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DOE OSTI2024

Airborne LiDAR to Improve Canopy Fuels Mapping for Wildfire Modeling

Increasing conflict between wildfire and the built environment has increased the need for more up-to-date and finer resolution canopy fuels data to improve wildfire modeling and associated risk forecasts. The US Forest Service and US Department of the Interior’s LANDFIRE product, which provides 30-m resolution canopy fuels data for the entire US, is one of the most widely used sources of fuels data. However, the last complete mapping effort for LANDFIRE is based on 2016 conditions, and subsequent updates reflect disturbances 1-2 years behind the release year. Airborne systems equipped with Light Detection and Ranging (LiDAR) sensors can be deployed to actively sense canopy structure and estimate canopy fuels data (cover, height, base height, bulk density) at finer resolutions. Canopy base height (CBH) and canopy bulk density (CBD) are difficult to measure both in the field and in LiDAR point clouds. Still, they are important for accurately modeling crown fires, which are often intense and difficult to contain. Additionally, point cloud datasets are large, and calculations require efficient utilization of computational resources. To address these challenges, we are working on an approach that uses openly available National Ecological Observatory Network (NEON) airborne LiDAR data, with calculations processed in the R programming language and parallelized through the lidR package. CBH and CBD are often derived from tree height, diameter at breast height, and species-specific allometries using the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS). We aim to test if airborne LiDAR can estimate CBH and CBD without the use of empirical equations. Reliable estimates of canopy fuels data directly from airborne LiDAR could streamline quick, fine-resolution updates for use in wildfire behavior models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Identification of Common Types of Plastics by Vibrational Spectroscopic Techniques

Polyethylene Terephthalate (PET), High-Density Polyethylene (HDPE), Polyvinyl Chloride (PVC), Low-Density Polyethylene (LDPE), Polypropylene (PP), and Polystyrene (PS) account for most plastic use worldwide, with production nearing 380 million tons annually. A considerable portion enters municipal solid waste and landfills, creating long-term environmental concerns. Scaling recycling operations requires automated sorting technologies, with spectroscopy and machine learning offering promising solutions. In this study, a six-class convolutional neural network (CNN) was developed for plastic identification using vibrational spectroscopies. Raman Scattering (RS) spectra collected from recycling samples enabled accurate chemical differentiation while assessing the influence of visible features such as color. A CNN trained on RS data achieved 100% classification accuracy. To strengthen field applicability, Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy was incorporated, achieving 95% accuracy with a similar CNN model. These findings demonstrate the potential of integrating spectroscopy with deep learning for reliable plastic classification, advancing development of scalable, field-ready recycling technologies.

Garcia Tovar, Maria P.↗

DOE OSTI2019

Reimagining the potential of Earth observations for ecosystem service assessments

The benefits nature provides to people, called ecosystem services, are increasingly recognized and accounted for in assessments of infrastructure development, agricultural management, conservation prioritization, and sustainable sourcing. These assessments are often limited by data, however, a gap with tremendous potential to be filled through Earth observations (EO), which produce a variety of data across spatial and temporal extents and resolutions. Despite widespread recognition of this potential, in practice few ecosystem service studies use EO. Here, we identify challenges and opportunities to using EO in ecosystem service modeling and assessment. Some challenges are technical, related to data awareness, processing, and access. These challenges require systematic investment in model platforms and data management. Other challenges are more conceptual but still systemic; they are byproducts of the structure of existing ecosystem service models and addressing them requires scientific investment in solutions and tools applicable to a wide range of models and approaches. We also highlight new ways in which EO can be leveraged for ecosystem service assessments, identifying promising new areas of research. More widespread use of EO for ecosystem service assessment will only be achieved if all of these types of challenges are addressed. Furthermore, this will require non-traditional funding and partnering opportunities from private and public agencies to promote data exploration, sharing, and archiving. Investing in this integration will be reflected in better and more accurate ecosystem service assessments worldwide.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Comparative genomics of Mollicutes-related endobacteria supports a late invasion into Mucoromycota fungi

Abstract Diverse members of early-diverging Mucoromycota, including mycorrhizal taxa and soil-associated Mortierellaceae, are known to harbor Mollicutes-related endobacteria (MRE). It has been hypothesized that MRE were acquired by a common ancestor and transmitted vertically. Alternatively, MRE endosymbionts could have invaded after the divergence of Mucoromycota lineages and subsequently spread to new hosts horizontally. To better understand the evolutionary history of MRE symbionts, we generated and analyzed four complete MRE genomes from two Mortierellaceae genera: Linnemannia (MRE-L) and Benniella (MRE-B). These genomes include the smallest known of fungal endosymbionts and showed signals of a tight relationship with hosts including a reduced functional capacity and genes transferred from fungal hosts to MRE. Phylogenetic reconstruction including nine MRE from mycorrhizal fungi revealed that MRE-B genomes are more closely related to MRE from Glomeromycotina than MRE-L from the same host family. We posit that reductions in genome size, GC content, pseudogene content, and repeat content in MRE-L may reflect a longer-term relationship with their fungal hosts. These data indicate Linnemannia and Benniella MRE were likely acquired independently after their fungal hosts diverged from a common ancestor. This work expands upon foundational knowledge on minimal genomes and provides insights into the evolution of bacterial endosymbionts.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Coupling-dependent antiferromagnetic-ferromagnetic ordering in a pinwheel artificial spin ice

Nanopatterned magnetic thin films offer a platform for exploration of tailored magnetic properties such as emergent long-range order. A prominent example is artificial spin ice (ASI), where an arrangement of nanoscale magnetic elements, acting as macrospins, interact via their dipolar fields. In this study, we discuss the transition from antiferromagnetic (AFM) to ferromagnetic (FM) long-range order in a square lattice ASI as the magnetic elements are gradually rotated through 45⁢° to a “pinwheel” configuration. The AFM-FM transition is observed experimentally using synchrotron radiation x-ray spectromicroscopy and occurs for a certain rotation angle of the nanomagnets, dependent on the dipolar coupling strength determined by the separation of the magnets in the lattice. Large-scale magnetic dipole simulations show that the point-dipole approximation fails to capture the correct AFM-FM transition angle. However, excellent agreement with experimental data is obtained using a dumbbell-dipole model, which better reflects the actual dipolar fields of the magnets. This model also explains the coupling dependence of the transition angle, another feature not captured by the point-dipole model. Our findings resolve a discrepancy between measurement and theory in previous work on “pinwheel” ASIs and establish the coupling dependence of the AFM-FM transition. The revised dipole model, with a more accurate representation of the stray field, offers more precise control of magnetic order in artificial spin systems.

Artificial spin ice↗

DOE OSTI2019

Radiological Survey of Pajarito Site (Technical Area 18) for the Manhattan Project Historical Park

Environmental Protection Compliance-Environmental Stewardship (EPC-ES) has evaluated radiological process history and all measurement data and found: (1) soil and interior surface sampling meet the data quality objectives contained in MARSSIM (2000) and MARSAME (2009), as reflected in LANL procedures (LANL 2015a, 2015b), (2) that the interiors of the Slotin Building, Pond Cabin and Battleship Bunker were within surface contamination limits for unrestricted access, and (3) the soils around the Slotin Building, Pond Cabin and Battleship Bunker are below Screening Action Levels for both construction work and recreational use (LANL 2016). Therefore, public access inside the Slotin Building and to the outside areas surrounding the Slotin Building, Pond Cabin and Battleship Bunker area are not restricted. This finding is consistent with the requirements of DOE Order 458.1 “Radiation Protection of the Public and the Environment” (DOE 2013) and LANL procedure “Environmental Radiation Protection” (LANL 2018).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Femtosecond Reflectance Spectroscopy for Energetic Material Diagnostics

Understanding the fundamental mechanisms underpinning shock initiation is critical to predicting energetic material (EM) safety and performance. Currently, the timescales and pathways by which shock-excited lattice modes transfer energy into specific chemical bonds remains an open question. Towards understanding these mechanisms, our group has previously measured the vibrational energy transfer (VET) pathways in several energetic thin films using broadband, femtosecond transient absorption spectroscopy. However, new technologies are needed to move beyond these thin film surrogates and measure broadband VET pathways in realistic EM morphologies. Herein, we describe a new broadband, femtosecond, attenuated total reflectance spectroscopy apparatus. Performance of the system is benchmarked against published data and the first VET results from a pressed EM pellet are presented. This technology enables fundamental studies of VET dynamics across sample configurations and environments (pressure, temperature, etc .) and supports the potential use of VET studies in the non-destructive surveillance of EM components.

42 ENGINEERING↗

DOE OSTI2024

Algal Biomass Production via Open Pond Algae Farm Cultivation: 2023 State of Technology and Future Research

The annual State of Technology (SOT) assessment is an essential activity for platform research conducted under the Bioenergy Technologies Office (BETO). It allows for the impact of research progress (both directly achieved in-house at the National Renewable Energy Laboratory [NREL] and furnished by partner organizations) to be quantified in terms of economic improvements in the overall biofuel production process for a particular biomass processing pathway, whether based on terrestrial or algal biomass feedstocks. As such, initial benchmarks can be established for currently demonstrated performance, and progress can be tracked toward out-year goals to ultimately demonstrate economically viable biofuel technologies. NREL's algae SOT benchmarking efforts historically focused both on front-end algal biomass production and separately on back-end conversion to fuels through NREL's "combined algae processing" (CAP) pathway. The production model is based on outdoor long-term cultivation data, enabled by comprehensive algal biomass production trials conducted under the Development of Integrated Screening, Cultivar Optimization, and Verification Research (DISCOVR) consortium efforts, driven by data furnished by Arizona State University (ASU) at the Arizona Center for Algae Technology and Innovation (AzCATI) testbed site. The CAP model is based on experimental efforts conducted primarily under NREL research and development projects. This report focuses on front-end algal biomass production, documenting the pertinent algal biomass cultivation parameters that were input to the NREL open pond algae farm model. Through partnerships under DISCOVR, collaborators at ASU furnished details on cultivation performance metrics including biomass productivity and harvest densities for recent growth trials done at the AzCATI site. The resulting biomass productivity was calculated at 16.7 g/m 2 /day (ash-free dry weight [AFDW], annual average) for seasonal cultivation of Picochlorum celeri TG2 and Monoraphidium minutum 26B-AM biomass strains at the ASU site. Picochlorum celeri achieved the best productivity from April to September, with Monoraphidium minutum 26B-AM being used between October and March. Tetraselmis striata LANL1001, usually part of the strain rotation in previous cultivation SOTs, was supplanted by Monoraphidium minutum 26B-AM in this year's outdoor cultivation trials. Finally, building from an industry case study presented in the 2022 SOT report, in the Appendix of this report we provide an update on further improved data furnished by an industry collaborator and resultant impacts on economics reflecting several seasonal scenarios. This case study provides a supplementary datapoint on work being performed elsewhere with a more dedicated focus on improved compositional quality, producing biomass enriched in lipids as may be more optimal for conversion upgrading to fuels and products.

09 BIOMASS FUELS↗

DOE OSTI2023

A Spectroscopic Study of Mars-analog Materials with Amorphous Sulfate and Chloride Phases: Implications for Detecting Amorphous Materials on the Martian Surface

The Chemistry and Mineralogy X-ray diffraction (XRD) instrument aboard the Curiosity rover consistently identifies amorphous material at Gale Crater, which is compositionally variable, but often includes elevated sulfur and iron, suggesting that amorphous ferric sulfate (AFS) may be present. Understanding how desiccating ferric sulfate brines affect the spectra of Martian material analogs is necessary for interpreting complex/realistic reaction assemblages. Visible and near-infrared reflectance (VNIR), mid-infrared attenuated total reflectance (MIR, FTIR-ATR), and Raman spectra, along with XRD data are presented for basaltic glass, hematite, gypsum, nontronite, and magnesite, each at three grain sizes (<25, 25–63, and 63–180 μm), mixed with ferric sulfate (+/–NaCl), deliquesced, then rapidly desiccated in 11% relative humidity or via vacuum. All desiccated products are partially or completely XRD amorphous; crystalline phases include starting materials and trace precipitates, leaving the bulk of the ferric sulfate in the amorphous fraction. Due to considerable spectral masking, AFS detectability is highly dependent on spectroscopic technique and minerals present. This has strong implications for remote and in situ observations of Martian samples that include an amorphous component. AFS is only identifiable in VNIR spectra for magnesite, nontronite, and gypsum samples; hematite and basaltic glass samples appear similar to pure materials. Sulfate features dominate Raman spectra for nontronite and basaltic glass samples; the analog material dominates Raman spectra of hematite and gypsum samples. MIR data are least affected by masking, but basaltic glass is almost undetectable in MIR spectra of those mixtures. NaCl produces similar FTIR-ATR and Raman features, regardless of analog material.

58 GEOSCIENCES↗

DOE OSTI2021

KAZRCORGE

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorge.c1 (ge=general sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

KAZRCORMD

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcormd.c1 (md=moderate sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

KAZRCORHI

For the Ka ARM Zenith Radar (KAZR) data stream, kazrcorhi.c1 (hi=high sensitivity), produces significant detection mask, corrects reflectivity for gaseous attenuation, and dealiases mean Doppler velocity.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

KAZRCORGE-c0

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

KAZRCORHI-c0

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

KAZRCORMD-c0

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Quantifying Adoption Rates and Energy Savings Over Time for Advanced Manufacturing Technologies

Energy-efficient manufacturing technologies can reduce energy consumption and lower operating costs for an individual manufacturing facility, but increased process complexity and the resulting risk of disruption means that manufacturers may be reluctant to adopt such technologies. In order to quantify potential energy savings at scales larger than a single facility, it is necessary to account for how quickly and how widely the technology will be adopted by manufacturers. This work develops a methodology for estimating energy-efficient manufacturing technology adoption rates using quantitative, objectively measurable technology characteristics, including energetic, economic and technical criteria. Twelve technology characteristics are considered, and each characteristic is assigned an importance weight that reflects its impact on the overall technology adoption rate. Technology characteristic data and importance weights are used to calculate the adoption score, a number between 0 and 1 that represents how quickly the technology is likely to be adopted. The adoption score is then used to estimate parameters for the Bass diffusion curve, which quantifies the change in the number of new technology adopters in a population over time. Finally, energy savings at the sector level are calculated over time by multiplying the number of new technology adopters at each time step with the technology's facility-level energy savings. The proposed methodology will be applied to five state-of-the-art energy-efficient technologies in the carbon fiber composites sector, with technology data obtained from the Department of Energy's 2016 bandwidth study. Because the importance weights used in estimating the Bass curve parameters are subjective, a sensitivity analysis will be performed on the weights to obtain a range of parameters for each technology. The potential energy savings for each technology and the rate at which each technology is adopted in the sector are quantified and used to identify the technologies which offer the greatest cumulative sector-level energy savings over a period of 20 years. Preliminary analysis indicates that relatively simple technologies, such as efficient furnaces, will be adopted more quickly and result in greater cumulative energy savings compared to more complex technologies that require process retrofitting, such as advanced control systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Remote Fiber Based Velocimeter For Interface Strength Measurements

The understanding of the performance of nuclear reactor plate fuels is essential when developing new fuel systems. The condition of pre and post irradiated plate fuels must be characterized effectively and efficiently prior to irradiation and then post irradiation within a high rad environment. The post characterization work must be performed remotely and in an environment hostile to instrumentation. Laser based characterization methods provide the capability to be remote and robust within a hot-cell environment. Laser based characterization can provide high spatial resolution appropriate for scanning and imaging large areas. The INL is currently developing a laser shock system for Post Irradiation Examination (PIE) station for use in the Hot Fuel Examination Facility (HFEF) at the INL. The laser shock system is designed to characterize fuel to cladding and cladding to cladding interface strength. The laser shock-technique induces large amplitude shock waves to mechanically characterize interfaces such as the fuel-clad interface. The shock wave propagates as a compressional wave through the fuel plate to the free (unconfined) backside surface and is reflected back through the test plate as a rarefaction (tensile) wave. This rarefaction wave is the physical mechanism that produces internal delamination failure. The focus or this paper is on the accurate measurement of surface velocity to quantify the amount of energy traveling through the interfaces to the back surface of the fuel plate. The original fiber based velocimeter design was biased by parasitic back reflections. This paper will discuss the discovery of the back-reflection issue and the design changes to resolve the issue. Supporting data will be presented.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗