Search NASA⌕ Search

SEARCH · Search NASA

Results for “Surface Engineering”

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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Generalized moving least squares vs. radial basis function finite difference methods for approximating surface derivatives

Approximating differential operators defined on two-dimensional surfaces is an important problem that arises in many areas of science and engineering. Over the past ten years, localized meshfree methods based on generalized moving least squares (GMLS) and radial basis function finite differences (RBF-FD) have been shown to be effective for this task as they can give high orders of accuracy at low computational cost, and they can be applied to surfaces defined only by point clouds. However, there have yet to be any studies that perform a direct comparison of these methods for approximating surface differential operators (SDOs). The first purpose of this work is to fill that gap. For this comparison, we focus on an RBF-FD method based on polyharmonic spline kernels and polynomials (PHS+Poly) since they are most closely related to the GMLS method. Additionally, we use a relatively new technique for approximating SDOs with RBF-FD called the tangent plane method since it is simpler than previous techniques and natural to use with PHS+Poly RBF-FD. Further, the second purpose of this work is to relate the tangent plane formulation of SDOs to the local coordinate formulation used in GMLS and to show that they are equivalent when the tangent space to the surface is known exactly. The final purpose is to use ideas from the GMLS SDO formulation to derive a new RBF-FD method for approximating the tangent space for a point cloud surface when it is unknown. For the numerical comparisons of the methods, we examine their convergence rates for approximating the surface gradient, divergence, and Laplacian as the point clouds are refined for various parameter choices. We also compare their efficiency in terms of accuracy per computational cost, both when including and excluding setup costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Identifying the Accuracy of Surface Roughness for Metal Additive Manufacturing Parts Captured with Computed Tomography Scanning

Conventional surface roughness measurement techniques require direct access to internal surfaces, often necessitating destructive sectioning of test articles. Computed tomography (CT) offers a non-destructive alternative for internal surface characterization, but its application in metrology remains unstandardized and sensitive to machine resolution and operator technique. This study investigates the feasibility of using CT scanning to quantify areal surface roughness in metal AM heat exchanger tubes with three distinct internal geometries: ribbed, discrete W, and featureless. CT-derived surface parameters were extracted using custom Python scripts and compared to measurements obtained from a focus variation microscope calibrated against a known standard. Results show that CT-based roughness measurements closely matched microscope values for the discrete W specimen, deviations between CT and microscope measurements were minimal—less than 1 µm—indicating reliable reconstruction. In contrast, the ribbed and featureless specimens, with lower roughness values showed greater discrepancies. The findings suggest that CT scanning can be a viable non-destructive metrology tool for AM parts with surface roughness above approximately 8 µm. For smoother surfaces, current CT capabilities may not provide sufficient accuracy, highlighting the need for resolution-aware workflows and further standardization in CT-based surface metrology.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Unlocking bulk and surface oxygen transport properties of mixed oxide-ion and electron conducting membranes with combined oxygen permeation cell and oxygen probe method

Surface exchange kinetics and bulk diffusion of oxygen are of paramount importance to the activity of oxygen electrocatalysis and performance of electrochemical devices such as fuel cell, metal-air batteries, and oxygen separation membranes. Conventional approaches to obtaining these transport properties are often limited to single property under a specific non-operation related condition. Here we use a combined oxygen permeation cell and oxygen probe methodology to simultaneously attain rates of oxygen surface exchange and bulk conductivity/chemical diffusivity of three representative mixed oxide-ion and electron conductors, namely SrCo 0.9 Ta 0.1 O 3-δ (SCT), La 0.6 Sr 0.4 CoO 3-δ (LSC) and La 0.6 Sr 0.4 FeO 3-δ (LSF), operated under a steady-state oxygen flux. The results explicitly show that SCT exhibit the highest oxide-ion conductivity/chemical diffusivity, fastest rates of surface oxygen exchange kinetics, thus promising to be the best oxygen electrocatalyst. Here, we have also mapped out the distribution of oxygen chemical potential gradient across the membranes and applied B-transport number concept to illustrate the rate-limiting steps in the overall oxygen permeation process.

42 ENGINEERING↗
DOE OSTI2025

Fractal Scaling of Explosively Driven Product Gases

ABSTRACT Characterization of the interface between explosive product gases and ambient air in an explosion is a complicated task due to the turbulent mixing and inherently three‐dimensional expansion of the interface. This study aims to quantify the evolution of the interface as a temporally varying Hausdorff dimension. Two test series were conducted with Composition C‐4 charges with masses of 105 and 880 g. Imaging data were collected from the time of detonation until shock wave detachment using ultra‐high‐speed cameras. Gas cloud profiles were extracted using automated image processing algorithms, and the Hausdorff dimension of these two‐dimensional slices of the gas cloud was then estimated using boxcounting algorithms. When scaled with standard gas dynamic nondimensional scalings, the Hausdorff dimension of all explosive events appears to collapse towards a single curve. The fireball was initially nonfractal and began to develop fractal properties as the shock wave separated from the detonation products. Artificial perturbation of the charged surface had no detectable impact on the evolution of the Hausdorff dimension in the early development of the fireball outside of error, despite visible phenomenological differences in the early development of mixing on the fireball surface.

42 ENGINEERING↗
DOE OSTI2023

Systematic modification of functionality in disordered elastic networks through free energy surface tailoring

A combined machine learning–physics–based approach is explored for molecular and materials engineering. Specifically, collective variables, akin to those used in enhanced sampled simulations, are constructed using a machine learning model trained on data gathered from a single system. Through the constructed collective variables, it becomes possible to identify critical molecular interactions in the considered system, the modulation of which enables a systematic tailoring of the system’s free energy landscape. To explore the efficacy of the proposed approach, we use it to engineer allosteric regulation and uniaxial strain fluctuations in a complex disordered elastic network. Its successful application in these two cases provides insights regarding how functionality is governed in systems characterized by extensive connectivity and points to its potential for design of complex molecular systems.

42 ENGINEERING↗
DOE OSTI2019

Method of thin film adhesion pretreatment

A circuit board comprising a substrate and a circuit trace. The substrate includes a surface etched via ion milling over a circuit area such that the surface has an increased roughness. The circuit trace forms portions of an electronic circuit and may be created from a thin conductive film deposited on the surface within the circuit area. The circuit trace adheres more strongly to the roughened substrate surface, which prevents the circuit trace from peeling or becoming delaminated from the substrate surface.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Additive Engineering for Efficient and Stable Perovskite Solar Cells

Perovskite solar cells (PSCs) have reached a certified 25.2% efficiency in 2019 due to their high absorption coefficient, high carrier mobility, long diffusion length, and tunable direct bandgap. However, due to the nature of solution processing and rapid crystal growth of perovskite thin films, a variety of defects can form as a result of the precursor compositions and processing conditions. The use of additives can affect perovskite crystallization and film formation, defect passivation in the bulk and/or at the surface, as well as influence the interface tuning of structure and energetics. Here, recent progress in additive engineering during perovskite film formation is discussed according to the following common categories: Lewis acid (e.g., metal cations, fullerene derivatives), Lewis base based on the donor type (e.g., O-donor, S-donor, and N-donor), ammonium salts, low-dimensional perovskites, and ionic liquid. Various additive-assisted strategies for interface optimization are then summarized; additives include modifiers to improve electron- and hole-transport layers as well as those to modify perovskite surface properties. To conclude, an outlook is provided on research trends with respect to additive engineering in PSC development.

14 SOLAR ENERGY↗
DOE OSTI2021

Kinetic description of site ensembles on catalytic surfaces

Significance The kinetic assessment of catalytic reactions is essential to the design and application of thermochemical processes useful in petrochemical synthesis, energy conversion, and environmental remediation. We demonstrate that the ubiquitously used Langmuir–Hinshelwood formalism is incomplete and fails to correctly describe even simple catalytic reactions (e.g., A + A → A 2 ). An accurate kinetic description requires explicit rate equations for multisite configurations (e.g., A*–A* pairs) to account for the nonrandom clustering/isolation of surface species. We rigorously describe clustering/isolation phenomena by derivation of coverage-dependent stoichiometric coefficients, which capture the distinct influence each elementary step exerts on each catalytic site ensemble—thereby overcoming limitations of the mean-field (i.e., Hinshelwood) assumption in describing reactions of mobile Langmuirian adsorbates on catalytic surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Statistical 3D morphology characterization of vaterite microspheres produced by engineered Escherichia coli

Hollow vaterite microspheres are important materials for biomedical applications such as drug delivery and regenerative medicine owing to their biocompatibility, high specific surface area, and ability to encapsulate a large number of bioactive molecules and compounds. We demonstrated that hollow vaterite microspheres are produced by an Escherichia coli strain engineered with a urease gene cluster from the ureolytic bacteria Sporosarcina pasteurii in the presence of bovine serum albumin. We characterized the 3D nanoscale morphology of five biogenic hollow vaterite microspheres using 3D high-angle annular dark field scanning transmission electron microscopy (HAADF-STEM) tomography. Using automated high-throughput HAADF-STEM imaging across several sample tilt orientations, we show that the microspheres evolved from a smaller more ellipsoidal shape to a larger more spherical shape while the internal hollow core increased in size and remained relatively spherical, indicating that the microspheres produced by this engineered strain likely do not contain the bacteria. The statistical 3D morphology information demonstrates the potential for using biogenic calcium carbonate mineralization to produce hollow vaterite microspheres with controlled morphologies.

3D morphology↗
DOE OSTI2025

Engineered Endosymbionts that Modulate Primary Macrophage Function and Attenuate Tumor Growth by Shifting the Tumor Microenvironment

Modulating gene expression in macrophages can be used to improve tissue regeneration and redirect tumor microenvironments (TMEs) toward positive therapeutic outcomes. We have developed Bacillus subtilis as an engineered endosymbiont (EES) capable of residing inside the eukaryotic host cell cytoplasm and controlling the fate of macrophages. Secretion of mammalian transcription factors (TFs) from B. subtilis that expresses listeriolysin O (LLO; allowing the EES to escape destruction by the macrophage) modulated expression of surface markers, cytokines, and chemokines, indicating functional changes in a macrophage/monocyte cell line. The engineered B. subtilis LLO TF strains were evaluated in murine bone marrow-derived macrophages (BMDMs) by flow cytometry, chemokine/cytokine profiling, metabolic assays, and RNA-Seq delivery of TFs by the EES shifted BMDM gene expression, production of cytokine and chemokines, and metabolic patterns, indicating that the TF strains could guide primary macrophage function. Thereafter, the ability of the TF strains to alter the TME was characterized in vivo in an orthotopic murine model of triple-negative breast cancer to assess therapeutic effects. The TF strains altered the TME by shifting immune cell composition and attenuating tumor growth. Additionally, multiple doses of the TF strains were well-tolerated by the mice. The use of B. subtilis LLO TF strains as EES showed promise as a unique cancer immunotherapy by directing the immune function intracellularly. The uses of EES could be expanded to modulate other mammalian cells over a range of biomedical applications.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Exchange-Driven Intermixing of Bulk and Topological Surface States by Chiral Excitons in Bi 2 Se 3

Topological surface states (TSS) in the prototypical topological insulator (TI) Bi 2 Se 3 are frequently characterized using optical probes, but electron-hole interactions and their effect on surface localization and optical response of the TSS remain unexplored. Here, we use ab initio calculations to understand excitonic effects in the bulk and surface of Bi 2 Se 3 . We identify multiple series of chiral excitons that exhibit both bulk and TSS character, due to exchange-driven mixing. Further, our results address fundamental questions about the degree to which electron-hole interactions can relax the topological protection of surface states and dipole selection rules for circularly polarized light in TIs by elucidating the complex intermixture of bulk and surface states excited in optical measurements and their coupling to light.

42 ENGINEERING↗
DOE OSTI2026

Concept of Dynamic Heat Insulation for Rotating Detonation Engines

This work introduces a new class of materials concept to dynamically reduce instantaneous heat fluxes in Rotating Detonation Engine (RDE) combustor chamber walls. The high-frequency and high-amplitude surface heat fluxes observed in RDEs arise from large instantaneous temperature differences between the detonation shockwave and chamber wall surface. These temperature gradients drive substantial energy loss and reduce the chamber gas pressure, ultimately limiting the cycle’s thermodynamic efficiency. This work introduces a concept for dynamically insulating the combustion chamber surfaces using surface layers or coatings with low thermal time scale. With such coatings, the surface temperature may follow the fluctuations of the cyclic detonation wave temperature, thus reducing the instantaneous heat flux therefore cycle-mean heat flux. To analyze these cyclic thermal phenomena, a one-dimensional analytical conduction solver was utilized with the capability to handle multilayered structures. Parametric modeling was performed using transient heat flux boundary conditions representative of a hydrogen–air RDE across a broad range of coating thermal properties and engine conditions. The coating effectiveness scaled with the product of thermal time constant and detonation wave frequency and the results were non-dimensionalized to guide future materials development. This strategy may offer benefits in increasing material survivability, reducing cooling requirements, and enhancing pressure gain.

heat transfer↗
DOE OSTI2025

First indirectly driven liquid-DT filled double shell implosions at the National Ignition Facility

Double shell implosions aim to explore material mixing under fusion conditions in a volume burn geometry using high-Z metal pushers. High-Z pushers are more compressible than low-Z pushers enabling high stagnation pressure which reduces the required implosion speed while maintaining a low pusher adiabat despite strong shock heating. Additionally, the use of a small fuel mass reduces the fuel internal energy required for ignition, thus achieving stable platforms with reasonable fusion output to conduct controlled experiments. These factors make volume burn in a double shell implosion highly promising. Recently, a series of liquid-DT filled, indirectly driven, double shell implosions were conducted at the National Ignition Facility with laser drives reaching up to 1.5 MJ. These experiments achieved a maximum DT neutron yield of 1.67 × 10 14 (yield—479 J), DT ion temperature of 2.6 keV, fuel areal density (ρR) of 0.15 g/cm 2 , and stagnation pressure of 79 Gbar. Over the course of these shots, the DT neutron yield has increased by an order of magnitude largely from improved mitigation of outer shell assembly joint driven instability growth using thicker gold plating at the assembly joint. Further performance improvements are expected by enhancing outer-to-inner shell kinetic energy transfer and refined mitigation of degradations from engineering features such as the outer shell joint, fill tube, and surface roughness.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

Optimization of La 2–x Sr x CuO 4 Single Crystal Film Growth via Molecular Beam Epitaxy

Atomic layer-by-layer molecular beam epitaxy (ALL-MBE) combined with ozone is one of the best methods to fabricate single-crystal thin films of complex oxides. Cuprate such as La 2-x Sr x CuO 4 (LSCO) is a representative complex-oxide high-temperature superconductor (HTS) material. Our group utilizes this method to produce high-quality single-crystal HTS films with atomically smooth surfaces and interfaces. In addition, ALL-MBE enables us to engineer multilayer heterostructures with atomic precision. This allows the fabrication of tunnel junctions, various nanostructures, and other HTS devices of interest for superconducting electronics. We have synthesized over three thousand LSCO thin films in the past two decades. These films’ structural and electronic properties have been studied and characterized by various methods. Here, we distill the extensive experience we accumulated into a step-by-step protocol to fabricate atomically perfect LSCO films. The recipe includes substrate preparation, ozone generation and distillation, source calibration, the in situ monitoring of the film synthesis, post-growth annealing, and ex situ characterization. It discloses a reproducible way to fabricate single-crystal LSCO films for basic research and HTS electronic applications.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Approaching PetaVolts per Meter Plasmonics Using Structured Semiconductors

A newly uncovered class of plasmons in the strongly excited limit opens access to unprecedented Petavolts per meter electromagnetic fields with wide-ranging, transformative impact. Unlike conventional plasmons, such plasmons are constituted by non-perturbative, large-amplitude oscillations of the ultradense, delocalized free electron Fermi gas inherent in conductive media. Here structured semiconductors doped to have an appropriate conduction electron density are introduced to tune the properties of the Fermi gas for matched excitation of large-amplitude plasmons using readily available electron beams which enables immediate experimental validation. Specifically, an electrostatic, surface “crunch-in” plasmon is collisionlessly excited by the beam launched inside a tube. Strong excitation due to matching results in relativistic oscillations of the electron gas and unravels unique phenomena. Relativistically induced ballistic electron transport comes about due to relativistic multifold increase in the mean free path and also leads to unconventional heat deposition beyond Ohm’s law. This explains the absence of observed damage or solid-plasma formation in past experiments on conductive samples interacting with electron bunches shorter than 10-13 seconds. Furthermore, relativistic momentum leads to copious tunneling of electron gas across the surface, which then crunches inside the tube. Relativistic effects along with large, localized electron density variations underlying these modes necessitate kinetic approach to theoretical and computational modeling. Kinetic model presented here demonstrates experimental viability of observing tens of gigavolts per meter plasmonic fields excited by matching readily available electron beams to plasmons in semiconductors with 1018cm-3 free electron density, and paves the way for Petavolts per meter plasmonics.

42 ENGINEERING↗