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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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Analysis of MHD stability and active mode control on KSTAR for high confinement, disruption-free plasma

Long-pulse plasma operation at high normalized beta, $β_N$, above the $\textit{n}$= 1 ideal MHD no-wall stability limit in KSTAR is presently limited by tearing instabilities rather than resistive wall modes. H-mode plasma operation during the recent KSTAR device campaign produced discharges having strong $\textit{m/n}$= 2/1 tearing instabilities at $β_N$ lower than the ideal MHD no-wall beta limit. The unstable tearing mode consequently reduced plasma confinement and toroidal plasma rotation significantly. We report the experiment confirmed that an extended duration of electron cyclotron heating (ECH) at the initial phase of the discharge plays a critical role in mode destabilization. To study destabilizing mechanisms that affect the mode growth, the stability of the observed tearing modes from plasmas with significantly different $β_N$ is computed by using the resistive DCON code and the M3D-C 1 code. Equilibrium reconstructions that include constraints from internal profile diagnostics, and computed fast particle pressure are used as input for reliable computation of stability. The classical tearing stability index, Δ', from resistive DCON is computed to be unstable when the island is fully saturated with large amplitude, while the unstable mode is not computed by M3D-C 1 . The modified Rutherford equation (MRE) describing the evolution of the island width has been constructed for KSTAR plasmas by using plasma parameters computed by the TRANSP code. The MRE model estimates a saturated island width corresponding to ~10% of the plasma minor radius for equilibrium at high $β_N$ 3 having a stable Δ' from the resistive DCON. In preparation for long-pulse plasma operation at higher beta utilizing increased plasma heating power, a resistive wall mode (RWM) active feedback control algorithm has been completed and enabled on KSTAR. To accurately determine the $\textit{n}$= 1component produced by RWMs, an algorithm has been developed that includes magnetic sensor compensation of the prompt applied field and the field from the induced current on the passive conductors. Use of multiple toroidal sensor arrays is enabled by modifying the sensor toroidal angles assumed in mode decomposition to include the effect of varied mode helicities in the outboard region where the mode measurement is made. This analysis on stability, transport, and control provides the required foundation for disruption prediction and avoidance research on KSTAR.

resistive wall mode (RWM)↗
DOE OSTI2020

Leveraging Real-World Driving Data for Design and Impact Evaluation of Energy Efficient Control Strategies

Modeling and simulation are crucial in the development of advanced energy efficient control strategies. Utilizing real-world driving data as the underlying basis for control design and simulation lends veracity to projected real-world energy savings. Standardized drive cycles are limited in their utility for evaluating advanced driving strategies that utilize connectivity and on-vehicle sensing, primarily because they are typically intended for evaluating emissions and fuel economy under controlled conditions. Real-world driving data, because of its scale, is a useful representation of various road types, driving styles, and driving environments. The scale of real-world data also presents challenges in effectively using it in simulations. A fast and efficient simulation methodology is necessary to handle the large number of simulations performed for design analysis and impact evaluation of control strategies. In this study, two methods are presented of leveraging real-world data in both design optimization of energy efficient control strategies and in evaluating the real-world impact of those control strategies upon large-scale deployment. Through these methodologies, strategies with highest impact on energy savings were selected to be implemented as control algorithms. The developed algorithms were incorporated into a vehicle dynamics and powertrain control architecture implemented on a Cadillac CT6 demonstration vehicle. The control algorithms were then exercised on real-world driving scenarios to determine their impact on collective energy savings. The methodology utilizes the large-scale driving data sets maintained by the National Renewable Energy Laboratory to extract real-world driving scenarios and efficient simulation software tools. The insights obtained through this research help in guiding technology selection for energy efficient driving controls.

ARPA-E↗
DOE OSTI2024

Intermediate soil acidification induces highest nitrous oxide emissions

Global potent greenhouse gas nitrous oxide (N 2 O) emissions from soil are accelerating, with increases in the proportion of reactive nitrogen emitted as N 2 O, i.e., N 2 O emission factor (EF). Yet, the primary controls and underlying mechanisms of EFs remain unresolved. Based on two independent but complementary global syntheses, and three field studies determining effects of acidity on N 2 O EFs and soil denitrifying microorganisms, we show that soil pH predominantly controls N 2 O EFs and emissions by affecting the denitrifier community composition. Analysis of 5438 paired data points of N 2 O emission fluxes revealed a hump-shaped relationship between soil pH and EFs, with the highest EFs occurring in moderately acidic soils that favored N 2 O-producing over N 2 O-consuming microorganisms, and induced high N 2 O emissions. Our results illustrate that soil pH has a unimodal relationship with soil denitrifiers and EFs, and the net N 2 O emission depends on both the N 2 O/(N 2 O + N 2 ) ratio and overall denitrification rate. These findings can inform strategies to predict and mitigate soil N 2 O emissions under future nitrogen input scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Tank 48H Tetraphenylborate Mitigation: Simulant Studies using Sodium Permanganate

Tank 48H currently holds legacy material containing organic tetraphenylborate (TPB) compounds from the operation of the In-Tank Precipitation (ITP) process. TPB was added during the ITP process to precipitate the otherwise soluble cesium as insoluble cesium TPB (CsTPB), but excessive benzene generation from TPB degradation curtailed this treatment method. The contents of Tank 48H, which include approximately 26.000 kg of potassium TPB (KTPB) and trace CsTPB, are not compatible with the waste treatment facilities at the Savannah River Site (SRS) since the organic content and the associated flammability issues pose a challenge to the salt processing and sludge processing facilities within the liquid waste system. An in-tank process to remove (or decompose) TPB safely would be of great value. Previous testing at Savannah River National Laboratory (SRNL) demonstrated the destruction of glycolate via chemical oxidation using sodium permanganate with simulated and radioactive waste. Scoping tests were performed to study the destruction of TPB to determine if the contents of Tank 48H would be amenable to the same type of destruction. Partial destruction of TPB was observed in Tank 48H simulants under mild conditions (e g., pH 11, room temperature) with no definitive indication of benzene generation. To build upon the success of the scoping tests, an additional study was requested to provide a better understanding of the underlying chemistry for Tank 48H content destruction using sodium permanganate. Three experiments were performed with Tank 48H simulants at 40 °C to determine the efficacy of using sodium permanganate for TPB destruction. Three starting pH values were selected: 1) pH 11 for comparison with the previous work at room temperature, 2) pH 10 as the minimum pH recommended by the Corrosion Control Program (CCP) for in-tank processing, and 3) pH 8 to determine the effectiveness of TPB destruction at near neutral pH. While below the allowable pH for the CCP, the experiment at pH 8 was performed to study the TPB-Permanganate reaction under more extreme conditions and further verify the potential for out-of-tank processing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Shell-mediated control of surface chemistry of highly stoichiometric magnetite nanoparticles

Magnetite (Fe 3 O 4 ) nanoparticles are one of the most studied nanomaterials for different nanotechnological and biomedical applications. However, Fe 3 O 4 nanomaterials gradually oxidize to maghemite (γ-Fe 2 O 3 ) under conventional environmental conditions leading to changes in their functional properties that determine their performance in many applications. Here we propose a novel strategy to control the surface chemistry of monodisperse 12 nm magnetite nanoparticles by means of a 3 nm-thick Zn-ferrite epitaxial coating in core/shell nanostructures. We have carried out a combined Mössbauer spectroscopy, dc magnetometry, X-ray photoelectron spectroscopy and spatially resolved electron energy loss spectroscopy study on iron oxide and Fe 3 O 4 /Zn 0.6 Fe 2.4 O 4 core/shell nanoparticles aged under ambient conditions for 6 months. Our results reveal that while the aged iron oxide nanoparticles consist of a mixture of γ-Fe 2 O 3 and Fe 3 O 4 , the Zn-ferrite-coating preserves a highly stoichiometric Fe 3 O 4 core. Therefore, the aged core/shell nanoparticles present a sharp Verwey transition, an increased saturation magnetization and the possibility of tuning the effective anisotropy through exchange-coupling at the core/shell interface. The inhibition of the oxidation of the Fe 3 O 4 cores can be accounted for in terms of the chemical nature of the shell layer and an epitaxial crystal symmetry matching between the core and the shell.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2026

Demonstration of tokamak vertical stability control based on non-inductive Faraday-effect polarimetry measurements

Long-pulse or steady-state fusion reactors are envisioned to control vertical stability based on non-inductive measurements, i.e. that do not rely on temporal change of magnetic field. For the first time, vertical stability control using non-inductive Faraday-effect polarimetry measurements has been demonstrated. The Radial Interferometer-Polarimeter system on DIII-D is capable of microsecond resolution and was used to absolutely determine the vertical position of the plasma magnetic axis Z 0 . A vertical stability controller was developed to robustly stabilize diverted plasmas using Faraday-based measurements. The system was able to stabilize against vertical displacement events with growth rates up to 350 s -1 in elongated and elliptical plasma shapes, and instabilities with even higher growth rates are likely to be controllable with further improvements to controller tuning. Tests show that the Faraday-based controller remains effective and is capable of recovering from loss of control even when the plasma vertical position is far from the region where the linear model used to calculate Z 0 is most valid. Faraday control has also been activated during plasma ramp-up, demonstrating the robustness of the technique to larger systematic diagnostic uncertainty at low electron density.

Faraday-effect polarimetry↗
DOE OSTI2020

Optimal charging scheduling and management for a fast-charging battery electric bus system

Herein we discuss how battery electric buses (BEBs) are rapidly being embraced by public transit agencies because of their environmental and economic benefits. To address the problems of limited driving range and time-consuming charging for BEBs, manufacturers have developed rapid on-route charging technology that utilizes typical layovers at terminals to charge buses in operation using high power. With on-route fast-charging, BEBs are as capable as their diesel counterparts in terms of range and operating time. However, on-route fast-charging makes it more challenging to schedule and manage charging events for a BEB system. First, on-route fast-charging may lead to high electricity power demand charges. Second, it may increase electricity energy charges because of charging that occurs during on-peak hours. Without careful charging scheduling and management, on-route fast-charging may significantly increase fuel costs and reduce the economic attractiveness of BEBs. The present study proposes a network modeling framework to optimize the charging scheduling and management for a fast-charging BEB system, effectively minimizing total charging costs. The charging schedule determines when to charge a BEB, while the charging management strategically controls the actual charging power. Charging costs include both electricity demand charges and energy charges. The charging scheduling and management problem is first formulated as a nonlinear nonconvex program with time-continuous variables. A discretizing method and a linear reformulation technique are then adopted to reformulate the model as a linear program, which can be easily solved using off-the-shelf solvers, even for large-scale problems. Finally, the model is demonstrated with extensive numerical studies based on two real-world bus networks. The results demonstrate that the proposed model can effectively determine the optimal charging scheduling and management for a fast-charging BEB system, which carries the potential for use in large-scale real-world bus networks.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Uncertainty Quantification and Sensitivity Analysis of Non-Nuclear Advanced Controls Testbed Reactor Mockup

The research presented in this report describes our progress in applying stochastic methods and uncertainty quantification, parametric study, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated with Griffin, with the goal of developing a reduced order (surrogate) model which can be rapidly sampled while perturbing multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums, which are controlled by a hybrid proportional, integral and derivative (PID) controller, actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time and $\beta$ from a transient Griffin simulation executed with the improved quasi-static method to provide the kinetic parameters) as inputs to functions which control the CD rotation angle. Using a number of stochastic method approaches, we developed a dual purpose training-surrogate model of the NTP system using polynomial regression. The trained model can be rapidly sampled while simultaneously perturbing various input parameters of the model, such as coefficients on the PID control, or temperature (directly affect the neutron cross section). The surrogate model delivers accurate results orders-of-magnitude faster (minutes, not days) than the base model. Once the base model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs and their effect on the output. For example, coefficients used in the PID control system may vary due to some physical interference, or there may be uncertainty in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters and operational boundaries can be determined. The goal of this work is to support development of an advanced control system to operate CDs in a functioning NTP system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

Distribution of nickel(II) ions adsorbed at the muscovite mica (001)-water interface determined by in-situ resonant anomalous X-ray reflectivity

Mineral-water interfaces mediate adsorption, ion exchange, and secondary mineral formation that control element mobility in natural and engineered systems. Reliable prediction and control of these processes require a fundamental understanding of the interfacial structure that links adsorbed ion speciation to macroscopic sorption capacity and strength. Here, we determine atomic-scale changes in hydration and distribution of Ni(II) at the muscovite mica (001)-water interface using in situ high-resolution X-ray reflectivity (XR) and resonant anomalous X-ray reflectivity (RAXR) at 1 mM NiCl2 and pH 5.7. XR reveals reorganization of the primary hydration structure relative to that in deionized water: the water layer adsorbed in the cavity sites at a height of ~1.3 Å disappears, while distinct solution layers emerge at ~2.3, ~4.1, and ~5.6 Å above the basal oxygen plane. RAXR resolves three interfacial Ni(II) species: a dominant outer-sphere complex at 3.65 Å (~80% of the total coverage), a minor inner-sphere complex at 0.75 Å, and a low-coverage, more distant outer-sphere species at 5.63 Å. These three adsorbed Ni(II) species account for a total Ni(II) coverage of 0.54 ± 0.02 ion per unit cell area that compensates for the surface charge. These results highlight the role of interfacial hydration in controlling the speciation and stability of adsorbate cations on the negatively charged mica surface, providing quantitative insight into predicting the geochemical behavior of divalent metal cations in the aqueous environments.

Lee, Sang Soo↗
DOE OSTI2021

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Uncertainty quantification and sensitivity analysis of a nuclear thermal propulsion reactor startup sequence

The research presented in this article describes progress in applying stochastic methods, uncertainty quantification, parametric studies, and variance-based sensitivity analysis (also known as Sobol sensitivity analysis) to a full-core model of a nuclear thermal propulsion (NTP) system simulated via the radiation transport code Griffin to simulate neutronics. Our goal is to develop a reduced-order (surrogate) model that can be rapidly sampled with perturbations to multiple input parameters. In this NTP system, reactivity and power feedback affect the rotation of control drums (CDs), which is itself controlled by a hybrid proportional-integral-derivative (PID) controller actuated by the power demand and reactivity feedback from the numerical model. This model uses reactor kinetic feedback (mean generation time [Λ] and effective delayed neutron fraction [ β eff ] from a transient Griffin simulation executed via Griffin’s improved quasi-static solver to provide the kinetic parameters) as inputs to functions that control the CD rotation angle. By investigating numerous stochastic approaches, we developed a dual-purpose surrogate model of the NTP system, using polynomial regression in the Multiphysics Object-Oriented Simulation Environment (MOOSE) Stochastic Tools Module (STM). The trained model can be rapidly sampled while simultaneously perturbing various input parameters, such as coefficients on the PID control or temperature (directly affecting the neutron cross section). The surrogate model delivers accurate (within 5%) results at speeds orders of magnitude faster (minutes, not days of computational time) than the base model. Once the surrogate model has been trained, distributions of the uncertain parameters can be changed at will to investigate the effects of perturbing multiple inputs as well as the effects of these inputs on the model output. For example, coefficients used in the PID control system may vary due to some type of physical interference, or uncertainty may exist in the temperature of the neutron cross sections in various regions of the reactor. A distribution can be placed on these parameters, and operational boundaries can be determined. The goal of this work is to support development of an advanced control system for operating CDs in a functioning NTP system. This work is a scoping study of the MOOSE STM.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2020

Chemically Controllable Porous Polymer–Nanocrystal Composites with Hierarchical Arrangement Show Substrate Transport Selectivity

Functional organic–inorganic hybrid materials with tunable properties are useful across many application areas, ranging from gas storage to electronics, flame retardants, separations, and catalysis. Combining polymers, with a suite of functional groups and conformational flexibility, and inorganic nanoparticles, with tunable surface chemistry and composition, yields hybrids with novel functional properties. Specifically, in catalysis, control of the electronic environment at a metal interface is paramount in determining the catalytic properties. In this contribution, we describe a modular process to prepare porous polymer–nanocrystal (NC) composites in a hierarchical, multilayered synthesis, in which multiple parameters can be accurately tuned: polymer functional groups and the corresponding pore structure, the polymer layer thickness, and the NC size, shape, and composition. This process provides for a variety of controlled materials with high surface area, tunable chemistry, and thermal and chemical stabilities. Furthermore, we demonstrate their utility for shape- and size-selective catalytic conversions both in oxidation and hydrogenation reactions, where they show increased selectivity by orders of magnitude compared to conventional polymer-supported metal catalysts. In light of the high degree of control in the composite structure, this method allows for the design and realization of catalysts for several reactions and reaction environments and for nanomaterials with other applications.

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

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Room-temperature fabrication of garnet-type solid-electrolyte: Optimizing particle size for high ionic conductivity

Dense and uniform Li 6.25 Al 0.25 La 3 Zr 2 O 12 (Al-doped LLZO) solid-electrolyte film of ~24 µm thickness is successfully fabricated by room temperature aerosol deposition (AD) method. The process optimization study revealed that careful control of particle size and morphology is one of critical determinants in the development of a compact AD membrane. Notably, our method facilitated an impressive ionic conductivity of approximately 10 –5 S cm –1 , bypassing the necessity for post-annealing processes, a milestone in itself. However, it is hypothesized that the attained conductivity is somewhat restrained by factors such as smaller grain size and potential surface degradation due to moisture exposure during fabrication, indicating avenues for further research. Looking forward, detailed investigations into the film's microstructure and its impact on transport properties will be a focal point, alongside potential enhancements through post-annealing and particle coating strategies. Finally, this research hints at a promising trajectory for the development of high-efficiency solid-state battery technology.

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

Analytical population dynamics underlying harmful algal blooms triggered by prey avoidance

A modified version of the NPZD ecosystem model is used to analytically examine the effects of predation avoidance, a possible mechanism for triggering harmful algal blooms (HAB). To resolve HAB development caused by predation avoidance, an additional phytoplankton functional group is considered, one that has slower nutrient uptake and better predation avoidance characteristics than the non-harmful phytoplankton group used in traditional NPZD models. Because the two phytoplankton groups (one non-harmful and one HAB) compete for only one resource within the same system, steady state (equilibrium) conditions cannot occur without the presence of zooplankton; only the non-harmful phytoplankton group, which defeats the HAB group in the resource competition, can survive in the equilibrium. The presence of sufficient zooplankton effectively acts to replenish the nutrient pool by consuming the non-harmful phytoplankton. When this occurs, two equilibrium states are found: one with both phytoplankton groups coexisting, and one that only includes the HAB group. Further, the condition required for equilibrium is that the total nitrogen within the system should be larger than a threshold determined by model coefficients. The threshold and feasibility of the equilibrium are sensitive to the relative HAB predation avoidance coefficient. If the coefficient is larger than the ratio of net growth rates between the HAB and non-harmful phytoplankton group, the threshold becomes infinite, and an equilibrium is not feasible. The time scale for the system to reach an equilibrium state that includes a HAB group is determined asymptotically. The dependence of a threshold condition as a controlling factor may explain the regime shift of dominant species causing HABs. The ecosystem model is fully implemented into the Regional Ocean Modeling System and applied to an idealized coastal embayment (with depths and geometry taken from San Francisco Bay) to show numerically the dominance of prey avoidance dynamics in a natural shallow water environment that includes advection and diffusion. The analytical results improve strategies for HAB modeling and provide guidance for setting model coefficients necessary to resolve a HAB event.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Impact of Diabetes and Insulin Use on Prognosis in Patients With Resected Pancreatic Cancer: An Ancillary Analysis of NRG Oncology RTOG 9704

Diabetes mellitus (DM) has been proposed to be tumorigenic; however, prior studies of the association between DM and survival are conflicting. The goal of this ancillary analysis of RTOG 9704, a randomized controlled trial of adjuvant chemotherapy in pancreatic cancer, was to determine the prognostic effects of DM and insulin use on survival.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Revealing the Influence of Binding Motifs on Electron Transfer and Recombination Kinetics for CdSe Quantum Dots Functionalized with a Modified Viologen

Anchoring of molecules to the surfaces of semiconductor nanocrystals (NCs) presents an opportunity to leverage the precise synthetic tunability of molecular function and the remarkable light harvesting properties of NCs to drive photochemical reactions. However, charge transfer between the two species depends not only on the energy level alignments but also on the details of their binding interactions, which are difficult to probe. Here, in this work, we characterize the binding between CdSe quantum dots (QDs) and a new phosphonated derivative of the electron acceptor methyl viologen, designed to attach to the QD surface via the phosphonate group. We use isothermal titration calorimetry to probe the thermodynamics of the QD–molecule interaction and use the parameters determined therein to analyze transient absorption spectroscopy measurements of forward and back electron transfer from QDs to the viologen. We find that the ligand-like phosphonate binding leads to an electron-transfer rate constant that is 3 orders of magnitude smaller than that for the face-on binding of the bipyridine ring of methyl viologen. Back electron transfer is also significantly slower in the derivative. Interestingly, a minor fraction of the phosphonated derivative also binds in the face-on configuration, with similar forward and back electron transfer kinetics as methyl viologen. Numerical simulations show that the ligand-like binding will lead to significantly improved quantum yields of photocatalysis over a wide range of reaction rates. By independently characterizing binding thermodynamics and charge transfer kinetics, this work reveals how the complexities underlying electron transfer at the NC–molecule interface determine photocatalytic outcomes. This work also represents a step toward controlling forward and back electron transfer kinetics via rational molecular design.

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

Selective Electrocatalytic Reduction of Nitrous Oxide to Dinitrogen with an Iron Porphyrin Complex

Nitrous oxide (N 2 O) is the third largest contributor to anthropogenic greenhouse gas emissions and plays a detrimental role in the depletion of ozone. Despite the contribution of N 2 O as an atmospheric pollutant, there are currently only a few examples of electrochemical N 2 O remediation to N 2 . Herein we report the electrocatalytic deoxygenation of N 2 O by iron tetraphenylporphyrin (FeTPP) to form N 2 with quantitative Faradaic efficiency and an observed rate of 1.91 s –1 . No degradation was observed after controlled potential electrolysis. Mechanistic investigation of the proposed catalytic steps determined that hydrogen-bond donors are critical for accelerating N 2 O activation and that proton transfer is involved in the rate-determining step.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗