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DOE OSTI2022

U.S. Pacific Coast Workshop Report on Preconstruction Research Recommendations (U.S. Offshore Wind Synthesis of Environmental Effects Research (SEER) Project)

In May 2022, the U.S. Offshore Wind Synthesis of Environmental Effects Research (SEER) project team hosted a stakeholder workshop focused on preconstruction (baseline) research needs for potential floating offshore wind (OSW) energy development on the U.S. Pacific Coast, including California, Oregon, and Washington. Prior to the workshop, the SEER team developed a set of initial synthesized research recommendations that were identified based on a review of relevant, publicly available resources and with advisory group input. The workshop covered three marine life breakout groups on subsequent days to discuss research recommendations related to 1) marine mammals and sea turtles, 2) fish and invertebrates, and 3) birds and bats. As part of the workshop, over a hundred participants from the public and private sectors provided feedback on various aspects of the initial research recommendations, including associated data and knowledge gaps, benefits/limitations of available methods and technologies, and technological advancements or infrastructure needed to address the recommendation. Approximately 1,000 total comments were received on the workshop MURAL boards and were synthesized in this report. Based on workshop feedback, SEER developed a final database of over 500 specific research recommendations based on more than 40 resources. In Fall 2022, the full database and a tool with updated synthesized research recommendations were disseminated on Tethys (https://tethys.pnnl.gov) to assist with informing future funding opportunities and research programming. There is a continued need to improve awareness of the potential environmental effects, monitoring technologies, and management strategies for floating OSW energy development on the U.S. Pacific Coast. Coordination of these activities will require the sustained involvement of multiple stakeholders from across sectors. Beyond the baseline considerations discussed in this workshop, future state-of-the-science activities should be planned to consider research needs across wind energy life cycle phases for all relevant wildlife taxa and associated habitat and ecosystem processes.

17 WIND ENERGY↗

DOE OSTI2022

National Community Solar Partnership (NCSP+) Resource List

NLR compiles and makes quarterly updates to this dataset of publications related to expanding access to solar through its support for the National Community Solar Partnership (NCSP+). The purpose of the list is to help NCSP+ partners and others to find relevant resources on community solar, low- and moderate-income residential rooftop solar + storage, community-serving commercial solar projects, microgrids, and distributed solar + storage aggregations such as virtual power plants serving low-income communities. Resource types include journal articles, reports, fact sheets, presentations, videos, datasets, modeling tools, webinars, and other formats. A web-based version of the database is available on the NCSP+ Data Hub at: https://openei.org/wiki/NCSP_Hub/Resources .

14 SOLAR ENERGY↗

DOE OSTI2023

Process-based cost estimation framework for assessing economic viability of environmentally and socially sustainable rare earth element feedstocks

Rare Earth Elements (REEs) are critical raw material inputs that are required to enable the energy transition. Constraints on the U.S. supply of conventional REE feedstocks and the desire to diversify U.S. reliance on foreign supply chains have motivated the pursuit of REE recovery from unconventional feedstocks including waste products. Recovery of REEs from unconventional sources is also motivated by environmental and social considerations, including the potential to offset new mining activity and subsidize the remediation of REE-containing wastes from coal, natural gas, and other mineral extraction activities. Sourcing REEs from unconventional feedstocks would reduce supply chain constraints and minimize the environmental impacts of REE extraction, but current processes to exploit these feedstocks have uncertain technical and economic performance and the processes have not been demonstrated at scale. This work aims to reduce uncertainty in evaluating the performance of new REE production pathways. A database of operating and capital expenses for REE recovery and establish consistent process unit costs for common stages in the conventional supply chain are developed. Market prices are used to develop well-defined methods for determining product values for a diverse set of products that can be generated (e.g. oxalates, alloys, end-use products such as magnets) but are not typically considered in technoeconomic analyses. Finally, to provide insight on the effects of economies of scale and feedstock purity on capital and operating costs, process unit level cost functions relating total costs per kilogram of REE produced are reported. These tools can be applied to assess and facilitate scale-up of REE production from diverse sources of REEs.

Fritz, Alison↗

DOE OSTI2022

High-Throughput Ion Irradiation and Microstructural Characterization of Multi-Principal-Element Alloys

Multi-principal-element alloys (MPEAs) have garnered interest in the nuclear community for their apparent resistance to microstructural damage under irradiation. While both experimentation and simulation have demonstrated reduced hardening and void swelling in MPEAs, the relationship between composition and irradiation response is still poorly understood since a rich experimental database for MPEAs does not yet exist. In this work, to accelerate the generation of experimental irradiation-response data for MPEAs, high-throughput synthesis, irradiation, and characterization techniques have been employed. Specifically, additive manufacturing has been used to produce arrays of various MPEAs (1-cm2 coupons), which have been subsequently irradiated using 4-MeV Ni2+ ions to a peak damage of 200 dpa at 500 °C. Hardening and void swelling have been assessed using nanoindentation and trenching via plasma focused ion beam (PFIB), respectively. Trends in the experimental data relating hardening and void swelling behavior to composition have been analyzed using different machine learning approaches.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Poisson-response Tensor-on-Tensor Regression and Applications

We introduce Poisson-response tensor-on-tensor regression (PToTR), a novel regression framework designed to handle tensor responses composed element-wise of random Poisson-distributed counts. Tensors, or multi-dimensional arrays, composed of counts are common data in fields such as inter national relations, social networks, epidemiology, and medical imaging, where events occur across multiple dimensions like time, location, and dyads. PToTR accommodates such tensor responses alongside tensor covariates, providing a versatile tool for multi dimensional data analysis. We propose algorithms for maximum likelihood estimation under a canonical polyadic (CP) structure on the regression coefficient tensor that satisfy the positivity of Poisson parameters and then provide an initial theoretical error analysis for PToTR estimators. We also demonstrate the utility of PToTR through three concrete applications: longitudinal data analysis of the Integrated Crisis Early Warning System database, positron emission tomography (PET) image reconstruction, and change-point detection of communication patterns in longitudinal dyadic data. These applications highlight the versatility of PToTR in addressing complex, structured count data across various domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Ultra-compact system for characterization of physical, chemical and ignition properties of fuels

The present disclosure relates to a miniaturized fuel laboratory system that makes use of a housing, a processor housed within the housing, and a fuel inlet port supported from the housing for receiving a quantity of fuel to be used as a fuel test sample. The system may also have at least one fuel sensor housed in the housing in communication with the fuel inlet port for receiving the fuel test sample and carrying out combustion thereof. An electronic component may be housed in the housing, which enables communication with an external remote subsystem. A database may be incorporated which contains at least one of stored fuel characteristics or stored fuel analysis models, accessible by the processor. The processor may use fuel oxidation information generated by the fuel sensor, and at least one of the stored fuel characteristics or stored combustion models, to determine at least one fuel characteristic of the fuel test sample.

McNenly, Matthew J.↗

DOE OSTI2024

Ab initio property predictions of quinary solid solutions using small binary cells

The Set of Small Ordered Structures (SSOS) approach is an ab initio technique for modelling random solid solutions in which many small structures are averaged so that their correlation functions match those of a desired composition. SSOS has been shown to be effective in reducing the cost of density functional theory calculations relative to other well-known techniques such as cluster expansions and special quasirandom structures for modelling solid solutions. Here in this work, we demonstrate that SSOS’s can be constructed using cells with only a subset of elements while still accurately modelling multi-component systems. Specifically, we show that small binary cells can effectively model two quinary high entropy alloys – NbTaTiHfZr and MoNbTaVW – accurately capturing properties such as formation energy, lattice parameters, elastic constants, and root-mean-square atomic displacements. Overall, this insight is useful for those looking to construct databases of such small structures for predicting the properties of multi-component solid solutions, as it greatly decreases the number of structures that needs to be considered.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Structured information extraction from scientific text with large language models

Extracting structured knowledge from scientific text remains a challenging task for machine learning models. Here, we present a simple approach to joint named entity recognition and relation extraction and demonstrate how pretrained large language models (GPT-3, Llama-2) can be fine-tuned to extract useful records of complex scientific knowledge. We test three representative tasks in materials chemistry: linking dopants and host materials, cataloging metal-organic frameworks, and general composition/phase/morphology/application information extraction. Records are extracted from single sentences or entire paragraphs, and the output can be returned as simple English sentences or a more structured format such as a list of JSON objects. This approach represents a simple, accessible, and highly flexible route to obtaining large databases of structured specialized scientific knowledge extracted from research papers.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2024

Isotope effects and Alfvén eigenmode stability in JET H, D, T, DT, and He plasmas

While much about Alfvén eigenmode (AE) stability has been explored in previous and current tokamaks, open questions remain for future burning plasma experiments, especially regarding exact stability threshold conditions and related isotope effects; the latter, of course, requiring good knowledge of the plasma ion composition. In the JET tokamak, eight in-vessel antennas actively excite stable AEs, from which their frequencies, toroidal mode numbers, and net damping rates are assessed. The effective ion mass can also be inferred using measurements of the plasma density and magnetic geometry. Thousands of AE stability measurements have been collected by the Alfvén Eigenmode Active Diagnostic in hundreds of JET plasmas during the recent Hydrogen, Deuterium, Tritium, DT, and Helium-4 campaigns. In this novel AE stability database, spanning all four main ion species, damping is observed to decrease with increasing Hydrogenic mass, but increase for Helium, a trend consistent with radiative damping as the dominant damping mechanism. These data are important for confident predictions of AE stability in both non-nuclear (H/He) and nuclear (D/T) operations in future devices. In particular, if radiative damping plays a significant role in overall stability, some AEs could be more easily destabilized in D/T plasmas than their H/He reference pulses, even before considering fast ion and alpha particle drive. Active MHD spectroscopy is also employed on select HD, HT, and DT plasmas to infer the effective ion mass, thereby closing the loop on isotope analysis and demonstrating a complementary method to typical diagnosis of the isotope ratio.

Alfvén eigenmodes↗

DOE OSTI2026

Optical Vibrational Spectroscopic Investigation of Natural and Synthetic Analogs of Uranyl Oxyhydroxyhydrate Minerals

Uranyl oxy-hydroxy-hydrate minerals are common alteration products of uraninite (UO2+x) which is chemically and structurally analogous to uranium dioxide nuclear fuel. Therefore, structural and spectroscopic investigations of these alteration minerals and their analogous anthropogenic counterparts can provide insight into the environmental behavior of nuclear fuel cycle materials. Previously, we compiled available vibrational spectroscopic data for uranyl minerals in the Compendium of Uranium Raman and Infrared Experimental Spectra (CURIES) and found that only 37% of known uranyl oxy-hydroxy-hydrate minerals had spectra readily available in the literature and existing databases for inclusion therein. Furthermore, no available infrared spectra for this mineral group were included in CURIES. To expand our understanding of the spectroscopic features of uranyl oxy-hydroxy-hydrates and the structural origins thereof, we collected, and now include in CURIES, Raman and infrared spectra for an additional 12 uranyl hydroxide phases. To better understand the impact of structural and compositional variations of these phases on their spectroscopic features, we compare Raman spectra of different anion sheet topological groups and of analogous phases hosting different counter cations. We identify spectroscopic variations related to differences in equatorial bonding and structural changes as a result of cation substitution. We also prepare a uranyl hydroxide phase via hydrolysis of uranyl fluoride (UO2F2) as an analog of hydrolysis reactions that occur in nuclear fuel cycle materials; and we find that the alteration product of UO2F2, despite chemical and structural similarities to uranyl oxy-hydroxy-hydrate minerals, is readily distinguishable from related mineral phases using Raman spectroscopy. In this work, we provide new insights into the structural origins of spectroscopic features in uranyl oxy-hydroxy-hydrate minerals, improve the average Raman spectrum for this group of minerals, and thereby improve capabilities for identifying these mineral species and related anthropogenic phases using Raman spectroscopy.

Barth, Brodie [ORNL] (ORCID:0000000256142601)↗

DOE OSTI2020

How Wrong Can the Operational AEP Uncertainty Estimate Be When We Ignore the Correlations Between the Uncertainty Components?

Calculations of wind farm annual energy production (AEP) on operational data are essential for a variety of financial transactions during the life of wind plants. The AEP estimate is associated with an uncertainty value, which is calculated by combining contributions connected to on-site measurements, long-term reference measurements, losses, regression, windiness adjustment, and wind resource interannual variability. Although very limited documentation on the topic exists, the conventional approach currently used by the wind energy community to assess the uncertainty connected to the operational AEP estimate assume that the different uncertainty components are uncorrelated and therefore calculates the overall uncertainty with a sum of squares approach. In this analysis, we contrast the traditional technique to estimate the overall AEP uncertainty by ignoring the correlation between its different components with a novel Monte Carlo based approach, which can instead take into account the correlation between different uncertainty categories. We consider monthly operational data from 472 wind farms from the Energy Information Administration (EIA) 923 database to assess the difference between the two approaches. Long-term wind speed data needed for the AEP assessment are taken from three reanalysis products: the Modern-Era Retrospective analysis for Research and Applications v2 (MERRA-2), the European Reanalysis Interim (ERA-interim), and the National Centers for Environmental Prediction v2 (NCEP-2). The results of the Monte Carlo approach show that three pairs of AEP uncertainty components do show a statistically significant correlation: the uncertainty connected with wind resource inter-annual variability is positively correlated with the one related to the windiness correction and negatively correlated with the one due to the regression, and the wind measurement uncertainty is positively correlated with the regression uncertainty. All these correlations, which are found between uncertainty components which are not only part of an operational analysis, but also of a wind resource assessment, are currently ignored in the conventional techniques used as industry standard. We further investigate the causes of these correlations, in terms of common dependencies of different uncertainty components on wind resource variability, number of data points, and quality of the regression between wind speed and energy production data. Next, we quantify the error in the current industry standard technique, in terms of the percentage difference in total uncertainty calculated with the two considered approaches, for all the analyzed wind farms. We find a mean absolute percentage difference of about 6%, with the largest differences being greater than 20%. The data clearly confirm that ignoring the actual correlation between the uncertainty components can lead to large errors in the assessment of the operational AEP uncertainty, and the proposed Monte Carlo approach should be preferred.

Monte Carlo↗

DOE OSTI2023

Group additivity values for entropy and heat capacities of C 2 –C 8 alkanes, alkyl hydroperoxides, and their radicals

Group additivity values for the thermodynamic properties of oxygenated radicals are poorly determined due to the absence of high quality reference data. Here, a set of 58 group additive values (GAV) for the standard entropy and heat capacity of relevance to alkane oxidation is derived from fits to an extensive and accurate database of standard entropies (298.15 K) and heat capacities (300–3000 K) recently calculated with the “STAR-1D” formalism. The 192 species in this database represent the alkanes (RH), alkyl radicals ($\dot{R}$), alkyl hydroperoxides (RO 2 H), alkyl-peroxy (R$\dot{O}$ 2 ) and hydroperoxy-alkyl ($\dot{Q}$OOH) radicals for all of the isomers of C 2 –C 5 alkane fuels and a select number of isomers of C 6 –C 9 species. The STAR-1D thermochemical data for this set of species was previously obtained from a coupling of scaled B2PLYPD3/cc-pVTZ vibrational analyses with scaled ωB97X-D/cc-pVTZ one-dimensional hindered rotor corrections. The 2σ uncertainties in the GAV results relative to the STAR-1D data set are 2.4 cal K –1 mol –1 for the entropies and at most 2.0 cal K –1 mol –1 for the heat capacities in the temperature range 500–800 K. The 2σ fitting uncertainties in the heat capacity gradually reduce at higher temperatures reaching a value of 0.8 cal K –1 mol –1 at 2000 K decreasing to only 0.5 cal K –1 mol –1 at 3000 K. The high degree of accuracy for the GAV representations is obtained through the introduction of various new group terms, together with the re-optimization of existing group terms. Among the full set of 58 GAV terms, 25 include non-next-nearest neighbor interactions (NNI) and β-corrections. The updated GAVs can be applied in the prediction of entropies and heat capacities for a wide range of hydrocarbons and hydroperoxide species and their radicals, which is important to the accurate prediction of fuel reactivity at low-temperatures in the range 600–1000 K.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Wildfire‐induced increases in photosynthesis in boreal forest ecosystems of North America

Abstract Observations of the annual cycle of atmospheric CO 2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape‐level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat‐derived land cover time series to characterize plant functional types (PFTs), and solar‐induced fluorescence (SIF) from the Orbiting Carbon Observatory‐2 (OCO‐2) as a proxy for photosynthesis. We analyze these datasets to characterize post‐fire changes in plant succession and photosynthetic activity using a space‐for‐time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid‐succession yield enhancements in SIF by 8–40% during June and July for 2‐ to 59‐year stands relative to pre‐fire controls. From the analysis of post‐fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long‐term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early‐ and mid‐summer. Second, fire appears to facilitate a long‐term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO 2 exchange without a close coupling to long‐term increases in carbon storage.

Biodiversity & Conservation↗

DOE OSTI2024

Update on Activities Related to the Library of Graphite Microstructures

This report provides an overview and update of the ongoing efforts to create a comprehensive library of microstructures for nuclear graphite and carbon-based materials under consideration for nuclear applications. The library includes data on microstructural characterization of unirradiated graphite materials, a guide to the techniques used to analyze graphite (which complements the ASME guidelines and ASTM standards), a summary of characterization data for neutron-irradiated or oxidized material, and a compendium of microstructural information for carbon-based materials. These efforts are being conducted at various length scales for the filler and binder phases in graphite to better understand graphite’s local structure and property relationships. This report is a follow-up to the previous milestone report titled Report on initial development of a database of nuclear graphite characteristics based on microstructural characterization, ORNL/TM/-2023/2992, published in July 2023.The effort to develop the library of microstructures supports the US Department of Energy Office of Advanced Reactor Technologies program objectives of aiding the material selection, licensing, management, and core assessments of a graphite core by documenting the unirradiated microstructure of relevant grades or characterizing the microstructure’s evolution under the reactor environment. Additionally, this project aims to provide (1) information and guidelines for the characterizing of graphite and (2) a protocol to assess a nuclear graphite grade.

36 MATERIALS SCIENCE↗

DOE OSTI2022

End-to-end optimization for battery materials and molecules by combining graph neural networks and reinforcement learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to design new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗

DOE OSTI2022

End-to-End Optimization for Battery Materials and Molecules by Combining Graph Neural Networks and Reinforcement Learning

The National Renewable Energy Laboratory (NREL), together with the Colorado School of Mines (CSM) and Colorado State University (CSU), has developed a machine learning-enhanced approach to the design of new battery materials. Currently, such materials are designed in part via numerous expensive high-fidelity computational simulations that predict the performance of a given composition. Even with computational screening tools, the vast landscape of possible molecular or crystal structures exceeds current and future computational capacity. Improving the efficiency by which new materials can be optimized will therefore disrupt the cost, risk, and time required to bring new energy solutions to the marketplace. Predicting the properties of an organic molecule or periodic crystalline material given its structure has grown increasingly common. These approaches leverage large-scale computational and experimental databases and ML approaches such as graph neural networks. The inverse design problem of finding a material that possesses desired properties is substantially more challenging, since enumerating all valid material structures is not feasible. In this project, we leveraged recent success in reinforcement learning to efficiently navigate this high-dimensional search space. Just as algorithms can find the optimal chess moves from nearly limitless options, we train an approach to evolve a simple starting structure into a complex structure that possess the desired properties. Our solution has been demonstrated by applying it to two related design application tasks for short- and long-term energy storage, respectively: (1) the design of solid-state ion conductors and (2) the design of organic redox-active materials. The project has resulted an open-source software library for material design, documented examples of applying the library to both organic and inorganic material optimization, and peer-reviewed publications detailing the data, computational models, and resulting candidate materials.

25 ENERGY STORAGE↗

DOE OSTI2022

H-2-SALT: Storing Fossil Energy as Hydrogen in Salt Caverns

Hydrogen storage in subsurface caverns is known as perhaps largest and longest-term energy storage system. While few such systems are currently in service (e.g., Texas; Teesside, UK), they are envisioned as being a pillar of the current energy transition that will allow intermittent power production from renewable sources (e.g., wind, solar) to be balanced with demand at grid-scale. In addition, hydrogen energy storage can allow existing fossil power plants to run more economically either by minimizing startups/shutdowns or by allowing them to take advantage of arbitrage. Finally, stored hydrogen can feed a variety of non-power users such as pipeline gas, heating, transportation, and manufacturing. The H-2-SALT paper study assessed the feasibility of a power-to-hydrogen system utilizing salt cavern storage of hydrogen in bedded salt in central Kansas, where over 750 such caverns have been constructed to date, of which approximately 350 are still in service. Legacy well and salt cavern data were collected to develop a regional geological database for two sites in Central Kansas and adjacent areas for use in more detailed geological and geomechanical site characterization efforts. This study found commercial viability and competitiveness of large-scale, electrolytic H 2 production and storage ($1.78 per kg H 2 ) that can be used for both electrical power supply to the grid during high-priced electricity periods (including co-firing with NG and fuel cell power production) and sale of H 2 for various industries, such as petrochemicals or transportation. The commercial potential of the H-2-SALT system is also relatively high because each of its components operate commercially today. It is recommended that a stratigraphic test well be drilled to collect log data and core samples that can reduce uncertainty surrounding geological and geomechanical properties of the salt beds that will be required for further cavern and energy storage system design studies.

03 NATURAL GAS↗

DOE OSTI2021

Development of an inexpensive matrix-assisted laser desorption—time of flight mass spectrometry method for the identification of endophytes and rhizobacteria cultured from the microbiome associated with maize

Many endophytes and rhizobacteria associated with plants support the growth and health of their hosts. The vast majority of these potentially beneficial bacteria have yet to be characterized, in part because of the cost of identifying bacterial isolates. Matrix-assisted laser desorption-time of flight (MALDI-TOF) has enabled culturomic studies of host-associated microbiomes but analysis of mass spectra generated from plant-associated bacteria requires optimization. In this study, we aligned mass spectra generated from endophytes and rhizobacteria isolated from heritage and sweet varieties of Zea mays. Multiple iterations of alignment attempts identified a set of parameters that sorted 114 isolates into 60 coherent MALDI-TOF taxonomic units (MTUs). These MTUs corresponded to strains with practically identical (>99%) 16S rRNA gene sequences. Mass spectra were used to train a machine learning algorithm that classified 100% of the isolates into 60 MTUs. These MTUs provided >70% coverage of aerobic, heterotrophic bacteria readily cultured with nutrient rich media from the maize microbiome and allowed prediction of the total diversity recoverable with that particular cultivation method. Acidovorax sp., Pseudomonas sp. and Cellulosimicrobium sp. dominated the library generated from the rhizoplane. Relative to the sweet variety, the heritage variety contained a high number of MTUs. The ability to detect these differences in libraries, suggests a rapid and inexpensive method of describing the diversity of bacteria cultured from the endosphere and rhizosphere of maize.

dereplication↗