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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 361 records · Page 20

Error quantification of the Arrhenius blending rule for viscosity of hydrocarbon mixtures

Six hundred and seventy-five measurements of dynamic viscosity and density have been used to assess the prediction error of the Arrhenius blending rule for kinematic viscosity of hydrocarbon mixtures. Major trends within the data show that mixture complexity–binary to hundreds of components—and temperature are more important determinants of prediction error than differences in molecular size or hydrogen saturation between the components of the mixtures. Over the range evaluated, no correlation between prediction error and mole fractions was observed, suggesting the log of viscosity truly is linear in mole fraction, as indicated by the Arrhenius blending rule. Mixture complexity and temperature also impact molar volume and its prediction. However, a linear regression between the two model errors explains less than 20% of the observed variation, indicating that mixture viscosity and/or molar volume are not linear with respect to temperature and/or mixture complexity. Extensive discussion of the intermolecular forces and the geometric arrangement of molecules and vacancies in liquids, which ultimately determines its viscosity, is brought into context with the implicit approximations behind the Arrhenius blending rule. The complexity of this physics is not compatible with a simple algebraic correction to the model. However, sufficient data is now available to determine confidence intervals around the prediction of fuel viscosity based on its component mole fractions and viscosities. At -40°C, when all identified components are pure molecules the modeling error is 13.2% of the predicted (nominal) viscosity times the root mean square of the component mole fractions.

10 SYNTHETIC FUELS↗

Circulating levels of micronutrients and risk of osteomyelitis: a Mendelian randomization study

Background Few observational studies have investigated the effect of micronutrients on osteomyelitis, and these findings are limited by confounding and conflicting results. Therefore, we conducted Mendelian randomization (MR) analyses to evaluate the association between blood levels of eight micronutrients (copper, selenium, zinc, vitamin B12, vitamin C, and vitamin D, vitamin B6, vitamin E) and the risk of osteomyelitis. Methods We performed the two-sample and multivariable Mendelian randomization (MVMR) to investigate causation, where instrument variables for the predictor (micronutrients) were derived from the summary data of micronutrients from independent cohorts of European ancestry. The outcome instrumental variables were used from the summary data of European-ancestry individuals ( n = 486,484). The threshold of statistical significance was set at p < 0.00625. Results We found a significant causal association that elevated zinc heightens the risk of developing osteomyelitis in European ancestry individuals OR = 1.23 [95% confidence interval (CI) [1.07, 1.43]; p = 4.26E-03]. Similarly, vitamin B6 showed a similar significant causal effect on osteomyelitis as a risk factor OR = 2.78 (95% CI [1.34, 5.76]; p = 6.04E-03; in the secondary analysis). Post-hoc analysis suggested this result (vitamin B6). However, the multivariable Mendelian randomization (MVMR) provides evidence against the causal association between zinc and osteomyelitis OR = 0.98(95% CI [−0.11, 0.07]; p = 7.20E-1). After searching in PhenoScanner, no SNP with confounding factors was found in the analysis of vitamin B6. There was no evidence of a reverse causal impact of osteomyelitis on zinc and vitamin B6. Conclusion This study supported a strong causal association between vitamin B6 and osteomyelitis while reporting a dubious causal association between zinc and osteomyelitis.

Zhang, Xu↗

Sex Differences in Odds of Brain Metastasis and Outcomes by Brain Metastasis Status after Advanced Melanoma Diagnosis

Sex differences in cancer are well-established. However, less is known about sex differences in diagnosis of brain metastasis and outcomes among patients with advanced melanoma. Using a United States nationwide electronic health record-derived de-identified database, we evaluated patients diagnosed with advanced melanoma from 1 January 2011–30 July 2022 who received an oncologist-defined rule-based first line of therapy (n = 7969, 33% female according to EHR, 35% w/documentation of brain metastases). The odds of documented brain metastasis diagnosis were calculated using multivariable logistic regression adjusted for age, practice type, diagnosis period (pre/post-2017), ECOG performance status, anatomic site of melanoma, group stage, documentation of non-brain metastases prior to first-line of treatment, and BRAF positive status. Real-world overall survival (rwOS) and progression-free survival (rwPFS) starting from first-line initiation were assessed by sex, accounting for brain metastasis diagnosis as a time-varying covariate using the Cox proportional hazards model, with the same adjustments as the logistic model, excluding group stage, while also adjusting for race, socioeconomic status, and insurance status. Adjusted analysis revealed males with advanced melanoma were 22% more likely to receive a brain metastasis diagnosis compared to females (adjusted odds ratio [aOR]: 1.22, 95% confidence interval [CI]: 1.09, 1.36). Males with brain metastases had worse rwOS (aHR: 1.15, 95% CI: 1.04, 1.28) but not worse rwPFS (adjusted hazard ratio [aHR]: 1.04, 95% CI: 0.95, 1.14) following first-line treatment initiation. Among patients with advanced melanoma who were not diagnosed with brain metastases, survival was not different by sex (rwOS aHR: 1.06 [95% CI: 0.97, 1.16], rwPFS aHR: 1.02 [95% CI: 0.94, 1.1]). This study showed that males had greater odds of brain metastasis and, among those with brain metastasis, poorer rwOS compared to females, while there were no sex differences in clinical outcomes for those with advanced melanoma without brain metastasis.

60 APPLIED LIFE SCIENCES↗

Probabilistic Deliverability Assessment of Distributed Energy Resources via Scenario-Based AC Optimal Power Flow

As electric grids decarbonize and distributed energy resources (DERs) become increasingly prevalent, interconnection assessments must evolve to reflect operational variability and control flexibility. This paper highlights key modeling limitations observed in practice and reviews approaches for modeling uncertainty. It then introduces a Probabilistic Deliverability Assessment (PDA) framework designed to complement and extend existing procedures. The framework integrates scenario-based AC optimal power flow (AC OPF), corrective dispatch, and optional multi-temporal constraints. Together, these form a structured methodology for quantifying DER utilization, deliverability, and reliability under uncertainty in load, generation, and topology. Outputs include interpretable metrics with confidence intervals that inform siting decisions and evaluate compliance with reliability thresholds across sampled operating conditions. A case study on Puerto Rico’s publicly available bulk power system model demonstrates the framework’s application using minimal input data, consistent with current interconnection practice. Across staged fossil generation retirements, the PDA identifies high-value DER sites and regions requiring additional reactive power support. Results are presented through mean dispatch signals, reliability metrics, and geospatial visualizations, demonstrating how the framework provides transparent, data-driven siting recommendations. The framework’s modular design supports incremental adoption within existing workflows, encouraging broader use of AC OPF in interconnection and planning contexts.

14 SOLAR ENERGY↗

Digital Biosurveillance for Zoonotic Disease Detection in Kenya

Infectious disease surveillance is crucial for early detection and situational awareness of disease outbreaks. Digital biosurveillance monitors large volumes of open-source data to flag potential health threats. This study investigates the potential of digital surveillance in the detection of the top five priority zoonotic diseases in Kenya: Rift Valley fever (RVF), anthrax, rabies, brucellosis, and trypanosomiasis. Open-source disease events reported between August 2016 and October 2020 were collected and key event-specific information was extracted using a newly developed disease event taxonomy. A total of 424 disease reports encompassing 55 unique events belonging to anthrax (43.6%), RVF (34.6%), and rabies (21.8%) were identified. Most events were first reported by news media (78.2%) followed by international health organizations (16.4%). News media reported the events 4.1 (±4.7) days faster than the official reports. There was a positive association between official reporting and RVF events (odds ratio (OR) 195.5, 95% confidence interval (CI); 24.01–4756.43, p < 0.001) and a negative association between official reporting and local media coverage of events (OR 0.03, 95% CI; 0.00–0.17, p = 0.030). This study highlights the usefulness of local news in the detection of potentially neglected zoonotic disease events and the importance of digital biosurveillance in resource-limited settings.

59 BASIC BIOLOGICAL SCIENCES↗

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature

Landslides are a global and frequent natural hazard, affecting many communities and infrastructure networks. Technological solutions are needed for long-term, large-scale condition monitoring of infrastructure earthworks or natural slopes. However, current instruments for slope stability monitoring are often costly, require a complex installation process and/or data processing schemes, or have poor resolution. Wireless sensor networks comprising low-power, low-cost sensors have been shown to be a crucial part of landslide early warning systems. Here, we present the development of a novel sensing approach that uses linear arrays of three-axis accelerometers for monitoring changes in sensor inclination, and thus the surrounding soil’s deformation. By combining these deformation measurements with depth-resolved temperature measurements, we can link our data to subsurface thermal–hydrological regimes where relevant. In this research, we present a configuration of cascaded I2C sensors that (i) have ultra-low power consumption and (ii) enable an adjustable probe length. From an electromechanical perspective, we developed a novel board-to-board connection method that enables narrow, semi-flexible sensor arrays and a streamlined assembly process. The low-cost connection method relies on a specific FR4 printed circuit board design that allows board-to-board press fitting without using electromechanical components or solder connections. The sensor assembly is placed in a thin, semi-flexible tube (inner diameter 6.35 mm) that is filled with an epoxy compound. The resulting sensor probe is connected to an AA-battery-powered data logger with wireless connectivity. We characterize the system’s electromechanical properties and investigate the accuracy of deformation measurements. Our experiments, performed with probes up to 1.8 m long, demonstrate long-term connector stability, as well as probe mechanical flexibility. Furthermore, our accuracy analysis indicates that deformation measurements can be performed with a 0.390 mm resolution and a 95% confidence interval of ±0.73 mm per meter of probe length. This research shows the suitability of low-cost accelerometer arrays for distributed soil stability monitoring. In comparison with emerging low-cost measurements of surface displacement, our approach provides depth-resolved deformation, which can inform about shallow sliding surfaces.

deformation monitoring↗

Impact of Glass Irradiation on Laser-Induced Breakdown Spectroscopy Data Analysis

Increased absorption of optical materials arising from exposure to ionizing radiation must be accounted for to accurately analyze laser-induced breakdown spectroscopy (LIBS) data retrieved from high-radiation environments. We evaluate this effect on two examples that mimic the diagnostics placed within novel nuclear reactor designs. The analysis is performed on LIBS data measured with 1% Xe gas in an ambient He environment and 1% Eu in a molten LiCl-KCl matrix, along with the measured optical absorption from the gamma- and neutron-irradiated low-OH fused silica and sapphire glasses. Significant changes in the number of laser shots required to reach a 3σ detection level are observed for the Eu data, increasing by two orders of magnitude after exposure to a 1.7 × 1017 n/cm2 neutron fluence. For all cases examined, the spectral dependence of absorption results in the introduction of systematic errors. Moreover, if lines from different spectral regions are used to create Boltzmann plots, this attenuation leads to statistically significant changes in the temperatures calculated from the Xe II lines and Eu II lines, lowering them from 8000 ± 610 K to 6900 ± 810 K and from 15,800 ± 400 K to 7200 ± 800 K, respectively, for exposure to the 1.7 × 1017 n/cm2 fluence. The temperature range required for a 95% confidence interval for the calculated temperature is also broadened. In the case of measuring the Xe spectrum, these effects may be mitigated using only the longer-wavelength spectral region, where radiation attenuation is relatively small, or through analysis using the iterative Saha–Boltzmann method.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

An evaluation of prognostic factors, oncologic outcomes, and management for primary and recurrent squamous cell carcinoma of the vulva

Objective: To evaluate prognostic factors, outcomes, and management patterns of patients treated for squamous cell carcinoma of the vulva. Methods: One hundred sixty-four women were retrospectively identified with primary squamous cell carcinoma of the vulva treated at our institution between 1/1996–12/2018. Descriptive statistics were performed on patient, tumor, and treatment characteristics. The χ 2 tests and t-tests were used to compare categorical variables and continuous variables, respectively. Recurrence free survival (RFS), overall survival (OS), and disease-specific survival (DSS) were analyzed with Kaplan-Meier estimates, the log-rank test, and Cox proportional hazards. Results: Median follow-up was 52.5 months. Five-year RFS was 67.9%, 60.0%, 42.1%, and 20.0% for stage I–IV, respectively. Five-year DSS was 86.2%, 81.6%, 65.0%, and 42.9% for stage I–IV, respectively. On multivariate analysis, positive margins predicted overall RFS (hazard ratio [HR]=3.55; 95% confidence interval [CI]=1.18–10.73; p=0.025), while presence of lichen sclerosus on pathology (HR=2.78; 95% CI=1.30–5.91; p=0.008) predicted local RFS. OS was predicted by nodal involvement (HR=2.51; 95% CI=1.02–6.13; p=0.043) and positive margins (HR=5.19; 95% CI=2.03–13.26; p=0.001). Adjuvant radiotherapy significantly improved RFS (p=0.016) and DSS (p=0.012) in node-positive patients. Median survival after treatment of local, groin, and pelvic/distant recurrence was 52, 8, and 5 months, respectively. Conclusion: For primary treatment, more conservative surgical approaches can be considered with escalation of treatment in patients with concurrent precursor lesions, positive margins, and/or nodal involvement. Further studies are warranted to improve risk stratification in order to optimize treatment paradigms for vulvar cancer patients.

60 APPLIED LIFE SCIENCES↗

Precise Transit and Radial-velocity Characterization of a Resonant Pair: The Warm Jupiter TOI-216c and Eccentric Warm Neptune TOI-216b

TOI-216 hosts a pair of warm, large exoplanets discovered by the TESS mission. These planets were found to be in or near the 2:1 resonance, and both of them exhibit transit timing variations (TTVs). Precise characterization of the planets’ masses and radii, orbital properties, and resonant behavior can test theories for the origins of planets orbiting close to their stars. Previous characterization of the system using the first six sectors of TESS data suffered from a degeneracy between planet mass and orbital eccentricity. Radial-velocity measurements using HARPS, FEROS, and the Planet Finder Spectrograph break that degeneracy, and an expanded TTV baseline from TESS and an ongoing ground-based transit observing campaign increase the precision of the mass and eccentricity measurements. We determine that TOI-216c is a warm Jupiter, TOI-216b is an eccentric warm Neptune, and that they librate in 2:1 resonance with a moderate libration amplitude of 60{sub −2}{sup +2} deg, a small but significant free eccentricity of 0.0222{sub −0.0003}{sup +0.0005} for TOI-216b, and a small but significant mutual inclination of 1.°2–3.°9 (95% confidence interval). The libration amplitude, free eccentricity, and mutual inclination imply a disturbance of TOI-216b before or after resonance capture, perhaps by an undetected third planet.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Physical and Visual Evaluation of Filters for Direct Observation of the Sun and the International Standard ISO 12312-2:2015

We investigated the compliance of 43 commercially available solar filters (eclipse glasses) with the ISO 12312-2:2015 standard by measuring their spectral transmittances (280–2000 nm) and calculating their luminous, solar ultraviolet A, ultraviolet B, and infrared (IR) transmittances. We also evaluated the filters for usability by observing the full midday Sun and rating the view on a seven-point balanced scale, from “far too dark, details seen only with great difficulty” to “far too light, uncomfortable to view the Sun.” The mean ratings of two observers, one experienced and one inexperienced in solar observing, differed by 0.28 (95% confidence interval of the mean = 0.26). The inexperienced observer tended to be less accepting of high transmittances. All 43 solar filters complied with the UV and IR requirements. Eighteen filters passed the luminous transmittance requirements, and 24 were borderline too light or too dark. Seven of the 15 solar filters with a luminous transmittance darker than the requirement were rated as acceptable. One filter that passed and another that was borderline too light were rated as too light or far too light. The ISO 12312-2 limits derive from welding filter standards and do not represent an appropriate evidence base for direct solar viewing. This work provides the evidence base for a maximum 0.0012% and a minimum 0.00004% luminous transmittance for solar filters. The results of this study also support the use of welding filters between shades 12 and 16. Lighter welding filters are more acceptable than solar filters of the same luminous transmittance.

79 ASTRONOMY AND ASTROPHYSICS↗

Dependence of Narrow-line Region Sizes on [O III ] Luminosity in Low-redshift Active Galactic Nuclei with Double-peaked Broad Balmer Emission Lines

In this paper, simple but interesting results are reported on the upper limits of narrow-line region (NLR) sizes of a small sample of 38 low-redshift (z < 0.1) active galactic nuclei (AGN) with double-peaked broad emission lines (double-peaked BLAGN), in order to check whether the NLR sizes in type-1 AGN (broad line) and type-2 AGN (narrow line) obey a similar empirical dependence on [O III ] luminosity. In order to correct the inclination effects on projected NLR sizes of type-1 AGN, the accretion disk origin is commonly applied to describe the double-peaked broad Hα line, leading to the determined inclination angles of central disk-like broad-line regions of 38 double-peaked BLAGN. Then, considering the fixed Sloan Digital Sky Survey (SDSS) fiber radius, the upper limits of the NLR sizes of the 38 double-peaked BLAGN can be estimated. Meanwhile, a strong linear correlation between continuum luminosity and [O III ] luminosity is applied to confirm that the [O III ] emissions of the 38 double-peaked BLAGN are totally covered in the SDSS fibers. Considering the reddening-corrected measured [O III ] luminosity, the upper limits of the NLR sizes of the 38 double-peaked BLAGN are within a 99.9999% confidence interval of the expected results from the empirical relation between NLR size and [O III ] luminosity in type-2 AGN. In the current understanding, there are no challenges to the unified model of AGN through the space properties of NLRs.

79 ASTRONOMY AND ASTROPHYSICS↗

Aristolochic acid-induced genotoxicity and toxicogenomic changes in rodents

Aristolochic acid (AA) is a group of structurally related nitrophenanthrene carboxylic acids found in many plants that are widely used by many cultures as traditional herbal medicines. AA is a causative agent for Chinese herbs nephropathy, a term replaced later by AA nephropathy. Evidence indicates that AA is nephrotoxic, genotoxic, and carcinogenic in humans; and it also induces tumors in the forestomach, kidney, renal pelvis, urinary bladder, and lung of rats and mice. Therefore, plants containing AA have been classified as carcinogenic to humans (Group 1) by the International Agency for Research on Cancer. In our laboratories, we have conducted a series of genotoxicity and toxicogenomic studies in the rats exposed to AA of 0.1–10 mg/kg for 12 weeks. Our results demonstrated that AA treatments induced DNA adducts and mutations in the kidney, liver, and spleen of rats, as well as significant alteration of gene expression in both its target and nontarget tissues. AA treatments altered mutagenesis- or carcinogenesis-related microRNA expression in rat kidney and resulted in significant changes in protein expression profiling. We also applied benchmark dose (BMD) modeling to the 3-month AA-induced genotoxicity data. The obtained BMDL10 (the lower 95% confidence interval of the BMD 10 that is a 10% increase over the background level) for AA-induced mutations in the kidney of rats was about 7 μg/kg body weight per day. This review constitutes an overview of our investigations on AA-induced genotoxicity and toxicogenomic changes including gene expression, microRNA expression, and proteomics; and presents updated information focused on AA-induced genotoxicity in rodents.

59 BASIC BIOLOGICAL SCIENCES↗

Uncertainty Quantification via Stable Distribution Propagation

We propose a new approach for propagating stable probability distributions through neural networks. Our method is based on local linearization, which we show to be an optimal approximation in terms of total variation distance for the ReLU non-linearity. This allows propagating Gaussian and Cauchy input uncertainties through neural networks to quantify their output uncertainties. To demonstrate the utility of propagating distributions, we apply the proposed method to predicting calibrated confidence intervals and selective prediction on out-of-distribution data. The results demonstrate a broad applicability of propagating distributions and show the advantages of our method over other approaches such as moment matching.

Artificial Intelligence (cs.AI)↗

Opinion: The importance of historical and paleoclimate aerosol radiative effects

Abstract. Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols affected the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem. Because of the lack of information we do not seek here to determine the change in the radiative forcing due to aerosol changes but rather to estimate the uncertainties in those changes. Here we argue that current uncertainties from emission uncertainties (90 % confidence interval range spanning 2.8 W m−2) are just as large as model spread uncertainties (2.8 W m−2) in calculating preindustrial to present-day aerosol radiative effects. There are no estimates of radiative forcing for important aerosols such as wildfire and dust aerosols in most paleoclimate time periods. However, qualitative analysis of paleoclimate proxies suggests that changes in aerosols between different past climates are similar in magnitude to changes in aerosols between the preindustrial and present day; plus, there is the added uncertainty from the variability in aerosols and fires in the preindustrial. From the limited literature we crudely estimate a paleoclimate aerosol uncertainty for the Last Glacial Maximum relative to preindustrial of 4.8 W m−2, and we estimate the uncertainty in the aerosol feedback in the natural Earth system over the paleoclimate (Last Glacial Maximum to preindustrial) to be about 3.2 W m−2 K−1. In order to more accurately assess the uncertainty in historical aerosol radiative effects, we propose a new model intercomparison project, which would include multiple plausible emission scenarios tested across a range of state-of-the-art climate models over the historical period. These emission scenarios would then be compared to the available independent aerosol observations to constrain which are most probable. In addition, future efforts should work to characterize and constrain paleo-aerosol forcings and uncertainties. Careful propagation of aerosol uncertainties in the literature is required to ensure an accurate quantification of uncertainties in projections of future climate changes.

Environmental Sciences & Ecology↗

Gearbox bearing crack growth prognostics and uncertainty quantification with physics-informed machine learning

This paper introduces the extreme theory of functional connections (X-TFC), a physics-informed machine learning algorithm, and tailors it to estimate the remaining useful life (RUL) of wind turbine gearbox bearings experiencing fatigue crack growth. Unlike purely data-driven methods, X-TFC embeds a physics model, based on Head's theory in this work, into its training objective. The core of X-TFC is a random-projection single-layer neural network trained via an extreme learning machine, which requires only limited damage progression data and solves for output weights with a least-squares optimization algorithm. A composite loss function balances the network's fit to observed degradation data against the residuals of the governing crack growth differential equation, ensuring the learned damage trajectory remains physically plausible. When applied to a vibration-based health-index (HI) dataset measured during the growth of a crack on the inner ring of a high-speed bearing in a wind turbine gearbox (Bechhoefer and Dubé, 2020), X-TFC achieves near-zero prediction bias. Even when trained on only the first 10 %–20 % of the damage progression data, with sufficient physics weighting its predictions remain monotonic and smooth, delivering high prognosability and trendability. To quantify the epistemic uncertainty, we employ a Monte Carlo ensemble of independently initialized X-TFC models trained on noise-perturbed data, which yields confidence intervals around each RUL estimate and captures both model-parameter and epistemic uncertainty. In addition to a vibration-based HI, we demonstrate that the proposed framework can be directly applied to a supervisory control and data acquisition (SCADA) data-based HI (Eftekhari Milani et al., 2026) measured during similar wind turbine gearbox bearing crack faults, preserving its accuracy and interpretability. This extension shows the versatility of our approach, which is applicable to bearings of multiple gearbox manufacturers, models, and ratings using only SCADA data. By integrating domain knowledge with machine learning, X-TFC offers a rapid, reliable tool for crack prognostics. Its adaptability to other bearing failure modes, such as pitch bearing ring cracks, positions X-TFC as a powerful enabler of data-driven, physics-informed asset management in the wind energy sector and beyond.

17 WIND ENERGY↗

AEROICESTUDY-Colorado State University Ice Spectrometer

The dataset comprises measures, using Colorado State University's Ice Spectrometer (IS, an immersion freezing device with a range from 0 °C down to -26 to -29 °C) of atmospheric ice nucleating particle (INP) concentrations taken at ARM's Southern Great Plains (SGP) Site Guest Instrument Facility (GIF), in Lamont, Oklahoma, during the Aerosol-Ice Formation Closure Pilot Study (AEROICESTUDY). A filter sampler was mounted approximately 6 m above ground level atop the GIF rooftop platform. Single-use filter units open to the atmosphere fitted with pre-cleaned and pre-sterilized, 47-mm diameter Nuclepore polycarbonate filters were used. Filters were typically drawn for either a full day period (9AM-5PM CDT) or broken up into morning (9AM-12PM CDT) and afternoon (1PM-5PM CDT) periods. Additionally, 4 overnight filters were taken (6PM-8AM CDT). The volume of air filtered was typically between 3000 and 8000 standard liters for daily samples. After collection, filters were stored at -20 °C until processed. Initial processing to obtain spectra of INP number concentration active via the immersion freezing mechanism versus temperature was conducted using CSU's IS instrument (McCluskey et al., 2018). For measurement of INPs, collected aerosol particles were re-suspended in 8 mL of 0.1 µm-filtered deionized (DI) water. Aliquots of each suspension, and serial dilutions, were dispensed into trays which were fit into aluminum blocks in the IS. Samples were cooled at 0.33 °C per min and the freezing temperatures of wells recorded automatically. Cumulative INP concentrations were determined by first calculating the INPs per mL of suspension based on Vali (1971) and then converting to concentration per standard liter of air using the proportion of the total liquid sample dispensed and the air sample volumes. Two-tailed, 95% confidence intervals for binomial sampling were based on Equation 2 from Agresti and Coull (1998). Aliquots of suspensions from selected samples were also heat treated (95 °C for 20 min) to denature and deactiv

54 ENVIRONMENTAL SCIENCES↗

MOSAiC-Colorado State University Ice Spectrometer

This data set contains atmospheric ice nucleating particle (INP) measurements, using Colorado State University&rsquo;s (CSU) Ice Spectrometer (IS), of filter collections taken at the U.S. DOE ARM AMF2 site onboard the R/V Polarstern P-deck during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign. Samples were collected from October 27, 2019 to September 24, 2020. A filter sampler was mounted approximately 15 m above ground level on a railing in proximity to (and approximately 3 m below) the Aerosol Observation System (AOS) inlet. Single-use filter units open to the atmosphere were pre-cleaned and pre-loaded with 47-mm diameter Nuclepore polycarbonate (0.2 &micro;m pore-diameter) filters. Filters were typically drawn for a three-day period, with an average volume of air filtered of 87,000 standard liters. Total volumes were calculated through recorded daily flow rates using a mass flow meter (TSI). After collection, filters were stored and transported frozen until analysis using CSU&rsquo;s IS instrument (McCluskey et al., 2018). Aerosol particles were first re-suspended in 8 mL of 0.1 &micro;m-filtered deionized (DI) water. Aliquots of each suspension, and corresponding 11-fold dilutions, were dispensed into polymerase chain reaction (PCR) trays and placed into the aluminum blocks of the IS. Samples were cooled at approximately 0.33 &deg;C min -1 and freezing detected optically with corresponding temperatures recorded. Cumulative INP concentrations were determined through calculating the number of INPs per mL of suspension (Vali, 1971) and converting to concentration per standard L of air by accounting for the proportion of liquid used and volume of air collected. All samples were corrected for the number of INPs on the average of four field blanks (cleaned, handled, transported, and analyzed in the same way without air flow). Two-tailed, 95% confidence intervals for binomial sampling are provided (Agresti and Coull, 1998). Select samples were also heat treated (95 &deg;C for 20 min) to denature and deactivate biological INPs present and digested in 10% H 2 O 2 at 95 &deg;C under UV-B for 20 min to remove any organic carbon INPs. Agresti, A, and BA Coull. 1998. "Approximate is better than &ldquo;exact&rdquo; for interval estimation of binomial proportions." American Statistics 52: 119&ndash;126. https://doi.org/10.2307/2685469 McCluskey, CS, J Ovadnevaite, M Rinaldi, J Atkinson, F Belosi, D Ceburnis, &hellip; and PJ DeMott. 2018. "Marine and Terrestrial Organic Ice-Nucleating Particles in Pristine Marine to Continentally Influenced Northeast Atlantic Air Masses." Journal of Geophysical Research: Atmospheres 123 (11): 6196&ndash;6212, https://doi.org/10.1029/2017JD028033 Vali, G. 1971. "Quantitative Evaluation of Experimental Results and the Heterogeneous Freezing Nucleation of Supercooled Liquids." Journal of the Atmospheric Sciences 28: 402-209. https://doi.org/10.1175/1520-0469(1971)028<0402:QEOERA>2.0.CO;2

54 ENVIRONMENTAL SCIENCES↗

Concentration-Discharge Relationships in the Six Largest Arctic Rivers, 2003-2019

This dataset provides the results of the analysis of the relationship of dissolved analyte concentrations and river discharges in the six largest Arctic rivers across the global panarctic region (see Figure 1 in documentation file *.pdf). Long-term measurements of dissolved analyte concentrations and river discharge have been collected for each of the Kolyma, Lena, Mackenzie, Ob, Yenisey, and Yukon rivers by the Arctic Great Rivers Observatory (ArcticGRO) project from ~2003-present (Shiklomanov, 2021). The relationship of dissolved analyte concentrations and discharges in each river was characterized by statistical analysis of the slope of the log(concentration) vs log(discharge) (b), the coefficient of variation ratio (CVc/CVq), the 2.5% and 97.5% confidence intervals of b, and assigning a chemostatic, flushing, diluting, or non-systematic behavior category according to Koger (2018). The summary of these analyses for all six rivers is provided in one .csv file. The concentrations of 20 dissolved analytes and discharge measurement data for the individual Kolyma, Lena, Mackenzie, Ob, Yenisey, and Yukon rivers are also provided with this dataset. There are seven *.csv files; one for each river plus the statistical summary. These public ArcticGRO data at "https://www.arcticgreatrivers.org" (Shiklomanov, 2021) were downloaded on Feb 13, 2020, but each river has different measurement dates over the sampling and analysis period. The ArcticGRO metadata document (*.pdf) downloaded on Feb 13, 2020 is also included in this dataset. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗