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DOE OSTI2020

Detecting differential protein abundance by combining peptide level P -values

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here in this paper, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗

NASA NTRS1995

Gas chromatography/ion mobility spectrometry as a hyphenated technique for improved explosives detection and analysis

Ion Mobility Spectrometry (IMS) is currently being successfully applied to the problem of on-line trace detection of plastic and other explosives in airports and other facilities. The methods of sample retrieval primarily consist of batch sampling for particulate residue on a filter card for introduction into the IMS. The sample is desorbed into the IMS using air as the carrier and negative ions of the explosives are detected, some as an adduct with a reagent ion such as Cl(-). Based on studies and tests conducted by different airport authorities, this method seems to work well for low vapor pressure explosives such as RDX and PETN, as well as TNT that are highly adsorptive and can be found in nanogram quantities on contaminated surfaces. Recently, the changing terrorist threat and the adoption of new marking agents for plastic explosives has meant that the sample introduction and analysis capabilities of the IMS must be enhanced in order to keep up with other detector developments. The IMS has sufficient analytical resolution for a few threat compounds but the IMS Plasmogram becomes increasingly more difficult to interpret when the sample mixture gets more complex.

Mercado, AL↗

DOE OSTI2023

High-dimensional isotomics, part 2: Observations of over 100 constraints on methionine's isotome

The abundances of different isotopic forms of a compound, or isotopologues, will vary based on its physical and chemical history. The number of isotopologues increases combinatorically with the size of a molecule, and even small molecules such as amino acids have thousands of potentially observable isotopic variants. However, due to the analytical challenges of separating and observing isotopologues, only a few dimensions of isotopic diversity are routinely measured. Overcoming these challenges requires both an experimental method to observe many isotopic properties and a theoretical framework for interpreting these experiments. In Part 1, we presented such a theoretical framework; here, we demonstrate an experimental method, which we apply to methionine. Our approach uses a Q Exactive HF Orbitrap to perform several “M + N experiments”, where a sample is ionized, a subset of its isotopologues with cardinal mass N daltons greater than the unsubstituted isotopologue is selected and fragmented, and the proportions of all detectable isotopic forms of those fragment ions are quantified. We perform M + 1, M + 2, M + 3, and M + 4 experiments of a methionine sample and standard where the sample has a 100 ‰ enrichment of 13C at the methyl carbon relative to the natural 13C abundance at that position in the standard, and is otherwise identical to the standard. We observe isotopic forms of 8 fragment ion species for each version of the M + N experiment. With the assistance of a forward model of expected mass spectra, we identify isotopic peaks for each fragment ion based on observed mass and abundance, screen these for data quality, and quantify abundances for 146 unique isotopic peaks at precisions of ≈ 0.3–3 ‰. We present our direct observations and use them to reconstruct the concentrations of 19 individual singly, doubly, and triply-substituted isotopologues; doing so gives fewer constraints and broader error bars than working with the direct observations, but may be more interpretable for some applications. We also examine possibilities for measuring additional peaks, which are primarily limited by the detection limit of the Orbitrap-IRMS method. We then suggest some possible uses of our direct measurements for chemical forensics and hypothesis testing. Furthermore, our results demonstrate the diversity of isotopic constraints currently observable and interpretable for organic molecules.

58 GEOSCIENCES↗

DOE OSTI2021

A simple numerical method for evaluating heat dissipation from curved wires with periodic applied heating

In many situations, the dual-purpose heater/thermometer wires used in the three-omega method – one of the most precise and sensitive techniques for measuring the thermal conductivity of thin films and interfaces – must include bends and curves to avoid obstructions on the surface of a sample. Although the three-omega analysis assumes that the heating wire is infinitely long and straight, recent experimental work has demonstrated that in some cases curved-wire geometries can be used without introducing detectable systematic error. We describe a general numerical method that can be used to calculate the temperature of three-omega heating wires with arbitrary wire geometries. Furthermore, this method provides experimentalists with a simple quantitative procedure for calculating how large the systematic error caused by a particular wire asymmetry will be. We show calculations of two useful cases: a straight wire with a single bend of arbitrary angle and a wire that forms a circle. We find that the amplitude of the in-phase temperature oscillations near a wire that forms a circle differs from the prediction using the analytic straight-line source solution by <12%, provided that the thermal penetration depth is less than ten times the radius of curvature of the wire path. The in-phase temperature amplitude 1.5 wire widths away from a 90° bend in a wire is within 11% of the straight-line source prediction for all penetration depths greater than the wire width. Our calculations indicate that the straight-line source solution breaks down significantly when the wire bend angle is less than 45°.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

NASA NTRS2016

Effect of the Presence of Chlorates and Perchlorates on the Pyrolysis of Organic Compounds: Implications for Measurements Done with the SAM Experiment Onboard the Curiosity Rover

The Mars Science Laboratory (MSL) Curiosity Rover carries a suite of instruments, one of which is the Sample Analysis at Mars (SAM) experiment. SAM is devoted to the in situ molecular analysis of gases evolving from solid samples collected by Curiosity on Mars surface/sub-surface. Among its three analytical devices, SAM has a gaschromatograph coupled to a quadrupole mass spectrometer (GC-QMS). The GC-QMS is devoted to the separation and identification of organic and inorganic material. Before proceeding to the GC-QMS analysis, the solid sample collected by Curiosity is subjected to a thermal treatment thanks to the pyrolysis oven to release the volatiles into the gas processing system. Depending on the sample, a derivatization method by wet chemistry: MTBSTFA of TMAH can also be applied to analyze the most refractory compounds. The GC is able to separate the organic molecules which are then detected and identified by the QMS (Figure 1). For the second time after the Viking landers in 1976, SAM detected chlorinated organic compounds with the pyrolysis GC-QMS experiment. The detection of perchlorates salts (ClO4-) in soil at the Phoenix Landing site suggests that the chlorohydrocarbons detected could come from the reaction of organics with oxychlorines. Indeed, laboratory pyrolysis experiments have demonstrated that oxychlorines decomposed into molecular oxygen and volatile chlorine (HCl and/or Cl2) when heated which then react with the organic matter in the solid samples by oxidation and/or chlorination processes.

Millan, M.↗

DOE OSTI2022

Optical sensing of aqueous nitrate anion by a platinum( II ) triimine salt based solid state material

Selective and quantitative measurement of aqueous nitrate (NO 3 – ) anion is achieved using solid [Pt(Cl-4-tpy)Cl]ClO 4 salt (Cl-4-tpy = 4-chloro-2,2':6'2''-terpyridine), and as the salt supported on controlled porous glass. This detection method relies on the color change of the Pt(II) complex from yellow to red and intense luminescence response upon ClO 4 – exchange with NO 3 – due to concomitant enhancement of Pt∙∙∙Pt interactions. Finally, the spectroscopic response is highly selective for NO 3 – over a large range of halides and oxoanions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Np-237 LoD Measurement by TIMS-Multi-Ion Counting Total Evaporation [Poster]

Requests for ultra-low-level 237 Np analyses highlight the importance of a robust chemical purification and measurement technique for Np. In FY2022, LANL is quantifying the instrumental 237 Np LoD. Need to incorporate a chemical purification procedure for TIMS-MICTE sample preparation. What is the recovery of the chemical purification procedure? What is the ultimate detection limit that can be achieved while incorporating a chemical purification procedure? We have adapted and refined the Pu TIMS-MICTE method for Np analyses. We have determined expected uncertainties for 237 Np TIMS-MICTE measurements. We have increased sensitivity through technique improvements. We have improved method reliability for Np analyses and increased throughput. Apply knowledge gained from progress made in FY2022 to further develop Np sample purification and analyses by TIMSMICTE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2020

Analysis of Sample Acquisition Dynamics Using Discrete Element Method

The analysis presented in this paper is conducted in the framework of the Ocean Worlds Autonomy Testbed for Exploration Research and Simulation (OceanWATERS) project, currently under development at NASA Ames Research Center. OceanWATERS aims at designing a simulation environment which allows for testing autonomy of scientific lander missions to the icy moons of our solar system. Mainly focused on reproducing the end effector interaction with the inherent terrain, this paper introduces a novel discrete element method (DEM)-based approach to determine forces and torques acting on the lander’s scoop during the sample acquisition process. An accurate force feedback from the terrain on the scoop is required by fault-detection and autonomous decision-making algorithms to identify when the requested torque on the robotic arm’s joints exceeds the maximum available torque. Knowledge of the terrain force feedback significantly helps evaluating the arm’s links structural properties and properly selecting actuators for the joints. Models available in literature constitute a partial representation of the dynamics of the interaction. As an example, Balovnev derived an analytical expression of the vertical and horizontal force acting on a bucket while collecting a sample as a function of its geometry and velocity, soil parameters and reached depth. Although the model represents an adequate approximation of the two force components, it ignores the direction orthogonal to the scoop motion and neglects the torque. This work relies on DEM analysis to compensate for analytical models’ deficiencies and inaccuracies, i. e. provide force and torque 3D vectors, defined in the moving reference (body) frame attached to the scoop, at each instant of the sample collection process. Results from the first presented analysis relate to the specific OceanWATERS sampling strategy, which consists of collecting the sample through five consecutive passes with increasing depth, each pass following the same circularlinear- circular trajectory. Data is collected given a specific scoop design interacting with two types of bulk materials, which may characterize the surface of icy planetary bodies: snow and ice. Although specifically concerned with the OceanWATERS design, this first analysis provides the expected force trends for similar sampling strategies and allows to deduce phenomenological information about the general scooping process. In order to further instruct the community on the use of DEM tools as a solution to the sampling collection problem, two more analyses have been carried out, mainly focused on reducing the DEM computation time, which increases with a decrease in particle size. After running a set of identical simulations, where the only changing parameter is the size of the spherical particle, it is observed that the resulting force trajectories, starting from a given particle size, converge to the true trend. It is deducible that a further decrease in size yields negligible improvements in the accuracy, while it sensibly increases computation time. A final analysis aims at discussing limitations of approximating bulk material particles having a complex shape, e. g. ice fragments, with spheres, by comparing force trends resulting in the two cases for the same simulation scenario.

Catanoso, Damiana↗

NASA NTRS1993

Carbon analyses of IDP's sectioned in sulfur and supported on beryllium films

Carbon is the only major element in interplanetary dust whose abundance, distribution and chemical state are not well understood. Information about carbon could clarify the relationship between the various classes of IDP's, conventional meteorites, and sources (e.g., comets vs. asteroids). To date, the most reliable estimates of C abundance in Interplanetary Dust Particles (IDP's) have been obtained by analyzing particles on thick-flat Be substrates using thin-window energy-dispersive spectroscopy in the SEM. These estimates of C abundance are valid only if C is homogeneously distributed, because detected C x-rays originate from the outer 0.1 micrometers of the particle. An alternative and potentially more accurate method of measuring C abundances is to analyze multiple thin sections (each less than 0.1 less than 0.1 micrometers thick) of IDP's. These efforts however, have been stymied because of a lack of a suitable non-carbonaceous embedding medium and the availability of C-free conductive substrates. We have embedded and thin-sectioned IDP's in glassy sulfur, and transferred the thin sections to Be support films approximately 25 nm thick. The sections were then analyzed in a 200 KeV analytical TEM. S sublimes rapidly under vacuum in the TEM, leaving non-embedded sections supported on Be. Apart from quantitative C (and O) analyses, S sectioning dramatically expands the range of analytical measurements that can be performed on a single IDP.

Bradley, J. P.↗

DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

Noble gas component organization in Apollo 14 breccia 14318 I-129 and Pu-244 regolith chronology

Drozd et al. (1972) have first detected the presence of Xe-129 in lunar material. Bernatowicz et al. (1979, 1980) established that this xenon component resides exclusively on grain surfaces and confirmed previous observations that it is less tightly bound than other surface components. The present investigation extends many of the observations made on the basis of a study of the 14301 to 14318, which is the only other lunar regolith breccia for which the isotopic structure of 'parentless xenon' has been accurately resolved. Actually this isotope has two parents, including Pu-244 and I-129, which are both now extinct. Attention is given to the analyzed samples, the employed analytical methods, the petrology and chemistry of the grain size separates, and stepwise heating experiments. On the basis of the obtained results, it is concluded that the I-Pu-Xe system can be used as a chronometer.

Swindle, T. D.↗

NASA NTRSDate not supplied

An Overview of Research in Nondestructive Evaluation at the National Aeronautics and Space Administration’s Langley Research Center

The primary focus of the nondestructive evaluation (NDE) effort at Langley Research Center (LaRC) is to pioneer advances in NDE and structural health monitoring (SHM) through fundamental scientific research, technology development, and transferring these to the scientific and aerospace communities. This research supports a variety of NASA missions in space operations, exploration and aeronautics. The work presented will focus on the development of advanced inspection systems to detect critical flaws in both metallic and non-metallic materials and structures for aerospace applications. The emphasis is on increasing the performance of inspection systems to provide defect detection and quantitative characterization of the material state. LaRC NDE utilizes expertise in computational ultrasonic, thermal and eddy current simulations to reduce measured responses to size and locations of flaws, establish technique limitations and optimize performance. This presentation will highlight research into five strategic areas: (1) The development of novel solutions using state-of-the-art NDE technologies such as eddy current, ultrasound, thermography and x-ray computed tomography; (2) Research in the development of unique NDE technologies such as terahertz, wavefield imaging and nonlinear ultrasound; (3) The application of both computational and analytic models to NDE problems; (4) The development of advanced analytic methods to interpret large complex data sets; and (5) Advanced methods of applying and interrogating sensors for structural health monitoring.

K Elliott Cramer↗

NASA NTRSDate not supplied

An Overview of Research in Nondestructive Evaluation at the National Aeronautics and Space Administration’s Langley Research Center

NDE↗

DOE OSTI2022

Implementation of a practical Markov chain Monte Carlo sampling algorithm in PyBioNetFit

Abstract Summary Bayesian inference in biological modeling commonly relies on Markov chain Monte Carlo (MCMC) sampling of a multidimensional and non-Gaussian posterior distribution that is not analytically tractable. Here, we present the implementation of a practical MCMC method in the open-source software package PyBioNetFit (PyBNF), which is designed to support parameterization of mathematical models for biological systems. The new MCMC method, am, incorporates an adaptive move proposal distribution. For warm starts, sampling can be initiated at a specified location in parameter space and with a multivariate Gaussian proposal distribution defined initially by a specified covariance matrix. Multiple chains can be generated in parallel using a computer cluster. We demonstrate that am can be used to successfully solve real-world Bayesian inference problems, including forecasting of new Coronavirus Disease 2019 case detection with Bayesian quantification of forecast uncertainty. Availability and implementation PyBNF version 1.1.9, the first stable release with am, is available at PyPI and can be installed using the pip package-management system on platforms that have a working installation of Python 3. PyBNF relies on libRoadRunner and BioNetGen for simulations (e.g. numerical integration of ordinary differential equations defined in SBML or BNGL files) and Dask.Distributed for task scheduling on Linux computer clusters. The Python source code can be freely downloaded/cloned from GitHub and used and modified under terms of the BSD-3 license (https://github.com/lanl/pybnf). Online documentation covering installation/usage is available (https://pybnf.readthedocs.io/en/latest/). A tutorial video is available on YouTube (https://www.youtube.com/watch?v=2aRqpqFOiS4&t=63s). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing (Final Report)

The objective of this project is to address challenges in the design and development of scalable in-situ data clustering and analytics algorithms and software. Our goal is to develop parallel software consisting of a set of spatio-temporal data clustering and anomaly detection functions, both of which are very important for large-scale analysis and have wide applicability for in-situ runs as well as post-processing analysis. Our design principles for in-situ analysis consider the following: (1) identify parts of the computation can be done close to the data within the nodes, while it is still in memory; (2) extract analysis components can (and should) be performed in remote staging and analysis nodes; (3) develop error-bound approximation methods for applications tolerable for small errors; (4) identify the type of derived distributions and statistics, for spatio-temporal data, that can be kept locally in order to both accelerate computations and meet energy constraints in subsequent iterations and phases; (5) use a self-describing data format so that data can be consistent and understood among local storage (memory and SSDs) and at staging and analysis nodes, thereby providing portability and flexibility; (6) develop service-oriented functions that can schedule in-situ and post-hoc analysis tasks based on the dynamic requirements of applications. Our development focus is to produce the parallel data analysis software/library that will be scalable, reusable, extensible, and generic for applications in different disciplines. The software will be able to run in-situ with the simulations as well as post-hoc analysis. This approach will satisfy many synergistic requirements for data intensive applications executed on data coming from instruments and experiments. In particular, the proposed multilevel approach is directly applicable to perform design tradeoffs for running part of the algorithms near the instruments and the rest on remote (analysis) systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Novel Data Driven Noise Emulation Framework using Deep Neural Network for Generating Synthetic PMU Measurements

Sensors play a critical role in supporting day-to-day grid operations and they are essential to operator’s decision-making process. Furthermore, sensors and sensor behaviors need to be emulated with grid simulations to perform modeling studies and to design cutting edge power systems applications. Ensuring the accurate behavior of these applications requires accurate emulation of sensors and pertinent signals. However, most grid simulators and modeling tools assume either zero error scenarios or simplistic noise models that may not always correlate to real-world sensors. To address the above issue, this work presents an initial study on the noise characteristics of phasor measurement units (PMUs), along with models for recreating their unique noise signatures. The proposed methods (both analytical and machine-learning-based) provide a substantial increase in a sensor’s model fidelity, a feature that can be leveraged by an end-user application to yield more accurate system representations. The proposed methods were then applied to micro PMU data from the EPFL microgrid campus to extract sensor noise profiles. This data was used to train a deep learning model, which was tested to emulate the noise characteristics present in actual signals. Based on the observed results and the employed data-driven methodology, the proposed methods may be adapted to replicate the behavior of other grid sensors and power new applications capable of detecting sensor degradation and eventual device failures in near real-time.

PMU, noise emulation, synthetic measurements, deep↗

DOE OSTI2022

Direct isotopic analysis of solid uranium particulates on cotton swipes by microextraction-ICP-MS

Direct isotope ratio analysis of solid uranium particulates on cotton swipes was achieved using a solution-based microextraction technique, coupled to a quadrupole inductively coupled plasma - mass spectrometer (ICP-MS). This microextraction-ICP-MS methodology provides rapid isotopic analysis which could be applicable to nuclear safeguards measurements. Particulates of uranyl nitrate hexahydrate (UO 2 (NO 3 ) 2 ·6H 2 O) and uranyl fluoride (UO 2 F 2 ) ranging from 6 μm to 40 μm in length were transferred to cotton swipes with a particle manipulator. The microextraction probe then delivers a 5% nitric acid (HNO3) solvent onto the swipe surface to extract the uranium species. The extracted sample is then delivered to the ICP-MS for isotopic determination. The majority of uranium signal (~99% and ~94% for UO2(NO3)2·6H2O and UO 2 F 2 , respectively) was detected in the first 15 s extraction, while subsequent extractions on the same location had low or no U signal, suggesting near complete removal of the solid uranium compounds from the swipe surface. Ten samples (for each of the uranium compounds), were analyzed for their isotopic composition. For UO 2 (NO 3 ) 2 ·6H 2 O, the determined isotope ratios resulted in a % relative difference (% RD) from the referenced isotope ratios of 0.97, 1.0, and 7.3% for 234 U/ 238 U, 235 U/ 238 U, and 236 U/ 238 U, respectively. The % RD of the UO 2 F 2 isotope ratios were 1.9 and 0.60% for 234 U/ 238 U and 235 U/ 238 U, respectively. The preliminary limits of detection were determined to be 0.002, 0.4, and 60 pg for 234 U, 235 U and 238 U, respectively This work demonstrates that microextraction ICP-MS is a rapid and sensitive method that could directly determine uranium isotope ratios of UO 2 (NO 3 ) 2 ·6H 2 O and UO 2 F 2 particulates on cotton swipes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗