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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

I/O performance studies of analysis workloads on production and dedicated resources at CERN

The recent evolutions of the analysis frameworks and physics data formats of the LHC experiments provide the opportunity of using central analysis facilities with a strong focus on interactivity and short turnaround times, to complement the more common distributed analysis on the Grid. In order to plan for such facilities, it is essential to know in detail the performance of the combination of a given analysis framework, of a specific analysis and of the installed computing and storage resources. This contribution describes performance studies performed at CERN, using the EOS disk-based storage, either directly or through an XCache instance, from both batch resources and highperformance compute nodes which could be used to build an analysis facility. A variety of benchmarks, both synthetic and based on real-world physics analyses and their corresponding input datasets, are utilized. In particular, the RNTuple format from the ROOT project is put to the test and compared to the latest version of the TTree format, and the impact of caches is assessed. In addition, we assessed the difference in performance between the use of storage system specific protocols, like XRootd, and FUSE. The results of this study are intended to be a valuable input in the design of analysis facilities, at CERN and elsewhere.

Sciabà, Andrea↗
DOE OSTI2021

Remote Sensing-Based Estimation of Advanced Perennial Grass Biomass Yields for Bioenergy

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

09 BIOMASS FUELS↗
DOE OSTI2025

ITreeForeCast: An integrated modeling software to simulate tree level growth and forest carbon storage

Healthy trees in forest act as a natural carbon sink, capturing carbon. As they grow, they store carbon in their trunks, leaves and roots. Not all trees store carbon at the same rate, or in the same quantities, as it depends on a variety of biophysical and climatic factors. Furthermore, although carbon estimation in trees can be complex, the precision of estimates is tightly linked to trees growth, both in diameter and height. However, the simulation of carbon uptake by forest and forest growth has each been modeled separately, and independently at differing levels of detail and spatial resolution. In this paper, we introduce ITreeForeCast, a simulation model combining the two types of modeling on a unified platform, enabling the investigation of impacts of management strategies on carbon sequestration and wood products. ITreeForeCast is a user-extendable framework that offers new opportunities to model, simulate, and visualize the dynamics of individual trees in a forest, simulate management strategies over time, and carbon uptake.

09 - BIOMASS FUELS↗
DOE OSTI2022

Protective Effect of Phloretin against Hydrogen Peroxide-Induced Oxidative Damage by Enhancing Autophagic Flux in DF-1 Cells

Phloretin (PHL) is a dihydrochalcone flavonoid isolated from the peel and root bark of apples, strawberries, and other plants with antioxidative characteristic. In this study, we aimed to investigate the protective effect and the potential mechanism of PHL on hydrogen peroxide (H2O2)-induced oxidative damage in DF-1 cells. The results showed that PHL exhibited no cytotoxic effect on DF-1 cells at concentration below 20 μM. PHL markedly increased H2O2-reduced cell viability, decreased H2O2-induced apoptosis, as evidenced by reduced apoptosis rate, the upregulation of gene and protein level of Bcl-2, and the downregulation of gene and protein level of Bax and Cleaved caspase3. In addition, PHL reduced H2O2-induced reactive oxygen species (ROS) production and restored antioxidant enzymes activities as well as mitochondrial membrane potential in a dose-dependent manner. Moreover, PHL prior to H2O2 further increased LC3-II level, promoted p62 turnover and improved lysosomal function. Importantly, autophagy inhibitor chloroquine (CQ) reversed the protective effect of PHL, and increased H2O2-induced apoptosis. Furthermore, PHL inhibited the phosphorylation levels of ERK, p38, and JNK. Collectively, these results indicate that PHL could attenuate H2O2-induced oxidative injury and apoptosis by maintaining lysosomal function and promoting autophagic flux, and MAPKs pathway may be involved in this process. Our study provides evidence that PHL could as a new strategy to against oxidative damage in poultry industry.

Song, Dan↗
NASA NTRS1993

The use of polar-orbiting satellite sounding data to estimate rural maximum and minimum temperatures

Atmospheric sounding products from NOAA's polar-orbiting satellites were used to derive and test predictive equations of rural shelter-level maximum and minimum temperatures. Sounding data from both winter and summer months were combined with surface data from over 5300 cooperative weather stations in the continental United States to develop multiple linear regression equations. Separate equations were developed for both maximum and minimum temperature, using the three types of sounding retrievals (clear, partly cloudy, and cloudy). Clear retrieval models outperformed others, and maximum temperatures were more accurately predicted than minimums. Average standard deviations of observed rural shelter temperatures within sounding search areas were of similar magnitude to root-mean-square errors from satellite estimates for most clear and partly cloudy cases, but were significantly less for cloudy retrieval cases. Model validation for surrogate polar and tropical climatic regions showed success in application of the four clear retrieval models (maximum and minimum temperature, for both winter and summer). This indicates the potential adaptability of these models to estimates of rural shelter temperature in areas outside of the United States.

Johnson, Gregory L.↗
NASA NTRS2011

Assimilation of Terrestrial Water Storage from GRACE in a Snow-Dominated Basin

Terrestrial water storage (TWS) information derived from Gravity Recovery and Climate Experiment (GRACE) measurements is assimilated into a land surface model over the Mackenzie River basin located in northwest Canada. Assimilation is conducted using an ensemble Kalman smoother (EnKS). Model estimates with and without assimilation are compared against independent observational data sets of snow water equivalent (SWE) and runoff. For SWE, modest improvements in mean difference (MD) and root mean squared difference (RMSD) are achieved as a result of the assimilation. No significant differences in temporal correlations of SWE resulted. Runoff statistics of MD remain relatively unchanged while RMSD statistics, in general, are improved in most of the sub-basins. Temporal correlations are degraded within the most upstream sub-basin, but are, in general, improved at the downstream locations, which are more representative of an integrated basin response. GRACE assimilation using an EnKS offers improvements in hydrologic state/flux estimation, though comparisons with observed runoff would be enhanced by the use of river routing and lake storage routines within the prognostic land surface model. Further, GRACE hydrology products would benefit from the inclusion of better constrained models of post-glacial rebound, which significantly affects GRACE estimates of interannual hydrologic variability in the Mackenzie River basin.

Forman, Barton A.↗
DOE OSTI2025

The Carbon Budget of Land Conversion: Sugarcane Expansion and Implications for a Sustainable Bioenergy Landscape in Southeastern United States

The expansion of sugarcane onto land currently occupied by improved (IMP) and semi-native (SN) pastures will reshape the U.S. bioenergy landscape. We combined biometric, ground-based and eddy covariance methods to investigate the impact of sugarcane expansion across subtropical Florida on the carbon (C) budget over a 3-year rotation. With 2.3- and 5.1-fold increase in productivity over IMP and SN pastures, sugarcane displayed a C use efficiency (CUE; i.e., fraction of gross C uptake allocated to plant growth) of 0.59, well above that of pastures (0.31–0.23). Sugarcane also had greater C allocation to aboveground productivity and hence, harvestable biomass relative to IMP and SN. Cane heterotrophic respiration over the 3-year rotation (903 ± 335 gC m −2 year −1 ) was 1% and 14% higher than IMP and SN pastures, respectively. These soil C losses responded largely to disturbance over the first year after conversion (1510 ± 227 gC m −2 year −1 ) but declined in subsequent years to an average 599 ± 90 gC m −2 year −1 —well below those of IMP (933 ± 140 gC m −2 year −1 ) and SN (759 ± 114 gC m −2 year −1 ) pastures—despite a significant 40%–61% increase in soil C inputs. Soil C inputs, however, shifted from root-dominated in pastures to litter-dominated in sugarcane, with only 5% C allocation to roots. Reduced decomposition rates in sugarcane were likely driven by changes in the recalcitrance and distribution rather than the size of the newly incorporated soil C pool. As a result, we observed a rapid shift in the net ecosystem C balance (NECB) of sugarcane from a large source immediately following conversion to approaching the net C losses of IMP pastures only 2 years after conversion. The environmental cost of converting pasture to sugarcane underscores the importance of implementing management practices to harness the soil C storage potential of sugarcane in advancing a sustainable bioeconomy in Southeastern United States.

09 BIOMASS FUELS↗
NASA NTRS2020

VEG-04: The Effects of Light Quality on Mizuna Mustard Growth, Nutritional Composition, and Organoleptic Acceptability for a Space Diet

Growing fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the astronaut dietary experience as we move toward longer-duration missions. Tending plants may also serve as a countermeasure for crew psychological stresses associated with spaceflight. However, requirements to support consistent growth of a variety of high quality, nutritious crops under spaceflight environmental conditions remain unclear. This study explores the potential to grow crops for consumption on the International Space Station (ISS) using the Veggie vegetable-production system. VEG-04A and B were two flight tests conducted in 2019 with the leafy green crop mizuna mustard. Mizuna was grown in two Veggie chambers simultaneously, with the chambers set to different red-to-blue light formulations; one Veggie was programmed as "red-rich" and the second as "blue-rich." Light quality is known to impact plant growth, nutrition, microbiology, and organoleptic characteristics on Earth, and the Veggie flight tests examined how these impacts might differ in microgravity. VEG-04A, a 35-day growth test with a single harvest, was initiated in June and harvested in July 2019. At harvest, the astronauts froze half of the edible plant tissue to return to Earth and weighed the remaining half using the Mass Measurement Device (MMD). Weighed samples were then cleaned with produce-sanitizing wipes, and consenting crew members participated in organoleptic evaluation of the fresh produce. The remaining sanitized produce was available for crew consumption as desired. Frozen flight samples were returned at the end of August for microbial and chemical analyses to assess food safety and nutritional quality. No pathogens were detected on VEG-04A flight or ground control samples. On average, bacterial and fungal counts were significantly lower on ground control samples than flight samples. VEG-04B, a 56-day test with multiple harvests from the same plants, assessed sustained productivity. VEG-04B was initiated in October 2019 with three harvests at four, six, and eight weeks after initiation. Challenges with the watering program occurred early during VEG-04A, and several plants failed to survive in both the flight and ground control operations. Thus, prior to VEG-04B, an extra test was conducted to tailor water timing and volumes. This test determined that mizuna grew best if the wicks inside the plant pillow were allowed to dry after plants germinated, reducing persistent water around the stem. The wicks changed from being a conduit for water out of the plant pillow to being a conduit for air into the root zone. This test allowed a fine tuning of methods for VEG-04B. It is our hope that these tests on ISS will help mitigate the risk of an inadequate food supply for long-duration missions by adding fresh vegetables to the crew diet. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

Massa, Gioia D.↗
NASA NTRS2023

Improving the MODIS LAI Compositing Using Prior Time-Series Information

The Moderate Resolution Imaging Spectroradiometer (MODIS) long-term leaf area index (LAI) products have significantly contributed to global energy fluxes, climate change, and biogeochemistry research. However, the maximum fraction of photosynthetically active radiation absorbed by vegetation (Max-FPAR) compositing strategy of the Collection 6 (C6) products dictates that the main or backup algorithm is always triggered by observations of different quality, which indirectly causes the observed instability in the LAI time-series. Based on MODIS daily LAI retrievals, this study develops a prior knowledge time-series compositing algorithm (PKA) using a linear kernel driven (LKD) model. Our results show that the newly proposed PKA can significantly improve the LAI composites compared to the Max-FPAR strategy using ground-based observations for validation. We found that the PKA performs better than Max-FPAR in various aspects (different sites, seasons, and retrieval index (RI) ranges), with R2 increasing from 0.69 to 0.76 and root means square error (RMSE) decreasing from 1.01 to 0.84 compared to GBOV ground truth. The same improvement was shown for the ground truth LAIs measured at the Honghe and Hailun sites in northeastern China, with R2 increasing from 0.23 to 0.41 and RMSE decreasing from 1.27 to 1.25. In addition, three newly proposed temporal uncertainty metrics (time-series stability, TSS and time-series anomaly, TSA and reconstruction error metric, RE (the proximity to the main RT-based retrievals)) were applied to compare the stability of LAI time-series before and after PKA implementation. We found that the time series stability of PKA LAI was improved, the time series anomalies were reduced, and the retrieval rates of the main algorithm were also greatly enhanced compared to Max-FPAR LAI. A case intercomparison for Max-FPAR-MODIS, Max-FPAR-VIIRS (Visible Infrared Imager Radiometer Suite), and PKA-MODIS LAIs in the Amazon Forest region showed that the PKA is also effective in improving the LAI retrieval over large regions with few qualified observations due to poor atmospheric conditions (RE decreased from 2.37/2.35 (Max-FPAR-MODIS/Max-FPAR-VIIRS) to 2.25 (PKA-MODIS) and RI increased from 61.94%/59.62% to 66.88%). The same improvement was seen in the BELMANIP 2.1 sites for almost all biomes except deciduous broadleaf forest, where the RE decreased from 1.85/2.13 to 1.15 overall. We note that the PKA has the potential to be easily implemented in the operational algorithms of subsequent MODIS and MODIS-like LAI Collections.

MODIS↗
DOE OSTI2022

Characterizing proteases in sorghum drought response using activity-based protein profiling (ABPP)

Sorghum (Sorghum bicolor) is a C4 grass species that is a major food and bioenergy crop grown worldwide. Understanding the response of sorghum to climate change is therefore critical to future sustainable food and bioenergy production. Major advancements in genome sequencing have provided a wealth of genomic data for DOE-relevant plant species, but the functions of many of these genes remain unknown. Genotype to phenotype characterization of sorghum and other bioenergy crops is needed to identify key genes responsible for plant resilience to drought and other environmental stressors. Proteases, which modulate protein degradation and cellular signaling processes by catalyzing the hydrolysis of proteins into smaller proteins, peptides, or amino acids, are highly implicated in plant drought stress responses. We developed a method for using activity-based protein profiling (ABPP) of selected protease activities (serine and cysteine proteases) to characterize drought stress responses in sorghum roots and leaves. Analysis of these ABPP data identified several known serine and cysteine proteases as well as uncharacterized sorghum proteins which may have protease activities that can be explored in future validation studies.

09 BIOMASS FUELS↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Ultra-filtration(UF) units are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square (RMSE) metric. Accurate prediction of initial TMP is critical for optimizing CCRO operations, as it enables the development of robust modelling frameworks that enhance process efficiency and reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2020

A Viable New Strategy for the Discovery of Peptide Proteolytic Cleavage Products in Plant-Microbe Interactions

Small peptides that are proteolytic cleavage products (PCPs) of less than 100 amino acids are emerging as key signaling molecules that mediate cell-to-cell communication and biological processes that occur between and within plants, fungi, and bacteria. Yet, the discovery and characterization of these molecules is largely overlooked. Today, selective enrichment and subsequent characterization by mass spectrometry–based sequencing offers the greatest potential for their comprehensive characterization, however qualitative and quantitative performance metrics are rarely captured. Herein, we addressed this need by benchmarking the performance of an enrichment strategy, optimized specifically for small PCPs, using state-of-the-art de novo–assisted peptide sequencing. As a case study, we implemented this approach to identify PCPs from different root and foliar tissues of the hybrid poplar Populus × canescens 717-1B4 in interaction with the ectomycorrhizal basidiomycete Laccaria bicolor . In total, we identified 1,660 and 2,870 Populus and L. bicolor unique PCPs, respectively. Qualitative results supported the identification of well-known PCPs, like the mature form of the photosystem II complex 5-kDa protein (approximately 3 kDa). A total of 157 PCPs were determined to be significantly more abundant in root tips with established ectomycorrhiza when compared with root tips without established ectomycorrhiza and extramatrical mycelium of L. bicolor . These PCPs mapped to 64 Populus proteins and 69 L. bicolor proteins in our database, with several of them previously implicated in biologically relevant associations between plant and fungus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Comparison of Energy Storage and Arbitrage Options for Nuclear Power

Nuclear power is the most reliable source of clean energy and plays a crucial role in decarbonization efforts and national energy security. Achievement of Net-Zero targets depends on the deployment of new Nuclear Power Plants (NPPs) – both advanced reactors and large-scale Light-Water Reactors (LWRs) – as well as continuing operations at existing LWR plants. The Light-Water Reactor Sustainability (LWRS) program seeks to extend the lifetime of existing NPPs and improve their economic performance through research into plant modernization, Flexible Plant Operation and Generation (FPOG), risk-informed systems analysis, materials research, and physical security. The FPOG pathway investigates alternative revenue streams for LWRs. When electricity prices are driven low by an oversupply of renewable energy, steam and electricity from an NPP can be used to make value-added products while taking advantage of the low energy price; alternatively, this cheap energy can be stored chemically, electrically, or thermally, and used to regenerate-generate electricity at a later time when energy demand, and therefore the wholesale price of electricity, is higher in an economic concept called arbitrage. The purpose of this report is to investigate energy storage technologies that can store 500 MW of electricity from an LWR for a wide spectrum of durations.

13 HYDRO ENERGY↗
DOE OSTI2020

Effects of nitrogen fertilization and bioenergy crop type on topsoil organic carbon and total Nitrogen contents in middle Tennessee USA

Nitrogen (N) fertilization affects bioenergy crop growth and productivity and consequently carbon (C) and N contents in soil, it however remains unclear whether N fertilization and crop type individually or interactively influence soil organic carbon (SOC) and total N (TN). In a three-year long fertilization experiment in switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.) croplands in Middle Tennessee USA, soil samples (0–15cm) were collected in plots with no N input (NN), low N input (LN: 84 kg N ha -1 yr -1 in urea) and high N input (HN: 168 kg N ha -1 yr -1 in urea). Besides SOC and TN, the aboveground plant biomass was also quantified. In addition to a summary of published root morphology data based on a separated mesocosm experiment, the root leachable dissolved organic matter (DOM) of both crops was also measured using archived samples. Results showed no significant interaction of N fertilization and crop type on SOC, TN or plant aboveground biomass (ABG). Relative to NN, HN (not LN) significantly increased SOC and TN in both crops. Though SG showed a 15–68% significantly higher ABG than GG, GG showed a 9.3–12% significantly higher SOC and TN than SG. The positive linear relationships of SOC or TN with ABG were identified for SG. However, GG showed structurally more complex and less readily decomposed root DOM, a larger root volume, total root length and surface area than SG. Collectively, these suggested that intensive N fertilization could increase C and N stocks in bioenergy cropland soils but these effects may be more likely mediated by the aboveground biomass in SG and root chemistry and morphology in GG. Future studies are expected to examine the root characteristics in different bioenergy croplands under the field fertilization experiment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Phosphorus budgets of intensively managed row crops at a long–term agroecosystem research site in the upper US Midwest

Phosphorus (P) budgets for cropping systems provide insights for keeping soil P at optimal levels for crops while avoiding excess inputs. We quantified 12 years of P inputs (fertilizer and atmospheric deposition) and outputs (harvest and leaching losses) for replicated maize (Zea mays L.)—soybean (Glycine max L.)—wheat (Triticum aestivum) crop rotations under conventional, no-till, reduced input, and biologically based (organic without compost or manure) management systems at the Kellogg Biological Station LTAR site in southwest Michigan. Conventional, no-till, and reduced input systems were fertilized between 13 and 50 kg P ha –1 depending on year. Soil test phosphorus (STP) was measured at 0- to 25-cm depth every autumn. Leached P was measured as dissolved P in the soil solution sampled beneath the rooting depth and combined with modeled percolation. Fertilization and harvest were the predominant P fluxes in the fertilized systems, whereas only harvest dominated P flux in the unfertilized organic system. Leaching losses were minor terms in the budgets, but leachate concentrations were nevertheless close to the range of concern for downstream eutrophication. Over the 12-year study period, the organic system exhibited a negative P balance (–82.0 kg P ha –1 ), coinciding with suboptimal STP levels, suggesting a need for P supplementation. In contrast, the fertilized systems showed positive P balances (mean: 70.1 kg P ha –1 ) with STP levels well above agronomic optima. Results underscore the importance of tailored P management strategies to sustain crop productivity while mitigating environmental impacts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗
DOE OSTI2023

A Metallurgical Inspection Method to Assess the Damage in Performance-Limiting Nb 3 Sn Accelerator Magnet Coils

The design and production of Nb 3 Sn-based dipole and quadrupole magnets is critical for the realization of the High-Luminosity Large Hadron Collider (HL-LHC) at the European Organization for Nuclear Research (CERN). Nb 3 Sn superconducting coils are aimed at enhancing the bending and focusing strengths of accelerator magnets for HL-LHC and beyond. Due to the brittle nature of Nb 3 Sn, the coil fabrication steps are very challenging and require very careful QA/QC. Flaws in the Nb 3 Sn filaments may lead to quenches, and eventually, performance limitation below nominal during magnet testing. A novel inspection method, including advanced non-destructive and destructive techniques, was developed to explore the root-causes of quenches occurring in performance-limiting coils. The most relevant results obtained for MQXF coils through this innovative inspection method are presented. This approach allows for precise assessment of the physical events associated to the quenches experienced b y magnet coils, mainly occurring under the form of damaged strands with transversely broken sub-elements. Coil-slice preparation, micro-optical observations of transverse and longitudinal cross-sections, and a deep etching technique of copper will be illustrated in the present work, with a focus on the results achieved for a CERN coil from a non-conforming quadrupole magnet prototype, and two coils fabricated in the US, in the framework of the Accelerator Upgrade Project (AUP) collaboration, from two different non-conforming quadrupole magnets, respectively. The results obtained through the proposed inspection method will be illustrated.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Aerobic Methoxydotrophy: Growth on Methoxylated Aromatic Compounds by Methylobacteriaceae

Pink-pigmented facultative methylotrophs have long been studied for their ability to grow on reduced single-carbon (C 1 ) compounds. The C 1 groups that support methylotrophic growth may come from a variety of sources. Here, we describe a group of Methylobacterium strains that can engage in methoxydotrophy: they can metabolize the methoxy groups from several aromatic compounds that are commonly the product of lignin depolymerization. Furthermore, these organisms can utilize the full aromatic ring as a growth substrate, a phenotype that has rarely been described in Methylobacterium . We demonstrated growth on p -hydroxybenzoate, protocatechuate, vanillate, and ferulate in laboratory culture conditions. We also used comparative genomics to explore the evolutionary history of this trait, finding that the capacity for aromatic catabolism is likely ancestral to two clades of Methylobacterium , but has also been acquired horizontally by closely related organisms. In addition, we surveyed the published metagenome data to find that the most abundant group of aromatic-degrading Methylobacterium in the environment is likely the group related to Methylobacterium nodulans , and they are especially common in soil and root environments. The demethoxylation of lignin-derived aromatic monomers in aerobic environments releases formaldehyde, a metabolite that is a potent cellular toxin but that is also a growth substrate for methylotrophs. We found that, whereas some known lignin-degrading organisms excrete formaldehyde as a byproduct during growth on vanillate, Methylobacterium do not. This observation is especially relevant to our understanding of the ecology and the bioengineering of lignin degradation.

59 BASIC BIOLOGICAL SCIENCES↗