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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Characterization of a novel HIV-1 circulating recombinant form, CRF91_cpx, comprising CRF02_AG, G, J, and U, mostly among men who have sex with men

Prospective molecular studies of HIV-1 pol region (2253–5250 in HXB2 genome) sequences from sequenced samples of 269 HIV-1-infected patients in Cyprus (2017–2021) revealed a transmission cluster of 14 unknown HIV-1 recombinants that were not classified as previously established CRFs. The earliest recombinant was collected in September 2017, and the transmission cluster continued to grow until November 2020. Near full-length HIV-1 genome sequences of the 11 of the 14 recombinants were successfully obtained (790–8795 in HXB2 genome) and aligned against a reference dataset of HIV-1 subtypes and CRFs. We employed MEGAX for maximum-likelihood tree construction (GTR model, 1000 bootstrap replicates), Cluster-Picker for phylogenetic clustering analysis (genetic distance ≤0.045, bootstrap support value ≥70%), and REGA-3.0 for subtype determination. Bootscan and similarity plot analyses (sliding window of 400 nucleotides overlapped by 40 nucleotides) were conducted using SimPlot-v3.5.1, and subregion confirmatory neighbour-joining tree analyses were conducted using MEGAX (Kimura two-parameter model, 1000 bootstrap replicates, ≥70% bootstrap-support value). Exclusive clustering of the HIV-1 recombinants revealed their uniqueness. The recombination analyses illustrated the same unique mosaic pattern with six putative intersubtype recombination breakpoints, seven fragments of subtypes CRF02_AG, G, J and an unclassified fragment. We conclusively characterized the mosaic structure of the novel HIV-1 CRF, named CRF91_cpx, by the Los Alamos HIV Sequence Database. Additionally, we identified a URF of CRF91_cpx with two additional recombination sites, generated by a recombination event between subtype B and CRF91_cpx. Since the identification of CRF91_cpx, two additional patient samples have been entered into the CRF91_cpx transmission cluster, demonstrating active growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Haar-Like Wavelets on Hierarchical Trees

Here, discrete wavelet methods, originally formulated in the setting of regularly sampled signals, can be adapted to data defined on a point cloud if some multiresolution structure is imposed on the cloud. A wide variety of hierarchical clustering algorithms can be used for this purpose, and the multiresolution structure obtained can be encoded by a hierarchical tree of subsets of the cloud. Prior work introduced the use of Haar-like bases defined with respect to such trees for approximation and learning tasks on unstructured data. This paper builds on that work in two directions. First, we present an algorithm for constructing Haar-like bases on general discrete hierarchical trees. Second, with an eye towards data compression, we present thresholding techniques for data defined on a point cloud with error controlled in the $L$ $\infty$ norm and in a Hölder-type norm. In a concluding trio of numerical examples, we apply our methods to compress a point cloud dataset, study the tightness of the $L$ $\infty$ error bound, and use thresholding to identify MNIST classifiers with good generalizability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation

Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R 2 , MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows.

Bayesian optimization↗
DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1991

Kingdoms in turmoil

How should the world's living organisms be classified? Into how many kingdoms should they be grouped? Scientists have been grappling with these questions since the time of Aristotle, drawing on a broad base of biological characteristics for clues. The fossil record, visible traits of living organisms and, more recently, results from cell biology have all shaped theories of biological classification. But last year a new and controversial concept emerged: a classification of life based solely on molecular traits. The focal point of the controversy is a tree of life, or "phylogeny", devised by Carl Woese of the University of Illinois, Otto Kandler of the University of Munich and Mark Wheelis of the University of California. The tree is unusual because, unlike all previous schemes, it is constructed solely from biochemical data such as DNA sequences rather than a range of different organism characteristics. But that is not all. The scheme also challenges the idea that life on Earth is best divided into five kingdoms, with the main split being between bacteria and all other organisms. Woese and his colleagues create three main groupings by dividing the bacteria in two and unifying all other organisms.

NASA Program Exobiology↗
DOE OSTI2022

A deep learning approach to real-time HIV outbreak detection using genetic data

Pathogen genomic sequence data are increasingly made available for epidemiological monitoring. A main interest is to identify and assess the potential of infectious disease outbreaks. While popular methods to analyze sequence data often involve phylogenetic tree inference, they are vulnerable to errors from recombination and impose a high computational cost, making it difficult to obtain real-time results when the number of sequences is in or above the thousands. Here, we propose an alternative strategy to outbreak detection using genomic data based on deep learning methods developed for image classification. The key idea is to use a pairwise genetic distance matrix calculated from viral sequences as an image, and develop convolutional neutral network (CNN) models to classify areas of the images that show signatures of active outbreak, leading to identification of subsets of sequences taken from an active outbreak. We showed that our method is efficient in finding HIV-1 outbreaks with R0 ≥ 2.5, and overall a specificity exceeding 98% and sensitivity better than 92%. We validated our approach using data from HIV-1 CRF01 in Europe, containing both endemic sequences and a well-known dual outbreak in intravenous drug users. Our model accurately identified known outbreak sequences in the background of slower spreading HIV. Importantly, we detected both outbreaks early on, before they were over, implying that had this method been applied in real-time as data became available, one would have been able to intervene and possibly prevent the extent of these outbreaks. This approach is scalable to processing hundreds of thousands of sequences, making it useful for current and future real-time epidemiological investigations, including public health monitoring using large databases and especially for rapid outbreak identification.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using machine learning algorithms greatly enhances our ability to achieve this objective. With the progress of quantum technologies, quantum machine learning could become a powerful tool for data analysis in high energy physics. In this study, using IBM gate-model quantum computing systems, we employ the quantum variational classifier method and the quantum kernel estimator method in two recent LHC flagship physics analyses: $t\bar{t}H$ (Higgs boson production in association with a top quark pair) and $H\rightarrow\mu\mu$ (Higgs boson decays to two muons). We have obtained early results with 10 qubits on the IBM quantum simulator and the IBM quantum hardware. On the quantum simulator, the quantum machine learning methods perform similarly to classical algorithms such as SVM (support vector machine) and BDT (boosted decision tree), which are often employed in LHC physics analyses. On the quantum hardware, the quantum machine learning methods have shown promising discrimination power, comparable to that on the quantum simulator. This study demonstrates that quantum machine learning has the ability to differentiate between signal and background in realistic physics datasets.

Chan, Jay↗
DOE OSTI2020

Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP

Large-scale scientific workflows rely heavily on high-performance file transfers. These transfers require strict quality parameters such as guaranteed bandwidth, no packet loss or data duplication. To have successful file transfers, methods such as predetermined thresholds and statistical analysis need to be done to determine abnormal patterns. Network administrators routinely monitor and analyze network data for diagnosing and alleviating these, making decisions based on their experience. However, as networks grow and become complex, monitoring large data files and quickly processing them, makes it improbable to identify errors and rectify these. Abnormal file transfers have been classified by simply setting alert thresholds, via tools such as PerfSonar and TCP statistics (Tstat). This paper investigates the feasibility of unsupervised feature extraction methods for identifying network anomaly patterns with three unsupervised classification methods—principal component analysis, autoencoder and isolation forest. Here, we collect file transfer statistics from two experiment sets—synthetic iPerf generated traffic and 1000 Genome workflow runs, with synthetically introduced anomalies. Our results show that while PCA and a simple autoencoder finds it difficult to detect clusters, the tree-variant isolation forest is able to identify anomalous packets by breaking down TCP traces into tree classes early.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2011

Prediction of Weather Impacted Airport Capacity using Ensemble Learning

Ensemble learning with the Bagging Decision Tree (BDT) model was used to assess the impact of weather on airport capacities at selected high-demand airports in the United States. The ensemble bagging decision tree models were developed and validated using the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) data and weather forecast at these airports. The study examines the performance of BDT, along with traditional single Support Vector Machines (SVM), for airport runway configuration selection and airport arrival rates (AAR) prediction during weather impacts. Testing of these models was accomplished using observed weather, weather forecast, and airport operation information at the chosen airports. The experimental results show that ensemble methods are more accurate than a single SVM classifier. The airport capacity ensemble method presented here can be used as a decision support model that supports air traffic flow management to meet the weather impacted airport capacity in order to reduce costs and increase safety.

Weather impact↗
NASA NTRS1993

Analysis of a hardware and software fault tolerant processor for critical applications

Computer systems for critical applications must be designed to tolerate software faults as well as hardware faults. A unified approach to tolerating hardware and software faults is characterized by classifying faults in terms of duration (transient or permanent) rather than source (hardware or software). Errors arising from transient faults can be handled through masking or voting, but errors arising from permanent faults require system reconfiguration to bypass the failed component. Most errors which are caused by software faults can be considered transient, in that they are input-dependent. Software faults are triggered by a particular set of inputs. Quantitative dependability analysis of systems which exhibit a unified approach to fault tolerance can be performed by a hierarchical combination of fault tree and Markov models. A methodology for analyzing hardware and software fault tolerant systems is applied to the analysis of a hypothetical system, loosely based on the Fault Tolerant Parallel Processor. The models consider both transient and permanent faults, hardware and software faults, independent and related software faults, automatic recovery, and reconfiguration.

Dugan, Joanne B.↗
NASA NTRS2020

The bioclimatic extent and pattern of the cold edge of the boreal forest: the circumpolar taiga-tundra ecotone

Current configurations of forest structure at the cold edge of the boreal may help understand the future of ecosystem functioning in high northern latitudes. The circumpolar biome boundary at the boreal (taiga) forest and tundra interface is an ecological transition zone (taiga-tundra ecotone; TTE) experiencing changes that affect its forest structure. We accounted for the TTE's horizontal forest structure with an estimate of its extent and pattern as represented by tree canopy cover (TCC). We quantified TCC patterns with an algorithm that describes its spatial gradient, and summarized landscape patterns of structure to represent heterogeneity, capturing abrupt, diffuse, and uniform forest at mesoscales. We used these landscape patterns to constrain the spatial extent of sparse and open canopy forest, and non-forest (forest-adjacent) edge that defines the TTE extent. The resulting map of the TTE extent is based on forest structure spatial patterns resolved at 30 m, highlights structural variability across landscapes, and helps distinguish tundra from boreal domains. We classified 14 594 landscapes as those associated with the TTE within a circumpolar bioclimatic envelope (11.575 million km2), where 44.83% of the area of these landscapes were forest and non-forest edge, yet 36.43% contributed to the TTE extent. We report the overall extent of the TTE (3.032 million km2) across North America and Greenland (53%), and Eurasia (47%), where 0.697 million km2 is non-forest edge, 0.549 million km2 is sparse forest, and 1.787 million km2 is open canopy forest. Diffuse forest landscapes dominate the TTE (79%), and abrupt landscapes (~19%) indicate portions of the TTE where sparse forest and non-forest edge are the prevailing structural patterns. This account of the TTE quantifies the area of the cold edge of the boreal forest where previous global estimates show high discrepancies, and can help target monitoring and prediction of circumpolar dynamics.

Paul M Montesano↗
DOE OSTI2024

Cacao pod transcriptome profiling of seven genotypes identifies features associated with post-penetration resistance to Phytophthora palmivora

Abstract The oomycete Phytophthora palmivora infects the fruit of cacao trees ( Theobroma cacao ) causing black pod rot and reducing yields. Cacao genotypes vary in their resistance levels to P. palmivora , yet our understanding of how cacao fruit respond to the pathogen at the molecular level during disease establishment is limited. To address this issue, disease development and RNA-Seq studies were conducted on pods of seven cacao genotypes (ICS1, WFT, Gu133, Spa9, CCN51, Sca6 and Pound7) to better understand their reactions to the post-penetration stage of P. palmivora infection. The pod tissue- P. palmivora pathogen assay resulted in the genotypes being classified as susceptible (ICS1, WFT, Gu133 and Spa9) or resistant (CCN51, Sca6 and Pound7). The number of differentially expressed genes (DEGs) ranged from 1625 to 6957 depending on genotype. A custom gene correlation approach identified 34 correlation groups. De novo motif analysis was conducted on upstream promoter sequences of differentially expressed genes, identifying 76 novel motifs, 31 of which were over-represented in the upstream sequences of correlation groups and associated with gene ontology terms related to oxidative stress response, defense against fungal pathogens, general metabolism and cell function. Genes in one correlation group (Group 6) were strongly induced in all genotypes and enriched in genes annotated with defense-responsive terms. Expression pattern profiling revealed that genes in Group 6 were induced to higher levels in the resistant genotypes. An additional analysis allowed the identification of 17 candidate cis -regulatory modules likely to be involved in cacao defense against P. palmivora . This study is a comprehensive exploration of the cacao pod transcriptional response to P. palmivora spread after infection. We identified cacao genes, promoter motifs, and promoter motif combinations associated with post-penetration resistance to P. palmivora in cacao pods and provide this information as a resource to support future and ongoing efforts to breed P. palmivora -resistant cacao.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1989

Neural computing for numeric-to-symbolic conversion in control systems

A type of neural network, the multilayer perceptron, is used to classify numeric data and assign appropriate symbols to various classes. This numeric-to-symbolic conversion results in a type of information extraction, which is similar to what is called data reduction in pattern recognition. The use of the neural network as a numeric-to-symbolic converter is introduced, its application in autonomous control is discussed, and several applications are studied. The perceptron is used as a numeric-to-symbolic converter for a discrete-event system controller supervising a continuous variable dynamic system. It is also shown how the perceptron can implement fault trees, which provide useful information (alarms) in a biological system and information for failure diagnosis and control purposes in an aircraft example.

Passino, Kevin M.↗
DOE OSTI2023

AmeriFlux FLUXNET-1F US-Ton Tonzi Ranch

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-Ton Tonzi Ranch. This is the FLUXNET version of the carbon flux data for the site US-Ton Tonzi Ranch produced by applying the standard ONEFlux (1F) software. Site Description - The Tonzi Ranch is located on the lower foothills of the Sierra Nevada Mountains. The site is classified as an oak savanna woodland (Davis et al, 2016) and is situated on privately owned land. This site is representative of the oak savanna ecosystem that rings the Great Central Valley of California and inhabits the lower reaches of the Sierra Nevada foothills. This ecosystem is a natural laboratory for studying how ecosystem scale carbon and water fluxes respond to environmental change. The temperatures range between 0 and 40 C, the autumn, winter and spring are wet and cool, while the summer growing season is hot, dry and rainless. The overstory is dominated by blue oak trees (40% of total vegetation) with intermittent grey pine trees (3 trees/ha). Understory species include a variety of grasses and herbs, including purple false brome, smooth cat's ear, and rose clover. These two distinctive layers operate in and out of phase from one another. Growing season of the understory is confined to the wet season only, typically from October to early May. In contrast, the deciduous blue oak trees are dormant and leafless during the rainy winter months. The trees tend to leaf out in March and reach maximum LAI in April. During the rainless summers, the grasses in the understory die and the leaves experience soil moisture deficit stress References Davis, F., et al. (2016). "Oak woodlands." Ecosystems of California: 509–529.

Ma, Siyan↗
DOE OSTI2022

Identification of a new circulating recombinant form of human immunodeficiency virus type 1, CRF124_cpx involving subtypes A, G, H, and CRF27_cpx in Angola

Angola, located in Central Africa, has around 320,000 (270,000–380,000) people living with human immunodeficiency virus (HIV)/AIDS, equivalent to 1% of the country’s population at the end of 2021. A previous study conducted in 2012, using Angolan samples collected between 2008 and 2010 revealed a high prevalence of HIV-1 recombinants, around 42% of sequences, with 21% showing the same UH profile in partial pol region which were grouped into a monophyletic cluster with high bootstrap support. Thus, the objective of the present work was to obtain complete genomes of those sequences and characterize them, aiming at a description of a new circulating recombinant form (CRF). Whole blood from nine HIV-1 UH pol -infected individuals had their genomic DNA extracted, and nested PCR was used to amplify seven overlapping fragments targeting the full-length HIV-1 genome. The final classification was based on maximum likelihood trees, and recombination analyses were performed using a bootscan from the Simplot program. BLAST and Los Alamos Database inspections were used to search other similar H-like pol sequences. Complete genome amplification was possible for three samples, partial genomes were obtained for the other three, and only pol was available for the remaining three sequences. Bootscan analysis of the two whole-genome and three partial genome sequences retrieved from people living with HIV/AIDS (PLHIVA) without epidemiological linkage showed the same complex recombination profile involving HIV-1 subtypes A/G/H/CRF27_cpx, with a total of six recombinant breakpoints, aiming to classify a new HIV-1 CRF124_cpx. We found no other full-length HIV-1 genomes with the same mosaic profile; however, we identified 33 partial pol sequences, mainly sampled from Angola between 2001 to 2019, with the same H-like profile. Bayesian analysis of H and H-like pol sequences indicates that CRF124_cpx probably originated in Angola at mid-1970s, indicating that this CRF has been circulating in the country for a long time. In summary, our study describes a new CRF circulating principally in Angola and highlights the importance of continuing molecular surveillance studies, especially in countries with high molecular diversity of HIV.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2008

From Informal Safety-Critical Requirements to Property-Driven Formal Validation

Most of the efforts in formal methods have historically been devoted to comparing a design against a set of requirements. The validation of the requirements themselves, however, has often been disregarded, and it can be considered a largely open problem, which poses several challenges. The first challenge is given by the fact that requirements are often written in natural language, and may thus contain a high degree of ambiguity. Despite the progresses in Natural Language Processing techniques, the task of understanding a set of requirements cannot be automatized, and must be carried out by domain experts, who are typically not familiar with formal languages. Furthermore, in order to retain a direct connection with the informal requirements, the formalization cannot follow standard model-based approaches. The second challenge lies in the formal validation of requirements. On one hand, it is not even clear which are the correctness criteria or the high-level properties that the requirements must fulfill. On the other hand, the expressivity of the language used in the formalization may go beyond the theoretical and/or practical capacity of state-of-the-art formal verification. In order to solve these issues, we propose a new methodology that comprises of a chain of steps, each supported by a specific tool. The main steps are the following. First, the informal requirements are split into basic fragments, which are classified into categories, and dependency and generalization relationships among them are identified. Second, the fragments are modeled using a visual language such as UML. The UML diagrams are both syntactically restricted (in order to guarantee a formal semantics), and enriched with a highly controlled natural language (to allow for modeling static and temporal constraints). Third, an automatic formal analysis phase iterates over the modeled requirements, by combining several, complementary techniques: checking consistency; verifying whether the requirements entail some desirable properties; verify whether the requirements are consistent with selected scenarios; diagnosing inconsistencies by identifying inconsistent cores; identifying vacuous requirements; constructing multiple explanations by enabling the fault-tree analysis related to particular fault models; verifying whether the specification is realizable.

Cimatti, Alessandro↗
DOE OSTI2020

Historic and modern approaches for discovery of abandoned wells for methane emissions mitigation in Oil Creek State Park, Pennsylvania

Hundreds of oil wells were drilled along Oil Creek in Pennsylvania in the mid-16 1800s, birthing the modern oil industry. No longer in operation, many wells are now classified as abandoned, and, due to their age, their locations are either unknown or inaccurately recorded. These historic well sites present environmental, safety, and economic concerns in the form of possible methane leaks and physical hazards. Airborne magnetic and LiDAR surveys were conducted in the Pioneer Run watershed in Oil Creek State Park to find abandoned wells in a historically significant but physically challenging location. Wells were drilled in this area prior to modern geolocation and legal documentation. Although a large number of old wells were abandoned summarily without remediation of the site, much of the land area within Oil Creek State Park is now covered in trees and dense underbrush, which can obscure wellheads. The thick vegetation and steep terrain limited the possibility of ground-based surveys to easily find well sites for methane emissions studies. The data from remote sensing surveys were used to corroborate potential well locations from historic maps and photographs. Potential well sites were verified in a ground-based field survey and monitored for methane emissions. Two historic photographs documenting oil activity in the late 1800s were georeferenced using a combination of magnetic and LiDAR data. LiDAR data, which were more useful in georeferencing and in field verification, identified 290 field locations in the Pioneer Run watershed, 86% of which were possible well sites. Sixty-two percent of the ground-verified wells remained unplugged and comprised the majority of leaking wells. The mean methane emissions factor for unplugged wells was 0.027 ± 0.099 kg/day, lower than other Appalachian Basin methane emissions estimates. LiDAR was used for the first time, in combination with an airborne magnetic survey, to reveal underground oil industry features and inform well identification and remediation efforts in difficult-to-navigate regions. In the oldest oil fields, where well casing has been removed or wood conductor casing was installed, historic photographs provide additional lines of evidence for oil wells where ground disturbances have concealed surface features. Identification of well sites is necessary for mitigation efforts, as unplugged wells emit methane, a potent greenhouse gas.

02 PETROLEUM↗
DOE OSTI2019

Quantification of Soil Organic Carbon in Biochar-Amended Soil Using Ground Penetrating Radar (GPR)

The application of biochar amendments to soil has been proposed as a strategy for mitigating global carbon (C) emissions and soil organic carbon (SOC) loss. Biochar can provide additional agronomic benefits to cropping systems, including improved crop yield, soil water holding capacity, seed germination, cation exchange capacity (CEC), and soil pH. To maximize the beneficial effects of biochar amendments towards the inventory, increase, and management of SOC pools, nondestructive analytical methods such as ground penetrating radar (GPR) are needed to identify and quantify belowground C. The use of GPR has been well characterized across geological, archaeological, engineering, and military applications. While GPR has been predominantly utilized to detect relatively large objects such as rocks, tree roots, land mines, and peat soils, the objective of this study was to quantify comparatively smaller, particulate sources of SOC. This research used three materials as C sources: biochar, graphite, and activated C. The C sources were mixed with sand—12 treatments in total—and scanned under three moisture levels: 0%, 10%, and 20% to simulate different soil conditions. GPR attribute analyses and Naïve Bayes predictive models were utilized in lieu of visualization methods because of the minute size of the C particles. Significant correlations between GPR attributes and both C content and moisture levels were detected. The accuracy of two predictive models using a Naïve Bayes classifier for C content was trivial but the accuracy for C structure was 56%. The analyses confirmed the ability of GPR to identify differences in both C content and C structure. Beneficial future applications could focus on applying GPR across more diverse soil conditions.

54 ENVIRONMENTAL SCIENCES↗