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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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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Study on ammonia and dimethyl ether oxidation and kinetic interaction up to 100 atm

NH 3 and DME dual fuel oxidation and kinetic coupling are experimentally studied by using a supercritical pressure jet-stirred reactor (SP-JSR) at 20 and 100 atm, over a temperature range of 500–900 K, and at fuel-lean and stoichiometric conditions with NH 3 to DME molar ratios of 4 and 0.62, respectively. An HP-Mech model for high-pressure NH3/DME oxidation is developed based on our previous studies and it shows generally better performance than other models in the literature on high-pressure oxidation experiments. Due to DME’s strong low-temperature reactivity, it dramatically enhances the oxidation of NH3 at low temperature. However, the effect of NH 3 on DME oxidation varies with temperature. At low temperatures, NH 3 inhibits low-temperature reactivity by consuming OH radicals. In addition, the NH 2 and NO x formation from NH 3 further suppresses the low-temperature DME reactivity by reducing alkylperoxyl and O 2 QOOH radicals via RO 2 + NH 2 = RO + H 2 NO, RO 2 + NO = RO + NO 2 and O 2 QOOH + NO = 2CH 2 O + HO 2 + NO 2 . At intermediate temperatures, due to enhanced kinetic coupling of NH 2 /NO x /HO 2 chemistry, DME oxidation is significantly promoted. It is found that there are two major NH 2 /NO x /HO 2 coupling pathways for OH radical production brought by NH 3 : (1) NH 2 + HO 2 = H 2 NO + OH and (2) NH 2 + NO 2 = H 2 NO + NO with NO + HO 2 = OH + NO 2 . Moreover, the resulting H 2 NO will further contribute to OH production via the H 2 NO/NO x /HONO coupling pathways: H 2 NO + NO 2 = HNO + HONO, HNO + NO 2 = HONO + NO, and HONO (+M) = OH + NO (+M). These new NH 2 /NO x /HO 2 and H 2 NO/NO x /HONO pathways play a critical role in promoting DME oxidation at intermediate temperatures. Novelty and Significance Statement: Blending NH 3 with DME can significantly enhance the reactivity of NH 3 and facilitate its practical application in advanced internal combustion engines. NH 3 /DME oxidation is studied in a novel supercritical pressure jet-stirred reactor up to 100 atm, a much higher pressure than previous studies. An interesting non-monotonic effect of NH3 addition on fuel oxidation has been found. Unique kinetic couplings of NH2/HO2/NOx and H2NO/NOx/HONO brough by NH3 and DME blending are identified to play important roles in radical generation and reactivity promotion. The valuable experimental data and key kinetics revealed in the current work can tremendously improve our understanding of NH 3 /DME oxidation via NO x /RO 2 and NH 2 / H 2 NO/HO 2 /NO x kinetic couplings at low to intermediate temperatures.

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

Flame dynamics and kinetic coupling of ammonia and dimethyl-ether in non-premixed cool and warm flames at elevated pressure

Developing advanced low-temperature combustion engines with ammonia-biofuel blends requires a comprehensive understanding of low-temperature flame dynamics and kinetic interactions between ammonia and oxygenated fuels at elevated pressures. This work aims to study the dynamics and kinetics of non-premixed Dimethyl Ether (DME)/Ammonia (NH 3 ) cool and warm flames, and their reignition to hot flames. A counterflow burner is employed to establish DME/NH 3 cool/warm flames at pressures up to 5 atm. The extinction limits of cool flame and the reignition limits of warm flame to hot flame are measured by varying NH 3 concentrations and compared to simulations to quantitatively examine the effects on DME/NH 3 flames. It is found that NH 3 inhibits low-temperature DME oxidation and results in lower cool flame extinction limits. Warm flames in the presence of NH 3 are observed for the first time, revealing a non-monotonic effect of NH 3 addition: a small amount of NH 3 presence enhances warm flame chemistry and promotes reignition to hot flames, while a high NH 3 concentration weakens the warm flame. This trend is further explained by 0-D PSR kinetic simulations and 1-D S-curve flame dynamic calculations. Three flame transition regimes between cool flames (CF), warm flames (WF), and hot flames (HF) by different levels of NH 3 additions at a specific strain rate are identified, namely WFHF reignition, WF-CF transition, and WF extinction. Reaction sensitivity analyses of OH at low temperatures show that NH3 inhibits DME oxidation through OH consumption via H-abstraction and the kinetic couplings of RO 2 /NH 2 , RO 2 /NO x , R/NO x , and O 2 QOOH/NO x further suppress the low-temperature branching. At intermediate-temperatures, NH 2 /NO x /HO 2 coupling promotes warm flames via the pathway NH 2 → H 2 NO → HNO → NO by converting O 2 → HO 2 → OH. At even higher NH₃ concentrations, radical termination reactions of NH 2 + NO/NO 2 and excessive OH consumption via H-abstraction inhibit the flame. The insights into the kinetic coupling between NH 3 and low-temperature chemistry at elevated pressure and its impact on the dynamics of cool-warm-hot flame transitions will contribute to advancing combustion technologies with reduced emissions and improved energy-efficiency.

42 ENGINEERING↗
DOE OSTI2025

Improving microstructures segmentation via pretraining with synthetic data

Image analysis of material microstructures through microscopy is an integral capability in the field of materials science. The topological and chemical information obtained through microscopy allow us to draw vital connections between material microstructures, properties, and processing. While scanning electron microscopy (SEM) is able to yield a considerable wealth of information interpretable by the intuition of experts, there has been considerable interest in using machine learning, convolutional neural networks (CNNs) in particular, for such image analysis task. Training CNNs for an image analysis task requires a large annotated dataset. However, in many materials science applications, obtaining a large annotated dataset is cost and labor intensive. In this work, we study the use of synthetic data to enlarge the available annotated experimental data of uranium oxide. We utilize a modified Potts model to simulate uranium oxide particles with morphologies similar to those observed experimentally. We then leverage an image-to-image translation model to synthesize the simulated particles as if they are acquired with SEM. Through this process, we obtain pairs of particle images and their corresponding SEM representations, which corresponds to pairs of annotations and images. Unlike previous works, we leverage synthetic data for pretraining a CNN model prior, and finetune that model further with experimental data. We experimentally demonstrate that using synthetic data as incremental learning process benefits the overall performance compared to training a model on combined synthetic and experimental data.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Automatic building energy model development and debugging using large language models agentic workflow

Building energy modeling (BEM) is a complex process that demands significant time and expertise, limiting its broader application in building design and operations. While Large Language Models (LLMs) agentic workflow have facilitated complex engineering processes, their application in BEM has not been specifically explored. This paper investigates the feasibility of automating BEM using LLM agentic workflow. Here, we developed a generic LLM-planning-based workflow that takes a building description as input and generates an error-free EnergyPlus building energy model. Our robust workflow includes four core agents: 1) Building Description Pre-Processing, 2) IDF Object Information Extraction, 3) Single IDF Object Generator Suite, and 4) IDF Debugging Agent. These agents divide the complex tasks into manageable sub-steps, enabling LLMs to generate accurate and reliable results at each stage. The case study demonstrates the successful translation of a building description into an error-free EnergyPlus model for the iUnit modular building at the National Renewable Energy Laboratory. The effectiveness of our workflow surpasses: 1) naive prompt engineering, 2) other LLM-based workflows, and 3) manual modeling, in terms of accuracy, reliability, and time efficiency. The paper concludes with a discussion on the interplay between foundational models and LLM agent planning design, advocating for the use of fine-tuned, specialized models to advance this field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

A multiscale packed-bed reactor model for sustainable ethylene production via chemical looping oxidative coupling of methane

The rising global warming concerns and shale gas discovery have prompted research in the direction of greenhouse gas (GHG), such as methane, reduction and conversion. Oxidative coupling of methane (OCM) offers a pathway to low carbon-intense valorization of methane while producing ethylene, a chemical regarded as central to the petrochemical industry. Even after decades of OCM discovery, researchers keep understanding the process and underlying chemical reactions in a pursuit to achieve industrial viability for OCM. Here, in general, OCM suffers from low C 2 selectivity, yield and reactor temperature runaways due to highly exothermic nature of its reactions. Computational Fluid Dynamics (CFD) tools help analyze spatial gradients within the reactor to deeply understand the diffusion of species, mass and heat transfer phenomena. Furthermore, challenges associated with scaling up such as hot spot formation and parametric sensitivity can be addressed without having to expend on costly experiments. The current paper presents a multiscale packed-bed reactor CFD model coupled with a chemical kinetic model for the chemical looping OCM. The CFD model includes two scales i.e., macroscale for catalyst bed and microscale for individual pellets. Moreover, a chemical kinetic model based on 10 gas-phase reactions is integrated with the CFD model. An additional surface reaction for the formation of gas-phase oxygen from catalyst surface is added to account for the absence of feed oxygen. The model is calibrated against experimental results. The calibrated model captures trends in CH 4 conversion, C 2 selectivity and C 2 yield within a ± 4.35 % range across a temperature range of 700-900 °C. Moreover, model fidelity is evaluated by varying key computational parameters such as mesh resolution and time step size. The model is also verified by varying the inlet methane concentration and the gas hourly space velocity (GHSV) and comparing the results with literature. A sensitivity analysis and scale-up of the current model is undergoing.

Chemical looping↗
DOE OSTI2026

Lattice-Oxygen-Driven Selective Oxidation Strategy for Stable Argyrodite Solid-State Lithium Metal Batteries

All-solid-state lithium metal batteries (ASSLMBs) with Li6PS5Cl argyrodite electrolytes and high-voltage LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes offer high energy density but suffer from rapid capacity fading due to the layered-to-rock-salt transition of NMC811 and structural degradation of Li6PS5Cl from parasitic interfacial reactions. Here, we demonstrate a catholyte engineering strategy using a Li2S scavenging additive to suppress interfacial reactivity and preserve the structural and electrochemical stability of both NMC811 and Li6PS5Cl. Incorporating 0.10 wt.% Li2S enables exceptional cycling stability, achieving 76% capacity retention after 550 cycles at C/10 and 88% retention after 800 cycles at C/3 at 60 degrees C, compared with rapid failure in pristine cells. Spectroscopic, electrochemical, and morphological analyses confirm that Li2S maintains electrode integrity by sustaining particle contact and suppressing phase decomposition. This work elucidates interfacial degradation pathways in NMC811/argyrodite systems and introduces a low-cost, scalable strategy to stabilize nickel-rich oxide cathodes in ASSLMBs, advancing their practical viability.

25 ENERGY STORAGE↗
DOE OSTI2025

A comprehensive review of diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) techniques in protonic ceramic cells (PCCs): Current status and future perspective

Protonic ceramic cells (PCCs) have emerged as a promising technology for power generation, energy storage, and value-added chemical synthesis, offering benefits such as fuel flexibility, low emissions, and efficient operation at intermediate temperatures (300–600 ​°C). Recently, significant breakthroughs in materials and manufacturing methods have markedly enhanced the performance of PCCs. However, establishing a fundamental understanding of their electrocatalytic reactions has gained less attention. As a fast and cost-effective method for physicochemical fingerprinting, diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) has proven to be a surface-sensitive analytical tool for structural and functional studies. This review critically examines the most up-to-date applications of DRIFTS for characterizing key components of PCCs, including oxygen electrodes, protonic electrolytes, and hydrogen electrodes for different applications, with a focus on revealing hydration properties and catalytic reactions, and guiding rational material design. The challenges for advancing DRIFTS, including quantitative capabilities and operando applications for PCC investigations, are highlighted and strategies to tackle these challenges are discussed. Ultimately, this review underscores the critical role of DRIFTS in accelerating the development of high-performance and durable PCCs for next-generation energy solutions, offering methodologies and insights broadly applicable to a wide range of electrochemical energy conversion and storage technologies.

Diffuse Reflectance Infrared Fourier Transform Spe↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗
DOE OSTI2025

A review on advances in oxidative coupling of methane (OCM) for industrial use and prospects of CO 2 –H 2 O splitting integration

The discovery of shale gas reserves has encouraged the development of direct methods for methane conversion into valuable chemicals, offering an alternative to indirect approaches that involve an energy-intensive and intermittent syngas production step, leading to high CO 2 emissions. Amongst the direct methods, the oxidative coupling of methane (OCM) is a potential pathway to reduce CO 2 emissions and can produce commodity chemicals such as ethylene, a chemical regarded as central to the petrochemical industry. Even though OCM has been studied for over four decades, the technology still has not found commercial application. Amongst the challenges regarding industrial deployment of OCM, the most significant one is the requirement of a high ethylene yield of 30 % which is currently reported to be around 20 %. Moreover, the highly exothermic nature of the process and controlling the carbon selectivity over oxides of carbon (COx) is the heart of the problem. Numerous researchers have presented promising results in terms of catalysts, reactor designs and feeding strategies for OCM. However, due to lack of inclusiveness in the results, none of the combination of catalysts, reactors and system optimizations has been able to bring about its industrial viability. The current paper presents an extensive review of the noteworthy attempts to achieve industrial targets for OCM. Moreover, a comprehensive criteria is presented which highlights the desired end state for the industrial deployment of OCM technology. Furthermore, the criteria is based on literature survey and a comparison with industrially deployed ethylene production plants i.e., naphtha or ethane steam cracker plants. Finally, a novel integration technology is presented which includes a combination of OCM and CO 2 -H 2 O splitting in a chemical looping reactor design to enable efficient energy utilization and minimal heat losses to the environment.

CO2 Splitting↗
DOE OSTI2025

Evaluating post-fire watershed response to varying burn severity and precipitation regimes using fully-distributed and integrated hydrologic models

Wildfires can cause significant changes in vegetation and soil, which may lead to increased surface runoff and soil erosion, thereby affecting water cycling within ecosystems. This study uses the Advanced Terrestrial Simulator (ATS), an integrated and fully distributed hydrologic model at the watershed scale, to examine post-fire hydrologic responses in selected watersheds with varying burn severities in the Pacific Northwest region of the United States. The model integrates surface overland flow, subsurface flow, and canopy biophysical processes. We have developed a new fire module in ATS to account for changes in soil hydraulic properties caused by fire in the topsoil layer. Modeling results indicate that, in the year following a high-severity burn, watershed-averaged evapotranspiration decreases by 25%. Additionally, post-fire peak flows increase by 18-29% in watersheds burned with medium to high severity due to changes in soil properties. Conversely, a low-severity burn results in less than a 1% increase in post-fire peak flow. Furthermore, a high-severity fire causes a 38% reduction in the infiltration rate within the affected watershed during the first post-fire wet season. Hypothetical numerical experiments with varying precipitation regimes after a high-severity fire show that post-fire peak flows can increase by 1-29% due to fire-induced changes in soil hydraulic properties. This study highlights the importance of using fully distributed hydrologic models to quantify disturbance-feedback loops, which are essential for understanding the complexities brought about by spatial heterogeneity in post-fire landscapes.

ATS↗
DOE OSTI2025

Carbon monoxide chemistry of α-V70I Mo-nitrogenase: Evidence from EPR- and IR-monitored photolysis – or, what a difference a methyl makes

A critical step in the global nitrogen cycle is the conversion of dinitrogen into biologically accessible ammonia. In Nature this is accomplished by the nitrogenase (N 2 ase) family of enzymes. Carbon monoxide (CO) has long been known as an inhibitor of dinitrogen reduction by N 2 ase, but it can also be a substrate of the enzyme, when it is catalytically reduced to hydrocarbons. Understanding the CO interactions with N 2 ases are thus relevant to both dinitrogen fixation and Fischer-Tropsch-like chemistry. Here, in this work, the interaction of CO with the α-V70I variant of Azotobacter vinelandii MoFe N 2 ase was investigated using electron paramagnetic resonance (EPR) and infrared (IR) monitored photolysis of bound CO under cryogenic conditions. This was supplemented by further analysis of stopped-flow Fourier transform IR (SF-FT-IR) data under turnover conditions. The α-V70I variant adds a single methyl group close to the FeMo-cofactor active site, and the results show that this inhibits and slows, but does not substantially chemically change, the binding of CO to the FeMocofactor. The EPR spectra of both the hi-CO and lo-CO states closely resemble those from the wild-type enzyme. Similarly, the SF-FT-IR spectra of CO inhibited α-V70I and wild-type enzyme are strikingly similar, showing only small shifts in band energies which allow better interpretation of the published wild-type spectra. The extra carbon does, however, impact and inhibit the photochemical release and migration of CO at cryogenic temperatures, resulting in novel CO-bound species. These include a product species, termed Lo-1*, which may involve CO photochemically migrating on the FeMo-cofactor.

Carbon monoxide↗
DOE OSTI2025

A low-cost, fluorine-free electrolyte for improved sodium batteries

Sodium batteries are an attractive alternative to lithium-ion technologies due to sodium’s lower cost and natural abundance—over 1,000 times greater than lithium, comprising approximately 2.4% of the Earth’s crust. However, most current electrolytes rely on fluorine-containing components, which raise economic and environmental concerns. This study explores fluorine-free, borate-based electrolytes that offer improved cycling stability and significant cost and sustainability benefits. We demonstrate stable sodium metal stripping and plating on aluminum foil, enabling anode-free cell configurations with over 50% capacity retention after 700 cycles. Spectroscopic and electrochemical analyses reveal the effects of solvents and salt composition on solvation, ionic conductivity, and oxidative stability. In full-cell configurations, the fluorine-free electrolyte maintains more than 98% capacity retention after 400 cycles. Furthermore, these findings represent a critical step toward the development of cost-effective, environmentally friendly, and high-performance sodium battery systems suitable for future electrification.

anode-free sodium batteries↗
DOE OSTI2025

Recrystallization, cracking, and erosion of dispersoid-strengthened tungsten materials during exposure to divertor plasmas

In this study, we investigated the effects of combined intense particle and heat flux exposure on advanced tungsten plasma-facing materials within the DIII-D fusion facility. Our test matrix included two types of dispersoid-strengthened tungsten (containing either 100 nm diameter TiO 2 or Ni particles), along with high-purity polycrystalline tungsten as a reference. This experiment relied on a sample geometry angled at 15° relative to the divertor surface, thereby allowing the surfaces to intercept steady-state perpendicular heat fluxes (q ⟂ ) ranging from 10.1 to 19.6 MW/m 2 . During each shot, the samples were exposed to 42 Hz edge-localized modes (ELMs), allowing us to test the material response to transient heating. We correlated the exposure conditions with extensive post-test surface composition analysis and microscopy to determine how the plasma modified each surface. The angled specimens closest to the strike point received the highest combined heat and particle flux and melted midway through the experiment. EBSD analysis revealed they were completely recrystallized throughout, with an average grain size >100 µm. On the other hand, the specimens that received a lower steady state heat flux survived with more superficial surface damage. Whereas the high-purity polycrystalline tungsten exhibited a higher surface roughness, the dispersoid-strengthened material exhibited more extensive shallow inter-granular cracking. In addition, the surface was depleted of dispersoids following plasma exposure, possibly because of evaporation and/or sputtering. The results described here provide insights into the performance of these materials in a fusion environment which can guide further optimization for use in long-pulse devices.

Kolasinski, Robert D. [Sandia National Laboratorie↗
DOE OSTI2025

An overview of HR-EBSD techniques for mapping local stress and dislocations in crystalline materials at sub-micron resolution

High resolution electron backscatter diffraction (HR-EBSD) is a technique used to map elastic strain, crystallographic orientation and dislocation density in a scanning electron microscope. Here, this review covers the background and mathematics of this technique, contextualizing it within the broader landscape of EBSD techniques and other materials characterization methods. Several case studies are presented showing the application of HR-EBSD to the study of plasticity in metals, failure analysis in microelectronics and defect quantification in thin films. This is intended to be a comprehensive resource for researchers developing this technique as well as an introduction to those wishing to apply it.

Ruggles, Timothy J. [Sandia National Laboratories ↗
DOE OSTI2026

Transforming E-Waste Into Strategic Resources: Techno-Economic Analysis of Gallium and By-products Recovery from LEDs via Bioleaching

The growing demand for gallium in optoelectronics and renewable energy applications raises concerns about supply security and production sustainability. This study evaluates the techno-economic feasibility of recovering gallium and by-products (copper and nickel) from waste GaN-based LEDs via bioleaching. A process flowsheet encompassing transportation, robotic disassembly, ball milling, bioleaching, solvent extraction/electrowinning, and refining was modeled. Based on mass balance analysis, more than 53 tons of LED waste are required annually to yield 1 kg of gallium alongside substantial copper and nickel co-products. Preliminary techno-economic analysis (TEA) shows an average total cost (ATC) of 6.84 USD/kg metal when costs are allocated by mass-weighted economic value (market price) fraction, corresponding to 6.75 USD/kg for copper, 15.91 USD/kg for nickel, and 470.95 USD/kg for gallium. For gallium, direct operational costs account for more than 70% of the cost. Monte Carlo simulations further quantify cost uncertainties under market price fluctuations. This work represents the first TEA of gallium recovery from GaN-based LEDs and highlights potential pathways for future cost reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Optimizing aluminum oxide passivation layers—Laser-induced plasmas for layer formation, real-time diagnostics, and layer evaluation

Molten salts have beneficial thermophysical and electrolytic properties, but the aggressive nature of molten salts requires corrosion mitigation strategies, such as structural material surface treatment. In this study, a laser-induced breakdown spectroscopy (LIBS) system was used to simultaneously ablate and monitor de-excitation spectra to form and identify dense alumina passivation layers. Additionally, high-frequency (kHz) LIBS imaging allowed rapid elemental mapping and depth profiling for surface O/Al ratios via minimally destructive analysis. The paired t-test rejected the null hypothesis that air and Ar cover gases were equivalent (p = 0.00012), and the analysis of variance (ANOVA) showed that overlap between shots (p = 0.03112) and cover gas flow rate (p = 0.02729) were statistically significant. At 75% overlap, the O/Al ratio increased to 10% ± 2%, and 90% overlap rose further to 31% ± 2%. Regardless of overlap, tuning the gas flow from 0.5 to 2.5 L min −1 enhanced the O/Al ratio by 24% ± 4%. LIBS depth profiling showed a 3× increase in layer thickness from 75% to 90% overlap. Molecular band peaks of AlO, a precursor to Al 2 O 3 found during treatment, suggest that band intensity could be used for real-time Al 2 O 3 optimization. Ultimately, by adjusting laser spot overlap and cover gas flow rate, a 266 nm laser was shown to form Al 2 O 3 layers on an Al6061 sample, with cover gas flow rate primarily affecting the O/Al ratio and layer thickness correlating with laser spot overlap. This combination of molecular spectra collection, imaging, and depth profiling demonstrated the robust layer analysis capabilities of LIBS.

Corrosion↗