Isomer-Tailored Carbon Molecular Sieve Membranes with High Gas Separation Performance
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Chemistry↗
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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Bifilar analysis study, volume 1
A coupled rotor/bifilar/airframe analysis was developed and utilized to study the dynamic characteristics of the centrifugally tuned, rotor-hub-mounted, bifilar vibration absorber. The analysis contains the major components that impact the bifilar absorber performance, namely, an elastic rotor with hover aerodynamics, a flexible fuselage, and nonlinear individual degrees of freedom for each bifilar mass. Airspeed, rotor speed, bifilar mass and tuning variations are considered. The performance of the bifilar absorber is shown to be a function of its basic parameters: dynamic mass, damping and tuning, as well as the impedance of the rotor hub. The effect of the dissimilar responses of the individual bifilar masses which are caused by tolerance induced mass, damping and tuning variations is also examined.
Miao, W.↗
Modulating Energy Level on an A‐D‐A′‐D‐A‐Type Unfused Acceptor by a Benzothiadiazole Core Enables Organic Solar Cells with Simple Procedure and High Performance
Unfused‐ring acceptors (UFAs) have gained considerable research attention as they offer simple chemical structures through simplified synthesis methods, which would boost the commercialization of organic solar cells (OSCs). Recently, a new small molecule acceptor (SMA) named Y6 was reported, yielding high‐performance OSCs. Herein, the Y6‐like A‐DA′D‐A framework is developed to A‐D‐A′‐D‐A‐type backbone adopted in constructing UFAs. Two new Y6‐like UFAs are synthesized within four steps and the effect of noncovalent atoms at the central electron‐deficient core on material properties and device performances is studied. It is found that the introduction of fluorine atoms can bring larger red‐shift in the absorption spectra and better aggregation of the resulting UFA film states compared with those of oxygen atoms. Interestingly, the variations in the noncovalent interaction atoms induce different intermolecular charge transfer between donors and UFAs. When blended with another economical donor, PTQ10, F substitution at the benzothiadiazole ring is more effective than O substitution, leading to the increased short‐circuit current density ( J SC ) and higher efficiency of over 12%, among the best performances of UFA‐based OSCs. This contribution demonstrates the appropriate introduction of noncovalent interaction is a promising method for tuning energy levels, absorption, and aggregation of UFAs for high‐performance OSCs.
Yu, Han↗
Autogenerating a Domain-Specific Question-Answering Data Set from a Thermoelectric Materials Database to Enable High-Performing BERT Models
We present a method for autogenerating a large domain-specific question-answering (QA) dataset from a thermoelectric materials database. We show that a small language model, BERT, once fine-tuned on this automatically generated dataset of 99,757 QA pairs about thermoelectric materials, affords better performance in the field of thermoelectric materials compared to a BERT model fine-tuned on the generic English-language QA data set, SQuAD-v2. We further show that mixing the two data sets (ours and SQuAD-v2), which have significantly different syntactic and semantic scopes, allows the BERT model to achieve even better performance. The best-performing BERT model fine-tuned on the mixed data set outperforms the models fine-tuned on the other two data sets by scoring an exact match of 67.93% and an F1 score of 72.29% when evaluated on our test data set. This has important implications as it demonstrates the ability to realize high-performing small language models, with modest computational resources, empowered by domain-specific materials data sets which can be generated according to our method.
biological databases↗
Constrained GAN-Generated X-Ray CT Data For Self-Supervised And Foundation-Model Segmentation Of Concrete Microstructures
Three-dimensional characterization of materials using X-ray computed tomography (XCT) is challenging due to the complexity of internal structures, noise, and variations in resolution. Traditional computer vision models often struggle to accurately segment these images, particularly in domain-specific applications like materials science. While supervised deep learning approaches have been developed to address the limitations of conventional algorithms, they typically require large amounts of labeled training data and often fail to generalize across different datasets. Self-supervised, few-and zero-shot learning methods have gained prominence in natural image processing and segmentation tasks, but their application to scientific imaging remains limited due to the unique structural complexity, noise, and textural artifacts present in materials science data. In this work, we investigate how domain adaptation, leveraging physics-based and GAN-generated synthetic data, impacts segmentation performance. We introduce a modified Contrastive Unpaired Translation (CUT) model designed to generate realistic labeled data, which can be used for training, pre-training, and fine-tuning segmentation models for real XCT microstructure data. We evaluate the performance of two segmentation approaches: a self-supervised network (SSL-ALPNet) and a foundation model (Segment Anything Model), assessing their improvements when pre-trained and/or fine-tuned on the synthesized data. Our results demonstrate that leveraging synthetic data significantly enhances segmentation performance, particularly in challenging materials science applications.
Ziabari, Amir [ORNL] (ORCID:000000034776457X)↗
A Chemocatalytic Route to Stereoregular Poly(3-hydroxyhexanoate) and Its Statistical and Tri-Block Copolymers
Poly(3-hydroxyhexanoate) (P3HHx)-based poly(3- hydroxyalkanoate)s (PHAs) are biologically produced, commercially implemented PHAs, but little is known in the open literature about the structure and property characterizations of discrete, authentic homopolymer P3HHx, and its copolymers with poly(3- hydroxybutyrate) (P3HB). Here, we introduce a chemocatalytic route to stereoregular (both isotactic and syndiotactic) P3HHx and PHA copolymers with P3HB, including statistical copolymer P3HBHx with various levels of 3HHx incorporation and discrete hard−soft−hard triblock copolymer P3HB-b-P3HHx-b-P3HB, and present extensive characterizations of their structures, thermal properties, and mechanical performance. The synthesis is efficiently achieved by one-pot polymerization of eight-membered di(n-propyl) and dimethyl (for copolymerization) diolides catalyzed by chiral molecular catalysts. Notably, P3HBHx can be rapidly produced in quantitative yield and exhibits high molar mass (M n up to 551 kg/mol) as well as both high modulus (up to 1.39 GPa) and ductility (up to 445%), while P3HB-b-P3HHx-b-P3HB further extends application temperature windows by possessing a unique combination of low T g (−18 °C) and high T m (154 °C) values. These findings highlight the stereomicrostructural and architectural versatility of chemocatalytic routes to PHAs, which, in turn, can be utilized to largely tune the PHA thermal properties and mechanical performance.
Biopolymers↗
Surrogate Model Based Optimization for Finding Robust Deep Learning Model Architectures
Deep Learning (DL) models are increasingly used throughout the sciences. However, their performance and usefulness depend greatly on their architecture which is defined by hyperparameters such as the number of nodes, layers, the learning rate, etc. Tuning these hyperparameters is time-consuming because evaluating their performance requires a lengthy training step. Stochastic optimizers used in training lead to performance variability and potentially prediction reliability issues. In this talk, we will describe an automated optimization method based on surrogate models and active learning strategies for tuning DL model architectures. We take into account the prediction variability with the goal to identify architectures that make reliable and robust predictions. We demonstrate our developments on an application arising in particle physics.
deep learning↗
How to Train Your Jacobian: Least-Squares System Identification for Space-Based Coronagraphy
Stellar coronagraphs use closed-loop focal-plane wavefront sensing and control algorithms to create high-contrast dark zones suitable for imaging exoplanets and exozodiacal dust clouds around nearby stars. Model-based algorithms are susceptible to model mismatch, wherein a departure of the coronagraph's true optical characteristics from the assumed model causes reduced control loop performance. Here, we describe a simple technique for empirically tuning the wavefront control Jacobian matrix using applied deformable mirror commands and observed images. This mitigates model mismatch and recovers near-optimal control loop performance.
coronagraphy↗
Structural mapping and tuning of mixed halide ions in amorphous sulfides for fast Li-ion conduction and high deformability
Amorphous sulfides are among the most promising candidates for solid electrolytes (SEs) owing to their excellent deformability and acceptable Li-ion conductivity (σion) at room temperature. However, the complex atomic structure of these amorphous materials without long-range ordering results in a lack of structural understanding and difficulties in tuning material properties. Here, in this study, we performed structural mapping of glassy sulfide SEs composed of various anion clusters using a combinatorial atomic level analysis of synchrotron X-ray-based pair distribution function (PDF) and reverse Monte-Carlo (RMC) methods, demonstrating the potential to tune σ ion in glass SEs. First, we prepared a new glassy sulfide with a mixed anion framework of two halogens (Br and I) as atomic anions, with PS 4 3- molecular anions in a Li 2 S–P 2 S 5 -based glass SE. At a specific Br content, [(Li 2 S) 0.658 (LiI 0.9 LiBr 0.1 ) 0.342 ] 0.825 [P 2 S 5 ] 0.175 recorded a σ ion of 2.27 mS cm -1 , the highest value for any glass sulfides reported to date; however, its elastic modulus was still suppressed to 14.48 GPa. PDF and RMC calculations successfully provided structural mapping of anion clusters, including two halogens. Molecular dynamics simulations of each composition confirmed that flexible coordination caused by the rattling of small polarizable Br ions in the mixed halogens of glassy SEs contributed to the superior σ ion . Our results may provide new insights into the design of superior glassy SEs that play key roles in all-solid-state batteries requiring fast Li-ion conduction and high deformability.
36 MATERIALS SCIENCE↗
Design, Processing, and Integration of Pouch-Format Cell for High-Energy Lithium-Sulfur Batteries
This project objective was to develop and demonstrate a lithium-sulfur (Li-S) battery in a pouch-format cell capable of achieving an energy density ≥ 500 Wh/kg while achieving a 1,000 cycle life. The research focused on cell optimization and fabrication addressing different technical barriers and challenges including: 1) thin lithium anode optimization; 2) current collector and tab attachment design; 3) cathode porosity control; 4) electrolyte to sulfur ratio control; 5) cell design; and 6) cell fabrication. The project developed and demonstrated various technologies to address these technical barriers and challenges. The project demonstrated a thin lithium anode by vapor deposition, a collector design and validation of laser welding of tab attachment method, a cathode porosity control strategy by binder optimization, a calendaring process control and surface/interface treatment, and an electrolyte to sulfur ratio control with influence on cell energy density. The sulfur cathode was optimized by tuning the formulation, optimizing the calendaring process, and introducing a novel electrode fabrication process. To address the electrolyte performance issue, the electrolyte optimization was achieved with additive and formulation tuning and the introduction of a dual-phase electrolyte system. In addition to the demonstration of the optimized electrode with a novel fast-curing coating process and dual-phase electrolyte, a coating separator was further developed to address the polysulfide shuttling issue. The resulting new cell design with these optimized cell components was demonstrated in the 1 Ah pouch cell with medium sulfur loading and moderate porosity (~ 4.5 mAh cm-2, 65% porosity) and showed an energy density of > 400 Wh/kg (with E/S ratio of 2.8). The Li-SPAN cell configuration was also evaluated in combination with a new dual-phase electrolyte system. Initial coin cell performance demonstrated cycle stability of >300 cycles with an estimated energy density of 300 Wh/kg at the pouch format level. The corresponding 1 Ah pouch format SPAN cells with the new polymer electrolyte were developed which has shown a stable capacity at 800-900 mAh for ~40 cycles so far.
25 ENERGY STORAGE↗
Interplay Between Thin Film Ferroelectric Composition, Microstructure and Microwave Phase Shifter Performance
One of the keys to successfully incorporating ferroelectric films into Ku-band (12 to 18 GHz) phase shifters is to establish the composition, microstructure, and thickness required to meet the tuning needs, and tailor the film properties to meet these needs. Optimal performance is obtained when the film composition and device design are such that the device performance is limited by odd mode dielectric losses, and these losses are minimized as much as possible while still maintaining adequate tunability. The parameters required to maintain device performance will vary slightly depending on composition, but we can conclude that the best tuning-to-loss figures of merit (K-factor) are obtained when there is minimal variation between the in-plane and out-of-plane lattice parameters, and the full-width half maximum values of the BSTO (002) peaks are less than approximately 0.04 deg. We have observed that for phase shifters in which the ferroelectric crystalline quality and thickness are almost identical, higher losses are observed in films with higher BaISr ratios. The best performance was observed in phase shifters with Ba:Sr = 30:70. The superiority of this composition was attributed to several interacting factors: the B a: Sr ratio was such that the Curie temperature (180 K) was far removed from room temperature, the crystalline quality of the film was excellent, and there was virtually no difference between the inplane and out-of-plane lattice parameters of the film.
Mueller, Carl H.↗
Review of data-driven models for quantifying load shed by non-residential buildings in the United States
Shifting and shedding power demand in buildings can be cost-effective techniques for grids to function reliably and for end users to earn compensation. Grid operators reimburse customers in proportion to the quantity of load shed. Simple data-driven methods are used to quantify this shed, which is the difference between a measured load during the event and modeled "baseline" that would have occurred in absence of the event. These methods have evolved over the years and in many cases have been integrated with building physics, to make them a hybrid between physics based and empirical models. However, there is no comprehensive analysis that provides guidance to building operators, grid operators and researchers in selecting appropriate models based on their specific needs and available data. Here, this work aims to fill this gap by critically assessing the performance of baseline models put forward from the year 2000 through 2023. The literature reviewed includes reports generated by grid operators, reports from national laboratories and academic journal articles. The work outlines modeling features like the inputs, training period, estimation method, adjustments to fine tune the predictions and metrics to evaluate the performance. A comprehensive list of 50 models has been provided. For each model, the study explores the applicability of the model to weather sensitive buildings, variability in the building profile, timing of the event, and whether the building reduces energy consumption before an event. The work identifies the situations in which a particular model works and draws lessons based on evidence of performance. Finally, recommendations to aid in model selection are given.
97 MATHEMATICS AND COMPUTING↗
Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor
Neuromorphic processors have garnered considerable interest in recent years for their potential in enabling energy-efficient and high-speed computing. The Locally Competative Algorithm (LCA) has been utilized for power efficient sparse coding on neuromophic processors, including the first Loihi processor \cite{appletospikes, loihi1}. With the Loihi 2 processor enabling custom neuron models and graded spike communication, more complex implementations of LCA are possible \cite{loihi2}. We present a new implementation of LCA designed for the Loihi 2 processor and perform an initial set of benchmarks comparing it to LCA on CPU and GPU devices. In these experiments LCA on Loihi 2 is faster and orders of magnitude more efficient, while maintaining similar reconstruction quality. We find this performance improvement increases as the LCA parameters are tuned towards greater representation sparsity. Our study highlights the potential of neuromorphic processors, particularly Loihi 2, in enabling intelligent,autonomous, real-time processing on small robots, satellite where there are strict SWaP (small, lightweighr, and low-power) requirement. By demonstrating the superior performance of LCA on Loihi 2 compared to conventional computing device, our study suggests that Loihi 2 could be a valuable tool in advancing these types of applications. Overall, our study highlights the potential of neuromorphic processors for efficient and accurate data processing on resource-constrained devices.
Parpart, Gavin G.↗
CMS PYTHIA 8 colour reconnection tunes based on underlying-event data
New sets of parameter tunes for two of the colour reconnection models, quantum chromodynamics-inspired and gluon-move, implemented in the PYTHIA 8 event generator, are obtained based on the default CMS PYTHIA 8 underlying-event tune, CP5. Measurements sensitive to the underlying event performed by the CMS experiment at centre-of-mass energies $\sqrt{s}=$7 and 13 TeV, and by the CDF experiment at 1.96 TeV are used to constrain the parameters of colour reconnection models and multiple-parton interactions simultaneously. The new colour reconnection tunes are compared with various measurements at 1.96, 7, 8, and 13 TeV including measurements of the underlying-event, strange-particle multiplicities, jet substructure observables, jet shapes, and colour flow in top quark pair ($t\bar{t}$) events. The new tunes are also used to estimate the uncertainty related to colour reconnection modelling in the top quark mass measurement using the decay products of $t\bar{t}$ events in the semileptonic channel at 13 TeV .
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Recent Progress on Cerium Oxide‐Based Nanostructures for Energy and Environmental Applications
Cerium oxide (CeO 2 ) photo/electrocatalysts for energy storage and environmental applications have attracted considerable interest because of stable crystal structure, low toxicity/cost, superior chemical stability, stable redox (Ce 3+ /Ce 4+ ) pairs, abundant oxygen defects, and capablility for intense interaction with other materials. However, the wide bandgap and poor conductivity lower the CeO 2 photo/electrocatalytic and energy storage performances. To overcome these limitations, various modification strategies (tuning morphology, doping or loading of metal nanoparticles, and heterostructures) have been applied for the improvement of photocatalytic (removal of organic contaminants from water/wastewater and H 2 production and CO 2 reduction reactions) efficiency, electrocatalytic (hydrogen/oxygen evolution reactions and CO 2 reduction reactions), and energy storage performances (supercapacitor) of CeO 2 ‐based materials. Herein, the recent progress of CeO 2 ‐based materials for electro(photo)catalysis and energy storage applications has been discussed. The challenges and possible direction of CeO 2 ‐based materials for electro(photo)catalysis and energy storage applications have been emphasized. Furthermore, this comprehensive review is expected to advance the design of CeO 2 ‐based materials and their applications in electro(photo)catalysis and energy.
Ray, Schindra Kumar [Department of Chemistry North↗
Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders
We demonstrate transfer learning capabilities in a machine-learned algorithm trained for particle-flow reconstruction in high energy particle colliders. This paper presents a cross-detector fine-tuning study, where we initially pretrain the model on a large full simulation dataset from one detector design, and subsequently fine-tune the model on a sample with a different collider and detector design. Specifically, we use the Compact Linear Collider detector (CLICdet) model for the initial training set and demonstrate successful knowledge transfer to the CLIC-like detector (CLD) proposed for the Future Circular Collider in electron-positron mode. We show that with an order of magnitude less samples from the second dataset, we can achieve the same performance as a costly training from scratch, across particle-level and event-level performance metrics, including jet and missing transverse momentum resolution. Furthermore, we find that the fine-tuned model achieves comparable performance to the traditional rule-based particle-flow approach on event-level metrics after training on 100,000 CLD events, whereas a model trained from scratch requires at least 1 million CLD events to achieve similar reconstruction performance. To our knowledge, this represents the first full-simulation cross-detector transfer learning study for particle-flow reconstruction. These findings offer valuable insights towards building large foundation models that can be fine-tuned across different detector designs and geometries, helping to accelerate the development cycle for new detectors and opening the door to rapid detector design and optimization using machine learning.
43 PARTICLE ACCELERATORS↗
Purification of Lithium-Ion Battery Black Mass through Tailored Alkaline Corrosion
Obtaining high-purity material outputs is crucial to the viability of novel process aimed at direct recycling of lithium-ion batteries. Metallic impurities in recycled cathodes have been shown to inhibit performance, thereby threatening mainstream acceptance of recycled battery products. Thus, shredded black mass (BM) must be purified to remove metallic contaminants, and specifically Al and Cu originating from the electrode current collectors. We herein explore a process to ionize solid copper and aluminum to ionic form based on tailored alkaline chemistry, without incurring damage to the target cathode material (Li(NixMnyCo1-x-y)O2; NMC). Al and Cu corrosion may be enhanced through the addition of chloride salt, elevated temperatures, and the use of ultrasonication - all of which disrupt the formation of passivating films on the metallic surface, and thereby increase corrosion rate. We demonstrate optimized parameters for Al and Cu corrosion both from a kinetic and overall process cost perspective. Further, we analyze the impact of these conditions on the structural (XRD, SEM), chemical (EDS, ICP), and electrochemical (impedance, cycling, dQ/dV) properties of NMC, and suggest that the present purification method does not significantly disrupt NMC performance. Finally, we present preliminary results from a promising bench-scale demonstration of this purification process applied to a simulated black mass.
ADVANCED PROPULSION SYSTEMS,ENERGY STORAGE↗
Adaptive control strategies for flexible robotic arm
The motivation of this research came about when a neural network direct adaptive control scheme was applied to control the tip position of a flexible robotic arm. Satisfactory control performance was not attainable due to the inherent non-minimum phase characteristics of the flexible robotic arm tip. Most of the existing neural network control algorithms are based on the direct method and exhibit very high sensitivity if not unstable closed-loop behavior. Therefore a neural self-tuning control (NSTC) algorithm is developed and applied to this problem and showed promising results. Simulation results of the NSTC scheme and the conventional self-tuning (STR) control scheme are used to examine performance factors such as control tracking mean square error, estimation mean square error, transient response, and steady state response.
Bialasiewicz, Jan T.↗