Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Performance optimizations”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Surface-Enhanced Raman Detection of the CO 2 Moisture Swing

The development of scalable, energy-efficient carbon dioxide (CO 2 ) capture technologies is critical for achieving net-zero emissions. Moisture swing (MS) sorbents offer a promising alternative to traditional thermal regeneration methods by enabling reversible CO 2 binding through humidity-driven ion hydrolysis. In this study, we investigate the anion speciation dynamics in two classes of MS materials─an anion-exchange resin with a bicarbonate anion and activated carbon impregnated with potassium bicarbonate salt─using both sorption measurements and in situ surface-enhanced Raman spectroscopy (SERS). Ni-coated Ag nanowires were employed as SERS substrates to enhance signal intensity and enable the real-time detection of carbonate (CO 3 2– ), bicarbonate (HCO 3 – ), and hydroxide (OH – ) species under controlled humidity conditions in both air and nitrogen atmospheres. The results reveal humidity-dependent interconversion between anionic species with significant spectral shifts confirming the reversible hydrolysis reactions that drive the MS mechanism. Under humid conditions, we observed the depletion of bicarbonate signals and a concurrent increase in carbonate species, consistent with moisture-induced desorption of CO 2 . With the activated carbon samples, we further observed the formation of hydroxide. These findings not only validate the mechanistic models of humidity-driven anion exchange in MS sorbents but also demonstrate the practical potential of SERS as an operando diagnostic tool for monitoring CO 2 capture media. The ability to resolve and semiquantitatively evaluate the reversible transformation of carbonate, bicarbonate, and hydroxide ions under realistic environmental conditions provides valuable insight for the rational design, performance optimization, and quality control of next-generation sorbent materials for direct air capture applications.

CO2 capture↗
DOE OSTI2020

Influence of Silane Coupling Agents on Filler Network Structure and Stress-Induced Particle Rearrangement in Elastomer Nanocomposites

Filled rubber materials are key in many technologies having broad impact on the economy and sustainability, the most obvious being tire technology. Adding filler dramatically improves the strength of rubber by reinforcement and tailoring the type of filler and the chemistry of the interface between filler and rubber matrix is important for optimizing performance metrics such as fuel efficiency. In a highly loaded, silica-filled, crosslinked model rubber closely mimicking commercial materials, both the filler network structure and the dynamics of the silica filler particles change when the silica surface is modified with silane coupling agents. Reduction in size scales characteristic of the structure are quantified using ultra-small-angle X-ray scattering (USAXS) measurements and the particle dynamics probed with X-ray photon correlation spectroscopy (XPCS). While the structure averaged over the scattering volume changes little with aging after step strain, the dynamics slow appreciably in a manner that varies with the treatment of the silica filler. The evolution of filler particle dynamics depends on the chemical functionality at the silica surface and observing these differences suggests a way of thinking about the origins of hysteresis in nanoparticle-reinforced rubbers. These microscopic filler dynamics are correlated with the macroscopic stress relaxation of the filled materials. The combination of static and dynamic X-ray scattering techniques with rheological measurements is a powerful approach for elucidating the microscopic mechanisms of rubber reinforcement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dehydration of Methyl Lactate on Alkali Cation-Exchanged Faujasite: Effects of Metal Cation Identity and Water Pressure

Turnover rates for the catalytic dehydration of methyl lactate (ML) over ion-exchanged faujasite (FAU) catalysts depend on the identity of alkali metal cations (Na + , K + , Cs + ) and local solvation effects. Analysis of rate measurements and in situ infrared spectroscopy gives evidence that the reaction involves kinetically relevant dissociation of adsorbed methyl lactate upon alkali metal cations. This process involves concerted methyl transfer to the surface and dissociation of the alkali metal from the framework, which occurs at cationic active sites that remain predominantly unoccupied under relevant conditions (0.5–10 kPa ML, 0.5–15 kPa H 2 O, 563–583 K). Despite the mechanistic similarities, apparent activation enthalpies (ΔH app ‡ ) decrease linearly (47 kJ mol –1 from Na + to Cs + ) with ionization energy and cationic radius, and apparent activation entropies (ΔS app ‡ ) decrease 74 J mol –1 K –1 . These trends reflect electrostatic interactions that stabilize the cations to the anionic sites on the zeolite: stronger association between these charges leads to increasingly endothermic processes to displace the alkali metal to form a cationic methoxy and an intrapore metal lactate intermediate. Water physisorption measurements suggest alkali metal ions bind superstoichiometric quantities of water within FAU pores, and in situ infrared spectra suggest the concerted adsorption of ML requires reorganization of this water. Consequently, these processes introduce entropic gains that partially offset entropy losses associated with ML adsorption. Hence, turnover rates differ only by a factor of 2 among Na-, K-, and Cs-FAU at 573 K (ΔΔG app ‡ = 5 kJ mol –1 ). These findings demonstrate the interplay of alkali metal ions with zeolite active sites and intrapore water clusters for ML dehydration, indicating that these interactions can be leveraged to deliver optimal performance under different reaction conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Monolayer Molecular Functionalization Enabled by Acid–Base Interaction for High-Performance Photochemical CO 2 Reduction

We report the development of a hybrid catalyst consisting of carbon nitride (CN x ) and cobalt phthalocyanine tetracarboxylic acid (CoPc-COOH), which converts CO 2 to CO with high reaction rate (1067 μmol/g·h) and high selectivity (over 98%), under simulated solar irradiation. The carboxylic acid substituents on the phthalocyanine ligands play a critical role as they bind to the amine groups of CN x to enable nearly ideal monolayer coverage of the molecular co-catalyst on the semiconductor surface and promote catalytic activity from the molecular complex. Specifically, the CN x /CoPc-COOH hybrid material achieves a reaction rate 16 times higher than a CN x material containing unsubstituted CoPc molecules. We further show that activation and deactivation of the CN x /CoPc-COOH composite, which are associated with the reduction and decomposition of CoPc-COOH, respectively, both proceed at a nearly constant rate regardless of the CO 2 reduction reaction rate. Here, the decoupling of charge carrier injection and CO 2 reduction catalysis has important mechanistic implications for future performance optimization and materials design of photocatalysts for CO 2 reduction.

14 SOLAR ENERGY↗
DOE OSTI2020

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. Yet, the intricate connectivity and complexity of biosystems pose a major hurdle in designing biosystems with desired features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find non-obvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocess. Lastly, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗
DOE OSTI2021

A Scalable Semi-Implicit Barotropic Mode Solver for the MPAS-Ocean

A scalable semi-implicit barotropic mode solver for the ocean component of the model for prediction across scales has been implemented as a competitor to an existing explicit-subcycling scheme to allow faster and more stable simulations while not sacrificing accuracy. The semi-implicit solver adopts the pipelined preconditioned bi-conjugate gradient stabilization algorithm as an iterative solver in conjunction with the restricted additive Schwarz preconditioner that accelerates the convergence rate of the iterative solver. The preconditioner is constructed from a linearized barotropic system that also reorders the system for optimal performance, while the semi-implicit solver deals with the fully nonlinear barotropic system that requires reassembly of the coefficient matrix for every time step. Several numerical experiments, from simple one-dimensional tests to three-dimensional real-world tests, demonstrate that the semi-implicit solver has almost the same accuracy and better parallel scalability compared with the existing scheme while allowing faster and more stable simulations. Furthermore, the semi-implicit solver accelerates the barotropic mode up to 2.9 times faster than the existing scheme on 16,320 processors, leading to an overall runtime speedup of 1.9.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bright and durable scintillation from colloidal quantum shells

Abstract Efficient, fast, and robust scintillators for ionizing radiation detection are crucial in various fields, including medical diagnostics, defense, and particle physics. However, traditional scintillator technologies face challenges in simultaneously achieving optimal performance and high-speed operation. Herein we introduce colloidal quantum shell heterostructures as X-ray and electron scintillators, combining efficiency, speed, and durability. Quantum shells exhibit light yields up to 70,000 photons MeV −1 at room temperature, enabled by their high multiexciton radiative efficiency thanks to long Auger-Meitner lifetimes (>10 ns). Radioluminescence is fast, with lifetimes of 2.5 ns and sub-100 ps rise times. Additionally, quantum shells do not exhibit afterglow and maintain stable scintillation even under high X-ray doses (>10 9 Gy). Furthermore, we showcase quantum shells for X-ray imaging achieving a spatial resolution as high as 28 line pairs per millimeter. Overall, efficient, fast, and durable scintillation make quantum shells appealing in applications ranging from ultrafast radiation detection to high-resolution imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

The northeast materials database for magnetic materials

The discovery of magnetic materials with high operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are limited by the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challenge by using Large Language Models (LLMs) to create a comprehensive, experiment-based, magnetic materials database named the Northeast Materials Database (NEMAD), which consists of 67,573 magnetic materials entries (www.nemad.org). The database incorporates chemical composition, magnetic phase transition temperatures, structural details, and magnetic properties. Enabled by NEMAD, we trained machine learning models to classify materials and predict transition temperatures. Our classification model achieved an accuracy of 90% in categorizing materials as ferromagnetic (FM), antiferromagnetic (AFM), and non-magnetic (NM). The regression models predict Curie (Néel) temperature with a coefficient of determination (R 2 ) of 0.87 (0.83) and a mean absolute error (MAE) of 56K (38K). These models identified 25 (13) FM (AFM) candidates with a predicted Curie (Néel) temperature above 500K (100K) from the Materials Project. This work shows the feasibility of combining LLMs for automated data extraction and machine learning models to accelerate the discovery of magnetic materials.

Ferromagnetism↗
DOE OSTI2025

Adaptive stretching of representations across brain regions and deep learning model layers

Prefrontal cortex (PFC) is known to modulate the visual system to favor goal-relevant information by accentuating task-relevant stimulus dimensions. Does the brain broadly re-configures itself to optimize performance by stretching visual representations along task-relevant dimensions? We considered a task that required monkeys to selectively attend on a trial-by-trial basis to one of two dimensions (color or motion direction) to make a decision. Although effects were most prominent in frontal areas, representations stretched along task-relevant dimensions in all sites considered: V4, MT, lateral PFC, frontal eye fields (FEF), lateral intraparietal cortex (LIP), and inferotemporal cortex (IT). Spike timing was crucial to this code. A deep learning model was trained on the same visual input and rewards as the monkeys. Despite lacking an explicit selective attention or other control mechanism, by minimizing error during learning, the model’s representations stretched along task-relevant dimensions, indicating that stretching is an adaptive strategy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Modulating coordinate site occupancy in high-entropy spinel electrocatalysts

High entropy spinel oxides provide a versatile platform for electrocatalysis because multiple metal cations can be incorporated into a single crystalline lattice, enabling tunable electronic structures. However, controlling how these cations distribute between tetrahedral and octahedral coordination sites remains a major challenge, limiting rational catalyst design. Here, we modulate cation coordination site occupancy between tetrahedral and octahedral sites in a Co–Fe–Cr–Mn–Ni framework by introducing a sixth cation (Zn, Ga, Mg, or Al) with distinct site preference energies. Using density functional theory, synchrotron X-ray absorption spectroscopy, and magnetic circular dichroism, we demonstrate that Zn preferentially occupies tetrahedral sites, driving increased octahedral occupancy of cobalt. This redistribution increases the population of octahedrally coordinated cobalt in mixed oxidation states, enhances electrical conductivity, and improves oxygen evolution reaction activity. Our findings establish coordination site occupancy as a critical design parameter, providing a strategic pathway for tailoring multicomponent spinel electrocatalysts with optimized performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Understanding important features of deep learning models for segmentation of high-resolution transmission electron microscopy images

Cutting edge deep learning techniques allow for image segmentation with great speed and accuracy. However, application to problems in materials science is often difficult since these complex models may have difficultly learning meaningful image features that would enable extension to new datasets. In situ electron microscopy provides a clear platform for utilizing automated image analysis. In this work, we consider the case of studying coarsening dynamics in supported nanoparticles, which is important for understanding, for example, the degradation of industrial catalysts. By systematically studying dataset preparation, neural network architecture, and accuracy evaluation, we describe important considerations in applying deep learning to physical applications, where generalizable and convincing models are required. With a focus on unique challenges that arise in high-resolution images, we propose methods for optimizing performance of image segmentation using convolutional neural networks, critically examining the application of complex deep learning models in favor of motivating intentional process design.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine Learning Augmented Predictive and Generative Model for Rupture Life in Ferritic and Austenitic Steels

The Larson-Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high temperature applications. However, owing to poor generalizability and dependence on the constant C, which is typically not known a-priori, estimations using the Larson-Miller parameter often result in suboptimal performance for a wide range of materials. At best it is useful in comparing alloys of similar composition. In this work, three machine learning (ML) schemes were developed for rupture life prediction for 9-12% Cr ferritic-martensitic steels and austenitic stainless steels, i.e., a hierarchical model to parameterize LMP using the LMP constant C to compute rupture life, a hierarchical model to parameterize both C and LMP to compute rupture life, and a direct prediction of rupture life. Specifically, we show that the third scheme, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pear-son Correlation Coefficient > 0.9 for 9-12% Cr and > 0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Furthermore, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9-12 % Cr ferritic-martensitic steel and austenitic stainless steel datasets. Finally, based on the predictive and generative model, a reinforcement learning strategy has been proposed to guide experimentalists into designing better heat resistant alloys.

Mamun, Md Osman G.↗
DOE OSTI2023

Ultimate precision limit of noise sensing and dark matter search

Abstract The nature of dark matter is unknown and calls for a systematical search. For axion dark matter, such a search relies on finding feeble random noise arising from the weak coupling between dark matter and microwave haloscopes. We model such process as a quantum channel and derive the fundamental precision limit of noise sensing. An entanglement-assisted strategy based on two-mode squeezed vacuum is thereby demonstrated optimal, while the optimality of a single-mode squeezed vacuum is found limited to the lossless case. We propose a “nulling” measurement (squeezing and photon counting) to achieve the optimal performances. In terms of the scan rate, even with 20-decibel of strength, single-mode squeezing still underperforms the vacuum limit which is achieved by photon counting on vacuum input; while the two-mode squeezed vacuum provides large and close-to-optimum advantage over the vacuum limit, thus more exotic quantum resources are no longer required. Our results highlight the necessity of entanglement assistance and microwave photon counting in dark matter search.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Elemental zoning enhances mass transport in zeolite catalysts for methanol to hydrocarbons

Mass transport limitations in zeolite catalysts pose major hurdles for their optimal performance in diverse chemical reactions. Most approaches to reduce these restrictions focus on the synthesis of either hierarchical or nanosized zeolites. Here we demonstrate that the existence of a siliceous, catalytically inactive exterior rim on ZSM-5 particles dramatically reduces the diffusion limitations, which leads to an enhanced catalyst lifetime for the methanol-to-hydrocarbon reaction. In this study, our findings reveal that binary inorganic and organic structure-directing agents enable a one-pot synthesis of Si-zoned ZSM-5 catalysts with diffusion properties that are characteristic of particles with a much smaller size. Operando ultraviolet–visible light diffuse reflectance spectroscopy reveals a marked reduction in external coking among Si-zoned samples. Molecular dynamics simulations to assess the diffusion of methanol and benzene in siliceous pores and in those with Brønsted acids reveal substantially reduced transport limitations in zoned regions, consistent with the improved catalyst activity of Si-zoned zeolites relative to that of ZSM-5 with a homogeneous acid-site distribution.

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

Atomic/molecular layer deposition for energy storage and conversion

Energy storage and conversion systems, including batteries, supercapacitors, fuel cells, solar cells, and photoelectrochemical water splitting, have played vital roles in the reduction of fossil fuel usage, addressing environmental issues and the development of electric vehicles. The fabrication and surface/interface engineering of electrode materials with refined structures are indispensable for achieving optimal performances for the different energy-related devices. Atomic layer deposition (ALD) and molecular layer deposition (MLD) techniques, the gas-phase thin film deposition processes with self-limiting and saturated surface reactions, have emerged as powerful techniques for surface and interface engineering in energy-related devices due to their exceptional capability of precise thickness control, excellent uniformity and conformity, tunable composition and relatively low deposition temperature. In the past few decades, ALD and MLD have been intensively studied for energy storage and conversion applications with remarkable progress. In this work, we give a comprehensive summary of the development and achievements of ALD and MLD and their applications for energy storage and conversion, including batteries, supercapacitors, fuel cells, solar cells, and photoelectrochemical water splitting. Moreover, the fundamental understanding of the mechanisms involved in different devices will be deeply reviewed. Furthermore, the large-scale potential of ALD and MLD techniques is discussed and predicted. Finally, we will provide insightful perspectives on future directions for new material design by ALD and MLD and untapped opportunities in energy storage and conversion.

25 ENERGY STORAGE↗
DOE OSTI2020

Review on organosulfur materials for rechargeable lithium batteries

Organic electrode materials have been considered as promising candidates for the next generation rechargeable battery systems due to their high theoretical capacity, versatility, and environmentally friendly nature. Among them, organosulfur compounds have been receiving more attention in conjunction with the development of lithium-sulfur batteries. Usually, organosulfide electrodes can deliver relatively high theoretical capacity based on reversible breakage and formation of disulfide (S-S) bonds. Here, we provide an overview of organosulfur materials for rechargeable lithium batteries, including their molecular structural design, structure related electrochemical performance study as well as electrochemical performance optimization. In addition, recent progress of advanced characterization techniques for investigation of the structure and lithium storage mechanism of organosulfur electrodes are elaborated. To further understand the perspective application, additive effect of organosulfur compounds for lithium metal anodes, sulfur cathodes as well as high voltage inorganic cathode materials are reviewed with typical examples. Finally, some remaining challenges and perspective of the organosulfur compounds as lithium batteries components are also discussed. This review is intended to serve as a general guidance for researcher to facilitate the development of organosulfur compounds.

25 ENERGY STORAGE↗
DOE OSTI2021

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↗