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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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DOE OSTI2021

Two-dimensional electronic–vibrational spectroscopy: Exploring the interplay of electrons and nuclei in excited state molecular dynamics

Two-dimensional electronic-vibrational spectroscopy (2DEVS) is an emerging spectroscopic technique which exploits two different frequency ranges for the excitation (visible) and detection (infrared) axes of a 2D spectrum. In contrast to degenerate 2D techniques, such as 2D electronic or 2D infrared spectroscopy, the spectral features of a 2DEV spectrum report cross correlations between fluctuating electronic and vibrational energy gaps rather than autocorrelations as in the degenerate spectroscopies. The center line slope of the spectral features reports on this cross correlation function directly and can reveal specific electronic-vibrational couplings and rapid changes in the electronic structure, for example. The involvement of the two types of transition moments, visible and infrared, makes 2DEVS very sensitive to electronic and vibronic mixing. 2DEV spectra also feature improved spectral resolution, making the method valuable for unraveling the highly congested spectra of molecular complexes. The unique features of 2DEVS are illustrated in this paper with specific examples and their origin described at an intuitive level with references to formal derivations provided. Although early in its development and far from fully explored, 2DEVS has already proven to be a valuable addition to the tool box of ultrafast nonlinear optical spectroscopy and is of promising potential in future efforts to explore the intricate connection between electronic and vibrational nuclear degrees of freedom in energy and charge transport applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Large-Scale Image Analysis for Investigating Spatio-Temporal Changes in Nuclear DNA Damage Caused by Nitrogen Atmospheric Pressure Plasma Jets

The effective clinical application of atmospheric pressure plasma jet (APPJ) treatments requires a well-founded methodology that can describe the interactions between the plasma jet and a treated sample and the temporal and spatial changes that result from the treatment. In this study, we developed a large-scale image analysis method to identify the cell-cycle stage and quantify damage to nuclear DNA in single cells. The method was then tested and used to examine spatio-temporal distributions of nuclear DNA damage in two cell lines from the same anatomic location, namely the oral cavity, after treatment with a nitrogen APPJ. One cell line was malignant, and the other, nonmalignant. The results showed that DNA damage in cancer cells was maximized at the plasma jet treatment region, where the APPJ directly contacted the sample, and declined radially outward. As incubation continued, DNA damage in cancer cells decreased slightly over the first 4 h before rapidly decreasing by approximately 60% at 8 h post-treatment. In nonmalignant cells, no damage was observed within 1 h after treatment, but damage was detected 2 h after treatment. Notably, the damage was 5-fold less than that detected in irradiated cancer cells. Moreover, examining damage with respect to the cell cycle showed that S phase cells were more susceptible to DNA damage than either G1 or G2 phase cells. The proposed methodology for large-scale image analysis is not limited to APPJ post-treatment applications and can be utilized to evaluate biological samples affected by any type of radiation, and, more so, the cell-cycle classification can be used on any cell type with any nuclear DNA staining.

cellular imaging↗
DOE OSTI2024

Rapid Prototyping for Nanoparticle-Based Photonic Crystal Fiber Sensors

The advent of nanotechnology has motivated a revolution in the development of miniaturized sensors. Such sensors can be used for radiation detection, temperature sensing, radio-frequency sensing, strain sensing, and more. At the nanoscale, integrating the materials of interest into sensing platforms can be a common issue. One promising platform is photonic crystal fibers, which can draw in optically sensitive nanoparticles or have its optical properties changed by specialized nanomaterials. However, testing these sensors at scale is limited by the the need for specialized equipment to integrate these photonic crystal fibers into optical fiber systems. Having a method to enable rapid prototyping of new nanoparticle-based sensors in photonic crystal fibers would open up the field to a wider range of laboratories that could not have initially studied these materials in such a way before. This manuscript discusses the improved processes for cleaving, drawing, and rapidly integrating nanoparticle-based photonic crystal fibers into optical system setups. The method proposed in this manuscript achieved the following innovations: cleaving at a quality needed for nanoparticle integration could be done more reliably (≈100% acceptable cleaving yield versus ≈50% conventionally), nanoparticles could be drawn at scale through photonic crystal fibers in a safe manner (a method to draw multiple photonic crystal fibers at scale versus one fiber at a time), and the new photonic crystal fiber mount was able to be finely adjusted when increasing the optical coupling before inserting it into an optical system (before, expensive fusion splicing was the only other method).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Determining the complex second-order optical susceptibility in macroscale van der Waals heterobilayers

In this work, we report on the experimental characterization of the second-order susceptibility in MoSe 2 /WS 2 heterobilayers, including their hidden complex phases. To this end, we developed a heterodyne-detection scheme for second-harmonic generation and applied it to macroscale heterobilayer samples prepared using the gold-tape exfoliation method. The heterodyne scheme enabled us to distinguish the relative orientation of the crystal domains, and furthermore, it allowed us to characterize the complex phases of the susceptibility relative to a reference quartz sample. By comparing the results from the monolayer regions and the heterobilayer region over several hundred microns of the sample area, we determined that the contribution of interlayer effects to second-harmonic generation is within the experimental uncertainty arising from the sample inhomogeneity. The results here provide fundamental quantitative information necessary for the precise design of nanophotonic systems based on stacking engineering.

2D materials↗
DOE OSTI2024

Colloidal InAs Quantum Dot-Based Infrared Optoelectronics Enabled by Universal Dual-Ligand Passivation

Solution-processed low-bandgap semiconductors are crucial to next-generation infrared (IR) detection for various applications, such as autonomous driving, virtual reality, recognitions, and quantum communications. In particular, III–V group colloidal quantum dots (CQDs) are interesting as nontoxic bandgap-tunable materials and suitable for IR absorbers; however, the device performance is still lower than that of Pb-based devices. Herein, a universal surface-passivation method of InAs CQDs enabled by intermediate phase transfer (IPT), a preliminary process that exchanges native ligands with aromatic ligands on the CQD surface is presented. IPT yields highly stable CQD ink. In particular, desirable surface ligands with various reactivities can be obtained by dispersing them in green solvents. Furthermore, CQD near-infrared (NIR) photodetectors are demonstrated using solution processes. Careful surface ligand control via IPT is revealed that enables the modulation of surface-mediated photomultiplication, resulting in a notable gain control up to ≈10 with a fast rise/fall response time (≈12/36 ns). Considering the figure of merit (FOM), EQE versus response time (or –3 dB bandwidth), the optimal CQD photodiode yields one of the highest FOMs among all previously reported solution-processed nontoxic semiconductors comprising organics, perovskites, and CQDs in the NIR wavelength range.

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

Quantification of manganese for ChemCam Mars and laboratory spectra using a multivariate model

In this work, we report a new calibration model for manganese using the laser-induced breakdown spectroscopy instrument that is part of the ChemCam instrument suite onboard the NASA Curiosity rover. The model has been trained using an expanded set of 523 manganese-bearing rock, mineral, metal ore, and synthetic standards. The optimal calibration model uses the Partial Least Squares (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO) multivariate techniques, with a novel “double blending” technique. We determined the detection limit for manganese is 82 ppm using a method blank procedure and is possibly as low as 27 ppm based on visual inspection of the spectra. Based on a representative test set consisting of measurements on 93 standards, the double blended multivariate model shows a Root Mean Squared Error of Prediction (RMSEP) accuracy of 1.39 wt% MnO for the full blended model. Employing a local RMSEP estimate where the model performance is evaluated based on nearby test samples, the accuracy is 0.03 wt% at the quantification limit (0.05 wt% MnO), 0.4 wt% accuracy at 1.0 wt% MnO, and 4.4 wt% accuracy at 100 wt% MnO. Precision is estimated using the standard deviation of the test set measurements, and is ±0.01 wt% MnO at the quantification limit, ±0.09 wt% MnO at 1.0 wt% MnO, and ± 2.1 wt% MnO at 100 wt% MnO (all 1 standard deviation). This new calibration is important for understanding the variation of manganese in the bedrock with the Curiosity rover on Mars, which provides insight into past redox conditions on Mars.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Calorimetric metal vapor adsorption energies for characterizing industrial catalyst support materials

Many important catalysts and electro-catalysts for energy and environmental technologies involve late transition metal atoms and nanoparticles dispersed across the surface of high-surface-area support materials, for example, oxide nanoparticles. The surface reactivity and long-term stability against deactivation of these materials depends strongly on the strength of bonding of the metal atom and nanoparticles to the support surface. Here, we introduce a novel calorimetry method that implements a LiTaO 3 crystal for heat detection and extends metal adsorption calorimetry to surfaces of nanomaterials that are deposited from liquid solutions, as is typical when preparing industrial catalyst supports. This allows one to measure the strength of metal atom bonding and the adhesion energy of metal nanoparticles to clean surfaces of technologically relevant catalyst supports. We demonstrate that here by measuring the heat of Ag vapor adsorption onto the clean surfaces of high-surface-area TiO 2 anatase powdered support materials (i.e., 5-nm-diameter nanoparticles). These heats of adsorption show a pulse-to-pulse standard deviation of only 9.3 kJ/mol (3.3%) for pulses containing only 1% of a monolayer of Ag vapor, with potential for a 5-fold reduction in noise if used with another metal evaporator. The calorimeter was also proven to work well measuring heats of Ag adsorption on calcium niobate(001) nanosheets deposited as thin films from liquid solution. Here, this calorimeter opens up the possibility of using metal adsorption calorimetry to screen catalyst support materials for the strengths with which they bind metal atoms and metal nanoparticles, offering new opportunities to discover better support materials or for basic scientific study of structure / function relationships with respect to metal / support bond energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics

Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.

08 HYDROGEN↗
DOE OSTI2023

Dynamic Species Reduction for Multi-Cycle CFD Simulations (Final Technical Report)

This project primarily sought to address some of the computational cost concerns of detailed simulations by developing improved methods of handling species transport, and chemical kinetics evaluation in a commercial 3D Computational Fluid Dynamics (CFD) environment. Secondary goals were to apply these techniques to fuels and conditions of interest to better understand the key species and reactions required to adequately capture cycle to cycle coupling. Improved modeling will lead to better understanding of these combustion modes and their dependence on fuel composition, which can then enable more clean and efficient engines, ultimately benefiting the consumer as well as the general public. Two approaches were used to address the computational cost. A “Dynamic Species Reduction” (DSR) procedure was developed to remove chemical species from the simulation during periods when chemical reactions were not expected to be important, particularly during gas exchange when temperatures are low and little fuel remains. This modelling procedure automatically detects relevant species to retain in the simulation domain based on their local concentration, removes species below a specified concentration threshold, and then adjusts the remaining species mass to conserve not only the number of H, C, and O atoms in each cell, but also the relative proportions between species and the heating value of the mixture in every cell. The second procedure was a “Product Directed Remapping” (PDR) updates the algorithm used to group individual computational cells for chemical kinetics evaluation to account for non-uniform temperature distributions and the CO to CO 2 ratio in the cells. The model techniques developed in this work successfully demonstrated computational performance improvements for a range of conditions relevant for Highly Dilute SI and HCCI engine operation. Runtime reductions of 10% were observed for small mechanisms, with further reductions of up to 36% observed for larger mechanisms. Improvements were primarily related to reducing the number of chemical species tracked during the gas exchange process using the Dynamic Species Reduction method. With smaller benefits observed from changes to the kinetics binning and evaluation strategy in the post combustion region using the PDR method. The results of this work show the potential for improved computational runtime for complicated simulations. They can and should be extended to additional conditions and new bio-derived and renewable fuels as they are developed and new kinetic mechanisms become available.

10 SYNTHETIC FUELS↗
DOE OSTI2022

A Review: PFAS Adsorption, Sensing, and Remediation with Engineered Nanoporous Materials

Per- and polyfluoroalkyl substances (PFAS) are among the most abundant environmental contaminant species. They are widespread due to uncontrolled industrial and commercial use and have been linked to health risks such as cancer. With rising global concerns about the public health effects of PFAS, there is an incentive to develop strategies for reliable monitoring and effective PFAS removal, particularly in drinking water. Traditional PFAS sensing techniques are inefficient due to long measurement times, high labor input, high costs, and limitations to ex situ analysis. Current commercially available sorbents for PFAS removal generally lack the ability to capture PFAS components rapidly and quantitatively. Furthermore, existing sorbents are notably inefficient in removing the more toxic, smaller PFAS chemical chains. Large-scale applications of these methods tend to be costly and resource-intensive. Pacific Northwest National Laboratory has developed unique strategies for PFAS sensing and removal with capture probe technology that has an affinity for fluorocarbons, including PFAS. For both sensing and removal, the customizable capture probes can target specific PFAS compounds since they used a metal-organic framework (MOF)-based technology that can be fine-tuned and molecularly tailored. This tailoring of the materials enables high PFAS sensitivity, selectivity, and faster uptake. PNNL’s capture probe materials have been developed into a sensing technique based on the interactions of the capture probes with PFAS at the molecular level and further transduced that to a quantifiable electrochemical response. The materials have been integrated into a sensor platform developed by New Jersey Institute of Technology (NJIT). Tunable capture probes with a range of detection sensitivities allow for faster and more sensitive PFAS detection limits as low as what appears to be 0.5 ng/L (compared to the U.S. Environmental Protection Agency health advisory level of 70 ng/L). Customization of the capture probe results in improved sorption capacities (fast kinetics and high capacities) compared to commercial granular activated carbons. This research applied various experimental and modeling tools to improve understanding the molecular level interactions between the sorbents towards PFAS adsorption properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Atom Identification in Bilayer Moiré Materials with Gomb-Net

Moiré patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, deconvoluting the moiré pattern. This enables layer-specific mapping of atomic positions and dopant distributions, unlike other commonly used segmentation models which struggle with moiré-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS 2 -WS 2(1–x) Se 2x heterostructure and found that layer-specific implantation sites are unaffected by the moiré pattern’s local energetic or electronic modulation. In conclusion, this advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.

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

AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state

The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ~104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M ⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Mechanistic Insights into Nonoxidative Ethanol Dehydrogenation on NiCu Single-Atom Alloys

Ethanol dehydrogenation presents a promising pathway towards the production of acetaldehyde, a valuable building block in chemicals production. Under non-oxidative conditions, the reaction is facilitated by supported Cu nanoparticles which afford reasonable activity and high selectivity. The stability issues associated with Cu nanoparticle sintering can be addressed by the addition of small amounts of Ni, which further boost reactivity while retaining selectivity. Despite the promise of NiCu single-atom alloys for non-oxidative ethanol dehydrogenation, little is known about the role of each component and the pathway of this mechanistically complex process. Herein, kinetic investigations from reactor tests identify C-H bond scission as the rate limiting step, while 1-hydroxyethyl is detected as the intermediate via IR spectroscopy. Temperature program desorption studies are employed to examine the effect of Ni coverage and to demonstrate that Ni atoms activate ethanol selectively at lower temperatures, resulting in higher acetaldehyde yield than pure Cu. Temperature program desorption experiments also reveal the spillover of intermediates from the Ni atom to neighboring Cu sites as a relevant step in the reaction pathway. Density functional theory calculations are used to investigate the 2 reaction energetics and to confirm that C-H bond scission is the initial reaction step, while a clear effect of H 2 partial pressure on the reaction pathway is realized. Further, counter to the expected behavior that all reaction steps take place on the Ni atoms, our degree of rate control analysis reveals that a mechanism involving spillover of the 1-hydroxyethyl intermediate from the Ni atom to the Cu surface, where it will dehydrogenate further, is more likely. Furthermore, our combined kinetic, spectroscopic, and theoretical approach sheds light on this complex reaction mechanism and represents a promising method for the understanding and designing of highly active, selective, and stable single-atom alloys for other multistep catalytic processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Application of Principal Component Analysis to Electrochemical Reprocessing PM and NMAC

In this report, data from an electrorefiner (ER) for nuclear fuel reprocessing is evaluated for process monitoring (PM) conclusions. This data comes from tests performed at the Idaho National Laboratory in 2022. Multivariate approaches utilizing methods of Principal Component Analysis (PCA) is applied. This is based off established work in process monitoring for fault detection in industrial facilities. This report will discuss the background, methods, and results of the application and some of the conclusions and applications that can be drawn from them. PCA is applied to two different electrorefiner (ER) operations that occurred at Idaho National Laboratory between August and October 2022. The first operation occurred with little incident while the second had several noted faults in the equipment in operational logs. The data from the first run was used to train the data for “normal” operations and applied to both sets of data to determine when operations were in an “off-normal” condition and identify where the fault occurs through PCA. PCA was able to identify off-normal events and identify the cause for off-normal operations. These identified off-normal events matched with the events and their causes in the operational logs. However, small amounts of variance in the data led to false detection of “off-normal” events. Thus, careful selection of training data and a-posteriori conclusions based off operator assessments will both be required for application of PCA to PM applications. This work demonstrated that multivariate approaches and latent variables are applicable to pyroprocessing PM applications and can be further expanded in future work as quality variables such as salt concentration from sensors and sampling become available.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Continuous MOF Membrane-Based Sensors via Functionalization of Interdigitated Electrodes

Three M-MOF-74 (M = Co, Mg, Ni) metal-organic framework (MOF) thin film membranes have been synthesized through a sensor functionalization method for the direct electrical detection of NO 2 . The two-step surface functionalization procedure on the glass/Pt interdigitated electrodes resulted in a terminal carboxylate group, with both steps confirmed through infrared spectroscopic analysis. This surface functionalization allowed the MOF materials to grow largely in a uniform manner over the surface of the electrode forming a thin film membrane over the Pt sensing electrodes. The growth of each membrane was confirmed through scanning electron microscopy (SEM) and X-ray diffraction analysis. The Ni and Mg MOFs grew as a continuous but non-defect free membrane with overlapping polycrystallites across the glass surface, whereas the Co-MOF-74 grew discontinuously. To demonstrate the use of these MOF membranes as an NO 2 gas sensor, Ni-MOF-74 was chosen as it was consistently fabricated as the best thin and homogenous membrane, as confirmed by SEM. The membrane was exposed to 5 ppm NO 2 and the impedance magnitude was observed to decrease 123× in 4 h, with a larger change in impedance and a faster response than the bulk material. Importantly, the use of these membranes as a sensor for NO 2 does not require them to be defect-free, but solely continuous and overlapping growth.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH 2 O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH 2 O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. Finally, it is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Low Count and Background Radionuclides Analysis - 20488

The US Department of Energy (DOE) is often faced with the need to evaluate radionuclides at low concentrations. When site sample data are likely to be close to threshold activity concentrations of interest, then the means by which the radiochemical analysis is performed and reported is critical. This situation can occur when differentiating from zero (presence/absence) for radionuclides that do not occur naturally, close comparison with environmental background for naturally occurring radionuclides, close comparison with a risk- or dose-based threshold concentrations of interest, or even comparisons across studies. There are several analytical issues that are of concern, but the two that appear to cause incorrect decisions to be made most often involve establishing detection limits and subtracting ambient background conditions in the laboratory. These issues are not critical when radionuclide activity concentrations are large relative to thresholds of concern, but they seem to be poorly understood when it matters. When the comparisons are important and are likely to be close to a threshold of interest, then the general contract with the analytical laboratories needs to be changed so that the right or appropriate data are obtained. The concern is that important decisions are made incorrectly more often as greater scrutiny is placed on DoE's radionuclide cleanup or monitoring decisions by the public and other stakeholders. Examples are presented of problems that have been observed for different projects, both within and outside the realm of DOE and NRC remediation and radioactive waste disposal problems, and solutions are offered that should lead to better data from which important decisions need to be made. The first example is from Los Alamos National Laboratory (LANL) and involves radionuclide concentrations in soil and rock beneath LANL's Material Disposal Area (MDA) G. An initial review of the data led to a conclusion that americium and plutonium are a long way present beneath MDA G. A more thorough review of the data that accounted properly for ambient background and the detection limits that had been established led to the opposite conclusion. Another example is from the Nevada National Security Site where tritium results from one of the wells were unexpectedly high. Proper understanding and analysis of ambient background led to the conclusion that the increased concentrations were not so obvious, and that a different contract with the analytical laboratory was needed to provide more appropriate data to support a better determination. Other examples are used from regulatory review of projects in Nevada, where background levels and secular equilibrium for naturally occurring radionuclides are not established correctly because of analytical issues. The same basic issues have also been found to create difficulties analyzing historical data from the West Valley Demonstration Project. There is evidence in the data that the apparent lack of secular equilibrium where it is expected to exist is related to ambient background subtraction or other analytical issues. A final example is presented for analysis of Tc-99 in samples of depleted uranium. In this case, two different studies that were performed only three months apart provide quite different results. The US Environmental Protection Agency (EPA) established the data quality objectives (DQO) process in the mid-1980's to establish decision performance criteria for data collection. EPA guidance (EPA G-4, for example) clearly distinguishes between DQOs and measurement performance objectives (MQOs) that should be addressed for laboratory analysis of samples. The language of DQOs and MQOs has become confused over time it seems, and the subsequent effects seem to include a lack of attention to decision performance and a routine approach to measurement quality. In order to better address radionuclide sample analysis when the concentrations are close to thresholds of concern, which might be zero for some radionuclides, background for others, and risk-based thresholds for yet others, it is important that routine laboratory analysis methods are adjusted, and that the project team and the laboratory work closely together to ensure that the data meets the MQO requirements of laboratory analysis and reporting of results, and that the MQOs effectively support project-specific DQOs. This basic approach will be applied in Los Alamos in the coming year to the collection of moisture data from underneath MDA T that will be analyzed for americium and neptunium isotopes. Proper understanding of the radiochemistry methods and reporting, and of appropriate statistical methods is critical to the success of such projects, ensuring that the right decisions are made. (authors)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2021

Assessment of TMT Labeling Efficiency in Large-Scale Quantitative Proteomics: The Critical Effect of Sample pH

Isobaric labeling via tandem mass tag (TMT) reagents enables sample multiplexing prior to LC–MS/MS, facilitating high-throughput large-scale quantitative proteomics. Consistent and efficient labeling reactions are essential to achieve robust quantification; therefore, embedded in our clinical proteomic protocol is a quality control (QC) sample that contains a small aliquot from each sample within a TMT set, referred to as “Mixing QC.” This Mixing QC enables the detection of TMT labeling issues by LC–MS/MS before combining the full samples to allow for salvaging of poor TMT labeling reactions. While TMT labeling is a valuable tool, factors leading to poor reactions are not fully studied. We observed that relabeling does not necessarily rescue TMT reactions and that peptide samples sometimes remained acidic after resuspending in 50 mM HEPES buffer (pH 8.5), which coincided with low labeling efficiency (LE) and relatively low median reporter ion intensities (MRIIs). To obtain a more resilient TMT labeling procedure, we investigated LE, reporter ion missingness, the ratio of mean TMT set MRII to individual channel MRII, and the distribution of log 2 reporter ion ratios of Mixing QC samples. We discovered that sample pH is a critical factor in LE, and increasing the buffer concentration in poorly labeled samples before relabeling resulted in the successful rescue of TMT labeling reactions. Moreover, resuspending peptides in 500 mM HEPES buffer for TMT labeling resulted in consistently higher LE and lower missing data. By better controlling the sample pH for labeling and implementing multiple methods for assessing labeling quality before combining samples, we demonstrate that robust TMT labeling for large-scale quantitative studies is achievable.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗