Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Process optimization”

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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Toward Low-Thermal-Budget Hafnia-Based Ferroelectrics via Atomic Layer Deposition

Since the first report of ferroelectricity in fluorite structure oxides a decade ago, significant attention has been devoted to studying hafnia-based ferroelectric material systems due to their promising properties and opportunities. To achieve such ferroelectric fluorite structure oxides at low temperatures (below 400 °C), stabilizing the metastable noncentrosymmetric orthorhombic phase is crucial. This review provides a comprehensive overview of atomic layer deposition (ALD) techniques for obtaining the orthorhombic phase for low-temperature ferroelectric applications. We discuss optimization of the ALD process for synthesizing high-quality, low-temperature crystallizing ferroelectric films, including doping, precursor and oxygen source selection, deposition temperature, and interface engineering. In addition, the techniques for stabilizing the ferroelectric phase by regulating the thermal budget and stress with various annealing methods and stressors are discussed. In conclusion, the review focuses on different techniques to reduce the thermal budget required to acquire ferroelectricity, making hafnia-based ferroelectric materials compatible with back-end-of-line and three-dimensional integration for a variety of future applications, including flexible electronics applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Microstructural Assessment of Molybdenum Disulfide Coatings Using Nanoindentation Hardness

MoS 2 coatings are used extensively in aerospace and defense applications due to their ultralow friction and high wear resistance. Burnished and resin-bonded MoS 2 coatings are commonly used in these applications due to simplicity in deposition and history of use, despite issues with consistency in coating properties and performance. Physical vapor deposition (PVD) of MoS 2 thin films has emerged as a process alternative in the past 50 years, promising far greater control over film structure and composition but at a greater cost. Despite PVD’s benefits, hesitance to adoption persists in high-consequence applications, not only due to increased costs but variability in resulting coating properties. These variations in properties and subsequent performance are in part due to the complexity of the PVD process and the sensitive interplay between coating process-structure-property relationships. This work aims to demystify the remaining uncertainties of the process-structure-property relationships in PVD MoS 2 . The microstructure and mechanical and tribological properties of 61 different PVD pure MoS 2 coatings are examined herein. Emphasis has been placed on developing performance-based (i.e., hardness, modulus) metrics that can assess microstructural changes (density, orientation, and crystallinity) and be utilized to accelerate process development and coating optimization. Relationships established within suggest that nanoindentation hardness can be used to infer coating performance (i.e., wear rate) and properties (i.e., density, crystalline texture, and stoichiometry). Furthermore, this work demonstrates that PVD MoS 2 coatings close to the theoretical density of MoS 2 consistently have the best tribological performance and can be reliably identified by their hardness.

MoS2↗
DOE OSTI2025

Self-Sensing Composites via an Embedded 3D-Printed PVDF-MoS 2 Nanosensor for Structural Health Monitoring

Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS 2 nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite’s structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180–294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS 2 filament architecture. Here, this embedded 3D printing method produces complex and multimaterial PVDF-MoS 2 sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17–35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS 2 nanocomposite sensor materials for real-time structural health monitoring in epoxy–CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.

PVDF-MoS2 sensor↗
DOE OSTI2025

In-Situ Atomic-Scale Revelation of Amorphous Metallic Iron Formation during Hydrogen-Driven Reduction of Iron Oxides

The transition to hydrogen as a green reductant in metal production is critical for decarbonizing the metallurgical industry, yet atomic-scale mechanisms governing reduction pathways and phase evolution remain unresolved. Using in-situ environmental transmission electron microscopy, we identify a hidden pathway that reveals dynamic formation of amorphous metallic iron (Fe) during the hydrogen-driven reduction of ferrous oxides of Fe 3 O 4 and FeO. Real-time imaging uncovers three coexisting transformation routes: (i) Fe 3 O 4 → FeO, (ii) Fe 3 O 4 → amorphous Fe, and (iii) FeO → amorphous Fe. The resulting amorphous Fe exhibits fluid-like mobility, enabling its rapid aggregation and crystallization into core-shell nanostructures, with a crystalline core enveloped by an amorphous shell. Complementary ab initio molecular dynamics simulations trace the amorphous Fe formation to interfacial strain at the metal/oxide interfaces, where large lattice mismatches destabilize the metal lattice during initial metallization. This interplay between thermodynamics and kinetics governs phase evolution: thermodynamics favors a self-limiting amorphous Fe overlayer, while rapid oxide reduction kinetics drives amorphous overgrowth. Our findings demonstrate that amorphous intermediates bypass rate-limiting crystalline steps, providing mechanistic insights to optimize H 2 -based processes for sustainable steelmaking. In conclusion, these insights bridge the gap between macroscopic process engineering and atomic-scale dynamics, with broader implications for catalysis and nanostructured material synthesis, where oxide reduction pathways critically shape functional phases and microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nitrate Reduction Modeling under Acidic Conditions with Late Transition Metals

The electrochemical reduction of nitrate (NO 3 R) to ammonia is a bold yet conceivable way of producing ammonia using renewable electricity. However, serious challenges remain in finding optimal electrocatalysts for the process. An atomistic understanding of the surface energetics behind the NO 3 R is needed in order to design an efficient catalyst. Herein, we combine energetics from density functional theory and microkinetic modeling to demonstrate how surface descriptors can help simplify the search for efficient NO 3 R electrocatalysts. We illustrate the strong correlations between transition-state energetics and O* binding energies for adsorbed nitrate and nitrite on transition metals. For intermediates from NO* and beyond, we compare the benefits of using either the N* or H* binding energies to predict reduction onset potentials. These insights enable us to develop a simple microkinetic model that elucidates the surface coverages of intermediates and the product selectivity of NO 3 R across a range of potentials and transition metals. As a result, we show that the model adequately corroborates with quasi-steady-state rates observed experimentally.

ammonia↗
DOE OSTI2024

Scalable Surface Micro-Texturing of LLZO Solid Electrolytes for Battery Applications

A challenge for lithium lanthanum zirconate (LLZO)-based solid-state batteries is to increase the critical current density (CCD) to enable high current cycling. A promising strategy is to modify the LLZO surface morphology to provide a larger contact area with the Li metal. Here, a surface-textured thin LLZO electrolyte was prepared through an easily scalable process. The texturing process is a simple pressing of green LLZO tapes between micro-textured substrates. A variety of textures can be produced, depending on the type of substrate, and texturing can be on either one side or both sides. For this work, after pressing and sintering, several micro-patterns are formed on thin LLZO (~118 μm thick). The properties of the various samples were characterized to investigate the impact of surface texturing, and the most promising ones were selected for electrochemical testing in symmetrical lithium cells and full cells. Li symmetric cells using a coarse ridge-textured LLZO exhibit ~2.5 times increased CCD compared to planar non-textured LLZO, and a solid-state full cell shows stable cycling and improved rate performance. Finally, we believe this process offers a favorable trade-off of processing complexity vs structural optimization to maximize CCD.

25 ENERGY STORAGE↗
DOE OSTI2025

Revealing the Crystalline Architecture of Semicrystalline Ion Exchange Membranes for the Design of Conductive and Durable Alkaline Anion Exchange Membranes

Alkaline anion exchange membrane (AAEM) fuel cells offer a cost-effective alternative to proton exchange membrane (PEM) fuel cells by eliminating the need for expensive precious metal catalysts. In both PEMs and AAEMs, semicrystalline polymers are a common choice, as the crystalline domains can act as mechanical reinforcements that limit swelling and promote mechanical durability in the material. However, spatially resolved characterization of crystalline organization in ion exchange membranes beyond ensemble-averaged X-ray scattering is underrepresented, likely in part due to ionization damage limitations in soft materials. Here, in this study, we resolve the nanometer-size crystallites in semicrystalline ion exchange membranes by applying cryogenic four-dimensional scanning transmission electron microscopy (cryo-4D-STEM) along with data-processing algorithms designed to optimize signals at a low dose to minimize radiation damage. We investigate the effects of synthesis components, including molecular weight and thermal treatment, on a model system of AAEMs in comparison to Nafion, the most commonly used and commercially successful PEM today. We find that excess water uptake in polymer membranes, a property directly associated with weak mechanical durability and with possible negative impacts on ion conductivity, can be reduced by over 30% by varying the polymer's crystalline morphology through changes in synthesis parameters such as molecular weight and thermal history. Our results indicate that this improvement is correlated with smaller crystalline domains with a more homogeneous distribution. More broadly, these results demonstrate how the crystalline architecture of polymer membranes can be tuned through their chemistry and thermal treatment in order to improve their conductivity and durability for commercial fuel cell performance.

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

High-Capacity Enzymatic Degradation of Postconsumer Poly(ethylene terephthalate): Modeling and Experimental Investigations

Enzymatic degradation is considered as one of the key steps for biorecycling of polyethylene terephthalate (PET), a widely used plastic. Recently, we have developed an optimized leaf-branch compost cutinase PelB-LCC ICCG and demonstrated ∼80% degradation of 200 g L −1 recycled PET (RPET) within two days in bioreactors. In this study, continued research efforts were made to achieve a complete degradation of up to 300 g L −1 RPET in bioreactors. First, low-capacity reaction conditions with ≤5 g L −1 PET were examined to identify the key variables for PET degradation with PelB-LCC ICCG , including temperature, enzyme loading, and product inhibition. To better understand and further optimize the enzymatic degradation process, a mechanism-based model was established to describe the kinetics of PET degradation and formation of the main product terephthalic acid (TPA) and byproducts MHET and BHET in high-capacity bioreactors. Model simulation suggested that a minimal enzyme loading of ∼1.4 mg PelB-LCC ICCG g −1 RPET is required to achieve a nearly complete degradation of RPET within 48 h, which was used to guide more high-capacity experiments with 100−300 g L −1 RPET in fully controlled 1 L bioreactors. A higher temperature (≥65 °C) was found not only to enable rapid degradation in the beginning but also to induce a gradual increase in RPET’s crystallinity and significantly slow down the degradation after 48 h. A high loading of RPET solids and the accumulation of the produced insoluble TPA pose a big challenge on mixing and mass transfer in the stirred bioreactor, which can be addressed by increasing the stirring speed. The results pave the way toward biorecycling of PET at a large scale.

enzymatic degradation↗
DOE OSTI2023

19.31% binary organic solar cell and low non-radiative recombination enabled by non-monotonic intermediate state transition

Non-fullerene acceptors based organic solar cells represent the frontier of the field, owing to both the materials and morphology manipulation innovations. Non-radiative recombination loss suppression and performance boosting are in the center of organic solar cell research. Here, we developed a non-monotonic intermediate state manipulation strategy for state-of-the-art organic solar cells by employing 1,3,5-trichlorobenzene as crystallization regulator, which optimizes the film crystallization process, regulates the self-organization of bulk-heterojunction in a non-monotonic manner, i.e., first enhancing and then relaxing the molecular aggregation. As a result, the excessive aggregation of non-fullerene acceptors is avoided and we have achieved efficient organic solar cells with reduced non-radiative recombination loss. In PM6:BTP-eC9 organic solar cell, our strategy successfully offers a record binary organic solar cell efficiency of 19.31% (18.93% certified) with very low non-radiative recombination loss of 0.190 eV. And lower non-radiative recombination loss of 0.168 eV is further achieved in PM1:BTP-eC9 organic solar cell (19.10% efficiency), giving great promise to future organic solar cell research.

14 SOLAR ENERGY↗
DOE OSTI2022

Learning two-phase microstructure evolution using neural operators and autoencoder architectures

Abstract Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and generalizable surrogate models are needed to alleviate the cost of computationally taxing processes such as in optimization and design of materials. The intrinsic discontinuous nature of the physical phenomena incurred by the presence of sharp phase boundaries makes the training of the surrogate model cumbersome. We develop a framework that integrates a convolutional autoencoder architecture with a deep neural operator (DeepONet) to learn the dynamic evolution of a two-phase mixture and accelerate time-to-solution in predicting the microstructure evolution. We utilize the convolutional autoencoder to provide a compact representation of the microstructure data in a low-dimensional latent space. After DeepONet is trained in the latent space, it can be used to replace the high-fidelity phase-field numerical solver in interpolation tasks or to accelerate the numerical solver in extrapolation tasks.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Intrinsic doping limitations in inorganic lead halide perovskites

Inorganic halide perovskites (HP's) of the CsPbX 3 (X = I, Br, Cl) type have reached prominence in photovoltaic solar cell efficiencies, leading to the expectation that they are a new class of semiconductors relative to the traditional ones. Peculiarly, they have shown an asymmetry in their ability to be doped by holes vs. electrons. Indeed, both structural defect-induced doping as well as extrinsic impurity-induced doping strangely often result in HP's in a unipolar doping (dominantly p-type) with low free carriers’ concentration. This raises the question whether such doping limitations presents just a temporary setback due to insufficient optimization of the doping process, or perhaps this represents an intrinsic, physically-mandated bottleneck. Here we study three fundamental Design Principles (DP's) for ideal doping, applying them via density functional doping theory to these HP's, thus identifying the violated DP that explains the doping limitations and asymmetry in these HP's. Here, the target DP are: (i) requires that the thermodynamic transition level between different charge states induced by the dopants must ideally be energetically shallow both for donors (n-type) or acceptors (p-type); DP-(ii) requires that the ‘Fermi level pinning energies’ for electrons E (n) pin and holes E (p) pin (being the limiting value of the Fermi level before a structural defect that compensate the doping forms spontaneously) should ideally be located inside the conduction band for n-type doping and inside the valence band for p-type doping. DP-(iii) requires that the doping-induced shift in equilibrium Fermi energy ΔE (n) F towards the conduction band for n-type doping (shift of ΔE (p) F towards the valence band, for p-type doping) to be sufficiently large. We find that, even though in HP's based on Br and Cl there are numerous shallow level dopants that satisfy DP-(i), in contrast DP-(ii) is satisfied only for holes and DP-(iii) fails for both holes and electrons, being the ultimate bottleneck for the n-type doping in Iodide HP's. This suggests an intrinsic mechanism for doping limitations in this class of semiconductors in terms of recognized physical mechanisms.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Preparation, analysis, and application of coated glass targets for the Wendelstein 7-X laser blow-off system

Coated glass targets are a key component of the Wendelstein 7-X laser blow-off system that is used for impurity transport studies. The preparation and analysis of these glass targets as well as their performance is examined in this paper. The glass targets have a high laser damage threshold and are coated via physical vapor deposition with μm thick films. In addition, nm-thin layers of Ti are used as an interface layer for improved ablation efficiency and reduced coating stress. Hence, the metallic or ceramic coating has a lateral homogeneity within 2% and contaminants less than 5%, being optimal for laser ablation processing. With this method, a short (few ms) and well defined pulse of impurities with about 1017 particles can be injected close to the last closed flux surface of Wendelstein 7-X. In particular, a significant amount of atoms with a velocity of about 1 km/s enters the plasma within 1 ms. The atoms are followed by a negligible concentration of slower clusters and macro-particles. This qualifies the use of the targets and applied laser settings for impurity transport studies with the laser blow-off system in Wendelstein 7-X.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Exploration of lattice Hamiltonians for functional and structural discovery via Gaussian process-based exploration–exploitation

Statistical physics models ranging from simple lattice to complex quantum Hamiltonians are one of the mainstays of modern physics that have allowed both decades of scientific discovery and provided a universal framework to understand a broad range of phenomena from alloying to frustrated and phase separated materials to quantum systems. Traditionally, exploration of the phase diagrams corresponding to multidimensional parameter spaces of Hamiltonians was performed using a combination of basic physical principles, analytical approximations, and extensive numerical modeling. However, exploration of complex multidimensional parameter spaces is subject to the classic dimensionality problem, and the behaviors of interest concentrated on low dimensional manifolds remain undiscovered. Here, we demonstrate that a combination of exploration and exploration–exploitation with Gaussian process modeling and Bayesian optimization allows effective exploration of the parameter space for lattice Hamiltonians and effectively maps the regions at which specific macroscopic functionalities or local structures are maximized. We argue that this approach is general and can be further extended well beyond the lattice Hamiltonians to effectively explore the parameter space of more complex off-lattice and dynamic models.

42 ENGINEERING↗
DOE OSTI2026

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations

Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Effects of stabilizer strength on the delamination strength of ReBCO coated conductors

Delamination strengths (DSs) of ReBCO tapes were evaluated using a transverse tensile anvil test. This study first demonstrated that the electromechanical DS is nearly equivalent to the purely mechanical DS for ReBCO tapes exhibiting abrupt delamination behavior. Mechanical DS was then examined for tapes with and without stabilizer edges to assess the influence of stabilizer strength. Scanning electron microscopy/energy-dispersive x-ray spectroscopy analysis revealed the delamination sites and crack propagation in ReBCO tapes. Further investigation revealed that the DS of ReBCO tapes is primarily determined by two factors: (1) interfacial bonding strength between the layers and (2) mechanical strength of the stabilizer edges. These findings point to a potential approach to enhancing DS by increasing the strength of the stabilizer (e.g. by optimizing the Cu electroplating process).

Cu stabilizer↗
DOE OSTI2025

Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts

Neutron diffraction based strain mapping is a useful technique for measuring residual strains in additively manufactured (AM) metal parts. The measurement is traditionally done by scanning the sample in a point-wise raster pattern to extract the strain at each position. Since the overall scan can span several hours, adaptive sampling approaches using Bayesian optimization based on Gaussian process (BO-GP) regression have been introduced—demonstrating that even with a fraction of the typically made measurements the dominant strain patterns in the sample can be reconstructed. However, the parameters of the BO-GP algorithm have to be carefully chosen for best performance, and the movement time between arbitrary points can offset the time savings from a reduced number of measurement locations. In this paper, we propose algorithms to refine the BO-GP based methods by using simulations of strain patterns in AM parts based on the materials and the process used to print them. We demonstrate that the simulated strain patterns can be used to help choose better parameters for the BO-GP based framework—leading to low reconstruction error for the final strain pattern. Furthermore, we show that the strain mapping experiment can be initialized with a sampling pattern learnt from the simulation data and ordered to reduce movement time, dramatically enabling reduction in the overall time required to run the baseline BO-GP method.

Gaussian process regression↗