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DOE OSTI2025

Thermophysical Properties of NaCl–UCl 3 –PuCl 3 Molten Salts: A Combined Computational and Experimental Study

Actinide-bearing molten salts for use as fuels are an essential part of next generation molten salt reactors. Yet, numerous multicomponent salt mixtures are underdeveloped or have not been investigated. Here, this study, based on a combination of experimental and modeling techniques, is dedicated to determining and understanding a variety of properties of the ternary system of NaCl–UCl 3 –PuCl 3 , which represents a scenario for burnup of NaCl–UCl 3 fuel, at two compositions (∼10 and 5 mol % PuCl 3 in eutectic NaCl–UCl 3 pseudobinary) and a range of temperatures. Evaluation of the heat flow and mass loss data showed the 0.61NaCl–0.30UCl 3 –0.09PuCl 3 salt had a melting temperature of 551 ± 5 °C. Two additional thermal effects were observed occurring at approximately 410 and 494 °C. The transition occurring at 410 °C may be due to the presence of oxide in the salt. Extrapolation of thermodynamic data indicates the transition occurring at 494 °C is due to the formation of a liquid phase. Experimental testing determined the density of this system is a linear function of temperature and can be represented by the equation ρ = 4.014–0.0010T(°C), R 2 = 0.992. Additionally, by using atomistic modeling, we found that increasing the PuCl 3 content from 5 to 10 mol % led to the formation of larger Pu 3+ clusters and slower transport of ions.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

New K-feldspar Pb isotope results for Mesozoic arc crust in the Pacific Northwest, U.S.A. and Canada: comparison with the Mojave-Salinia province of southern California and Implications for Baja-BC

Measurements of lead isotopic compositions in detrital K-feldspar have been increasingly used as a tool to assess sediment provenance. We compiled a database of previously published Pb isotope data from 700 bedrock K-feldspar samples and 1,423 age-corrected bedrock whole rock samples from western North American igneous and metamorphic bodies. Additionally, we report 66 new K-feldspar Pb isotope data for plutons throughout the Pacific Northwest region of the United States and British Columbia. Results show that the Pb isotope values of plutonic K-feldspar depend on the isotopically juvenile or evolved nature of underlying crust. Samples obtained from the mid Cretaceous – mid Eocene Coast Plutonic Complex, North Cascades, and Intermontane superterrane that occur west of the initial 87 Sr/ 86 Sr (Sri) = 0.706 isopleth exhibit a highly restricted 207 Pb/ 206 Pb and 208 Pb/ 206 Pb values centred upon 0.83 and 2.03, respectively. Conversely, rocks overlying older continental crust further east such as the Middle Jurassic – Late Cretaceous Omineca crystalline belt, Idaho batholith, and Boulder Batholith exhibit far greater variation of Pb isotope values that parallel the 100 Ma isochron calculated from a two-stage Pb evolution model. We demonstrate that Pb isotopic results from the Idaho and Boulder Batholith region can be used to define distinctive subregions for Pb isotopic provenance analysis, and compare these signatures to the Mojave-Salinian batholith of southern California and western Arizona, as these two areas have previously been proposed as source regions for extraregional sediment that was deposited within the Nanaimo Basin during the Campanian – Maastrichtian. Future Pb isotopic analysis of detrital K-feldspar from the Nanaimo Basin of southwestern British Columbia may effectively distinguish between potential extraregional sources separated by thousands of kilometres.

Coast Plutonic Complex↗

DOE OSTI2025

2024 IUFRO Tree Biotechnology Conference (Aug 4-8, 2024)

The 2024 IUFRO Tree Biotechnology Conference is the biennial meeting on genomics, molecular biology, and biotechnology of forest trees, associated with the IUFRO Working Party 2.04.06. This year's meeting was held in Annapolis, MD, USA from August 4th to 8th and was hosted by Yiping Qi (University of Maryland), Edward Eisenstein (University of Maryland), Gary Coleman (University of Maryland), and Heather Coleman (Syracuse University). The conference covered seven topics over the course of five days: 1) Biological and ecological insights from OMICS, 2) Advancing technologies for targeted trait manipulation and acceptability to diverse tree species, 3) Genes, development, and physiology, 4) Translating genomics and biotechnology to practice, 5) Trees in a changing world, 6) Genetic and phenotypic diversity for breeding and genomic selection, and 7) Biotechnology for biomaterials and bioeconomy. In addition to the sessions, there were two plenary sessions, provided by John Ralph (University of Wisconsin) and Tanja Pyrhäjärvi (University of Helsinki). The meeting celebrated the second awardees of the newly created IUFRO WG 2.04.06 Award: Excellence in Forest Molecular Biology and Genomics, which was presented to Chung-Jui (C.J.) Tsai (University of Georgia). Greg Goralogia (Oregon State University) was the recipient of the associated Early Career Award. The scientific presentations at the conference highlighted cutting-edge advancements in many facets of forest biotechnology research, including applications of genomic selection in forest genetics and breeding, the use of genetic editing, tree physiology, stress response, molecular breeding, wood development, "omics" technologies, and the social and economic impacts of genetically modified (GM) trees. Scientific take homes from the meeting include the power of NMR to dissect the composition of lignin, the genomic diversity of forest trees that has enormous potential for tree improvement and the integration of systems biology with climate and geographical data. The conference attracted a mix of students (25), postdoctoral fellows (32), and scientists from academia (66) and industry (18). In all, the conference was attended by 141 registered participants, representing 20 countries that participated in 23 invited lectures (including 6 'early-career' keynotes), 27 voluntary talks and 61 poster presentations. Support for the conference was drawn from a wide variety of Academia, Industry, and Government sources, and included financial support from several tree improvement companies. Overall, the conference was a great success, providing an exceptional mix of science and social activities in a relaxed and collegial atmosphere. More information about the meeting can be found at treebiotech.org. The next meeting will be held in Stellenbosch, South Africa, in 2026, hosted jointly by Zander Myburg, Dave Drew (University of Stellenbosch,) and Sanushka Naidoo (University of Pretoria, FABI).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

TEM Characterization of Neutron Irradiated HfAl3-Al Composite Specimens

Particles comprised of a thermal neutron absorbing material (HfAl3) are dispersed in a metal matrix material with high thermal conductivity (aluminum) to conduct the heat generated by neutron capture away from the fuel and materials. This metal matrix composite is very promising for use as a conduction-cooled neutron absorber and has the potential to be useful as a shroud or heat sink for testing advanced fast reactor fuels and materials in an existing thermal reactor. To design and optimize the absorber block system for advanced reactor designs, fundamental understanding of the irradiation effect on material properties is necessary. This dataset contains TEM characterization results of neutron irradiated HfAl3-Al composite specimens. It is focused on the irradiation induced defects (dislocation lines and loops) characterization using the on-zone axis bright field STEM technique. This data was collected using a FEI Tecnai G2 F30 S/TEM at the Microscopy and Characterization Suite (MaCS), Center for Advanced Energy Studies (CAES).

Guillen, Donna↗

DOE OSTI2024

TEM Characterization of Neutron Irradiated HfAl3-Al Composite Specimens

Guillen, Donna↗

DOE OSTI2025

Data from a throughfall exclusion experiment: Fine root dynamics, morphology, chemistry, and AMF colonization across four lowland Panamanian forests

Fine roots regulate forest nutrient, carbon, and water cycling, yet their variation within and among tropical forests remains under-characterized. We quantified root productivity, disappearance, and stocks to 1 m using minirhizotron imaging, and we measured morphology, elemental composition [root carbon (C), root nitrogen (N), root phosphorus (P)], and arbuscular mycorrhizal fungi (AMF) colonization to 20 cm using ingrowth cores and sequential coring. Sampling took place in four distinct lowland Panamanian forests (32 plots; 8 per forest) from 2018 through 2022 under control and throughfall-exclusion (drought) treatments in the Panama Rainforest Changes with Experimental Drying (PARCHED) experiment.The dataset is presented as an Excel workbook with six tabs. The first tab is the data dictionary. Tab S1 contains ingrowth-core production and mortality, morphology and soil moisture. Tab S2 contains sequential-coring standing stocks with associated morphology and soil moisture. Tab S3 contains minirhizotron row data records to 1 m depth, including per-frame root length and diameter, normalized length metrics, and session timing. Tab S4 contains AMF colonization. Tab S5 contains fine-root chemistry at 0–10 cm, reporting %P, %C, %N, and C:N for samples collected via ingrowth cores and sequential-coring standing stocks. CSV mirrors for each tab are provided, and a KML file supplies coordinates for all 32 plots.Key variables span live and dead fine-root biomass (and coarse fractions where applicable), specific root length (SRL) and area (SRA), diameter, root tissue density (RTD), soil moisture, AMF colonization, root %N, %C, %P, and C:N, along with minirhizotron root length and diameter. Depth, season, treatment, and plot/site identifiers are included to support cross-tab integration and analysis from 0–100 cm (minirhizotron) and 0–20 cm (cores).Units are reported in-column and missing values are coded as NA. No special software is required to open or use the files (Excel, CSV, and KML compatible).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Sources and Composition of Organic Aerosols in the Central Arctic during Spring and Summer

Organic aerosols (OA) are a major component of Arctic aerosol mass and influence the region’s radiation budget, yet their sources and physicochemical properties remain largely unknown. We investigate OA sources and climate-relevant characteristics over the central Arctic Ocean in spring and summer, by applying positive matrix factorization to aerosol mass spectrometry data from two ship-based expeditions (2018 and 2020), complemented by total and interstitial aerosol measurements during fog periods. Six distinct OA factors were identified: haze-related OA, Arctic oxygenated OA, two mixed-OA types resembling biomass burning and primary marine OA linked to warm-air intrusions, marine OA, and hydrocarbon-related OA. Seasonal transitions strongly shaped OA composition. Following polar sunrise, highly oxygenated OA, likely formed secondarily from photo-oxidized volatile organic compounds, became dominant. After the polar vortex collapsed in May, episodic spikes in marine OA from the marginal ice zone appeared, alongside a reduced influence from Eurasian anthropogenic sources. These transitions influenced OA oxidation state and related properties including volatility, acidity, and hygroscopicity, highlighting the role of the Arctic spring atmosphere as an active photochemical reactor. Overall, OA was highly oxidized, with particles activated in fog during summer showing even greater oxidation, suggesting that central Arctic OA can be highly cloud active.

Arctic haze↗

DOE OSTI2024

Guest Editorial for Nondestructive Testing and Evaluation (NDT&E) Special Section

Nondestructive testing and evaluation (NDT&E) are interdisciplinary fields that require a significant amount of interconnection between fundamental physics, measurement techniques, data processing, decision making, and reporting to have a significant impact on industry. NDT&E practitioners are challenged to keep up with the fast-paced evolution of materials, structures, processing, and manufacturing technologies. The development of engineered materials, complex structures and composites, and novel forming techniques require NDT&E to rapidly evolve to meet the needs of industry.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Sulfur isotopes from the Paleoproterozoic Francevillian Basin record multigenerational pyrite formation, not depositional conditions

Bulk-rock sulfur isotope data from pyrite in the ~2.1 billion-year sedimentary rocks of the Francevillian Basin, Gabon, have underpinned ideas about initial oxygenation of Earth’s surface environments and eukaryote evolution. In this paper, we show, using micro-scale analytical methods, that the bulk sulfur isotope record represents progressive diagenetic modification. Our findings indicate no significant change in microbial sulfur cycling processes and seawater sulfate composition throughout that initial phase of atmosphere-ocean oxygenation of Paleoproterozoic time. This offers an alternative view of Earth system evolution during the transition from an anoxic to an oxic state and highlights the need for a judicious reappraisal of conceptual models using sulfur isotope data as primary depositional signals linked to global-scale biogeochemical processes.

58 GEOSCIENCES↗

DOE OSTI2026

Quantitative Analysis and Prediction of Thermal Runaway Metrics of High-Nickel Oxide Cathodes by Machine Learning Models

The pursuit of higher energy density in lithium-ion batteries has made high-nickel (Ni) layered oxides leading cathode candidates for next-generation electric vehicles. However, their poor thermal stability, particularly at Ni contents ≥ 90%, increases the risk of cathode-initiated thermal runaway. Furthermore, we present a data-driven framework combining linear and nonlinear machine learning models to predict key thermal runaway descriptors from a high-throughput differential scanning calorimetry database. With cathode composition and state of charge (SOC) as input features, the ensemble model accurately predicts peak temperature, heat release, and peak heat flow. SHAP analysis identifies Ni content and SOC as the dominant factors controlling thermal runaway temperature, while SOC primarily governs heat release and peak heat flow. Al, Mg, and Mn improve thermal stability by strengthening metal–oxygen bonding and delaying structural transformation, whereas B mainly reduces heat release through surface passivation. Validation with a new cathode composition confirms accurate prediction of SOC-dependent thermal runaway behavior and critical SOC.

25 ENERGY STORAGE↗

DOE OSTI2024

A Mechanism-Based Reaction–Diffusion Model for Accelerated Discovery of Thermoset Resins Frontally Polymerized by Olefin Metathesis

Frontal ring-opening metathesis polymerization (FROMP) involves a self-perpetuating exothermic reaction, which enables the rapid and energy-efficient manufacturing of thermoset polymers and composites. Current state-of-the-art reaction–diffusion FROMP models rely on a phenomenological description of the olefin metathesis kinetics, limiting their ability to model the governing thermo-chemical FROMP processes. Furthermore, the existing models are unable to predict the variations in FROMP kinetics with changes in the resin composition and as a result are of limited utility toward accelerated discovery of new resin formulations. In this work, we formulate a chemically meaningful model grounded in the established mechanism of ring-opening metathesis polymerization (ROMP). Our study aims to validate the hypothesis that the ROMP mechanism, applicable to monomer-initiator solutions below 100 °C, remains valid under the nonideal conditions encountered in FROMP, including ambient to >200 °C temperatures, sharp temperature gradients, and neat monomer environments. Through extensive simulations, we demonstrate that our mechanism-based model accurately predicts the FROMP behavior across various resin compositions, including polymerization front velocities and thermal characteristics (e.g., T max ). Additionally, we introduce a semi-inverse workflow that predicts FROMP behavior from a single experimental data point. Notably, the physiochemical parameters utilized in our model can be obtained through DFT calculations and minimal experiments, highlighting the model’s potential for rapid screening of new FROMP chemistries in pursuit of thermoset polymers with superior thermo-chemo-mechanical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Electronic structure prediction of multi-million atom systems through uncertainty quantification enabled transfer learning

The ground state electron density — obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations — contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the computational expense of KS-DFT scales cubically with system size which tends to stymie training data generation, making it difficult to develop quantifiably accurate ML models that are applicable across many scales and system configurations. Here, we address this fundamental challenge by employing transfer learning to leverage the multi-scale nature of the training data, while comprehensively sampling system configurations using thermalization. Our ML models are less reliant on heuristics, and being based on Bayesian neural networks, enable uncertainty quantification. We show that our models incur significantly lower data generation costs while allowing confident — and when verifiable, accurate — predictions for a wide variety of bulk systems well beyond training, including systems with defects, different alloy compositions, and at multi-million-atom scales. Moreover, such predictions can be carried out using only modest computational resources.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Life Cycle Analysis of Natural Gas Extraction and Power Generation: U.S. 2020 Emissions Profile

This analysis expands upon previous life cycle analyses (LCAs) of natural gas systems performed by the National Energy Technology Laboratory (NETL). It provides a complete inventory of emissions to air and water, water consumption, and land use change. These environmental burdens are detailed for all supply chain steps from natural gas production through natural gas distribution. This package includes the report, the NETL Natural Gas Model, and appendices that include several Excel workbooks and a python script to provide transparent access to the calculations and resulting data. This is revision 1 of the 2024 study (published December 17, 2024 and updated on January 24, 2025) and corrects a modeling error in natural gas composition. See the errata on page 2 for more information.

03 NATURAL GAS↗

DOE OSTI2025

Determination of Scale Bar for AGHCF Metallography Data

The DOE Nuclear Energy Advanced Reactor Technologies (ART) Fast Reactor Program (FRP) has supported the development of several databases containing information on the safety performance of fast reactors, components, and fuels. This growing collection of legacy experimental data, operating data, and analysis is available online to registered users. Metallography data represents one of the most critical types of post-irradiation examination (PIE) data being collected, organized, and archived in several ART Fast Reactor Databases (https://frdb.ne.anl.gov), including the Metallic Fuels Irradiation & Physics Database (FIPD), Out-of-Pile Transient Database (OPTD), and TREAT (the Transient Reactor Test Facility) Experimental Relational Database (TREXR). These databases contain three principal sets of metallography data. The first set comprises metallography data from Experimental Breeder Reactor-II (EBR-II) and Fast Flux Test Facility (FFTF) irradiated fuel pins examined in the Hot Fuel Examination Facility (HFEF). The second set consists of metallography data from EBR-II irradiated fuel pins examined in the Alpha-Gamma Hot Cell Facility (AGHCF). The third set includes metallography data from transient-tested fuel pins (including both out-of-pile furnace tests and TREAT tests) examined in AGHCF. Since both the second and third sets were generated in AGHCF, they are governed by identical specifications. The metallography data in the databases consist of digital images scanned from either positive or negative photographic films. To analyze the microstructure of a fuel pin, a series of preparatory steps are required, including sectioning, epoxy mounting, mechanical grinding and polishing, and etching. Following sample preparation, specimens are transferred for metallographic examination. The AGHCF and HFEF metallography data were generated using optical microscopes manufactured by Leitz and Bausch and Lomb (B&L). Images were recorded on Polaroid film at preset magnifications. Magnification verification for the Leitz and B&L metallographs was conducted every two months prior to 1989 and at least every six months from 1989 through the conclusion of the IFR program. Magnifications determined from imaging of microslide standards were compared to the instrument settings for magnifications ranging from 50× to 500×. If the magnifications determined from standards deviated from the instrument settings, adjustments were made to the bellows extension until agreement was achieved. The specifications for AGHCF and HFEF legacy metallography data have been established based on available hard-copy and digital records, most of which have been incorporated into the data repositories associated with FIPD, OPTD, and TREXR. Detailed specifications including hard-copy records, digitized records, cutting diagrams and sectioning schemes, high-magnification photographs, photomosaics (composites), information tags, scale bars, and magnification verification procedures can be found in a separate report.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Integrated Direct Air Capture and H₂-Free CO₂ Valorization

This project advances fundamental understanding of a novel integrated direct air capture (DAC) and CO₂ conversion process that valorizes atmospheric CO₂ without external H₂. The research encompasses four critical components: (1) design of task-specific ionic liquids for efficient CO₂ capture under ambient conditions, (2) development of H₂-free tandem catalytic systems using ethane as a reductant, (3) advanced operando characterization to elucidate capture and conversion mechanisms, and (4) data science-driven predictive computation to accelerate material discovery. Over the project period, we developed five high-performance DAC sorbent systems—including CaO/superbase ionic liquid composites, Ni-MOF/Ionic Liquid (IL) hybrids, fluorinated covalent organic frameworks with ion-pair functional groups, defect-engineered UiO-66, and a validated kinetic model for humid-condition operation, achieving CO₂ capacities up to 1.86 mmol/g at 400 ppm with excellent cycling stability. For H₂-free conversion, we constructed atomically synergistic Zn–O–Cr binuclear catalytic sites that achieve 100% ethylene selectivity, ~9.6% ethane conversion, and 99% CO₂ utilization in equimolar co-conversion of ethane and CO₂. We further demonstrated downstream valorization pathways converting CO and C₂H₄ into polyketones and C₃ chemicals. These advances strengthen the scientific foundation for producing value-added materials from ambient CO₂.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Evaluating Limits of Machine Learning-Assisted Raman Spectroscopy in Classification of Biological Samples

Machine learning (ML)-assisted Raman spectroscopy has become a powerful analytical tool for the classification and identification of analytes; however, technical challenges impacting its detection accuracy have not been thoroughly investigated. This study explores experimental factors affecting classification performance. Among the evaluated ML models, ML algorithms show minimal impact on classification accuracy. Instead, experimental factors, including spectral similarity between tested samples and data quality, dominate detection performance. Increases in spectral noise and spectral similarity significantly reduce classification accuracy. In well-controlled samples with low experimental noise, ML-assisted Raman spectroscopy can discriminate lipid mixtures with a composition difference of 1.85 mol %. To assess the effect of biological heterogeneity, we analyzed single-cell Raman spectra from Saccharomyces cerevisiae strains carrying single, double, or triple gene mutations. Intrinsic cell-to-cell variability introduced substantial spectral differences, severely reducing the accuracy of multiclass classification of these genetically similar strains at the single-cell level. Averaging Raman spectra across multiple cells improved classification accuracy by reducing this spectral variability. We also assess the effectiveness of transfer learning across different Raman spectrometers, specifically by applying an ML model trained on one instrument to another Raman spectrometer. Transfer learning can be improved with proper instrument calibration, highlighting the importance of instrument standardization. Overall, our results demonstrate that data quality and spectral similarity are the primary bottlenecks in ML-assisted Raman spectroscopy. Careful attention to sample preparation, data acquisition, measurement conditions, and instrument calibration is critical to achieving robust and reliable classification performance.

Fungi↗

DOE OSTI2025

Data-driven design of electrolyte additives supporting high-performance 5 V LiNi 0.5 Mn 1.5 O 4 positive electrodes

LiNi 0.5 Mn 1.5 O 4 (LNMO) is a high-capacity spinel-structured material with an average lithiation/de-lithiation potential at ca. 4.6–4.7 V vs Li + /Li, far exceeding the stability limits of electrolytes. An efficient way to enable LNMO in lithium-ion batteries is to reformulate an electrolyte composition that stabilizes both graphitic (Gr) negative electrode with solid-electrolyte-interphase and LNMO with cathode-electrolyte-interphase. In this study, we select and test a diverse collection of 28 single and dual additives for the Gr||LNMO battery system. Subsequently, we train machine learning models on this dataset and employ the trained models to suggest 6 binary compositions out of 125, based on predicted final area-specific-impedance, impedance rise, and final specific-capacity. Such machine learning-generated new additives outperform the initial dataset. This finding not only underscores the efficacy of machine learning in identifying materials in a highly complicated application space but also showcases an accelerated material discovery workflow that directly integrates data-driven methods with battery testing experiments.

batteries↗

DOE OSTI2024

Daily water stable isotopes, transpiration, and matrix potential data for an aspen and engelmann stand in the East River Watershed (version 2)

We provide daily stable isotope (2H & 18O) ratios in soil water and xylem (plant stem) water, as well as the sap flow (transpiration) and the soil's matric potential at a forested site near Gothic, Colorado, in the East River catchment. We measured the stable isotopic composition of the transpiration and the daily transpiration flux sum of three aspen and three engelmann spruce. In both forest stands, we installed a soil profile and measured the soil matric potential at 15, 30, and 60 cm depth as well as the stable isotopes of soil pore water at 5, 10, 30, 60, and 90 cm depths. All isotope measurements were done in situ via vapor probes connected to a cavity ring down spectrometer (Picarro L1240i).We further report the daily meteorological data observed at billy barr near our study site. We also provide for each tree the relative share of root water uptake derived from the isotope measurements via a Bayesian mixing model (MixSIAR).The daily data is provided as a time series in "Iso_MP_Sap_DataDaily_ESSDiveUpload.csv" and the units are provided in "dd.csv"; the location of the instrumented trees and soil profiles are given as latitude and longitude coordinates saved as CSV and KMZ files; and a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata.The data was gathered to investigate the short-term changes of the water sources (i.e., variation of root water uptake from different soil depths) of the studied subalpine trees.Update 07/16/2025: The relative and absolute plant water uptake depths were grouped to ensure that the MixSIAR model was applied with endmembers that differed in their d2H value by at least 3 permill and at least 1 permill for d18O. Whenever the difference between observed d2H values for two or more probes at neighboring depths was less than 3 permill, we used the average value for the source water endmember. For days at which probes that were not next to each other measurements did not differ at least 3 permill, the average of all probes between these two depths was used as the water source endmember representing the depth range between these two probes.

54 ENVIRONMENTAL SCIENCES↗