiber Bragg Gratings Based Thermal Profiling of Submerged Entry Nozzles

This research focuses on the application of sapphire fiber Bragg gratings (FBGs) for instrumentation in submerged entry nozzles (SEN) within the steelmaking industry. The SEN is pivotal for transferring molten steel from a tundish to a mold, while preventing the infiltration of oxygen and nitrogen from the surrounding environment. Maintaining optimal flow conditions in the mold is crucial for ensuring casting process stability and maintaining high quality steel. Sapphire FBG sensors have been instrumented in SENs to enable distributed thermal mapping for monitoring the health of the SEN. The optical sensor comprises three cascaded sapphire FBGs inscribed using femtosecond laser technology into a one-meter-long sapphire crystalline fiber. The sensor underwent characterization in a laboratory setting up to 1600°C and was tested for long-term stability over 40 hours under extreme environmental conditions. The coupling between silica and sapphire fibers was investigated and implemented during sensor packaging. Here, the sensor successfully captured the pre-heat sequence of the SEN in real-world steelmaking operations. Compared to conventional thermocouples, sapphire FBG sensors demonstrated exceptional efficiency and precision. They offer potential benefits such as increased productivity, reduced energy consumption, and minimized carbon footprint in the steel industry.

Sapphire Fiber Bragg Grating↗
DOE OSTI2023

Convolutional Neural Networks for Problems in Transport Phenomena: A Theoretical Minimum

Convolutional neural network (CNN), a deep learning algorithm, has gained popularity in technological applications that rely on interpreting images (typically, an image is a 2D field of pixels). Transport phenomena is the science of studying different fields representing mass, momentum, or heat transfer. Some of the common fields are species concentration, fluid velocity, pressure, and temperature. Each of these fields can be expressed as an image(s). Consequently, CNNs can be leveraged to solve specific scientific problems in transport phenomena. Herein, we show that such problems can be grouped into three basic categories: (a) mapping a field to a descriptor (b) mapping a field to another field, and (c) mapping a descriptor to a field. After reviewing the representative transport phenomena literature for each of these categories, we illustrate the necessary steps for constructing appropriate CNN solutions using sessile liquid drops as an exemplar problem. If sufficient training data is available, CNNs can considerably speed up the solution of the corresponding problems. Finally, the present discussion is meant to be minimalistic such that readers can easily identify the transport phenomena problems where CNNs can be useful as well as construct and/or assess such solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

The Anatomy of an Unusual Edge-on Protoplanetary Disk. I. Dust Settling in a Cold Disk

As the earliest stage of planet formation, massive, optically thick, and gas-rich protoplanetary disks provide key insights into the physics of star and planet formation. When viewed edge-on, high-resolution images offer a unique opportunity to study both the radial and vertical structures of these disks and relate this to vertical settling, radial drift, grain growth, and changes in the midplane temperatures. In this work, we present multi-epoch Hubble Space Telescope and Keck scattered light images, and an Atacama Large Millimeter/submillimeter Array 1.3 mm continuum map for the remarkably flat edge-on protoplanetary disk SSTC2DJ163131.2–242627, a young solar-type star in ρ Ophiuchus. We model the 0.8 μm and 1.3 mm images in separate Markov Chain Monte Carlo (MCMC) runs to investigate the geometry and dust properties of the disk using the MCFOST radiative transfer code. In scattered light, we are sensitive to the smaller dust grains in the surface layers of the disk, while the submillimeter dust continuum observations probe larger grains closer to the disk midplane. An MCMC run combining both data sets using a covariance-based log-likelihood estimation was marginally successful, implying insufficient complexity in our disk model. The disk is well characterized by a flared disk model with an exponentially tapered outer edge viewed nearly edge-on, though some degree of dust settling is required to reproduce the vertically thin profile and lack of apparent flaring. A colder than expected disk midplane, evidence for dust settling, and residual radial substructures all point to a more complex radial density profile to be probed with future, higher-resolution observations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Vibronic mixing enables ultrafast energy flow in light-harvesting complex II

Since the discovery of quantum beats in the two-dimensional electronic spectra of photosynthetic pigment-protein complexes over a decade ago, the origin and mechanistic function of these beats in photosynthetic light-harvesting has been extensively debated. The current consensus is that these long-lived oscillatory features likely result from electronic-vibrational mixing, however, it remains uncertain if such mixing significantly influences energy transport. Here, we examine the interplay between the electronic and nuclear degrees of freedom (DoF) during the excitation energy transfer (EET) dynamics of light-harvesting complex II (LHCII) with two-dimensional electronic-vibrational spectroscopy. Particularly, we show the involvement of the nuclear DoF during EET through the participation of higher-lying vibronic chlorophyll states and assign observed oscillatory features to specific EET pathways, demonstrating a significant step in mapping evolution from energy to physical space. These frequencies correspond to known vibrational modes of chlorophyll, suggesting that electronic-vibrational mixing facilitates rapid EET over moderately size energy gaps.

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

A novel conjugative transposon carrying an autonomously amplified plasmid

Tetracyclines serve as broad-spectrum antibiotics to treat bacterial infections. The discovery of new tetracycline resistance genes has led to new questions about the underlying mechanisms of resistance, gene transfer, and their relevance to human health. We tracked changes in the abundance of a 55-kbp conjugative transposon (CTn214) carrying tetQ, a tetracycline resistance gene, within a Bacteroides fragilis metagenome-assembled genome derived from shotgun sequencing of microbial DNA extracted from the ileal pouch of a patient with ulcerative colitis. The mapping of metagenomic reads to CTn214 revealed the multi-copy nature of a 17,044-nt region containing tetQ in samples collected during inflammation and uninflamed visits. B. fragilis cultivars isolated from the same patient during periods of inflammation harbored CTn214 integrated into the chromosome or both a circular, multi-copy, extrachromosomal region of the CTn214 containing tetQ and the corresponding integrated form. The tetracycline-dependent mechanism for the transmission of CTn214 is nearly identical to a common conjugative transposon found in the genome of B. fragilis (CTnDOT), but the autonomously amplified nature of a circular 17,044-nt region of CTn214 that codes for tetQ and the integration of the same sequence in the linear chromosome within the same cell is a novel observation. Genome and transcriptome sequencing of B. fragilis cultivars grown under different concentrations of tetracycline and ciprofloxacin indicates that tetQ in strains containing the circular form remains actively expressed regardless of treatment, while the expression of tetQ in strains containing the linear form increases only in the presence of tetracycline.

59 BASIC BIOLOGICAL SCIENCES↗