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DOE OSTI2020

LIvermore SEM image Tools

Scanning Electron Microscopy (SEM) images provide a variety of structural and morphological information for the characterization of the nanomaterials. This code offers automatic recognition and quantitative analysis of SEM images in a high-throughput manner using computer vision and machine learning techniques. The main function of this application is to extract particle size and morphology information of overlapping nanoparticles and core-shell nanostructures in a user friendly interface. The code is written in C++ with QT environment, and has been tested on MacOSX.

KIM, HYOJIN↗

DOE OSTI2021

Fast All-sky Radiation Model for Solar applications (FARMS) [SWR-16-18]

The Fast All-sky Radiation Model for Solar applications (FARMS) is used to compute cloudy irradiance. Radiative transfer (RT) models simulating broadband solar radiation have been widely used by atmospheric scientists to model solar resources for various energy applications such as operational forecasting. Due to the complexity of solving the RT equation, the computation under cloudy conditions can be extremely time consuming though many approximations (e.g. two-stream approach and delta-M truncation scheme) have been utilized. Thus, a more efficient RT model is crucial for model developers as a new option for approximating solar radiation at the land surface with minimal loss of accuracy. We have developed a fast all-sky radiation model for solar applications (FARMS) using the simplified clear-sky RT model, REST2, and simulated cloud transmittances and reflectances from the Rapid Radiation Transfer Model (RRTM) with a sixteen-stream Discrete Ordinates Radiative Transfer (DISORT). Simulated lookup tables (LUTs) of cloud transmittances and reflectances were created by varying cloud optical thicknesses, cloud particle sizes, and solar zenith angles. Equations with optimized parameters were fitted to the cloud transmittances and reflectances to develop the model. Using this model the all-sky solar irradiance at the land surface can be computed rapidly by combining REST2 with the cloud transmittances and reflectances. This new RT model is more than 1000 times faster than those currently utilized in solar resource assessment and forecasting since it does not explicitly solve the RT equation for each individual cloud condition. Our results indicate the accuracy of the fast radiative transfer model is comparable to or better than two-stream approximation in term of computing cloud transmittance and solar radiation.

Xie, Yu↗

DOE OSTI2021

TomoEncoders: 3D Autoencoders for feature extraction in X-ray tomography

Real-time steering of time-resolved or in-situ X-ray tomography requires capturing changes in morphological descriptors in a sample (e.g., porosity, particle size, and crack width) during continuous data acquisition. Image segmentation (2D or3D) followed by quantitative measurement is the conventional method for tracking changes in these descriptors with respect to a previous time-step or a 3D search in a volume. However, image segmentation is expensive. As a faster and unsupervised alternative, a feature-extraction approach using a convolutional autoencoders was developed, where the latent space of the encoder responds to relative changes in morphology with-out prior knowledge of the morphological descriptors.

TEKAWADE, ANIKET↗

DOE OSTI2021

Radar-Based Bayesian Estimation of Ice Crystal Growth Parameters within a Microphysical Model

The potential for polarimetric Doppler radar measurements to improve predictions of ice microphysical processes within an idealized model–observational framework is examined. In an effort to more rigorously constrain ice growth processes (e.g., vapor deposition) with observations of natural clouds, a novel framework is developed to compare simulated and observed radar measurements, coupling a bulk adaptive-habit model of vapor growth to a polarimetric radar forward model. Bayesian inference on key microphysical model parameters is then used, via a Markov chain Monte Carlo sampler, to estimate the probability distribution of the model parameters. The statistical formalism of this method allows for robust estimates of the optimal parameter values, along with (non-Gaussian) estimates of their uncertainty. To demonstrate this framework, observations from Department of Energy radars in the Arctic during a case of pristine ice precipitation are used to constrain vapor deposition parameters in the adaptive habit model. The resulting parameter probability distributions provide physically plausible changes in ice particle density and aspect ratio during growth. A lack of direct constraint on the number concentration produces a range of possible mean particle sizes, with the mean size inversely correlated to number concentration. Consistency is found between the estimated inherent growth ratio and independent laboratory measurements, increasing confidence in the parameter PDFs and demonstrating the effectiveness of the radar measurements in constraining the parameters. Furthermore, the combined Doppler and polarimetric observations produce the highest-confidence estimates of the parameter PDFs, with the Doppler measurements providing a stronger constraint for this case.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Vertical Structure and Ice Production Processes of Shallow Convective Postfrontal Clouds over the Southern Ocean in MARCUS. Part II: Modeling Study

Abstract Part I of this series presented a detailed overview of postfrontal mixed-phase clouds observed during the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) field campaign. In Part II, we focus on a multiday (23–26 February 2018) case with the aim of understanding ice production as well as model sensitivity to ice process parameterizations using the Weather Research and Forecasting (WRF) Model. The control simulation with the Predicted Particle Properties (P3) microphysics scheme underestimates the ice content and overestimates the supercooled liquid water content, contrary to the bias common in global climate models. The simulations targeted at ice production processes show negligible sensitivity to cloud droplet number concentrations. Further, neither increasing ice nuclei particle (INP) concentrations to an unrealistic level nor adjusting it to MARCUS field estimations alone guarantees more ice production in the model. However, the simulated clouds are found to be highly sensitive to the implementation of immersion freezing, the thresholding of condensation/deposition freezing initiation, and the rime splintering process. By increasing immersion freezing of cloud droplets, relaxing thresholds for condensation/deposition freezing, or removing rime splintering thresholds, the model significantly improves its performance in producing ice. The relaxation of the immersion freezing temperature threshold to the observed cloud-top temperature suggests an in-cloud seeder–feeder mechanism. The results of this work call for an increase in observations of INP, especially over the remote Southern Ocean and at relatively high temperatures, and measurements of ice particle size distributions to better constrain ice nucleating processes in models.

Meteorology & Atmospheric Sciences↗

DOE OSTI2021

Exploring the potential benefits of high-efficiency dual-fuel combustion on a heavy-duty multi-cylinder engine for SuperTruck I

In support of the Daimler SuperTruck I team’s 55% brake thermal efficiency (BTE) pathway goal, researchers at Oak Ridge National Laboratory performed an experimental investigation of the potential efficiency and emissions benefits of dual-fuel advanced combustion approaches on a modified heavy-duty 15-L Detroit™ DD15 engine. For this work, a natural gas port fuel injection system with an independent injection control for each cylinder was added to the DD15 engine. For the dual-fuel strategies investigated, 65%–90% of the total fuel energy was supplied through the added port fuel injection natural gas (NG) fueling system. The remaining fuel energy was supplied by one or more direct injections of diesel fuel using the production high pressure diesel fueling system. The production DD15 air handling system and combustion geometry were unmodified for this study. Efficiency and emissions with dual-fuel strategies including both low temperature combustion (LTC) and non-LTC approaches such as dual fuel direct-injection were investigated along with control authority over combustion phasing. Parametric studies of dual-fuel NG/diesel advanced combustion were conducted in order to experimentally investigate the potential of high-efficiency, dual-fuel combustion strategies to improve BTE in a multi-cylinder engine, understand the potential reductions in engine-out emissions, and characterize the range of combustion phasing controllability. Characterization of mode transitions from mixing-controlled diesel pilot ignition to kinetically controlled ignition is presented. Key findings from this study included a reproducible demonstration of BTE approaching 48% at up to a 13-bar brake mean effective pressure with significant reductions in engine-out NOx and soot emissions. Additional results from investigating load transients in dual-fuel mode and initial characterization of particle size distribution during dual-fuel operation are presented.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Enhancing anaerobic digestion of lignocellulosic biomass by mechanical cotreatment

Abstract Background The aim of this study was to increase the accessibility and accelerate the breakdown of lignocellulosic biomass to methane in an anaerobic fermentation system by mechanicalcotreatment: milling during fermentation, as an alternative to conventionalpretreatment prior to biological deconstruction. Effluent from a mesophilic anaerobic digester running with unpretreated senescent switchgrass as the predominant carbon source was collected and subjected to ball milling for 0.5, 2, 5 and 10 min. Following this, a batch fermentation test was conducted with this material in triplicate for an additional 18 days with unmilled effluent as the ‘status quo’ control. Results The results indicate 0.5 – 10 min of cotreatment increased sugar solubilization by 5– 13% when compared to the unmilled control, with greater solubilization correlated with increased milling duration. Biogas concentrations ranged from 44% to 55.5% methane with the balance carbon dioxide. The total biogas production was statistically higher than the unmilled control for all treatments with 2 or more minutes of milling (α = 0.1). Cotreatment also decreased mean particle size. Energy consumption measurements of a lab-scale mill indicate that longer durations of milling offer diminishing benefits with respect to additional methane production. Conclusions Cotreatment in anaerobic digestion systems, as demonstrated in this study, provides an alternative approach to conventional pretreatments to increase biogas production from lignocellulosic grassy material.

Biotechnology & Applied Microbiology↗

DOE OSTI2023

Co-Harvest and Anaerobic Co-Storage of Corn Grain and Stover as Biomass Feedstocks

This research investigated the utility of co-harvesting and anaerobic co-storage of corn grain and stover to positively influence their physical and chemical characteristics as a biomass feedstock. Corn grain and stover were harvested in 2019 and 2020 with a self-propelled forage harvester. Stover yield, moisture content, and composition were altered by the harvest date, stubble height, and header configuration. Harvest date had the utility of varying the stover moisture content (p < 0.001) from 42.3% to 53.5% (w.b.) and 43.1% to 53.9% (w.b.) for the 2019 and 2020 harvest years, respectively. Stubble height was also utilized to vary stover moisture content. A negative linear relationship was established between stubble height and stover moisture content for the early (R2 = 0.76) and late harvest (R2 = 0.91) dates for both years. Stover yield also showed a negative linear relationship (R2 = 0.76) with stubble height over both years. Regardless of the stubble height, the row-crop header collected more stover (p < 0.001) than the ear-snapper header. In 2020, harvested stover ranged from 5.0 to 10.5 Mg ha-1, with ha-1 representing 41% to 85% of the total available stover. In both years, stover ash content was less than 64 g kg-1. Material stored in pilot-scale silos (19 L) was well conserved during anaerobic storage, with average DM losses of 4.8% and 3.4% in 2019 and 2020, respectively. Grain moisture content averaged 23.6% (w.b.) at harvest, and 31.0% (w.b.) after storage as moisture migrated from the moist stover to the drier grain. Harvesting whole-plant corn with a forage harvester had the unwanted effect of reducing the particle size of the grain fraction, which would complicate downstream utilization. However, reducing the harvester cutterhead speed increased the fraction of intact kernels from 47% to 85% by mass. The studied system was a viable alternative to conventional corn grain and stover systems for producing feedstocks for biochemical conversion.

09 BIOMASS FUELS↗

DOE OSTI2024

Epoxy Coating of Biofiber: An Effective Modifier of Biofiber Physical and Flow Properties for Improved Tensile Behavior of Biofiber-Reinforced Biocomposite

Biocomposites combine renewable, plant-based fibers with degradable polymers and are an attractive option for sustainable, lightweight, and cost-effective materials with a low carbon footprint, especially for large-scale additive manufacturing. One of the major challenges in the widespread adoption of biocomposites is that their mechanical performance is significantly inferior to that of synthetic composites. Surface treatment is a common and effective technique to improve the mechanical properties of the biofibers used in biocomposites. This study aims to investigate the physical and flow properties of surface-treated biofibers, as well as the tensile properties of their PLA-based biocomposite, to gain insights into how surface treatment changes the fiber‘s characteristics and biocomposite‘s mechanical properties. Surface treatment was created using a two-component epoxy system by reacting poly(bisphenol A-co-epichlorohydrin) glycidyl end-capped (PBG) and dicyandiamide (DICY). The treatment was tested on two different biofibers (loblolly pine and corn stover fibers) with three different PBG/DICY molar ratios (0.25, 0.5, and 2). Results showed that surface-treated fibers improved the tensile strength and Young‘s modulus of the biocomposites. Loblolly pine biocomposites from fibers treated with a PBG/DICY ratio of 0.25 exhibited the best tensile properties. The surface treatment resulted in a more loosely dispersed fiber bulk structure, as evidenced by less fiber agglomeration into smaller particle sizes, higher fiber sphericity, and lower loose bulk density. This can enhance stress distribution and the overall mechanical performance of the biocomposites. Additionally, surface-treated fibers exhibited better dynamic flow properties.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Diffraction and thermal effect of a Bessel-Gaussian laser for Ag nanoparticle deposition

Nanoparticles are known to sinter at much lower temperatures than the corresponding bulk or micro size particles. A laser-assisted sintering process is considered in this study to sinter Ag nanoparticles by dispensing Ag paste onto an indium tin oxide-coated Si substrate. The Gaussian beam of a CO 2 laser source is propagated through axicon and biconvex lenses, and the resulting hollow beam is focused on the Ag paste with a hollow parabolic mirror. A Bessel-Gaussian irradiance distribution is obtained at the focal plane of the parabolic mirror due to the interference of the hollow laser cone. The Fresnel diffraction approximation is considered to determine the phasor of the laser and an analytical approach is implemented to calculate the irradiance distribution of the Bessel-Gaussian beam. This irradiance distribution is utilized as a heat source in a heat conduction model and the temperature distribution is analyzed for thin Ag films formed during the laser sintering of Ag nanoparticles. An analytical expression is obtained for the temperature distribution by solving the heat conduction equation using Fourier transform for finite media. The widths of the deposited Ag lines are predicted from the temperature profiles and the model predictions compare well with the experimental results. The isotherms are found to be geometrically noncongruent with convex and concave tips depending on the locally maximum and minimum irradiances of the Bessel-Gaussian beam, respectively. The convex and concave tips, however, appear in the same isotherm for sufficiently high substrate speed relative to the laser beam.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2021

Gamma radiation sterilization of N95 respirators leads to decreased respirator performance

In response to personal protective equipment (PPE) shortages in the United States due to the Coronavirus Disease 2019, two models of N95 respirators were evaluated for reuse after gamma radiation sterilization. Gamma sterilization is attractive for PPE reuse because it can sterilize large quantities of material through hermetically sealed packaging, providing safety and logistic benefits. The Gamma Irradiation Facility at Sandia National Laboratories was used to irradiate N95 filtering facepiece respirators to a sterilization dose of 25 kGy(tissue). Aerosol particle filtration performance testing and electrostatic field measurements were used to determine the efficacy of the respirators after irradiation. Both respirator models exhibited statistically significant decreases in particle filtering efficiencies and electrostatic potential after irradiation. The largest decrease in capture efficiency was 40–50% and peaked near the 200 nm particle size. The key contribution of this effort is correlating the electrostatic potential change of individual filtration layer of the respirator with the decrease filtration efficiency after irradiation. This observation occurred in both variations of N95 respirator that we tested. Electrostatic potential measurement of the filtration layer is a key indicator for predicting filtration efficiency loss.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Processed Lab Data for Neural Network-Based Shear Stress Level Prediction

Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted features and labels from lab data (experiment p4679). The features are extracted with a non-overlapping window from the original acoustic data. The first column is the time of the window. The second and third columns are the mean and the variance of the acoustic data in this window, respectively. The 4th-11th column is the the power spectrum density ranging from low to high frequency. And the last column is the corresponding label (shear stress level). The name of the file means which driving velocity the sequence is generated from. Data were generated from laboratory friction experiments conducted with a biaxial shear apparatus. Experiments were conducted in the double direct shear configuration in which two fault zones are sheared between three rigid forcing blocks. Our samples consisted of two 5-mm-thick layers of simulated fault gouge with a nominal contact area of 10 by 10 cm^2. Gouge material consisted of soda-lime glass beads with initial particle size between 105 and 149 micrometers. Prior to shearing, we impose a constant fault normal stress of 2 MPa using a servo-controlled load-feedback mechanism and allow the sample to compact. Once the sample has reached a constant layer thickness, the central block is driven down at constant rate of 10 micrometers per second. In tandem, we collect an AE signal continuously at 4 MHz from a piezoceramic sensor embedded in a steel forcing block about 22 mm from the gouge layer The data from this experiment can be used with the deep learning algorithm to train it for future fault property prediction.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Association between soil organic carbon and calcium in acidic grassland soils from Point Reyes National Seashore, CA

All data is collected from across 3 soil cores spanning a pH gradient at Point Reyes National Seashore, as part of a recent publication. The bulk data csv includes hygroscopic moisture content, soil pH, total C and N values, particle size distributions, cation exchange capacity and extractable cations. The STXM data package includes raw spectra data from scanning transmission X-ray microscopy C near-edge X-ray absorption fine structure spectroscopy analysis, extracted from STXM Image Reader analysis and then subsequently normalised in Athena (see details in paper). The data file also contains a bulk chemistry and mineralogy dataset, including total and trace element contents from 3 depths and mineral compositions as measured at BL 11-3 SSRL and semi-quantified in HighScore. Finally the final data file is calcium K-edge X-ray absorption near-edge structure spectroscopy (BL 4-3) and micro-XANES and micro-X-ray fluorescence (BL 14-3b) data.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

TEMPEST3 surface runoff water chemistry and organic matter composition

Coastal flooding, driven by storm surges and sea level rise, can mobilize organic matter (OM) via runoff, while introducing compositionally distinct OM (e.g., estuarine OM) into the system. To understand event-scale OM dynamics, we monitored source waters and surface runoff during an ecosystem-scale field manipulation experiment, TEMPEST (Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments), in June 2024. The TEMPEST experiment is part of the COMPASS-FME (Coastal Observations, Mechanisms, and Predictions Across Systems and Scales – Field, Measurements, and Experiments) project and designed to investigate biogeochemical and ecological impacts of freshwater and seawater flooding on coastal terrestrial-aquatic interface ecosystems by simulating freshwater and seawater storm events in two 2000m2 coastal upland forest plots (freshwater and brackish seawater plots). The temporal coverage of this dataset is during the TEMPESTⅢ event (June 11-13, 2024). This dataset contains: - Surface runoff discharge measured by flumes - Sensor data (specific conductivity, salinity, dissolved oxygen, and temperature) - Particle size distribution - Total suspended sediment concentrations (TSS), particulate and dissolved organic carbon (POC, DOC) concentrations, total nitrogen and total dissolved nitrogen (TN, TDN) concentrations - Bulk particulate and dissolved OM compositions (stable C and N isotopes of particulates and optical measurements of chromophoric dissolved OM) - High resolution mass spectrometry analysis data - Water isotope data All data files are plain-text CSV (comma-separated value), and no special software is required to read them.

COMPASS-FME↗

DOE OSTI2021

Investigating the dielectric properties of barium titanate nanocomposites using transmission electron microscopy image processing

Abstract Barium titanate (BTO) is a ferroelectric perovskite material used in energy storage applications because of its high dielectric constant. A previous study showed that the dielectric constant for BTO nanoparticles drastically increases to over 15,000 at a particle size of 70 nm. This result is highly contested, but its implications to energy storage motivated our investigation into the dielectric constants of BTO nanoparticles that are incorporated into a polymer matrix. We developed a novel method of using image processing techniques on transmission electron microscope images of BTO-polymer nanocomposites. Data on the positions, shapes, sizes, and orientations of BTO nanoparticles were used to build more realistic computational models that simulate the dielectric behavior of the nanocomposites. Here, we investigate the relationship between regions of enhanced electric field and the composite dielectric constant.

36 MATERIALS SCIENCE↗

DOE OSTI2023

AmeriFlux FLUXNET-1F US-Lin Lindcove Orange Orchard

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Lin Lindcove Orange Orchard. This is the FLUXNET version of the carbon flux data for the site US-Lin Lindcove Orange Orchard produced by applying the standard ONEFlux (1F) software. Site Description - The experimental site was a citrus orchard about three km west of the UC Lindcove Research and Experiment Station. The site is characterized by a Mediterranean climate typical of Central California, with warm dry summers and cool wet winters. The soil texture for the upper horizon (0-33 cm) was loam with a particle size distribution of 42% sand, 38% silt, and 20% clay (Kearney Ag Center). Soil pH was 7.4 (Valley Tech Soil Agricultural Laboratory Services). The orchard was drip-irrigated with water applied twice per week in the warm season to ensure water avail- ability close to field capacity. The site was kept clean from understory vegetation and mechanical pruning operations took place twice a year to limit the size ofthe orange trees for harvesting and site maintenance. The block of trees in which the tower and instruments were located was ‘Valencia’ orange on trifoliate rootstock, with a planting date in the 1960’s. The square 4 ha block had dimensions of 200 m NeS and EeW. At the end of the study, we harvested a ‘Valencia’ citrus tree from within the study block to measure citrus leaf area index (LAI) and biomass density. The tree height was 3.7 m as measured with a telescoping pole. We measured canopy radii in the four cardinal directions to calculate a planar area (as seen from above) of 12.2 m2. The mean specific leaf area (SLA) of citrus leaves was 85.4 cm2 g-1 obtained by measuring leaf area of five groups of leaf samples with a LiCor Leaf area meter (mod. LI-3100C) followed by drying and weighing. LAI for the orchard was 3.00, derived using the SLA value and the total dry mass of leaves from the harvested tree, multiplied by the plant population of 237 plants per ha as obtained from spacing measurements and Google Maps imagery.

Fares, Silvano↗

DOE OSTI2025

SOC Microstructural Property Estimator

This pre-trained ML model is a tool that uses basic compositional parameters for porous solid oxide cell (SOC) electrodes - the phase fractions and mean particle/pore diameters – as inputs and uses them to estimate additional electrochemical performance parameters: active (i.e., connected) TPB density, all tortuosity factors, and phase pair specific interfacial areas. The electrode is assumed to be composed of two solid phases and a pore phase. The property calculations are performed using neural network regression models trained on a large bank of synthetic electrode microstructural data that NETL has generated using the program DREAM3D (that bank is also hosted on EDX: https://edx.netl.doe.gov/dataset/soc-synthetic-microstructure-bank). This means the generated parameters are based on training from actual measured properties from 3D microstructures, not estimated from geometric simplifications. This tool was developed and is intended to replace percolation theory calculations in models that use hypothetical electrode properties. An example use case would be running SOC performance simulations across a parametric sweep of electrode designs (e.g., varying phase fractions and particle sizes) and assessing how it impacts the electrochemical performance of the SOC. Within the parameter space of the training data (statistics of that parameter space is provided in the readme file), this model achieves sub-5% mean absolute percent errors, an order of magnitude less error than percolation theory across the same parameter space. However, be aware that this tool was developed with parametric simulations in mind, and users are encouraged to assess accuracy for their own specific use case rather than taking accuracy metrics at face value. More info, including a usage guide, is in the included readme file. This tool should be cited with the DOI number provided.

Electrode Microstructure↗

DOE OSTI2020

Industrial Scalable Additives for Enhanced Decomposition of Plastic Waste Through Photocatalysis

Polymer pollution is a dangerous environmental problem that can lead to the spread of toxic chemicals and microplastics throughout aquatic biomes. Commonly littered plastic from consumer goods and products with a limited or single use can be embedded with photocatalysts to avoid an increase in the amount of plastic present in Earth’s bodies of water. In this work, the photocatalyst nanoparticles, less than 100 nm particle size, were integrated into polyethylene terephthalate, high-density polyethylene, low-density polyethylene, and polystyrene and exposed under ultraviolet light over two independent experimental trials examining both the environmental factors impacting decay and the physical parameters required to implement this solution on a large scale. The plastics were chosen due to their prolific use in items such as grocery bags, bottles, and packaging. The first half of the project examined the effects of water and light frequency on polymer decay, and the second half of the project examined photodecomposition of polymer films. We report that low-density and high-density polyethylene films with titanium dioxide decayed approximately 11.7 and 9.6 faster than control samples, respectively. With an estimated price of less than half the cost of a shopping bag for titanium catalyst integration and a retention of the polymer’s tensile strength with a 2 wt% catalyst concentration, photocatalytic decomposition is a feasible, scalable method for the prevention and future reduction of polymer waste from realistic products littered in the environment.

36 MATERIALS SCIENCE↗