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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Mainstream nitrogen removal from low temperature and low ammonium strength municipal wastewater using hydrogel-encapsulated comammox and anammox

Application of partial nitritation (PN)-anammox to mainstream wastewater treatment faces challenges in low water temperature and low ammonium strength. In this study, a continuous flow PN-anammox reactor with hydrogel-encapsulated comammox and anammox was designed and operated for nitrogen removal from mainstream wastewater with low temperature. Long-term operation with synthetic and real wastewater as the feed demonstrated nearly complete ammonium and total inorganic nitrogen (TIN) removal by the reactor at temperatures as low as 10 °C. A significantly decreased nitrogen removal performance and biomass activity was observed in the reactor at 4 °C before a selective heating strategy was employed. A novel heating technology using radiation to heat carbon black co-encapsulated in the hydrogel matrix with biomass was used to selectively heat biomass but not water in the treatment system. This selective heating technology enabled nearly complete ammonium removal and 89.4 ± 4.3 % TIN removal at influent temperature of 4 °C and reactor temperature 5 °C. Activity tests suggested selective heating brought the biomass activity at influent temperatures of 4 °C and reactor temperature 5 °C to a level comparable to that at 10 °C. Comammox and anammox were consistently present in the system and spatially organized in the hydrogel beads as revealed by qPCR and fluorescence in-situ hybridization (FISH). The abundance of comammox largely decreased by 3 orders of magnitude during the operation at 4 °C, and rapidly recovered after the application of selective heating. The anammox-comammox technology tested in this study essentially enabled mainstream shortcut nitrogen removal, and the selective heating ensured good performance of the technology at temperature as low as 5 °C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis of CdTe photovoltaic cells for ambient light energy harvesting

This paper investigates the suitability of CdTe photovoltaic cells to be used as power sources for wireless sensors located in buildings. We fabricate and test a CdTe photovoltaic cell with a transparent conducting oxide front contact that provides for high photocurrents and low series resistance at low light intensities and measures the photovoltaic response of this cell across five orders of magnitude of AM1.5G light intensity. Efficiencies of 10% and 17.1% are measured under ~1 W m -2 AM1.5G and LED irradiance respectively, the highest values for a CdTe device under ambient lighting measured to date. We use our results to assess the potential of CdTe for internet of things devices from an optoelectronic, as well as a techno-economic perspective, considering its established manufacturing know-how, potential for low-cost, proven long-term stability and issues around the use of cadmium.

14 SOLAR ENERGY↗
DOE OSTI2023

The effect of ambient ozone exposure on three types of diabetes: a meta-analysis

Background: Ozone as an air pollutant is gradually becoming a threat to people's health. However, the effect of ozone exposure on risk of developing diabetes, a fast-growing global metabolic disease, remains controversial. Objective: To evaluate the impact of ambient ozone exposure on the incidence rate of type 1, type 2 and gestational diabetes mellitus. Method: We systematically searched PubMed, Web of Science, and Cochrane Library databases before July 9, 2022, to determine relevant literature. Data were extracted after quality evaluation according to the Newcastle Ottawa Scale (NOS) and the agency for healthcare research and quality (AHRQ) standards, and a meta-analysis was used to evaluate the correlation between ozone exposure and type 1 diabetes mellitus (T1D), type 2 diabetes mellitus (T2D), and gestational diabetes mellitus (GDM). The heterogeneity test, sensitivity analysis, and publication bias were performed using Stata 16.0. Results: Our search identified 667 studies from three databases, 19 of which were included in our analysis after removing duplicate and ineligible studies. Among the remaining studies, three were on T1D, five were on T2D, and eleven were on GDM. The result showed that ozone exposure was positively correlated with T2D [effect size (ES) = 1.06, 95% CI: 1.02, 1.11] and GDM [pooled odds ratio (OR) = 1.01, 95% CI: 1.00, 1.03]. Subgroup analysis demonstrated that ozone exposure in the first trimester of pregnancy might raise the risk of GDM. However, no significant association was observed between ozone exposure and T1D. Conclusion: Long-term exposure to ozone may increase the risk of T2D, and daily ozone exposure during pregnancy was a hazard factor for developing GDM. Decreasing ambient ozone pollution may reduce the burden of both diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Application of artificial intelligence methods in the international roughness index prediction of rigid and composite pavements: a systematic review

The International Roughness Index (IRI) is a widely adopted metric for quantifying pavement roughness, directly influencing vehicle safety, ride comfort, and overall roadway performance. In recent years, the use of Machine Learning (ML) models for IRI prediction has gained momentum, with the goal of improving the allocation of maintenance and rehabilitation resources by enabling accurate assessments of pavement conditions. Most prior reviews, however, have concentrated on flexible pavements, leaving a notable gap regarding rigid and composite pavements. To address this gap, the present study conducts a systematic review of Artificial Intelligence (AI) methods applied to IRI prediction for rigid and composite pavements. Literature published between 2004 and 2025 is synthesized to highlight prevailing trends, methodological contributions, and directions for future research. Particular attention is given to the types of models employed, the datasets used for training and validation, and the role of input variables and data-processing strategies. Across the included studies, ensemble learning methods (especially gradient boosting variants such as XGBoost), artificial neural networks, and hybrid architectures frequently achieved high predictive skill, with several models reporting test-set coefficients of determination approaching 0.9–0.96, indicating strong potential for capturing the influence of traffic, pavement structure, and climatic factors. Since these results are obtained from heterogeneous datasets and evaluation protocols, they are interpreted qualitatively rather than as strict cross-study rankings. Analysis of input variables revealed that pavement age and initial IRI were included in 91% (21 of 23) and 78% (18 of 23) of studies, respectively. Climatic variables such as the freezing index appeared in 57% (13 of 23), while traffic-related factors were considered in 65% (15 of 23). The findings underscore the importance of standardized, high-quality datasets, such as those from the Long-Term Pavement Performance (LTPP) program, along with data consistency, model interpretability, computational efficiency, and replicability in enhancing IRI prediction. Future research should focus on incorporating input variable selection techniques to identify the most influential predictors, thereby improving accuracy and robustness. Integrating these approaches with advanced non-linear data-driven models, coupled with robust hyperparameter optimization, holds considerable promise for strengthening the reliability of IRI prediction and supporting resilient pavement management strategies.

42 ENGINEERING↗
DOE OSTI2022

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Source Physics Experiment (SPE) Science Plan

The Source Physics Experiment (SPE) series is a long-term NNSA research and development effort designed to improve U.S. arms control and nuclear nonproliferation verification and monitoring capabilities. The findings from the SPE will advance the United States’ nuclear explosion monitoring capabilities, particularly with respect to detection, discrimination and determination of yields associated with small nuclear explosions that can be lost amid the noisy seismo-acoustic background from other sources. The data generated from the SPE, a series of well-designed and recorded chemical explosions, will contribute to the development and validation of first-principles explosive source generated seismo-acoustic modeling codes. These codes will then facilitate the update of semi-empirical methods, currently based on historic test site data, such that key explosion observables can be reproduced, thus improving confidence in nuclear test monitoring in new areas and/or under novel emplacement conditions. The overall SPE project is comprised of both the development of the new explosion simulation codes and the chemical explosion test series. The chemical explosion test series will generate the empirical data required to both develop and validate the new simulation codes.

58 GEOSCIENCES↗
DOE OSTI2024

Time-lapse seismic inversion for CO 2 saturation with SeisCO2Net: An application to Frio-II site

Seismic monitoring of geological CO 2 storage (GCS) involves highly nonlinear seismic inversion and petrophysical inversion, making it challenging to estimate CO 2 volume efficiently and detect possible early CO 2 leakages. Deep learning (DL) using convolutional neural networks (CNNs) has shown promise in solving highly nonlinear seismic inversion problems. However, direct estimation of CO 2 plume extent/saturation from time-lapse seismic gathers using DL is still underexplored, with no reported field applications to date. The investigation of field data is primarily hindered by scarcity of field data for neural network training. Other obstacles include highly nonlinear seismic-petrophysics inverse relationship, and presence of noise in field seismic data. We introduce SeisCO2Net, a deep CNN that predicts CO 2 saturation maps directly from time-lapse full waveform shot gathers. For training, we use site-specific geological information, fluid flow physics, rock physics, and seismic modeling to generate synthetic datasets that closely resemble the CO 2 storage site. Synthetic tests show promising results, inspiring us to apply SeisCO2Net's trained weights on field data collected at Frio-II GCS site by leveraging transfer learning principles. As reference, we compare SeisCO2Net's predicted CO 2 saturation maps with results obtained from physics-based inversion. Our analyses show both methods display similar CO 2 plume shapes, reasonable CO 2 plume characteristics, and comparable saturation values. Our results suggest pre-training CNNs on physics-informed synthetic datasets and then applying the learned weights to field data is a viable approach to estimating field CO 2 saturation. This method effectively addresses the scarcity of field training data, thus encouraging the feasibility of long-term GCS monitoring.

58 GEOSCIENCES↗
DOE OSTI2025

Model validation and error attribution for a drifting qubit

Qubit performance is often reported in terms of a variety of single-value metrics, each providing a facet of the underlying noise mechanism limiting performance. However, the value of these metrics may drift over long timescales, and reporting a single number for qubit performance fails to account for the low-frequency noise processes that give rise to this drift. Here, in this work, we demonstrate how we can use the distribution of these values to validate or invalidate candidate noise models. We focus on the case of randomized benchmarking (RB), where typically a single error rate is reported but this error rate can drift over time when multiple passes of RB are performed. We show that using a statistical test as simple as the Kolmogorov-Smirnov statistic on the distribution of RB error rates can be used to rule out noise models, assuming the experiment is performed over a long enough time interval to capture relevant low frequency noise. With confidence in a noise model, we show how care must be exercised when performing error attribution using the distribution of drifting RB error rate.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2022

Universal control of a six-qubit quantum processor in silicon

Future quantum computers capable of solving relevant problems will require a large number of qubits that can be operated reliably. However, the requirements of having a large qubit count and operating with high fidelity are typically conflicting. Spins in semiconductor quantum dots show long-term promise but demonstrations so far use between one and four qubits and typically optimize the fidelity of either single- or two-qubit operations, or initialization and readout. Here, we increase the number of qubits and simultaneously achieve respectable fidelities for universal operation, state preparation and measurement. We design, fabricate and operate a six-qubit processor with a focus on careful Hamiltonian engineering, on a high level of abstraction to program the quantum circuits, and on efficient background calibration, all of which are essential to achieve high fidelities on this extended system. State preparation combines initialization by measurement and real-time feedback with quantum-non-demolition measurements. These advances will enable testing of increasingly meaningful quantum protocols and constitute a major stepping stone towards large-scale quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Mapping brucellosis risk in Kenya and its implications for control strategies in sub-Saharan Africa

In Sub-Saharan Africa (SSA), effective brucellosis control is limited, in part, by the lack of long-term commitments by governments to control the disease and the absence of reliable national human and livestock population-based data to inform policies. Therefore, we conducted a study to establish the national prevalence and develop a risk map for Brucella spp. in cattle to contribute to plans to eliminate the disease in Kenya by the year 2040. We randomly generated 268 geolocations and distributed them across Kenya, proportionate to the area of each of the five agroecological zones and the associated cattle population. Cattle herds closest to each selected geolocation were identified for sampling. Up to 25 cattle were sampled per geolocation and a semi-structured questionnaire was administered to their owners. We tested 6,593 cattle samples for Brucella immunoglobulin G (IgG) antibodies using an Enzyme-linked immunosorbent assay (ELISA). We assessed potential risk factors and performed spatial analyses and prevalence mapping using approximate Bayesian inference implemented via the integrated nested Laplace approximation (INLA) method. The national Brucella spp. prevalence was 6.8% (95% CI: 6.2–7.4%). Exposure levels varied significantly between agro-ecological zones, with a high of 8.5% in the very arid zone with the lowest agricultural potential relative to a low of 0.0% in the agro-alpine zone with the highest agricultural potential. Additionally, seroprevalence increased with herd size, and the odds of seropositivity were significantly higher for females and adult animals than for males or calves. Similarly, animals with a history of abortion, or with multiple reproductive syndromes had higher seropositivity than those without. At the herd level, the risk of Brucella spp. transmission was higher in larger herds, and herds with a history of reproductive problems such as abortion, giving birth to weak calves, or having swollen testes. Geographic localities with high Brucella seroprevalence occurred in northern, eastern, and southern regions of Kenya all primarily characterized by semi-arid or arid agro-ecological zones dominated by livestock pastoralism interspersed with vast areas with mixed livestock-wildlife systems. The large spatial extent of our survey provides compelling evidence for the widespread geographical distribution of brucellosis risk across Kenya in a manner easily understandable for policymakers. Our findings can provide a basis for risk-stratified pilot studies aiming to investigate the cost-effectiveness and efficacy of singular and combined preventive intervention strategies that seek to inform Kenya’s Brucellosis Control Policy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Host Species–Microbiome Interactions Contribute to Sphagnum Moss Growth Acclimation to Warming

Sphagnum moss is the dominant plant genus in northern peatlands responsible for long-term carbon accumulation. Sphagnum hosts diverse microbial communities (microbiomes), and its phytobiome (plant host + constituent microbiome + environment) plays a key role in nutrient acquisition along with carbon cycling. Climate change can modify the Sphagnum -associated microbiome, resulting in enhanced host growth and thermal acclimation as previously shown in warming experiments. However, the extent of microbiome benefits to the host and the influence of host–microbe specificity on Sphagnum thermal acclimation remain unclear. Here, we extracted Sphagnum microbiomes from five donor species of four peatland warming experiments across a latitudinal gradient and applied those microbiomes to three germ-free Sphagnum species grown across a range of temperatures in the laboratory. Using this experimental system, we test if Sphagnum 's growth response to warming depends on the donor and/or recipient host species, and we determine how the microbiome's growth conditions in the field affect Sphagnum host growth across a range of temperatures in the laboratory. After 4 weeks, we found that the highest growth rate of recipient Sphagnum was observed in treatments of matched host–microbiome pairs, with rates approximately 50% and 250% higher in comparison to maximum growth rates of non-matched host–microbiome pairs and germ-free Sphagnum , respectively. We also found that the maximum growth rate of host–microbiome pairs was reached when treatment temperatures were close to the microbiome's native temperatures. Our study shows that Sphagnum's growth acclimation to temperature is partially controlled by its constituent microbiome. Strong Sphagnum host–microbiome species specificity indicates the existence of underlying, unknown physiological mechanisms that may drive Sphagnum 's ability to acclimatize to elevated temperatures. Together with rapid acclimation of the microbiome to warming, these specific microbiome–plant associations have the potential to enhance peatland resilience in the face of climate change.

acclimation↗
DOE OSTI2021

Enhanced Hanford Low-Activity Waste Glass Property Data Development: Phase 2

The U.S. Department of Energy’s (DOE) Office of River Protection (ORP) requested Pacific Northwest National Laboratory (PNNL) to support the River Protection Project vitrification in an effort to support operations upon completion of startup activities (DOE 2012). This work was performed under the PNNL project titled “ORP Glass Support Work.” One task of this project—Enhanced Hanford Waste Glass Models—is the subject of this report. A previous task focused on generating property-composition data and models for the Hanford site low-activity waste (LAW) glasses with lower waste loadings, which are relevant to the commissioning of the LAW vitrification facility. The current task has the long-term objective of expanding the Hanford site LAW glass database and property composition models for the balance of the Hanford site tank waste treatment and immobilization mission. During the balance of the mission, LAW glasses with higher waste loadings will be produced. This report presents the glass compositions and glass property data developed in Phase 2 of the enhanced Hanford LAW glass property data development effort. When this effort is complete, enhanced LAW glass property models will be developed. Section 1.1 summarizes the status of the LAW glass composition regions and waste loading constraints prior to the data development effort documented in this report. Section 1.2 summarizes the LAW Phase 2 glass composition region and test matrix. Section 1.3 documents the quality assurance program used in performing the work discussed in this report.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Storage Stability of 50% Biodiesel Blends and Neat Biodiesel Produced in North America

This study investigated aging of two petroleum diesel, seven B100, three B20 blends, and 14 B50 blends representing the full range of fuels in the North American market for 12-weeks at 43 degrees Celsius under the ASTM D4625 accelerated aging protocol. Twelve-weeks under D4625 aging represents approximately 12 months of storage time in clean, dry conditions. Samples were taken every two weeks for measurement of Rancimat induction period (IP), peroxide content, and TAN; and total insoluble were measured at the end of the 12-week test.

09 BIOMASS FUELS↗
DOE OSTI2023

Role of Fe in long-range ordered Ni 2 Cr precipitates in Ni-Cr-Fe model alloys during isothermal aging

The precipitation of new phases during long-term service at elevated temperatures is a concern for the thermal stability of engineering alloys. In Ni-Cr-based alloys, e.g., Alloy 690, the formation of long-range ordered Ni 2 Cr causes embrittlement and may impact the lifetime of nuclear power plant components. In this work, we quantify the formation and evolution of Ni 2 Cr precipitation in eleven Ni-Cr-Fe model alloys with 0, 5, 7, and 10wt % Fe contents, and with Ni/Cr atomic ratios of 1.8, 2.0, 2.2, 2.4. These alloys were isothermally aged up to 10,000h at temperatures between 330 and 475°C. The alloys were characterized by synchrotron-based x-ray diffraction and Vickers hardness testing to quantify Ni 2 Cr precipitate size, and the impact of precipitate size on the mechanical properties as a function of Fe content. After 10,000 h of aging at 475°C and 418°C, the formation of Ni 2 Cr was observed in all alloys with 0 and 5wt % Fe. After 10,000h of aging at 418°C, Ni 2 Cr precipitates were also observed in the 7wt % Fe containing Ni/Cr=2.0 sample. Further, no clear evidence of Ni 2 Cr was observed in any of the 10wt % Fe samples at any time and temperature combination. We find that the face-centered cubic matrix lattice contraction and Vickers hardness are correlated with the Ni 2 Cr formation. The greatest change in hardness and lattice contraction occurs in stoichiometric alloys (Ni/Cr=2.0) with 0wt % Fe at 475°C. The rate of change in the material properties for the 5wt % Fe alloys is reduced, however the magnitude of changes is similar to 0wt % Fe alloys. A precipitation hardening model developed for Ni-Cr alloys based on critical resolved shear stress with weakly coupled dislocations shows a clear link between Ni 2 Cr precipitate size and hardness. This trend held across all alloys with Ni 2 Cr formation regardless of Fe concentration. This important structure-property relationship can potentially help define Ni-Cr-Fe-based component lifetimes directly through an understanding of how Ni 2 Cr formation impacts mechanical properties as a function of Fe content.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evaluation of Oxygen Consumption as a Sensitive Measure of Electrical Cable Polymer Insulation Degradation

Polymers used as dielectric materials in nuclear grade electrical cables, such as cross-linked polyethylene (XLPE) and ethylene-propylene rubber (EPR), are long-lived and durable. Estimating the decades-long useful service life of polymers experimentally in a feasible timeframe of weeks or months requires greatly accelerated aging in the laboratory at elevated temperatures and/or gamma dose rates. Many limitations of accelerated aging, such as due to artefacts of the process including diffusion limited oxidation and dose rate effects, have been identified that can make accurate lifetime prediction at service conditions challenging. This can be due to differences in degradation mechanisms between rapid and extended aging or to the presence of induction periods during which early aging is undetectable using common metrics of polymer insulation characterization such as tensile elongation at break. Conceptually, an ideal measure of aging would track clearly and predictably with material changes throughout the lifecycle of the evolving material state from early aging through end of useful life. With such a metric accelerated aging could be performed at much milder conditions, conditions closer to those found in long term service, and end of life calculated from the early data. Oxygen consumption has previously been proposed as a sensitive measure of insulation polymer oxidation that might meet such desirable characteristics. In this work, nuclear cable insulation samples of the two most common types, XLPE and EPR, from two of the most sourced manufacturers, RSCC and The Okonite Company, were subjected to gamma irradiation at dose rates of 100, 200, and 300 Gy/hr for up to 42 days at 26°C. The aged samples were used to evaluate oxygen consumption as a sensitive measure of aging for these materials and these conditions. For comparison and validation, the aged samples were also characterized using the more conventional tests of mass change, density, carbonyl index, and yellowness index. For both materials, oxygen consumption during irradiation was found to track consistently with applied dose—oxygen consumption rate was linear with time. Mass change and carbonyl index were also clearly proportional to exposure dose. Yellowness index, a surface sensitive measure, was observed to increase linearly with dose at low exposure levels before plateauing. Sample density was not observed to change under the conditions explored. Oxygen consumption was found to be a particularly sensitive metric to measure the degradation of polymeric cable insulation exposed to radiation. Oxygen consumption was compared to Arrhenius and time-temperature-superposition methods for activation energy calculation using literature date and found to be a useful and viable alternative for lifetime prediction. The techniques developed in this work are anticipated to be valuable in the estimation of insulation aging rates under mild conditions approaching service conditions. Direction comparison of useful life prediction by oxygen consumption with prediction via Arrhenius methodology for thermal aging and existing methods for radiation and combined thermal/radiation aging would serve to further validate this promising approach and refine our understanding of material-specific aging rates.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An exploration of benchtop X–ray emission spectroscopy for precise characterization of the sulfur redox state in cementitious materials

The evolution of sulfur chemistry in cements is best known in the bailiwick of failure mechanisms via sulfate attack but is equally important for its contributions to the reduction capacity of cementitious materials often used for immobilizing nuclear waste streams destined for long-term storage, e.g., cementitious waste forms (CWF). The total reduction capacity of CWFs, encompassing contributions from both S and Fe reductants, and its implications toward radionuclide immobilization is most often studied by destructive wet chemistry methods requiring acid digestion in the presence of Ce(IV) and subsequent titration and colorimetric interpretation. Here, we investigate a similarly analytical but nondestructive alternative, benchtop high resolution wavelength-dispersive x-ray fluorescence spectroscopy, most commonly known as x-ray emission spectroscopy (XES), for probing the bulk sulfur oxidation state distribution. We present here an initial investigation of S XES, including an improved experimental protocol for lab XES of inhomogeneous samples, both as a complement to the Ce(IV) test and for new scientific opportunities that it enables for observing changes in sulfur chemistry. We discuss future improvements and opportunities, including: (1) the practical challenges associated with coordinating XES and Ce(IV) liquid extraction for a more comprehensive perspective on reduction capacity and for a high-precision evaluation of uncertainties in the Ce(IV) test; and (2) new opportunities, due to the nondestructive nature of XES, for controlled evolution studies aimed at elucidating specific chemical responses of CWF’s exposed to invasive gas or liquid species or to accelerated aging by radiative dose or thermal treatment.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Evidence of a South Asian Proto‐Monsoon During the Oligocene‐Miocene Transition

Abstract The geological history of the South Asian monsoon (SAM) before the Pleistocene is not well‐constrained, primarily due to a lack of available continuous sediment archives. Previous studies have noted an intensification of SAM precipitation and atmospheric circulation during the middle Miocene (∼14 Ma), but no records are available to test how the monsoon changed prior to this. In order to improve our understanding of monsoonal evolution, geochemical and sedimentological data were generated for the Oligocene‐early Miocene (30–20 Ma) from Indian National Gas Hydrate Expedition 01 Site NGHP‐01‐01A in the eastern Arabian Sea, at 2,674 m water depth. We find the initial glaciation phase (23.7–23.0 Ma) of the Oligocene‐Miocene transition (OMT) to be associated with an increase in water column ventilation and water mass mixing, suggesting an increase in winter monsoon type atmospheric circulation, possibly driven by a relative southward shift of the intertropical convergence zone. During the latter part of the OMT, or “deglaciation” phase (23.0–22.7 Ma), a long‐term decrease in Mn (suggestive of deoxygenation), increase in Ti/Ca and dissolution of the biogenic carbonate fraction suggest an intensification of a proto‐summer SAM system, characterized by the formation of an oxygen minimum zone in the eastern Arabian Sea and a relative increase of terrigenous material delivered by runoff to the site. With no evidence at this site for an active SAM prior to the OMT we suggest that changes in orbital parameters, as well as possibly changing Tethyan/Himalayan tectonics, caused this step change in the proto‐monsoon system at this intermediate‐depth site.

Beasley, C.↗