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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A spatially regularized detector for emergent/re-emergent disease outbreaks

Early detection of outbreaks caused by emergent pathogens, using epidemiological surveillance data i.e., daily case counts, is difficult. This is because the data tend to be noisy during the early epoch of the outbreak. In contrast, the spread-rate of the disease tends to be well-behaved, as it depends only on the mixing patterns of the population and the characteristics of the pathogen, neither of which behave erratically in space-time. In this report, we explore whether the spread-rate can be used for epidemiological surveillance, conditional on case count data. Estimating the spread-rate from case count data allows us to exploit exogenous information, e.g., incubation period distributions etc., which can considerably smooth out any erratic temporal behavior. Further, epidemiological dynamics are spatially correlated, and if case counts are available for multiple areal units e.g., counties, these correlations could potentially be used to suppress noise in the early epoch data. These exogenous information and structure are not exploited by conventional syndromic surveillance detectors to extract a well-behaved latent variable for monitoring purposes. The technical challenge lies in the estimation of the spread-rate field jointly over a collection of areal units; further, the spread-rate varies over time. We develop a method based on mean-field variational inference to approximately estimate the spread-rate field, using a Gaussian Random Field Model for spatial regularization. The method is tested on the estimation of spread-rate in the thirty-three counties of New Mexico and detect the arrival of the Fall 2020 COVID-19 wave in September 2020. We find that the method is scalable, but underestimates the uncertainty in the estimated spread-rate field. We detect the arrival of the Fall 2020 wave a week ahead of conventional syndromic surveillance algorithms, but our simplistic detection algorithm, based on simple anomaly detection, suffers from a high false positive rate, similar to conventional detectors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Contaminant Transport Modeling for Technology Evaluation and Long-Term Monitoring in the Tims Branch Testbed, SC - 20343

Studies conducted at U.S. DOE sites have shown the presence of heavy metals, radionuclides, and volatile organic compounds in surface water, groundwater, and soil as a result of nuclear activity in the Cold War era. Since the 1990's, innovative cleanup methods have been implemented in the Tims Branch watershed at Savannah River Site (SRS) to limit the contaminant flux to the stream that have reduced the contaminant concentrations to acceptable regulatory levels in the dissolved phase. A tin-based treatment which effectively eliminated all local anthropogenic mercury inputs to this ecosystem resulted in a known step function addition of inert tin oxide particles which now serve as a potential tracer for sedimentation and particle transport processes in the stream. The long-term effectiveness of this and other remediation techniques and the potential for remobilization of adsorbed contaminant in sediment during extreme hydrologic conditions however remains unclear. It is therefore important to understand not only the fate and transport of dissolved contaminants, but also the movement of sediment and the relevant interactions with dissolved contaminant. To narrow this knowledge gap, a study is being conducted using the Tims Branch watershed as a stream-scale ecosystem test-bed to identify the primary transport processes of major contaminants of concern (such as mercury, nickel and uranium) with an emphasis on interactions with sediment transport. This involves the development of a fully distributed hydrologic watershed model of the Tims Branch watershed to predict streamflow under extreme weather conditions, as well as the development of a comprehensive contaminant transport model that can properly account for coupled contaminant and sediment transport. Review of relevant research reports and peer-reviewed journals revealed that aside from advection-dispersion transport of dissolved contaminants, adsorption and desorption with suspended solids and bed sediment also play an important role in the transport of those contaminants of concern. To develop the fully distributed hydrologic watershed model, the MIKE SHE 2-dimensional (2D) land surface/3D groundwater model that simulates surface/subsurface hydrologic processes (such as overland flow, evapotranspiration, and infiltration) was coupled with a 1D streamflow model that accounts for stream water hydraulics (such as hydraulic structures, cross-sections, and network). To model contaminant transport, the MIKE 11 streamflow component was coupled with the MIKE 11 AD module that simulates solute transport through advection and dispersion, and MIKE ECO Lab module that accounts for both sediment transport and interactions with dissolved contaminant. At this stage, the development and optimization of the fully distributed hydrologic model has been completed, achieving satisfactory statistical results between observed and predicted discharge as indicated by a root mean square error (RMSE) of 0.039 cms and a Nash-Sutcliffe efficiency coefficient (NSE) of 0.764. The ongoing development of the contaminant transport model has also yielded realistic results from preliminary tests. Results from this study are a key to evaluating the effectiveness of tin (II)-based mercury treatment of wetlands at the SRS site, and are also relevant to evaluating the potential of using this type of novel remediation technology in other mercury-contaminated stream systems. Knowledge acquired from this research will also support interpretation of historical data on the trends of contaminant concentration distribution in Tims Branch, particularly considering the effect of extreme hydrological events on the stream flow and pollutant transport. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Phase Composition and Phase Transformation of Additively Manufactured Nickel Alloy 718 AM Bench Artifacts

Additive manufacturing (AM) technologies offer unprecedented design flexibility but are limited by a lack of understanding of the material microstructure formed under their extreme and transient processing conditions and its subsequent transformation during post-build processing. As part of the 2022 AM Bench Challenge, sponsored by the National Institute of Standards and Technology, this study focuses on the phase composition and phase evolution of AM nickel alloy 718, a nickel-based superalloy, to provide benchmark data essential for the validation of computational models for microstructural predictions. Here, we employed high-energy synchrotron X-ray diffraction, in situ synchrotron X-ray scattering, as well as high-resolution transmission electron microscopy for our analyses. The study uncovers critical aspects of the microstructure in its as-built state, its transformation during homogenization, and its phase evolution during subsequent aging heat treatment. Specifically, we identified secondary phases, monitored the dissolution and coarsening of microstructural elements, and observed the formation and stability of γ ’ and γ ” phases. The results provide the rigorous benchmark data required to understand the atomic and microstructural transformations of AM nickel alloy 718, thereby enhancing the reliability and applicability of AM models for predicting phase evolution and mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Disturbance legacies regulate coastal forest soil stability to changing salinity and inundation: A soil transplant experiment

Coastal forests worldwide are vulnerable to a dramatic transition from upland to wetland as sea-level rise accelerates and regimes of precipitation and storms change. However, the biogeochemical impacts of shifting salinity and inundation disturbance that foreshadow forest to wetland state transitions are largely unknown. This experiment used a natural salinity gradient in a tidal creek in eastern Maryland, U.S.A., to examine how soil respiration and chemistry may change under novel salinity and inundation disturbance regimes. Soil monoliths were transplanted in a reciprocal design among plots varying in seawater exposure and elevation above the creek. We monitored the monoliths’ carbon dioxide (CO 2 ) flux for two years and performed soil chemical analyses at the end of the experiment. Soil CO 2 flux was affected by changing disturbance regimes and responses were dependent upon the salinity and inundation legacies associated with each study location. Lowland soil CO 2 flux was resistant to changing salinity and inundation disturbance, with transplanted soil monolith chemistry composition in between that of its origin and destination. Conversely, upland soil CO 2 flux was sensitive to changing salinity and inundation disturbance and remained suppressed throughout the 2-year study when exposed to wetter, saline conditions. Additionally, transplanted upland soil chemistry composition was like that of its destination, rather than origin, with upland soils displaying higher pH, base saturation, and nutrient availability relative to lowland soils. Here, we hypothesize that reductions in soil respiration rates were driven by loss of soil nutrients and osmotic and redox stress on microbial communities following exposure to seawater. Together, our results suggest that disturbance legacies shape coastal forest soil responses to changing salinity and inundation disturbance regimes. However, fully understanding the dependence of system responses on disturbance legacies requires future study across a variety of systems and spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Uncovering the Role of Hole Traps in Promoting Hole Transfer from Multiexcitonic Quantum Dots to Molecular Acceptors

The understanding of electronic dynamics in multiexcitonic quantum dots (QDs) is important for designing efficient systems useful in high power scenarios, such as solar concentrators and multielectron charge transfer. The multiple charge carriers within a QD can undergo undesired Auger recombination events, which rapidly annihilate carriers on picosecond time scales and generate heat from absorbed photons instead of useful work. Compared to the transfer of multiple electrons, the transfer of multiple holes has proven to be more difficult due to slower hole transfer rates. To probe the competition between Auger recombination and hole transfer in CdSe, CdS, and CdSe/CdS QDs of varying sizes, we synthesized a phenothiazine derivative with optimized functionalities for binding to QDs as a hole accepting ligand and for spectroscopic observation of hole transfer. Transient absorption spectroscopy was used to monitor the photoinduced absorption features from both trapped holes and oxidized ligands under excitation fluences where the averaged initial number of excitons in a QD ranged from ~1 to 19. We observed fluence-dependent hole transfer kinetics that last around 100 ps longer than the predicted Auger recombination lifetimes, and the transfer of up to 3 holes per QD. Theoretical modeling of the kinetics suggests that binding of hole acceptors introduces trapping states significantly different from those in native QDs passivated with oleate ligands. Holes in these modified trap states have prolonged lifetimes, which promotes the hole transfer efficiency. These results highlight the beneficial role of hole-trapping states in devising hole transfer pathways in QD-based systems under multiexcitonic conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Extending Component Lifetime And Improving Inverter Reliability (ECLAIIR)

Inverter reliability remains one of the most persistent challenges limiting the performance, availability, and economic viability of utility‑scale photovoltaic (PV) plants. Industry data consistently show that inverters account for the highest share of corrective maintenance events and unplanned outages across PV fleets. These failures result in energy losses, increased O&M costs, and reduced confidence in long‑term solar asset performance. Motivated by these challenges, this project—Extending Component Lifetime and Improving Inverter Reliability (ECLAIIR)—was undertaken to systematically investigate inverter degradation and failure mechanisms, develop predictive maintenance capabilities, and establish data‑driven pathways to improve service life and reduce the Levelized Cost of Energy (LCOE) for large‑scale PV systems. The primary goal of the project was to identify pre‑failure signatures in string inverters using both lab‑based accelerated lifetime testing and field‑based data and to develop predictive maintenance algorithms that can anticipate inverter faults before they occur. Through collaboration with inverter testing laboratory, solar PV plant owner, and failure‑analysis experts, the project advanced the technical understanding of inverter reliability. By instrumenting inverters with thermistors, humidity sensors, power‑quality meters, and acoustic sensors, the research established how multiple sensing modalities can reliably detect deviations from normal behavior hours to days before failure. These findings substantially enhance scientific understanding of inverter failure kinetics and provide the PV industry with the most comprehensive cross‑OEM characterization of early‑stage failure indicators reported to date. Technically, the project demonstrated the effectiveness of predictive maintenance by developing and validating the PreDICT (Predictive Diagnostics of PV Inverters Using Condition Monitoring and Trend Analysis) framework—a multi‑layer diagnostic architecture combining peer‑to‑peer analytics, historical trend modeling, and advanced machine‑learning techniques such as the Sequential Conditional Variational Autoencoder (SCVAE). This predictive model achieved more than 90% accuracy in detecting pre‑failure conditions and provided up to four days of lead time before inverter failure in field scenarios. Economically, the project’s LCOE analysis showed that predictive maintenance can reduce lifetime energy losses and minimize corrective maintenance interventions. Modeling indicated that, depending on inverter failure rates and replacement timelines, predictive maintenance can significantly reduce LCOE impacts associated with inverter downtime: from as high as 19.4% under conventional maintenance strategies to 0.1%–10.17% when predictive analytics are adopted. These results confirm that predictive maintenance is both technically feasible and economically advantageous for utilities and plant operators. The project’s findings also have broad public benefit. By improving inverter reliability and reducing downtime, predictive maintenance directly increases electricity generation from existing PV assets. Enhanced reliability lowers operational costs for utilities, which can translate over time into lower energy costs for consumers. Furthermore, the project’s technical publications, conference presentations, and industry workshops ensure that knowledge gained is shared broadly across the solar industry, supporting workforce development and enabling utilities of all sizes to adopt modern asset‑health monitoring practices. The retrofitting case study and service‑life prediction framework further support informed decision‑making for aging PV fleets, helping operators extend system life and reduce electronic waste. In summary, the ECLAIIR project significantly advanced the state of knowledge on inverter degradation, demonstrated the technical and economic value of predictive maintenance, and delivered actionable tools and insights that support more reliable, cost‑effective, and sustainable PV plant operation. The outcomes of this project will continue to inform utility practices, guide inverter design improvements, and strengthen the long‑term performance of solar assets nationwide.

14 SOLAR ENERGY↗
DOE OSTI2024

Near-Field Passive Wireless Sensor for High-Temperature Metal Corrosion Monitoring

This work focuses on the fabrication and evaluation of a passive wireless sensor for the monitoring of the temperature and corrosion of a metal material at high temperatures. An inductor–capacitor (LC) resonator sensor was fabricated through the screen printing of Ag-based inks on dense polycrystalline Al 2 O 3 substrates. The LC design was modeled using the ANSYS HFSS modeling package, with the LC passive wireless sensors operating at frequencies from 70 to 100 MHz. The wireless response of the LC was interrogated and received by a radio frequency signal generator and spectrum analyzer at temperatures from 50 to 800 °C in real time. The corrosion kinetics of the Cu 110 was characterized through thermogravimetric (TGA) analysis and microscopy images, and the oxide thickness growth was then correlated to the wireless sensor signal under isothermal conditions at 800 °C. The results showed that the wireless signal was consistent with the corrosion kinetics and temperature, indicating that these two characteristics can be further deconvoluted in the future. In addition, the sensor also showed a magnitude- and frequency-dependent response to crack/spallation events in the oxide corrosion layer, permitting the in situ wireless identification of these catastrophic events on the metal surface at high temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Enabling Microscale Processing: Combined Raman and Absorbance Spectroscopy for Microfluidic On-Line Monitoring

Microfluidics have many potential applications including characterization of chemical processes on a reduced scale, spanning the study of reaction kinetics using on-chip liquid–liquid extractions, sample pretreatment to simplify off-chip analysis, and for portable spectroscopic analyses. The use of in situ characterization of process streams from laboratory-scale and microscale experiments on the same chemical system can provide comprehensive understanding and in-depth analysis of any similarities or differences between process conditions at different scales. A well-characterized extraction of Nd(NO 3 ) 3 from an aqueous phase of varying NO 3– (aq) concentration with tributyl phosphate (TBP) in dodecane was the focus of this microscale study and was compared to an earlier laboratory-scale study utilizing counter current extraction equipment. Here, we verify that this same extraction process can be followed on the microscale using spectroscopic methods adapted for microfluidic measurement. Concentration of Nd (based on UV–vis) and nitrate (based on Raman) was chemometrically measured during the flow experiment, and resulting data were used to determine the distribution ratio for Nd. Extraction distributions measured on the microscale were compared favorably with those determined on the laboratory scale in the earlier study. Both micro-Raman and micro-UV–vis spectroscopy can be used to determine fundamental parameters with significantly reduced sample size as compared to traditional laboratory-scale approaches. This leads naturally to time, cost, and waste reductions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Reliable statistics-based detection and investigation of anomalies in a SMART valve system

Reliable anomaly detection and diagnosis are critical for the safe operation of complex engineered systems. This study presents a unified framework that integrates statistical, model-based, and data-driven techniques for anomaly detection and investigation, demonstrated on SMART valve systems in hybrid energy applications. Four detection methods—mean deviation, seasonal extreme studentized deviate, ARIMA forecasting, and matrix profiling—were implemented and compared. Matrix profiling was particularly effective in revealing subtle deviations and hidden relationships among variables. Anomaly investigation was performed by analyzing variable-level and grouped signal profiles, with system topology incorporated to distinguish primary faults from propagated effects. Grouping signals by type enhanced interpretability, enabling accurate localization of anomalies across multi-dimensional datasets. Experimental results confirmed the framework's capability to consistently detect and isolate anomalies while providing actionable insights into system interdependencies. The proposed methodology offers a robust, interpretable, and scalable solution for condition monitoring, with potential applications in safety-critical domains such as nuclear energy, aerospace, and process industries.

ARIMA models↗
DOE OSTI2020

Air quality impact of the Northern California Camp Fire of November 2018

The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM 2.5 ) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM 2.5 , black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM 2.5 to over 50 µg m –3 for nearly 2 weeks, with localized peaks exceeding 300 µg m –3 . Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM 2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM 2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM 2.5 . Inclusion of aerosol radiative feedback moderately improves PM 2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems

Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.

AI/ML↗
DOE OSTI2022

Quantifying Subsurface Flow and Solute Transport in a Snowmelt‐Recharged Hillslope With Multiyear Water Balance

Abstract Quantifying flow and transport from hillslopes is vital for understanding water quantity and quality in rivers, but remains obscure because of limited subsurface measurements. Using measured hydraulic conductivity K profiles and water balance over a single year to calibrate a transmissivity feedback model for a hillslope in the East River watershed (Colorado) proved unsatisfactory for predicting flow over the subsequent years. Well‐constrained field‐scale K were obtained by optimizing subsurface flux predictions over years having large differences in recharge, and by including estimates of interannual transfer of excess snowmelt recharge. Water and solute exports during high snowmelt recharge occur predominantly via shallow groundwater flow through weathered rock and soil because of their enlarged transmissivities under saturated conditions. Conversely, these shallow pathways are less active in snow drought years when the water table remains deeper within the weathering zone. Hillslope soil water monitoring showed that rainfall does not infiltrate deeply during summer and fall months, and revealed water losses consistent with model ET predictions. By combining water table‐dependent fluxes with pore water chemistry in different zones, time‐dependent rates of solute exports become predictable. As an example, calibrated K were combined with dissolved nitrogen concentrations in pore waters to show the snowmelt‐dependence of reactive nitrogen exported from the hillslope, further supporting the recent finding that the weathering zone is the dominant source of reactive nitrogen at this site. Subsurface export predictions can now be obtained for wide ranges of recharge based on measurements of water table elevation and profiles of pore water chemistry.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗
DOE OSTI2020

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Monitoring Local Electric Fields using Stark Shifts on Napthyl Nitrile-Functionalized Silicon Photoelectrodes

in this paper, we report spectroscopic measurements of the local electric field using vibrational Stark shifts of napthyl nitrile-functionalized silicon under electrochemical working conditions. The C$\equiv$N bond is particularly sensitive to applied electric fields and serves as a good probe for the local electric fields at the silicon–aqueous interface. Here, surface-enhanced Raman spectra (SERS) are collected at a silicon surface using a water immersion lens as a function of the reference potential in a three-terminal potentiostat. In deionized (DI) water and KCl solutions, the nitrile (i.e., C$\equiv$N) stretch downshifts by 4.7 and 8.6 cm –1 , respectively, under an applied potential of –1 V vs Ag/AgCl. Density functional theory (DFT) calculations of the napthyl nitrile complex carried out under various electric fields establish the Stark tuning rate to be 0.5622 cm–1/(MV cm –1 ). Based on this relation, electric fields of –8.4 and –15.2 MV/cm were obtained under negative applied potentials. These measurements report the electric field strength within the double (i.e., Helmholtz) layer, which is responsible for pulling positively charged ions (e.g., H + ) toward the surface in reduction reaction processes.

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

Voltage and Temperature Limits of Advanced Electrolytes for Lithium-Metal Batteries

Several advanced electrolytes (mainly ether-based) have recently demonstrated excellent electrochemical performance in high energy density lithium (Li)-metal batteries. However, the safety of these ether-based electrolytes is still unknown. This work evaluates the thermal stability of these new formulations to understand their safety limits of operation in comparison to carbonate electrolytes typically used in Li-ion batteries. Electrolyte stability is assessed in conjunction with LiNi 0.8 Mn 0.1 Co 0.1 O 2 cathode and Li-metal anode at ultra-high voltages (≤ 4.8 V) and temperatures (≤ 300°C) to trigger thermal runaway, where onset and extent of heat release are monitored via isothermal microcalorimetry and differential scanning calorimetry Most ether-based electrolytes show improved thermal resilience over commercial carbonate formulations when heated up to 300°C. The thermal behavior in presence of charged cathode and Li-metal suggests that the new electrolytes may be better at stabilizing active material surfaces than carbonate electrolytes. Although extreme voltages severely destabilize the ether-based formulations, prompting thermal runaway, a phosphate based localized high concentration electrolyte exhibits superior stability over commercial carbonate electrolytes at all tested temperatures (32°C, 45°C, and 60°C). Although thermal analysis during the first charge process (as adopted in this preliminary study) may be insufficient to conclude the long-term advantages of these electrolytes, a more stable electrolyte identified under extreme voltage and temperature conditions will provide valuable guidance for the safety of future electrolyte designs.

25 ENERGY STORAGE↗
DOE OSTI2024

The use of a benign fast-growing cyanobacterial species to control microcystin synthesis from Microcystis aeruginosa

Introduction Microcystis aeruginosa(M. aeruginosa), one of the most abundant blue-green algae in aquatic environments, produces microcystin by causing harmful algal blooms (HABs). This study investigated the combined effects of nutrients and competition among cyanobacterial subpopulations on the synthesis of microcystin-LR. Methods Under varying nitrogen and phosphorus concentrations, cyanobacterial coculture, and the presence of algicidal DCMU, the growth was monitored by optical density analysis or microscopic counting, and the microcystin production was analyzed using high-performance liquid chromatography-UV. Furthermore, growth and toxin production were predicted using a kinetic model. Results and discussion First, coculture with the fast-growing cyanobacteriumSynechococcus elongatusUTEX 2973 (S. elongatus) reducedM. aeruginosabiomass and microcystin production at 30°C. Under high nitrogen and low phosphorus conditions,S. elongatuswas most effective, limitingM. aeruginosagrowth and toxin synthesis by up to 94.7% and 92.4%, respectively. Second, this biological strategy became less effective at 23°C, whereS. elongatusgrew more slowly. Third, the photosynthesis inhibitor DCMU (3-(3,4-dichlorophenyl)-1,1-dimethylurea) inhibitedM. aeruginosagrowth (at 0.1 mg/L) and microcystin production (at 0.02 mg/L). DCMU was also effective in controlling microcystin production inS. elongatus–M. aeruginosacocultures. Based on the experimental results, a multi-substrate, multi-species kinetic model was built to describe coculture growth and population interactions. Conclusion Microcystin from representative toxin-producingM. aeruginosacan be controlled by coculturing fast-growing benign cyanobacteria, which can be made even more efficient if appropriate algicide is applied. This study improved the understanding of the biological control of microcystin production under complex environmental conditions.

Microbiology↗