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NASA NTRS2020

Restricted and Uncontained: Health Care and Public Health Considerations in the Event of Loss of Containment During the Restricted Earth Return of Extraterrestrial Samples

The normal scope of an adequate public health response to released biological material is framed by working with biological vectors with known pathogenicity and virulence. Defining the scope of a response to the release of biological material with unknown pathogenicity and virulence enters into a novel and yet to be framed domain. A current case, the case of returning samples from a location other than Earth, extraterrestrial samples from a location such as Mars, which may harbor life as we know it, requires framing a public health response. An unintentional release of biological material with unknown pathogenicity and virulence may occur when biological containment mechanisms in the Earth-returning transport method are lost. This paper raises initial public health and healthcare response questions during a return of extraterrestrial samples to Earth, in the event of its release from biological containment mechanisms: How does the public health community prepare for a response when there is release of samples that may contain potential extraterrestrial organisms from a planetary body or hardy terrestrial organisms surviving a round trip? If a mishap occurs during the return of these samples, what considerations need to be made to confine, decontaminate, and collect material in regions around the mishap? How will the public health community work with relevant government organizations to prepare the general public? The unknowns of exposure, potential extraterrestrial pathogenicity, and decontamination approaches, underscore gaps in biopreparedness for this novel case from the federal to local levels.

Betsy Pugel↗

NASA NTRSDate not supplied

Estimation With Range Dependent Sensor Model

This paper focuses on the improvement of target tracking accuracy taking into account the sensor’s reading degradation as the relative range increases. The approach is based on the assumption that range measurement error depends on the actual range. Specifically, we model the measurement error as a proportional to the actual range term plus a zero-mean, Gaussian distributed, and uncorrelated process. We present two approaches to tracking a target with constant velocity model. The first approach uses Extended Kalman Filter (EKF) framework to estimate target's states and the unknown proportionality coefficient with linearization on the observation model on the predicted states at each time step. The second one uses a measurement conversion method to estimate the target's scaled by the unknown constant term states, which is shown to be unbiased. This conversion results in linear state and observation models, hence the standard Kalman filter can be applied. The actual target state is computed by application of back scaling with the estimate of unknown scale factor. We evaluate the approaches in desktop simulations.

Target tracking↗

NASA NTRSDate not supplied

VERNE: Revealing the Mysteries and Histories of Venus

Introduction: The three Venus missions that were recently selected for upcoming flight (VERITAS, DAVINCI+, and EnVision) will be incredibly valuable to our understanding of Venus’ history, geology, and atmosphere. However, even once completed, key gaps in our knowledge of Venus, and more generally the formation and active processes on rocky, Earth-like planets, will still persist. Remaining questions include 1) how global intrinsic magnetic fields might be maintained on rocky worlds, and how they could then go extinct, and 2) what role atmospheric sulfur chemistry plays in climates of Earth-like planets, which is an increasingly timely subject as Earth’s own atmospheric sulfur content is climbing due to human activity. These questions require in-situ observations from Venus’ cloud deck, at the altitudes at which the UV absorber exists. The Venus Environment Research and Novel Exploration (VERNE) mission will address these questions with an aerial platform that will drift around the equatorial region of the planet for 9 days. VERNE will collect data to determine the identity of the mysterious UV absorber, while also taking magnetic field measurements over the tesserae, the regions on Venus that are most likely to retain remanent crustal magnetization, in order to understand the potential role of a past intrinsically-generated global magnetic field on Venus. Mission Objectives: The two major science objectives that drive the VERNE mission are: 1) Determine the identity of the Venusian unknown ultraviolet absorber(s). First observed approximately a century ago [1], the composition of Venus’ ultraviolet (UV) absorber is one of the oldest mysteries in Venus atmospheric chemistry [2,3]. While several candidate UV absorbers (mostly sulfur species) have been proposed, no consensus has been reached on its composition and its specific interactions with the atmosphere. Determining the identity of the UV absorber will aid climate models by showing how and where incident solar energy is absorbed by the atmosphere and make progress towards understanding the chemical and energetic processes taking place above Venus’ upper cloud deck [4]. 2) Determine if Venus retains evidence of a past, internally-generated magnetic field. While Venus does not currently have an intrinsically-generated magnetic field, evidence for the existence of a past field on Venus and a timeline of its decay will fill in a more holistic picture of the evolution of Venus’ geological record and atmosphere. As the oldest geologic units on the surface, Venus’ tesserae may still have remanent crustal magnetization signatures within the rocky composition [5]. In any case, the signatures detected will provide insight into Venus’ past geological and core dynamo activity. Mission Summary: VERNE includes a 3-part flight system made up of 1) an entry system with a HEEET (Heatshield for Extreme Entry Environment Technology) aeroshell, 2) an orbiter for data relay, and 3) an in-situ balloon and gondola. After entry into Venus’ atmosphere, the balloon will be deployed within the upper cloud deck, at an altitude of 62 km, over a tesserae region. The in-situ data collection will last for the duration of 2 full circumnavigations of the planet, which will take ~9 days. Instrumentation Suite: The four instruments that comprise VERNE’s instrument payload will enable the identification of the unknown UV absorber and the detection of remanent crustal magnetization if it exists in the tesserae. The proposed instrument suite cycle during mission operations is shown in Fig. 1. The four instruments are described below: 1) Adams (ion neutral mass spectrometer) has a range of 18-257 AMU and will detect and distinguish the mixing ratios of O₂, H₂O, H₂SO₄, S, S₂, S₃, S₄, S₅, S₆, S₇, S₈, SO, SO₂, OSSO, SO₃, Cl₂, FeCl₃ and other trace sulfur and organic species. The spatial (longitudinal) and temporal (day/night) variations will be observed throughout 2 circumnavigations with a sample cadence of 12 minutes. 2) Shelley (nephelometer) will determine the size distribution of the aerosols (0.4 to 36 um) in the atmosphere. With a size resolution of <0.7 um, it can determine which mode of H2SO4 is present and characterize the large (>30 um) organic particles previously detected by the Venera and Galileo missions [6,7]. 3) Herbert (UV imager) will measure UV radiance at 283 nm (the wavelength of SO2 absorption) and 365 nm (the unknown part of the absorber). UV images will be taken concurrently with the INMS and nephelometer to correlate UV absorption with abundances of the UV absorber species. 4) Vonnegut (magnetometer) has a range of >600 nT and a precision and accuracy of 1 nT. If magnetized by a past field, the crust may have retained a magnetization of up to 3 A/m2 [5]. With a noise floor of 10 nT, the magnetometer will be able to detect RCM from an altitude of 62 km, even if the thickness of the magnetized crust is just 1 km (Fig. 2). Mission Concept Design: VERNE will be launched with a mass of 3300 kg in an intermediate-high performance class vehicle with a 4-m fairing. The 475-day mission includes 466 days for the cruise, coasting, and orbit initialization phases before entry, descent, and balloon deployment. During the 9-day science phase, the aerial platform will make 2 circumnavigations of the planet at an altitude of 62 km. The INMS and the nephelometer will acquire data for 2 hours during the daytime and nighttime during each circumnavigation, while the UV imager will be on for the duration of the daytime, and the magnetometer will be operational throughout the entirety of the science phase. Data will be stored and processed with the JPL-designed Sphinx command and data handling system. Data will be sent from the balloon to the orbiter using an S-band relay link, stored on the orbiter, and then forwarded to Earth where it will be received by the DSN. Conclusion: VERNE will fill key gaps in our understanding of the history and ongoing processes related to the geology and atmosphere of Venus and rocky worlds in general. Even with adequate flight system contingencies and expected costs below the $900M New Frontiers cost cap, VERNE is not without its risks and challenges. Further trade spaces to explore include 1) using solely battery power vs. including solar panels to increase the mission duration and 2) investigating the use of lightweight materials and 3D-printed structures to reduce the gondola mass, among others. Acknowledgments: We would like to thank the JPL Planetary Science Summer School, especially our mentors Troy Hudson, Karl Mitchell, and Leslie Lowes, as well as our Team-X study lead Al Nash and the members of Team-X. We’d additionally like to thank our review panel for asking insightful questions and providing valuable feedback. References: [1] Ross, F. E. (1928) Astrophysical J., 68, 57-92. [2] Rossow, W. B. et al. (1980) J. Geophysical Research, 85, 8107-8128. [3] Pinto, J. P. et al. (2021) Nature Communications, 12, 175. [4] Titov, D. V. et al. (2007) Cosmic Research, 21, 401. [5] O’Rourke, J. et al. (2019) Geophysical Research Lett., 46, 5768–5777. [6] Limaye, S. S. et al. (2018) Astrobiology, 18(9), 1181-1198. [7] Grinspoon, D. H. et al. (2013) Planetary and Space Sci., 41(7), 515-542. [8] Parker, R. L. (2003) J. Geophysical Research, 108, 5006. *The cost information contained in this document is of a budgetary and planning nature and is intended for informational purposes only. It does not constitute a commitment on the part of JPL and/or Caltech.

H Alpert↗

NASA NTRS2023

Forward for the new edition of: “What Every Engineer Should Know About Risk Engineering and Management"

In aerospace and engineering in general, the major metrics are capability, cost, and safety/risk. With the increasingly rapid emergence and utilization of new technologies and systems of increasing complexity, ensuring safety becomes more difficult. The technology and practice/applications of safety/risk technologies require updating in concert with capability and systems technology changes. Hence the new, updated edition of this risk engineering book. Major changes in technology and applications, now and going forward, increasingly involve artificial intelligence (AI)/autonomy and complex systems, which are rapidly developing and moving targets when it comes to risk/safety analysis. These introduce both new risks and safety issues, and they alter more usual ones. The current reality is that often the best AI is when it is “Black Boxed”, developed “independently”, without detailed human understanding of how and by what processes decisions and results are produced. There are ongoing efforts to make the AI processes more understandable by humans, with results to be determined. Also, trusted and true autonomy is free of human intervention, which would require machines to ideate to solve in real time issues that arise due to unknown unknowns and even known unknowns. Machines using generative adversarial network (GANs) and other approaches are beginning to ideate. Overall, the capabilities and practice of AI/autonomy utilization is a work in progress with the rate of progress substantial and the impacts upon system risk/safety major.

Dennis M. Bushnell↗

NASA NTRSDate not supplied

An Overview of Spacecraft Aerosols: Sources, Dynamics, and Knowledge Gaps

NASA has prioritized development of airborne particulate monitoring systems and mitigation strategies for future Lunar, Martian, and deep-space exploration missions. Airborne particles, also called aerosols, can cause human health problems ranging from mild respiratory discomfort to more severe disease. NASA’s suspended cabin particle and Lunar dust concentration requirements are motivated by protecting crew health and comfort during spaceflight, especially as mission durations lengthen and become increasingly ground-independent. Spacecraft cabin aerosols can originate from a variety of sources, including skin flakes, fibers from clothes and other materials, mechanical wear from equipment, and personal care products (e.g., antiperspirant). As NASA plans future expeditions to the Moon and Mars, Lunar and planetary dust compose an additional, under-characterized source for airborne particles. In addition to these known sources, recent studies on ISS cabin aerosols also revealed particles with unknown origins and poorly understood formation mechanisms, including some that may be formed by oxidative and/or heterogeneous chemical processes. The aerosol monitors payload, to be launched February 2025, will focus on understanding these unknown sources and processes, as well as demonstrating novel technologies for particulate monitoring to be infused in future space flight programs. In this presentation, we will present our knowledge of spacecraft cabin aerosols to date, summarizing aerosol measurement experiments from Shuttle through ISS. Cabin aerosol sources, known and unknown, will be summarized, and knowledge gaps for future long-duration space exploration missions will be discussed.

Claire Fortenberry↗

NASA NTRSDate not supplied

Implementing Artificial Thinking Autonomy with Model-Based System Engineering

Complex autonomous systems capable of successfully operating independently under ‘known unknowns’ and harsh conditions require paradigm innovation in modern development strategies. In the field of autonomy, developing a system-of-systems which can ostensibly think for itself in the face of ‘unknown unknowns’ is still a field of ongoing research. Maturing the systems architecting and modeling methodologies for developing henceforth named Thinking Autonomous Systems, which are verified with digital mission simulation, can potentially usher in the next generation of artificial intelligence for space exploration. The concept presented in this paper incorporates multiple Model-Based Systems Engineering and simulation methodologies combined as a new paradigm to design a novel, biomimetic thinking autonomy strategy. Anachronistic concepts from classical Kantian philosophy will be leveraged to inspire architectural designs that could be used for complex distributed systems in deep space. To accomplish this, digital transformation of a document-based implementation plan for Thinking Autonomous Systems, generated by experienced NASA software engineers, is implemented for NASA’s Platform for Autonomous Systems by creating descriptive and executable software models in SysML to prototype real-time operating capabilities. This conceptual implementation has been developed by incorporating model-based digital simulations to theorize how a cyberphysical thinking system would achieve specific strategies without crew reliance, while simultaneously being resilient to all operating conditions and remaining functional when devoid of ground communication. Additionally, ensuring that an autonomous system framework is an ethical Artificial Intelligence requires careful consideration of system behavior and accountability, human factors for teaming with a thinking autonomous system, and comparison to other modern approaches used for implementing true autonomy. This paper presents the first steps in formalizing the metacognition required for instantiating a truly Thinking Autonomous System; the approach described symphonizes autonomy characteristics from classical philosophical into a unified software architecture describing human thought. In the future, the foundational models described in this paper can be further leveraged to help advance research into thinking autonomy requirements for future deep space missions as well as for current near-term applications, i.e., living aboard crewed spacecraft like a NASA Gateway cislunar habitat.

Artificial Thought↗

DOE OSTI2021

Low-observability matrix completion

An example device includes at least one processor configured to receive electrical parameter values corresponding to at least one first location within a power network. The at least one processor is further configured to determine, using matrix completion and based on the at least one electrical parameter value, an estimated value of at least one unknown electrical parameter. The at least one unknown electrical parameter corresponds to a second location within the power network. The at least one processor is also configured to cause at least one device within the power network to modify operation based on the estimated value of the at least one unknown electrical parameter.

Bernstein, Andrey↗

DOE OSTI2024

Multi-Omics integration can be used to rescue metabolic information for some of the dark region of the Pseudomonas putida proteome

In every omics experiment, genes or their products are identified for which even state of the art tools are unable to assign a function. In the biotechnology chassis organism Pseudomonas putida, these proteins of unknown function make up 14% of the proteome. This missing information can bias analyses since these proteins can carry out functions which impact the engineering of organisms. As a consequence of predicting protein function across all organisms, function prediction tools generally fail to use all of the types of data available for any specific organism, including protein and transcript expression information. Additionally, the release of Alphafold predictions for all Uniprot proteins provides a novel opportunity for leveraging structural information. We constructed a bespoke machine learning model to predict the function of recalcitrant proteins of unknown function in Pseudomonas putida based on these sources of data, which annotated 1079 terms to 213 proteins. Among the predicted functions supplied by the model, we found evidence for a significant overrepresentation of nitrogen metabolism and macromolecule processing proteins. These findings were corroborated by manual analyses of selected proteins which identified, among others, a functionally unannotated operon that likely encodes a branch of the shikimate pathway.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Detecting Unclassified Electromagnetic Signals for Secure Wireless Communication Using Open Set Recognition

We developed multiple machine learning methods for the detection and classification of new wireless communication waveforms, which is critical for targeted attacks in wireless networks and electronic warfare. Our machine learning models are capable of dynamically detecting security threats in near real time through our advanced open set recognition (OSR) approach. This model has demonstrated significant improvements in the detection of unknown waveforms, thereby enhancing the security and reliability of mission critical communications. Our approach to detecting uncertain security threats is novel; we advanced OSR techniques by incorporating domain knowledge of wireless signals. Specifically, we combined time and frequency domain model features to enhance the model’s performance. Utilizing an OSR approach eliminates the need for training data to be distributed similarly to the deployment environment and removes the requirement for the training set to contains all possible threat classes. This is crucial because it is often infeasible to determine and characterize all potential security threats in advance. Our model were trained on simulated data, generated in partnership with the University at Albany, State of New York. The data set contained a diverse array of wireless signals, including those with additive white Gaussian noise and multipath signals, with and without line of sight. This comprehensive training set allowed us to optimize our models to detect unknown waveforms under various challenging scenarios, such as low signal-to-noise ratios. By training on various waveforms, varying signal-to-noise ratio, and different sample sizes under normal conditions, our models were fine tuned to perform effectively in challenging environments.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals

Deep Learning based classification techniques have shown excellent performance in static environments, where the training and testing samples are drawn from the same distribution. However, real world scenarios often present samples that do not belong to the known set of classes chosen during training. This is quite common for electromagnetic signals, where it is impractical to assume that all possible waveforms are known a-priori, specially in scenarios like warfare. To address this problem, we propose a deep learning based adversarial model where the generator learns to generate waveform features that can deceive the discriminator model as true samples. We introduce domain knowledge of wireless signals by decomposing the signal into a lower dimensional unique feature set, which is used for classifying known versus unknown signals. We further introduce multiple domain representations of the signal to extract features and combine them together to accurately classify new waveforms as an unknown class. Our results show that combined features from multiple domains outperform any single domain representation, especially at low SNR regimes with fewer number of samples to classify.

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Exploiting Multi-Domain Features for Detection of Unclassified Electromagnetic Signals (Presentation)

99 - GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

One Collision—Two Substituents: Gas–Phase Preparation of Xylenes under Single–Collision Conditions

The fundamental reaction pathways to the simplest dialkylsubstituted aromatics—xylenes (C 6 H 4 (CH 3 ) 2 )—in high-temperature combustion flames and in low-temperature extraterrestrial environments are still unknown, but critical to understand the chemistry and molecular mass growth processes in these extreme environments. Exploiting crossed molecular beam experiments augmented by state-of-the-art electronic structure and statistical calculations, this study uncovers a previously elusive, facile gas-phase synthesis of xylenes through an isomer-selective reaction of 1-propynyl (methylethynyl, CH 3 CC) with 2-methyl-1,3-butadiene (isoprene, C 5 H 8 ). The reaction dynamics are driven by a barrierless addition of the radical to the diene moiety of 2-methyl-1,3-butadiene followed by extensive isomerization (hydrogen shifts, cyclization) prior to unimolecular decomposition accompanied by aromatization via atomic hydrogen loss. This overall exoergic reaction affords a preparation of xylenes not only in high-temperature environments such as in combustion flames and around circumstellar envelopes of carbon-rich Asymptotic Giant Branch (AGB) stars, but also in low-temperature cold molecular clouds (10 K) and in hydrocarbon-rich atmospheres of planets and their moons such as Triton and Titan. Furthermore, our study established a hitherto unknown gas-phase route to xylenes and potentially more complex, disubstituted benzenes via a single collision event highlighting the significance of an alkyl-substituted ethynyl-mediated preparation of aromatic molecules in our Universe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

One Collision—Two Substituents: Gas–Phase Preparation of Xylenes under Single–Collision Conditions

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Structure‐Aware Representation Learning for Effective Performance Prediction

ABSTRACT Application performance is a function of several unknowns stemming from the interactions between the application, runtime, OS, and underlying hardware, making it challenging to model performance using deep learning techniques, especially without a large labeled dataset. Collecting such labeled longitudinal datasets can take weeks. Intuitively, developers could save analysis time during code development by taking a comparative approach between multiple applications. However, the unknown dynamic interactions between applications and execution environments make it difficult for deep learning‐based models to predict the performance of new applications. In this paper, we address these problems by presenting a labeled dataset for the community and taking a comparative analysis approach to explore the source code differences between different correct implementations of the same problem. This paper assesses the feasibility of using purely static information, for example, Abstract Syntax Tree (AST), of applications to predict performance change based on code structure. We evaluate several deep learning‐based representation learning techniques for source code and propose an architecture for the tree‐based Long Short‐Term Memory (LSTM) models to discover latent representations for a source code's hierarchical structure. We demonstrate that our proposed architecture enables feed‐forward predictive models to predict change in performance using source code with up to 84% accuracy.

Ramadan, Tarek [Department of Computer Science Tex↗

DOE OSTI2020

Response of Pseudomonas putida to Complex, Aromatic-Rich Fractions from Biomass

There is strong interest in the valorization of lignin to produce valuable products; however, its structural complexity has been a conversion bottleneck. Chemical pretreatment liberates lignin-derived soluble fractions that may be upgraded by bioconversion. Here, cholinium ionic liquid pretreatment of sorghum produced soluble, aromatic-rich fractions that were converted by Pseudomonas putida (P. putida), a promising host for aromatic bioconversion. Growth studies and mutational analysis demonstrated that P. putida growth on these fractions was dependent on aromatic monomers but unknown factors also contributed. Proteomic and metabolomic analyses indicated that these unknown factors were amino acids and residual ionic liquid; the oligomeric aromatic fraction derived from lignin was not converted. A cholinium catabolic pathway was identified, and the deletion of the pathway stopped the ability of P. putida to grow on cholinium ionic liquid. This work demonstrates that aromatic-rich fractions obtained through pretreatment contain multiple substrates; conversion strategies should account for this complexity.

09 BIOMASS FUELS↗

DOE OSTI2023

A dynamic variational multiscale method on unstructured meshes for stationary transport problems

Here, this paper presents a variational multiscale (VMS) based finite element method where the stabilization parameter is computed dynamically. The current dynamic procedure takes in a general structure/form of the stabilization parameter with unknown coefficients and computes them dynamically in a local fashion resulting in a dynamic VMS-based finite element method. Thus, a static stabilization parameter with pre-defined coefficients is not needed. A variational Germano identity (VGI) based local procedure suitable for unstructured meshes is developed to perform the dynamic computation in a local fashion. The local VGI based procedure is applied for each interior vertex in the mesh and unknown coefficients are first determined locally at each vertex, and subsequently, for each element a maximum value is taken over the vertices of the element. To make the current procedure practical, a coarser secondary solution is constructed from the primary coarse-scale solution, which is done locally over a patch of elements around each interior vertex. Further, averaging steps are employed to make the local dynamic procedure robust. Currently, the new dynamic VMS formulation is applied to steady problems governed by the advection-diffusion and incompressible Navier-Stokes equations in both 1D and 2D to demonstrate its efficacy and effectiveness.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Analysis of degradation products in thermally treated TATB

Delineating the chemical composition of TATB (1,3,5-triamino-2,4,6-trinitrobenzene) residues produced from the exposure to abnormal thermal environments should lead to a better understanding of the decomposition paths. Identifying and quantifying each compound in thermally produced residues, monitors which compounds are degrading or forming along the decomposition route, as well as providing input for the kinetic models of those pathways. Here, in this paper, we report the methodology of isolating, identifying, and where possible, quantifying soluble compounds present in solid residues of thermally treated TATB (330 °C for tens of minutes). Samples were extracted with DMSO, separated using chromatography, and quantified using their absorption at 354 nm. Identification of unknown compounds was accomplished using high resolution mass spectrometry. TATB, F1 (diamino-dinitro-benzofurazan), HO-TATB (2,4,6-triamino-1-hydroxyl-3,5-dinitrobenzene), and T4A (1-chloro-3,5-dinitro-2,4,6-triaminobenzene) were trace compounds detected in the unreacted TATB. Ten more compounds that formed in the residues were structurally identified including F2 (amino-nitro-difurazan). Several more compounds were observed but not completely identified. We propose possible structures for the unknowns. Of the compounds formed, F1 was the most abundant compound reaching 4.5 % by weight of the degraded solid sample. Other degradation compounds were estimated to sum to trace levels, well below 1 %. Most compounds were new, having not been detected and identified in previous studies of production grade and thermally aged TATB. Many compounds only reached detectable concentrations after several min of thermal exposure.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Accelerating Discovery of Solid‐State Thin‐Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment

Thin‐film solid‐state metal dealloying (thin‐film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high‐throughput characterization of thermal treatment parameters is demonstrated using a laser‐based thermal treatment to create temperature gradients on single thin‐film samples of Nb‐Al/Sc and Nb‐Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X‐ray multimodal and high‐throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified. While the oxidation may influence nanostructure formation during thin‐film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. Further, this approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin‐film SSMD systems with targeted nanostructures.

36 MATERIALS SCIENCE↗