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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantum-Inspired Power System Reliability Assessment

To enable an in-depth study of power system operation and planning, the assessment of standard reliability indices is inevitable. The Monte Carlo Simulation (MCS) approach is a broadly used method in replacing the analytical methods in reliability indices assessment. The accuracy of MCS, however, highly depends on the sampling size, and hence, a complicated system with large number of components requires a large sampling size and daunting computational effort. To address this shortcoming, we, in this paper attempt to take advantage of potentials of the quantum computing (QC) for power system reliability assessment by realizing the following contributions: 1) an innovative quantum model designed for reliability assessment; 2) a quantum circuit that achieves the quadratic speed up compared to the classical MCS method; 3) an efficient quantum amplitude estimation (QAE) algorithm to accurately evaluate the reliability indices. The accuracy and efficacy of the quantum reliability method are extensively verified and demonstrated on both radial and mesh distribution systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Anomalous size effect on yield strength enabled by compositional heterogeneity in high-entropy alloy nanoparticles

High-entropy alloys (HEAs), although often presumed to be random solid solutions, have recently been shown to display nanometer-scale variations in the arrangements of their multiple chemical elements. Here, we study the effects of this compositional heterogeneity in HEAs on their mechanical properties using in situ compression testing in the transmission electron microscope (TEM), combined with molecular dynamics simulations. We report an anomalous size effect on the yield strength in HEAs, arising from such compositional heterogeneity. By progressively reducing the sample size, HEAs initially display the classical “smaller-is-stronger” phenomenon, similar to pure metals and conventional alloys. However, as the sample size is decreased below a critical characteristic length (~180 nm), influenced by the size-scale of compositional heterogeneity, a transition from homogeneous deformation to a heterogeneous distribution of planar slip is observed, coupled with an anomalous “smaller-is-weaker” size effect. Atomic-scale computational modeling shows these observations arise due to compositional fluctuations over a few nanometers. These results demonstrate the efficacy of influencing mechanical properties in HEAs through control of local compositional variations at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Plasma catalytic ammonia synthesis on Ni nanoparticles: The size effect

Herein we demonstrate the synthesis of ammonia via atmospheric DBD plasma discharge over nickel nanoparticles supported on silica. We evaluated the performance of nickel nanoparticles with average sizes of 5.6nm and 13.5 nm. The smallest nanoparticle size sample resulted in an ammonia synthesis rate at least 2 times higher than that of the larger nanoparticle size sample and nickel bulk at equimolar composition. This allowed us to observe experimentally, for the first time, the size effect in the area of plasma catalysis. Moreover, the presented results allowed us to investigate a hydrogen sink metal such as nickel under atmospheric plasma conditions and determine the gas composition that resulted beneficial for this surface chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Flaw-insensitive fracture of a micrometer-sized brittle metallic glass

Brittle materials, such as oxide glasses, are usually very sensitive to flaws, giving rise to a macroscopic fracture strength that is much lower than that predicted by theory. The same applies to metallic glasses (MGs), with the important difference that these glasses can exhibit certain plastic strain prior to catastrophic failure. Here we consider the strongest metallic alloy known, a ternary Co 55 Ta10B 35 MG. In this work, we show that this macroscopically brittle glass is flaw-insensitive at the micrometer scale. This discovery emerges when testing pre-cracked specimens with self-similar geometries, where the fracture stress does not decrease with increasing pre-crack size. The fracture toughness of this ultra-strong glassy alloy is further shown to increase with increasing sample size. Both these findings deviate from our classical understanding of fracture mechanics, and are attributed to a transition from toughness-controlled to strength-controlled fracture below a critical sample size.

36 MATERIALS SCIENCE↗
DOE OSTI2026

An Overview of Manufacturing Controls for Production of High-Consequence, Single-Use Systems

Extended Testing (Crowder, et al. (2025) ) is a reliability demonstration technique that can be used to dramatically reduce sample size requirements. Manufacturing controls are needed to supplement extended testing by identifying production issues that a reduced sample sizes might overlook, especially built-in, or latent, manufacturing defects. This report focuses on some of the most commonly used, yet most impactful, manufacturing control tools that are used to limit production-related defects and efficiently screen any remaining defects at final inspection. These tools include statistical process control (SPC), acceptance sampling, environmental stress screening (HASS and ESS), and mistake proofing. The goal is to minimize the probability that built-in defects ever reach the customer. In terms of nuclear weapons (NW), the goal is to prevent defective units from ever entering the nation’s NW stockpile. Examples of each of the techniques are illustrated with case studies.

42 ENGINEERING↗
DOE OSTI2025

Verification of the Uniformly-Ordered Binary Decision Algorithm in Correlated-Benchmark Whisper Calculations

Whisper is a nuclear criticality safety code package that aids analysts in validation exercises by computing upper subcritical limits (USL) for applications of interest. To obtain statistically meaningful, significant, and conservative USLs, the analyst must ensure that Whisper selects a sufficient number of benchmarks that are neutronically similar to the application. Many of the available benchmarks are correlated but are currently treated as independent, leading to an artificially small sample size, as their individual information contributions will be overestimated. To aid the analyst in obtaining a sufficient sample size, prior work [2] demonstrated application of the Uniformly-Ordered Binary Decision (UOBD) algorithm in adjusting benchmark weights to account for benchmark correlations. This work provides verification of the Whisper implementation and considers the impact of updated benchmark correlations compared to those available previously. We demonstrate that the UOBD algorithm performs as expected with an analytic example. With HEU-SOL-THERM-001 cases 1 through 10 as the applications, we compare the USLs computed with benchmark correlations available in the Whisper 1.1 release only to those computed with additional benchmark correlations from DICE 2023 and demonstrate substantive differences.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Normative Ranges for Oculomotor and Reaction Time Tests in U.S. Military Service Members and Veterans

AbstractBackground Oculomotor and reaction time tests are frequently used assessments of vestibular symptoms, traumatic brain injury (TBI), or other neurological disorders in both clinical and research contexts. When interpreting these tests it is important to have a reference interval (RI) as a comparison for what constitutes a typical/expected response; however, the current body of research has only limited information regarding normative ranges calculated according to established standards or for a military-specific sample.Purpose The purpose of the present study was to describe RIs for oculomotor and reaction time tests in a cohort of service members and veterans (SMVs) for use as comparators by clinicians and scientists.Research Design Descriptive.Study Sample Participants were prospectively enrolled in the Defense and Veterans Brain Injury Center-Traumatic Brain Injury Center of Excellence 15-year Longitudinal Traumatic Brain Injury Study. Only SMVs without a history of TBI or blast exposure were included in the RI calculations.Data Collection and Analysis The test paradigms included in this analysis were: smooth pursuit, prosaccades, antisaccades, saccades and reaction time, predictive saccades, optokinetic nystagmus, auditory reaction time, and visual reaction time. Nonparametric methods, based on the U.S. Food and Drug Administration's recognized consensus standards, were used to calculate 95% RIs. A comparison between the calculated RIs and those available from previously published research is provided.Results Summary statistics and RIs were calculated for 47 outcome parameters from 13 oculomotor and reaction time tests. Sample sizes and age ranges varied across outcome parameters depending on the availability of reference values for RI calculations. The sample sizes used to calculate RIs ranged from 51 to 69. The age of SMVs included in each RI ranged from 19 to 61 years with mean ages ranging from 37 to 39 years. Similarities/differences between the RIs in the present study and those in previously published research are highly dependent on the outcome parameter; however, in general, the RIs in the present study tended to be somewhat wider.Conclusion The RIs provided in this paper can serve as comparisons for clinicians and scientists who are utilizing these oculomotor and reaction time testing paradigms in similar cohorts of patients or research participants.

Audiology & Speech-Language Pathology↗
DOE OSTI2023

Establishing the accuracy of position–specific carbon isotope analysis of propane by GC–pyrolysis–GC–IRMS

Rationale: Position-specific (PS) δ 13 C values of propane have proven their ability to provide valuable information on the evolution history of natural gases. Two major approaches to measure PS δ 13 C values of propane are isotopic 13 C nuclear magnetic resonance (NMR) and gas chromatography-pyrolysis-gas chromatography-isotope ratio mass spectrometry (GC-Py-GC-IRMS). Measurement accuracy of the isotopic 13 C NMR has been verified, but the requirements of large sample size and long experimental time limit its applications. GC-Py-GC-IRMS is a more versatile method with a small sample size, but its accuracy has not been demonstrated. Methods: We measured the PS δ 13 C values of propane from nine natural gases using both 13 C NMR and GC-Py-GC-IRMS, then evaluated the accuracy of the GC-Py-GC-IRMS method. Results: The results show that large carbon isotope fractionations occurred for both terminal and central carbons within propane during pyrolysis. The isotope fractionations during the pyrolysis are reproducible at optimum conditions, but vary between the two GC-Py-GC-IRMS systems tested, affected by experimental conditions (e.g., pyrolysis temperature, flow rate, and reactor conditions). Conclusions: It is necessary to evaluate and calibrate each GC-Py-GC-IRMS system using propane gases with accurately determined PS δ 13 C values. Finally, this study also highlights a need for PS isotope standards for propane and other molecules (e.g., butane and acetic acid).

03 NATURAL GAS↗
DOE OSTI2022

Micro- to nano-scale areal heterogeneity in pore structure and mineral compositions of a sub-decimeter-sized Eagle Ford Shale

Mineral and organic matter compositions & pore structures of fine-grained shale influence reservoir properties. To improve the understanding of spatial heterogeneity in core-sized samples, methods of microscale X-ray fluorescence (μXRF) mapping, (ultra-) small-angle x-ray scattering [(U)SAXS] and wide-angle X-ray scattering (WAXS) have been used to determine elemental, pore-structure variations at scales up to ~10 cm on two samples prepared at circular and rectangular orientations from a piece of Eagle Ford Shale outcrop in South Texas, USA. In addition, thin section petrography and field emission-scanning electron microscopy observations, X-ray diffraction (XRD), total organic carbon, and pyrolysis were utilized to investigate the potential spatial heterogeneity of pore types, mineral and organic matter compositions for cm-sized samples at both orientations. Overall, the siliceous-carbonate mineral contents in these two samples (8 cm×8 cm×0.08 cm and 5 cm × 8 cm × 0.08 cm, in terms of width×length×thickness) of carbonate-rich Eagle Ford Shale vary between laminations at mm scales. For the circular sample, porosity and specific surface area (SSA) variations range from 0.82 to 3.04% and 1.51 to 14.1 m 2 /g, respectively. For the rectangular sample, values for porosity and SSA vary from 0.93 to 2.50% and 3.95 to 10.8 m 2 /g. By analyzing six selected sub-samples on each of two samples with (U)SAXS and XRD techniques, nm-sized pores are mainly interparticle ones in the higher calcite regions, where the porosity is also relatively lower, while the lower calcite regions consist of both interparticle and intraparticle pore types with higher porosity. Lastly, the μXRF and (U)SAXS mappings are combined to generate porosity distribution maps to provide more insights about sample heterogeneity related to the laminations and fractures at our observational scales.

58 GEOSCIENCES↗
DOE OSTI2024

Toward the validation of crowdsourced experiments for lightness perception

Crowdsource platforms have been used to study a range of perceptual stimuli such as the graphical perception of scatterplots and various aspects of human color perception. Given the lack of control over a crowdsourced participant’s experimental setup, there are valid concerns on the use of crowdsourcing for color studies as the perception of the stimuli is highly dependent on the stimulus presentation. Here, we propose that the error due to a crowdsourced experimental design can be effectively averaged out because the crowdsourced experiment can be accommodated by the Thurstonian model as the convolution of two normal distributions, one that is perceptual in nature and one that captures the error due to variability in stimulus presentation. Based on this, we provide a mathematical estimate for the sample size needed to produce a crowdsourced experiment with the same power as the corresponding in-person study. We tested this claim by replicating a large-scale, crowdsourced study of human lightness perception with a diverse sample with a highly controlled, in-person study with a sample taken from psychology undergraduates. Our claim was supported by the replication of the results from the latter. These findings suggest that, with sufficient sample size, color vision studies may be completed online, giving access to a larger and more representative sample. With this framework at hand, experimentalists have the validation that choosing either many online participants or few in person participants will not sacrifice the impact of their results.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Genome‐wide association and genomic prediction for yield and component traits of Miscanthus sacchariflorus

Abstract Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic‐enabled breeding tools, like marker‐assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome‐wide association (GWA) and genomic prediction of biomass yield and 14 yield‐components traits in Miscanthus sacchariflorus . We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single‐nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield‐component traits in M. sacchariflorus that may facilitate marker‐assisted breeding for biomass yield. Genomic prediction accuracy for the yield‐related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia ( N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus . This study indicated that MAS and genomic prediction will likely be beneficial for conducting population‐improvement of M. sacchariflorus .

09 BIOMASS FUELS↗
DOE OSTI2023

Data for Genome-Wide Association and Genomic Prediction for Yield and Component Traits of Miscanthus sacchariflorus

Accelerating biomass improvement is a major goal of miscanthus breeding. The development and implementation of genomic-enabled breeding tools, like marker-assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of miscanthus breeding. The present study conducted genome-wide association (GWA) and genomic prediction of biomass yield and 14 yield-components traits in Miscanthus sacchariflorus . We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across four years in one subtropical and three temperate locations and genotyped with 268,109 single-nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield-component traits in M. sacchariflorus that may facilitate marker-assisted breeding for biomass yield. Genomic prediction accuracy for the yield-related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus . This study indicated that MAS and genomic prediction will likely be beneficial for conducting population-improvement of M. sacchariflorus .

Biomass Analytics↗
DOE OSTI2022

Classification of animal sounds in a hyperdiverse rainforest using convolutional neural networks with data augmentation

To protect tropical forest biodiversity, we need to be able to detect it reliably, cheaply, and at scale. Automated detection of sound producing animals from passively recorded soundscapes via machine-learning approaches is a promising technique towards this goal, but it is constrained by the necessity of large training data sets. Using soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN), we investigate i) the minimum viable training data set size for accurate prediction of call types (‘sonotypes’), and ii) the extent to which data augmentation and transfer learning can overcome the issue of small and imbalanced training data sets. We found that even relatively high sample sizes (>80 per sonotype) lead to mediocre accuracy, which however improved significantly with data augmentation and transfer learning, including at extremely small sample sizes (3 per sonotype), regardless of taxonomic group or call characteristics. Neither transfer learning nor data augmentation alone achieved high accuracy. Our results suggest that transfer learning and data augmentation could make the use of CNNs to classify species’ vocalizations feasible even for small soundscape-based projects with many rare species. Retraining our open-source model requires only basic programming skills which makes it possible for individual conservation initiatives to match their local context, in order to enable more evidence-informed management of biodiversity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Plasma GFAP for populational enrichment of clinical trials in preclinical Alzheimer's disease

Abstract INTRODUCTION Cognitively unimpaired (CU) amyloid beta (Aβ)+ individuals with elevated plasma glial fibrillary acidic protein (GFAP) have an increased risk of Alzheimer's disease (AD)‐related progression. We tested the utility of plasma GFAP for population enrichment CU populations in clinical trials. METHODS We estimated longitudinal progression, effect size, and costs of hypothetical clinical trials designed to test an estimated 25% drug effect on reducing tau positron emission tomography (PET) accumulation in the medial temporal lobe (MTL) and temporal neocortical region (NEO‐T). RESULTS CU GFAP+/Aβ+ individuals present an increased annual rate of change and effect size in tau PET MTL and tau PET NEO‐T compared to the other groups. An enrichment strategy selecting CU GFAP+/Aβ+ individuals would require a smaller sample size (≈ 57% reduction) and fewer Aβ PET scans (≈ 74% reduction) than trials enriched with Aβ PET alone, reducing total clinical trial costs by up to 64%. DISCUSSION Our results suggest that clinical trials focusing on preclinical AD recruiting Aβ+ individuals with elevated GFAP levels would improve cost effectiveness. Highlights Cognitively unimpaired (CU) glial fibrillary acidic protein (GFAP)+/amyloid beta (Aβ)+ shows increased changes in tau positron emission tomography (PET) . CU GFAP+/Aβ+ enriched clinical trials require a reduced sample size compared to Aβ+ only. CU GFAP+/Aβ+ enrichment reduces Aβ PET scans required and costs. CU GFAP+/Aβ+ enrichment allows the selection of individuals at early stages of the Alzheimer's disease continuum.

Neurosciences & Neurology↗
DOE OSTI2021

The Initial–Final Mass Relation for Hydrogen-deficient White Dwarfs

The initial–final mass relation represents the total mass lost by a star during the entirety of its evolution from the zero age main sequence to the white-dwarf cooling track. The semiempirical initial–final mass relation (IFMR) is largely based on observations of DA white dwarfs, the most common spectral type of white dwarf and the simplest atmosphere to model. We present a first derivation of the semiempirical IFMR for hydrogen-deficient (non-DA) white dwarfs in open star clusters. We identify a possible discrepancy between the DA and non-DA IFMRs, with non-DA white dwarfs ≈0.07 M {sub ⊙} less massive at a given initial mass. Such a discrepancy is unexpected based on theoretical models of non-DA formation and observations of field white dwarf mass distributions. If real, the discrepancy is likely due to enhanced mass loss during the final thermal pulse and renewed post-AGB evolution of the star. However, we are dubious that the mass discrepancy is physical and instead is due to the small sample size, to systematic issues in model atmospheres of non-DAs, and to the uncertain evolutionary history of Procyon B (spectral type DQZ). A significantly larger sample size is needed to test these assertions. In addition, we also present Monte Carlo models of the correlated errors for DA and non-DA white dwarfs in the initial–final mass plane. We find the uncertainties in initial–final mass determinations for individual white dwarfs can be significantly asymmetric, but the recovered functional form of the IFMR is grossly unaffected by the correlated errors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Creating Accurate Methane Emission Inventories through Data-Driven Airborne Survey Strategies

Because natural gas emits less carbon than other fossil fuels, it holds promise as a green energy transition fuel. However, the overall carbon footprint of natural gas is significantly elevated by methane emissions that occur during its production and transmission (Cusworth et al. 2022). Methane “super-emitters,” while comprising only about 1% of sites, are responsible for the majority of oil- and gas-sourced methane emissions, making their detection and mitigation critical in reducing the climate impact of natural gas and in meeting national and global sustainability goals (Sherwin et al. 2024). Yet, despite advancements in detection, significant uncertainties remain regarding the size, frequency, and duration distributions of methane emissions (e.g., Frankenberg et al. 2016, Cusworth et al. 2022, Chen, Sherwin et al. 2022, Conrad et al. 2023, Johnson et al. 2023, Sherwin et al. 2024) underscoring the need for comprehensive emissions inventories segmented by basin across the US. Airborne surveys are well-suited for collecting data to build these comprehensive, basin-level inventories because they allow for extensive spatial coverage, and have the spatial resolution, and the sensitivity to pinpoint individual methane sources. As remote sensing technologies enable rapid basin-scale surveys, it is imperative to establish scientifically and statistically robust standards to generate reliable and actionable emissions inventories. Recent work has shown that differences in airborne sampling strategies, detection technologies, and analysis can lead to large differences between survey conclusions if not correctly accounted for (Chen et al. 2024). This elevates the importance of incorporating proper sampling and analysis techniques when designing a methane emissions monitoring campaign to produce accurate results and facilitate cross-study comparisons. In this paper, we describe a survey strategy designed using the latest conclusions from the literature to align results from different aerial surveys. We identify several sampling and analysis principles, including large sample sizes, balanced sampling across oil and gas production, careful survey area definition, and a unified protocol for analysis, to be vital to producing an unbiased estimate of basin-scale emissions. We present results from a Department of Energy-funded project that deployed this survey strategy in two understudied oil and gas- producing regions in the United States: the Haynesville Basin in Texas and Louisiana, and the Woodford Shale in the Anadarko Basin in Oklahoma.

03 NATURAL GAS↗
DOE OSTI2023

A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial with a Pairwise Null Clustering Prior

A multi-armed trial based on ordinal outcomes is proposed that leverages a flexible non-proportional odds cumulative logit model and numerical utility scores for each outcome to determine treatment optimality. This trial design uses a Bayesian clustering prior on the treatment effects that encourages the pairwise null hypothesis of no differences between treatments. A group sequential design is proposed to determine which treatments are clinically different with an adaptive decision boundary that becomes more aggressive as the sample size or clinical significance grows, or the number of active treatments decreases. A simulation study is conducted for 3 and 5 treatment arms, which shows that the design has superior operating characteristics (family wise error rate, generalized power, average sample size) compared to utility designs that do not allow clustering, a frequentist proportional odds model, or a permutation test based on empirical mean utilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Hollow fiber mid-ID spectrometer with UV laser ablation sampling for fine spatial reoslution of isotope ratios in solids

We describe a system that combines isotope ratio analysis via mid-infrared (Mid-IR) laser absorption spectroscopy with fine spatial resolution sampling using a UV pulsed laser. The UV laser ablates a pit in a solid on the order of 10 microns in diameter. The sample-derived particulates resulting from laser ablation pass through a micro-combustor, and the resulting gas is analyzed using Mid-IR laser absorption spectroscopy in a capillary absorption spectrometer (CAS). The CAS uses a hollow fiber optic waveguide with a reflective inner coating and a small internal volume on the order of 1ml. The hollow fiber both guides the laser light from source to detector and contains the gas sample at reduced pressure. Near unity overlap between the laser beam and sample enables sensitive analysis with ultra-small sample size. A prototype system has been demonstrated to enable stable carbon isotopic analysis (d13C) with 1 per mil precision using <1 picomole of carbon and is currently being used to study nutrient exchange in soil/root/microbial rhizosphere studies. The smaller sample size of this system is enabling fine spatial resolution analysis (on the order of 10 microns), which is roughly an order of magnitude smaller than was possible with an isotope ratio mass spectrometer (IRMS). In addition to rhizosphere studies, the system can provide a useful tool for fine scale isotope analysis with applications in biological, forensic, and environmental science.

capillary absorption spectroscopy, laser ablation,↗