An Artificial Protective Coating toward Dendrite-Free Lithium-Metal Anodes for Lithium–Sulfur Batteries
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Energy & Fuels↗
SEARCH · Engineering Papers
Results for “Performance Tuning”
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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Rational optimization of substituted α–MnO 2 cathode for aqueous zinc–ion battery
Density functional theory (DFT) is utilized to explore the effects of increasing concentrations of vanadium (V) and chromium (Cr) substitution on the discharge of α-MnO 2 cathode in hydrated zinc-ion batteries (ZIBs). During H + -intercalation and Zn 2+ -intercalation, Cr-substitution proves to be more effective than V-substitution in promoting discharge behaviors. Transitioning from Mn 0.875 Cr 0.125 O 2 , Mn 0.75 Cr 0.25 O 2 to Mn 0.625 Cr 0.375 O 2 is found to consistently enhance the discharge voltage, along with improved tunnel structure retention and volume expansion suppression. In comparison, the promoting effect of increasing V-substitution is relatively small at initial discharge stages and leads to degradation at later stages, primarily due to an increased concentration of unstable Mn 2+ ion. The superior effect of Cr-substitution is attributed to the unique atomic and electronic structures of substituted Cr 4+ and reduced Cr 3+ ions during discharge. These ions serve as active electron acceptors to limit the formation of Mn 3+ and Mn 2+ ions, and as anchors to stabilize the α-MnO 2 framework and intercalated H + /Zn 2+ ions, respectively. Finally, our study highlights fine-tuning through substitution to enhance the performance of α-MnO 2 -based cathode materials in ZIBs.
25 ENERGY STORAGE↗
Lewis Acid Site Engineering in Chromite Spinels Orchestrated Surface Reconstruction and Surpasses RuO 2 in Oxygen Evolution
Atomic-scale engineering of chromite spinels featuring redox-active tetrahedral A-sites and strong Cr–O covalency offers a promising route to superior platinum-group-metal-free oxygen evolution reaction (OER) catalysts. However, comprehensive studies addressing how cation substitution influences surface chemistry and governs OER activity and durability in chromite spinels remain limited. Here, in this work, a systematic investigation of the multicationic chromite series Ni x Fe y Cr 3−x−y O 4 is presented, identifying composition-dependent Lewis acidity as a descriptor of superior OER performance. It is further demonstrated that tuning surface acidity directly controls dynamic reconstruction processes and lattice-oxygen participation during spinel-based electrocatalysis. Following activation, the optimized Ni 0.8 Fe 0.3 Cr 1.9 O 4 catalyst delivers a current density of 10 mA cm −2 at an overpotential of 235 mV, surpassing RuO 2 , with excellent long-term stability. Integrating microscopic and spectroscopic analysis with operando impedance spectroscopy, it shows that activation generates an oxyhydroxide overlayer and reveals a previously unrecognized link between surface Lewis acidity and the growth kinetics and activity of these shells. Density functional theory calculations indicate that Fe incorporation at octahedral sites raises the O 2p-band center and lowers oxygen-vacancy formation energy, promoting lattice-oxygen activation and triggering reconstruction, yielding enhanced OER. This work integrates cation-driven surface-acidity modulation, acidity-governed reconstruction, and OER activity enhancement into a unified predictive framework for designing earth-abundant spinel-based catalysts.
operando impedance spectroscopy↗
The impact of neutrino-nucleus interaction modeling on new physics searches
Accurate neutrino-nucleus interaction modeling is an essential requirement for the success of the accelerator-based neutrino program. As no satisfactory description of cross sections exists, experiments tune neutrino-nucleus interactions to data to mitigate mis-modeling. In this work, we study how the interplay between near detector tuning and cross section mis-modeling affects new physics searches. We perform a realistic simulation of neutrino events and closely follow NOvA’s tuning, the first published of such procedures in a neutrino experiment. We analyze two illustrative new physics scenarios, sterile neutrinos and light neutrinophilic scalars, presenting the relevant experimental signatures and the sensitivity regions with and without tuning. While the tuning does not wash out sterile neutrino oscillation patterns, cross section mis-modeling can bias the experimental sensitivity. In the case of light neutrinophilic scalars, variations in cross section models completely dominate the sensitivity regardless of any tuning. Our findings reveal the critical need to improve our theoretical understanding of neutrino-nucleus interactions, and to estimate the impact of tuning on new physics searches. We urge neutrino experiments to follow NOvA’s example and publish the details of their tuning procedure, and to develop strategies to more robustly account for cross section uncertainties, which will expand the scope of their physics program.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Examining Metal Identity and Proximity Effects on Acetylene Hydrogenation with Azolate-Based MOFs
Liquid organic hydrogen carriers (LOHCs) are an attractive fuel source due to their compatibility with existing transportation methods and ease of use. However, they suffer from sluggish (de)hydrogenation kinetics. One promising platform for developing next-generation catalysts is metal–organic frameworks (MOFs), which can enable systematic interrogation into the influence of metal identity and spatial arrangement. In this study, the effect of the coordination environment was investigated using Ni- and Co-based azolate MOFs: MFU-4l-OH (M x Zn 5–x (OH) 4 (BTDD) 3 ; x = 4 for M = Co and x = 3 for M = Ni, H 2 BTDD = bis(1H-1,2,3-triazolo[4,5-b][4′,5′-i])dibenzo[1,4]dioxin), composed of single-site nodes, and M(OH) 2 BBTA (M = Ni, Co; H 2 BBTA = 1H,5H-benzo(1,2-d:4,5-d’)bistriazole), composed of extended chain-type nodes. The catalysts were characterized by isotherms, powder X-ray diffraction (PXRD), scanning electron microscopy (SEM), inductively coupled plasma-optical emission spectroscopy (ICP-OES), and X-ray photoelectron spectroscopy (XPS) analysis. Acetylene hydrogenation activity under steady state conditions (150 °C, 1:1 C 2 H 2 :H 2 ) revealed higher turnover frequencies (TOFs) up 1.17 × 10 –2 mol C 2 H 2 mol −1 Ni min −1 and 1.98 × 10 –3 mol C 2 H 2 mol −1 Co min −1 for Ni-MFU-4l-OH and Co-MFU-4l-OH, respectively, compared to their BBTA analogues. However, the Co-based MOFs, particularly Co 2 (OH) 2 -BBTA, exhibited greater selectivity (up to 19%) for the fully hydrogenated ethane product. Isosteric heat of adsorption (Q st ) measurements for ethylene and ethane revealed that the BBTA framework had stronger binding to the products than MFU-4l. Furthermore, these findings demonstrate that metal identity and coordination environment may modulate acetylene hydrogenation performance, leading to design principles for tuning LOHC hydrogenation catalysts.
catalysts↗
Compositional heterogeneity in Cs y FA 1- y Pb(Br x I 1- x ) 3 perovskite films and its impact on phase behavior
Hybrid organic inorganic lead halide perovskite semiconductors of the form Cs y FA 1-y Pb(Br x I 1-x ) 3 are promising candidate materials for high-efficiency photovoltaics. Notably, cation and anion substitution can be used to tune the band gaps to optimize performance and improve stability. However, multi-component materials can be prone to compositional and structural inhomogeneity and the extent, length scale and impact of this heterogeneity on lead halide perovskite properties are not well understood. Here we use synchrotron X-ray diffraction to probe the evolution of crystal structure across the tetragonal-to-cubic phase transition for a series of Cs y FA 1-y Pb(Br x I 1-x ) 3 thin films with x = 0.05 to 0.3 and y = 0.17 to 0.40. We find that the transition occurs across a broad temperature range of approximately 40 °C, much larger than for pure compounds such as MAPbI 3 and MAPbBr 3 . Finally, we hypothesize that this smearing of the phase transition is due to compositional inhomogeneities that give rise to a distribution of local transition temperatures and we estimate the composition varies by about 10% to 15% with likely greater heterogeneity for the halide anion than the cation. This approach of mapping the transition is a simple and effective method of assessing heterogeneity, enabling evaluation of its impact.
36 MATERIALS SCIENCE↗
Molecular engineering of fluoroether electrolytes for lithium metal batteries
Fluoroether solvents are promising electrolyte candidates for high-energy-density lithium metal batteries, where high ionic conductivity and oxidative stability are important metrics for design of new systems. Recent experiments have shown that these performance metrics, particularly stability, can be tuned by changing the fraction of ether and fluorine content. However, little is known about how different molecular architectures influence the underlying ion transport mechanisms and conductivity. Here, we use all-atom molecular dynamics simulations to elucidate the ion transport and solvation characteristics of fluoroether chains of varying length, and having different ether segment and fluorine terminal group contents. The design rules that emerge from this effort are that solvent size determines lithium-ion transport kinetics, solvation structure, and solvation energy. In particular, the mechanism for lithium-ion transport is found to shift from ion hopping between solvation sites located in different fluoroether chains in short-chain solvents, to ion–solvent co-diffusion in long-chain solvents, indicating that an optimum exists for molecules of intermediate length, where hopping is possible but solvent diffusion is fast. Consistent with these findings, our experimental measurements reveal a non-monotonic behavior of the effects of solvent size on lithium-ion conductivity, with a maximum occurring for medium-length solvent chains. Finally, a key design principle for achieving high ionic conductivity is that a trade-off is required between relying on shorter fluoroether chains having high self-diffusivity, and relying on longer chains that increase the stability of local solvation shells.
25 ENERGY STORAGE↗
Charge breeders: Development of diagnostic tools to probe the underlying physics
Charge breeders were developed more than 20 years ago in the context of radioactive ion beam (RIB) production. The main goal is to boost the charge state of a singly charged RIB to a higher value matching the A/Q of a post-accelerator. In that way, the RIB produced at some tens of keV can efficiently be accelerated to energies in a range of few MeV/u up to several tens of MeV/u, which is of interest for nuclear structure and nuclear astrophysics experiments. Two families of charge breeders have been developed: one based on Electron Cyclotron Resonance Ion Sources (ECRISs) suitable for Continuous Wave (CW) operation and another built around Electron Beam Ion Sources (EBISs) used for pulsed post-accelerators. For many years, experimental studies have been carried out to enhance the charge breeding process and improve the extracted beam quality, i.e., purity, intensity, emittance, and time structure. The evolution of the charge breeders is also closely related to the emergence of new facilities delivering even more exotic beams. Diagnostic tools, such as Faraday cups, beam profile monitors, and emittance scanners, are mandatory to tune the machines and evaluate their performances. However, to go beyond that, a better understanding of plasma physics (in ECRIS) and electron-ion interactions (in EBIS), as well as the 1+ beam characteristics, for instance, energy spread transverse emittance, is needed. Therefore, simulation codes modeling the physics phenomena in both type of charge breeders were developed to support the understanding of experimental outputs and to gain insights into non-observable parameters. Nevertheless, more sophisticated diagnostic tools are required to improve the charge breeding performance and to extend the potential applications toward new RIB facilities. This Review will present the two kinds of charge breeders and their technical evolutions. It will review the diagnostic tools and simulation codes employed for operation and evaluation of the internal physics processes.
47 OTHER INSTRUMENTATION↗
SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression
Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).
Tsoi, Ho Fung (ORCID:0000000225502184)↗
Analysis of Long-Term Quality Control Data for a 137 Cs Dosimetry Calibration Source
Strict quality assurance programs are required for many radiological applications, but these seldom exist for verifying dosimetry calibration sources. After initial characterization of a dosimetry calibration facility, quality control procedures are recommended to ensure the early detection of any changes or malfunctions. These also result in refined knowledge about average dose rate and experimental variations in dose delivery. This paper describes the implementation of a phase I quality control protocol for a 137 Cs dosimetry calibration source and includes an analysis of the resulting data collected over a 24-mo period. During this time, substantial data was collected to establish trial control limits. Air kerma rate measurements were obtained using an ion chamber and were adjusted for decay, corrected for ambient temperature, pressure and humidity, and then analyzed using quality control charts. Three variations of rational subgrouping methods were used in order to find assignable causes of error, and Nelson's Rules were followed to detect any non-random statistical variations. Measurements were subgrouped according to same-day measurements in order to detect positional errors as well as atmospheric correction errors. Additionally, measurements were subgrouped according to analogous experimental setups in order to detect failure in equipment or incorrect settings. Both were analyzed using the X-bar and R chart method. Similarly, individuals and moving ranges charts were used to carefully examine each position in order to observe any situational errors that may occur which include timing, positional, or interference errors. Each method was successful in identifying unique out-of-control data points that occurred during the phase I application of forming control limits. Furthermore, over the 24-mo period, enough data points were deemed in-control to establish reliable trial limits. Future experiments will include the phase II application of gaining more reliable measurements in order to fine-tune the limits, as well as performing a designed experiment, where variables are purposefully changed in order to test the variation of the data.
61 RADIATION PROTECTION AND DOSIMETRY↗
MPC-Based Local Voltage Control Strategy of DGs in Active Distribution Networks
The increasing penetration of distributed generators (DGs) exacerbates the voltage violations in active distribution networks (ADNs). The commonly used centralized voltage control may aggravate the burden of computation and communication. Local voltage control can realize fast response to frequent voltage fluctuations. However, effective local control for DGs still needs to be further investigated. In this paper, a model predictive control (MPC)-based local voltage control strategy of DGs is proposed to cope with voltage violation problems and enhance the adaptation to the DG volatility. DGs are regulated with local Q-V control curves to respond to voltage fluctuations rapidly. Based on the dynamic network partition, a unified control curve for DGs is set in the same area to realize efficient operation. To further improve the performance of voltage control, the parameter tuning of control curves is regularly conducted based on decentralized inter-area coordination with the rolling of the control horizon. Finally, case study is verified on the modified IEEE 33-node distribution system. The proposed local control strategy of DGs can effectively improve the voltage profile and reduce power losses of the whole network without high computation and communication burden.
active distribution network (ADN)↗
network-pruner (Neural network pruning analysis) [SWR-25-113]
This repository implements an iterative magnitude pruning algorithm for pruning neural networks in PyTorch. The pruning method involves gradually removing less significant weights from the model to achieve a specified sparsity, followed by fine-tuning the pruned model to recover performance.
Griffin, Kevin [National Renewable Energy Laborato↗
Tuning Surface Stoichiometry of SOFC Electrodes at the Molecular and Nano-scale for Enhanced Performance and Durability
This project achieved the following objectives. Different cation segregation behaviors of different common SOFC cathodes, including La 0.6 Sr 0.4 Co0.2Fe 0.8 O 3-δ (LSCF), Sr 0.5 Sm 0.5 O 3-δ (SSC), and PrBa 0.5 Sr 0.5 Co 1.5 Fe 0.5 O5 +δ (PBSCF), were determined. We showed how oxygen partial pressure and gas impurities impact the stability of SOFC cathodes. We developed atomic layer deposition (ALD) coating techniques for electrodes and showed that by introducing different elements and different ALD oxidizers, electrode surface chemistry can be altered, resulting in enhanced oxygen reduction kinetics. In addition, we developed solution infiltration technique to enhance performance and durability of electrodes. Different infiltrates as well as different thermal treatment processes were screened to identify the optimal surface modification process that yields both low impedance and high durability. The modified cathode shows excellent stability and has a low area specific resistance (ASR) of only 0.2 Ωcm 2 at 600 °C after over 2000 hours of operation. Further, we developed ceramic anodes, SrFe(Co,Mo)O 3 (SFCM) and SrFe(Ni,Mo)O 3 (SFNM), and enhanced anode oxidation kinetics by solution infiltration or in situ catalyst exsolution from the ceramic anode surface. The modified anodes showed improved performance in full SOFCs, and the optimized anode modification shows high stability for over 400 hours at 550 °C. Moreover, the modified anode also demonstrates high durability in methane. This work provides fundamental understanding of electrode surface chemistry and demonstrates a simple, facile, cost-effective approach to enhance catalytic activity and durability of SOFC electrodes.
20 FOSSIL-FUELED POWER PLANTS↗
PATHS: Career Pathways to Advance the Trades in HVAC Services
The Career Pathways to Advance the Trades in HVAC Services (“PATHS”) project was designed to advance EERE/BTO goals of dramatically reducing the energy consumed in homes nationwide. Installing HVAC systems correctly and going back to provide tune-ups (maintenance) can improve their performance by at least 30%, so HVAC Technicians are critical to achieving GHG goals. However, there is a lack of trained technicians: residential HVAC installers and service technicians are retiring faster than they are being recruited, and workers with the advanced skills needed to install and service more complicated heat pump systems are even more scarce. Paradoxically, at the same time, unemployment and underemployment are still problems, particularly in disadvantaged communities.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Design, Selection, and Evaluation of Reinforcement Learning Single Agents for Ground Target Tracking
Previous approaches for small fixed-wing unmanned air systems that carry strapdown rather than gimbaled cameras achieved satisfactory ground target tracking performance using both standard and deep reinforcement learning algorithms. However, these approaches have significant restrictions and abstractions to the dynamics of the vehicle, such as constant airspeed and constant altitude, because the number of states and actions was necessarily limited. Thus, extensive tuning was required to obtain good tracking performance. The expansion from 4 state–action degrees of freedom to 15 enabled the agent to exploit previous reward functions that produced novel yet undesirable emergent behavior. This paper investigates the causes of and various potential solutions to undesirable emergent behavior in the ground target tracking problem. A combination of changes to the environment, reward structure, action space simplification, command rate, and controller implementation provides insight into obtaining stable tracking results. Consideration is given to reward structure selection and refinement to mitigate undesirable emergent behavior. Results presented in the paper for a simulated environment of a single unmanned air system tracking a randomly moving single ground target show that a soft actor–critic algorithm can produce feasible tracking trajectories without limiting the state space and action space, provided that the environment is properly posed.
Engineering↗
OECD/NEA MPCMIV Benchmark - Preliminary fuel performance results
The on-going OECD/NEA Multi-physics Pellet Cladding Mechanical Interaction Validation (MPCMIV) benchmark aims to provide guidance on multi-physics validation through the modelling of two cold ramps. In this paper, the first results for the base irradiation of the father rod and fuel rodlet (refabricated from the father rod), and the first cold ramp are presented. The base irradiation consists of 3 years of irradiation in the Forsmark-2 reactor. The cold ramp encompasses a steady-state pre-ramp period of less than one hour at a low constant linear heat rate (LHR) followed by a ramp test (< 1 min) with a much higher maximal LHR. The base irradiation is modelled using the fuel performance codes FRAPCON and FAST, while the cold ramp modeling is using the fuel performance code FRAPTRAN. Several missing parameters for FRAPCON/FAST base irradiation models have been selected using multiple references such as the OECD/NEA light water reactor Uncertainty Analysis in Modelling (UAM) benchmark specifications and the FRAPCON Integral Assessment report. The obtained results for the base irradiation such as the cladding outer diameter show reasonable agreement with the experimental measurements. The results for the cold ramp show larger differences with the experimental measurements (e.g., cladding axial elongation). However, such differences have been observed as well in other studies involving pellet cladding mechanical interaction analyses and are attributed to the fuel performance modelling assumptions and to the tuned modelling parameters that were not covered by the specifications. Uncertainty and sensitivity analysis might allow a better quantification of these missing parameters. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
End-to-end protocol for high-quality quantum approximate optimization algorithm parameters with few shots
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for combinatorial optimization that has been demonstrated to scale better than state-of-the-art classical solvers for some problems. For a given problem instance, QAOA performance depends crucially on the choice of the parameters. While average-case optimal parameters are available in many cases, meaningful performance gains can be obtained by fine-tuning these parameters for a given instance. This task is especially challenging, however, when the number of circuit executions (shots) is limited. In this work, we develop an end-to-end protocol that combines multiple parameter settings and fine-tuning techniques. We use large-scale numerical experiments to optimize the protocol for the shot-limited setting and observe that optimizers with the simplest internal model (linear) perform best. We implement the optimized pipeline on a trapped-ion processor using up to 32 qubits and 5 QAOA layers, and we demonstrate that the pipeline is robust to small amounts of hardware noise. To the best of our knowledge, these are the largest demonstrations of QAOA parameter fine-tuning on a trapped-ion processor in terms of two-qubit gate count.
quantum algorithms & computation↗
Automated ICRF heating surrogate modeling via machine learning
This work introduces automated machine learning workflows that address critical bottlenecks in surrogate model development for Ion Cyclotron Range of Frequencies (ICRF) heating applications. The automated framework includes data analysis tools that transform raw datasets into actionable insights in seconds, replacing weeks of manual exploratory effort and ensuring consistent, reproducible dataset characterization. By integrating advanced hyperparameter optimization (HPO) methods including Bayesian optimization via BoTorch and Tree-structured Parzen Estimators (TPE), the framework significantly reduces model development time from weeks to hours, decreasing computational cost and required expertise, while enabling high-accuracy surrogate models. Compared to traditional hyperparameter scanning (HPS) techniques such as methodical, randomized, and grid searches, HPO methods achieve superior convergence and predictive performance, even when compared to already well-tuned reference models. On NSTX High Harmonic Fast Wave (HHFW) heating datasets, both Random Forest Regressor (RFR) and neural network surrogates demonstrate improved accuracy, achieving R 2 values beyond 0.97 and 0.98, respectively. The results show that while HPO gains are modest for robust architectures like RFR, they become essential for more sensitive models such as neural networks, highlighting the trade-offs across optimization strategies. Through automated workflows that eliminate manual hyperparameter tuning and require minimal ML expertise, this work enables widespread adoption of high-fidelity surrogate models across the fusion community for real-time plasma control, uncertainty quantification, rapid experimental scenario development, and integrated system optimization.
Sanchez-Villar, Alvaro [Princeton Plasma Physics L↗