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DOE OSTI2024

Robust surfactant-assisted one-pot sample preparation for label-free single-cell and nanoscale proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell to cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in recent 5 years there are a surge of small sample preparation methods developed for robust effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: specific device- and standard PCR tube- or multi-well plate-based methods. Herein we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of a MS-compatible surfactant, DDM (n-Dodecyl-ß-D-maltoside), and standard low-bind PCR tube or multi-well plate for ‘all-in-one’ one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in the biological and biomedical research.

Single-cell proteomics, nanoscale proteomics, SOP-↗

DOE OSTI2022

Transitioning from File-Based HPC Workflows to Streaming Data Pipelines with openPMD and ADIOS2

This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexibly extended for in situ analysis. The openPMD-api is a library for the description of scientific data according to the Open Standard for Particle-Mesh Data (openPMD). Its approach towards recent challenges posed by hardware heterogeneity lies in the decoupling of data description in domain sciences, such as plasma physics simulations, from concrete implementations in hardware and IO. The streaming backend is provided by the ADIOS2 framework, developed at Oak Ridge National Laboratory. This paper surveys two openPMD-based loosely-coupled setups to demonstrate flexible applicability and to evaluate performance. In loose coupling, as opposed to tight coupling, two (or more) applications are executed separately, e.g. in individual MPI contexts, yet cooperate by exchanging data. This way, a streaming-based workflow allows for standalone codes instead of tightly-coupled plugins, using a unified streaming-aware API and leveraging high-speed communication infrastructure available in modern compute clusters for massive data exchange. We determine new challenges in resource allocation and in the need of strategies for a flexible data distribution, demonstrating their influence on efficiency and scaling on the Summit compute system. The presented setups show the potential for a more flexible use of compute resources brought by streaming IO as well as the ability to increase throughput by avoiding filesystem bottlenecks.

Poeschel, Franz↗

DOE OSTI2023

Optimizing Data Movement for GPU-Based In-Situ Workflow Using GPUDirect RDMA

The extreme-scale computing landscape is increasingly dominated by GPU-accelerated systems. At the same time, in-situ workflows that employ memory-to-memory inter-application data exchanges have emerged as an effective approach for leveraging these extreme-scale systems. In the case of GPUs, GPUDirect RDMA enables third-party devices, such as network interface cards, to access GPU memory directly and has been adopted for intra-application communications across GPUs. In this paper, we present an interoperable framework for GPU-based in-situ workflows that optimizes data movement using GPUDirect RDMA. Specifically, we analyze the characteristics of the possible data movement pathways between GPUs from an in-situ workflow perspective, and design a strategy that maximizes throughput. Furthermore, we implement this approach as an extension of the DataSpaces data staging service, and experimentally evaluate its performance and scalability on a current leadership GPU cluster. The performance results show that the proposed design reduces data-movement time by up to 53% and 40% for the sender and receiver, respectively, and maintains excellent scalability for up to 256 GPUs.

Zhang, Bo↗

DOE OSTI2023

Asynchronous Execution of Heterogeneous Tasks in ML-Driven HPC Workflows

Heterogeneous scientific workflows consist of numerous types of tasks that require execution on heterogeneous resources. Asynchronous execution of those tasks is crucial to improve resource utilization, task throughput and reduce workflows' makespan. Therefore, middleware capable of scheduling and executing different task types across heterogeneous resources must enable asynchronous execution of tasks. In this paper, we investigate the requirements and properties of the asynchronous task execution of machine learning (ML)-driven high-performance computing (HPC) workflows. We model the degree of asynchronicity permitted for arbitrary workflows and propose key metrics that can be used to determine qualitative benefits when employing asynchronous execution. Our experiments represent relevant scientific drivers, we perform them at scale on Summit, and we show that the performance enhancements due to asynchronous execution are consistent with our model.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Gamma-Ray and Cosmic Ray Muon Modalities for Cargo Inspection

Screening and inspection of cargo containers are two essential methods to nondestructively examine the contents of shipment. These methods enable the detection of illicit transportation of unauthorized materials such as nuclear and radioactive materials, explosives, drugs, and so on, typically at borders or secure facilities. Although high-energy X-ray transmission is a standard system and is widely used for cargo inspection, the inherent challenges of high false-positive rates and high attenuation factors necessitate the development of complementary techniques that can increase the detection efficiency and accuracy in large and dense materials. Gamma-rays, which possess higher penetration characteristics because of their high energy, offer an alternative nonintrusive modality for cargo scanning. They represent a promising inspection method when compared to X-rays for three reasons: (1) improved ability to detect nuclear and radioactive materials, (2) higher inspection throughput rates, and (3) lower false-positive rates. Currently, there are two main gamma-ray inspection techniques, active and passive interrogation. Active interrogation can be further grouped into (1) gamma-ray transmission imaging and (2) neutron-induced gamma-ray emission detection. Gamma-ray transmission imaging utilizes differences in material densities for mapping the shipment contents and detecting anomalies. It is analogous to the X-ray transmission method; however, the high-energy photons make it more difficult to shield against, which enables more efficient performance in large and dense material inspection. Neutron-induced gamma-ray emission inspection is designed for the detection of nuclear and radioactive material because those materials emit characteristic gamma-rays when they are activated by neutron absorption. On the other hand, passive interrogation techniques rely on high-efficiency detectors to detect radiation emitted from hidden special nuclear or other radioactive materials. Similar to passive interrogation, cosmic ray muon monitoring and imaging are relatively new techniques that do not require external radioactive sources. These techniques have received attention as a potential next-generation radiographic probe to identify illicit transportation of nuclear and radioactive materials in cargo containers. Cosmic ray muons have unique features, (1) much higher energies than X-rays or gamma-rays (on the order of 10−1—104 GeV), (2) enhanced penetration capability, and (3) natural occurrence, thereby eliminating the need for induced radiation sources. These features enable cosmic ray muons to be utilized for detection of special nuclear materials in high-background-noise environments. By analyzing incoming and outgoing muon trajectories, scattering angles, and energies, it has been shown that it would be possible to locate hidden and well-shielded materials in cargo containers via three-dimensional muon tomography images or signal analysis. Gamma-rays, cosmic ray muons, and other nonintrusive cargo inspection modalities are complementary to each other, allowing them to address various cargo inspection conditions (i.e., scanning time, cost, radiation exposure level, and types of target materials). This chapter presents a detailed review of the theoretical fundamentals and technical principles behind the current gamma-ray and cosmic ray muon modalities for cargo inspection. Additionally, critical assessments and suggestions for the future directions to advance the use of gamma and muon modalities are discussed.

Bae, Junghyun↗

DOE OSTI2021

Quantitation of amiodarone and N-desethylamiodarone in single HepG2 cells by single-cell printing-liquid vortex capture-mass spectrometry

Quantitative measure of a drug and its associated metabolite(s) with single-cell resolution is often limited by sampling throughput or other compromises that limit broad use. Here, we demonstrate the use of single-cell printing-liquid vortex capture-mass spectrometry (SCP-LVC-MS) to quantitatively measure the intracellular concentrations of amiodarone (AMIO) and its metabolite, N-desethylamiodarone (NDEA), from thousands of single cells across several AMIO incubation concentrations ranging from 0 to 10 μM. Concentrations obtained by SCP-LVC-MS were validated through comparison with average assays and traditional measurement of cells in bulk. Average of SCP-LVC-MS measurements and aggregate vial collection assay the concentrations differed by < 5%. Both AMIO and NDEA had clear log-normal distributions with similar standard deviation of concentrations in the cell population. The mean of both AMIO and NDEA intracellular concentrations were positively correlated with AMIO incubation concentration, increasing from 0.026 to 0.520 and 0.0055 to 0.048 mM for AMIO and NDEA, respectively. The standard deviation of AMIO and NDEA log-normal distribution fits were relatively similar in value across incubation concentrations, 0.15–0.19 log 10 (mM), and exhibited a linear trend with respect to each other. The single cell-resolved conversion ratio of AMIO to NDEA increased with decreasing incubation concentration, 7 ± 2%, 18 ± 3%, and 20 ± 7% for 10.0, 1.0, and 0.1 μM AMIO incubation concentrations, respectively. Finally, association with simultaneously measured lipids had several ions with statistically significant difference in intensity but no clear correlations with AMIO intracellular content was observed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Characterization of the biofilm landscape of Bacillus subtilis by spatial microproteomics

Bulk proteomics has been demonstrated to differentiate subpopulations within bacterial colonies, yet advanced analyses by mass spectrometry imaging (MSI) hold even greater promise for the future. This technology can enable high-throughput spatial phenotyping that can reshape biological discovery by providing visualization of components of various biomolecular mechanisms. With high mass resolving power and high spatial resolution analyses being routine, we can confidently enable intact protein imaging directly from samples with minimal preparation. Pairing those analyses with bulk experimental libraries can provide high confidence in annotations of post-translational modifications (PTMs) and truncations. Revealing PTM localization within the samples unlocks a direct window into unknown biology at the microscale. However, top-down proteomics (TDP) is not commonplace for microbial species, largely due to challenges in identifying detected peptides and proteins; considering the theoretical proteome of even the well-studied model bacterium Bacillus subtilis was only partially mapped recently. With little still known about the form and function of many of these proteins – let alone proteoforms, where PTMs and truncations of the same protein may possess unique physiological roles – there is a wealth of work to be done. Here we jointly apply TDP and MSI to describe the microscale spatial proteomic landscape within B. subtilis and further demonstrate the feasibility of detecting differentiated subpopulations through proteoforms across the biofilm landscape.

bacterial biofilms↗

DOE OSTI2020

Peatland Microbial Community Composition Is Driven by a Natural Climate Gradient

Peatlands are important players in climate change–biosphere feedbacks via long-term net carbon (C) accumulation in soil organic matter and as potential net C sources including the potent greenhouse gas methane (CH 4 ). Interactions of climate, site-hydrology, plant community, and groundwater chemical factors influence peatland development and functioning, including C dioxide (CO 2 ) and CH 4 fluxes, but the role of microbial community composition is not well understood. To assess microbial functional and taxonomic dissimilarities, we used high throughput sequencing of the small subunit ribosomal DNA (SSU rDNA) to determine bacterial and archaeal community composition in soils from twenty North American peatlands. Targeted DNA metabarcoding showed that although Proteobacteria, Acidobacteria, and Actinobacteria were the dominant phyla on average, intermediate and rich fens hosted greater diversity and taxonomic richness, as well as an array of candidate phyla when compared with acidic and nutrient-poor poor fens and bogs. Moreover, pH was revealed to be the strongest predictor of microbial community structure across sites. Predictive metagenome content (PICRUSt) showed increases in specific genes, such as purine/pyrimidine and amino-acid metabolism in mid-latitude peatlands from 38 to 45° N, suggesting a shift toward utilization of microbial biomass over utilization of initial plant biomass in these microbial communities. Overall, there appears to be noticeable differences in community structure between peatland classes, as well as differences in microbial metabolic activity between latitudes. Finally, these findings are in line with a predicted increase in the decomposition and accelerated C turnover, and suggest that peatlands north of 37° latitude may be particularly vulnerable to climate change.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Merging Fungal and Bacterial Community Profiles via an Internal Control

Integrated measurements of fungi and bacteria are critical to understand how interactions between these taxa drive key processes in ecosystems ranging from soils to animal guts. High-throughput amplicon sequencing is commonly used to census microbiomes, but the genetic markers targeted for fungi and bacteria (typically ribosomal regions) are domain-specific so profiling must be performed separately, obscuring relationships between these groups. To solve this problem, we developed a spike-in method with an internal control (IC) construct containing primer sites commonly used for bacterial and fungal taxonomic profiling. The internal control offers several advantages: estimation of absolute abundances, estimation of fungal to bacterial ratios (F:B), integration of bacterial and fungal profiles for holistic community analysis, and lower costs compared to other quantitation methods. To validate the IC as a scaling method, we compared IC-derived measures of F:B to measures from quantitative PCR (qPCR) using a commercial mock community (the ZymoBiomic Microbial Community DNA Standard II, containing two fungi and eight bacteria) and complex environmental samples. For both the mock community and the environmental samples, the IC produced F:B values that were statistically consistent with qPCR. Merging the environmental fungal and bacterial profiles based on the IC-derived F:B values revealed new relationships among samples in terms of community similarity. As a result, this IC method is the first spike-in method to employ a single construct for cross-domain amplicon sequencing, offering more reliable measurements.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

New approaches to secondary metabolite discovery from anaerobic gut microbes

The animal gut microbiome is a complex system of diverse, predominantly anaerobic microbiota with secondary metabolite potential. These metabolites likely play roles in shaping microbial community membership and influencing animal host health. As such, novel secondary metabolites from gut microbes hold significant biotechnological and therapeutic interest. Despite their potential, gut microbes are largely untapped for secondary metabolites, with gut fungi and obligate anaerobes being particularly under-explored. To advance understanding of these metabolites, culture-based and (meta)genome-based approaches are essential. Culture-based approaches enable isolation, cultivation, and direct study of gut microbes, and (meta)genome-based approaches utilize in silico tools to mine biosynthetic gene clusters (BGCs) from microbes that have not yet been successfully cultured. In this mini-review, we highlight recent innovations in this area, including anaerobic biofoundries like ExFAB, the NSF BioFoundry for Extreme & Exceptional Fungi, Archaea, and Bacteria. These facilities enable high-throughput workflows to study oxygen-sensitive microbes and biosynthetic machinery. Such recent advances promise to improve our understanding of the gut microbiome and its secondary metabolism.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Advanced urothelial cancer: a radiology update

The recent genomic characterization of urothelial carcinoma by the Cancer Genome Atlas Project, made possible by the introduction of high throughput, reduced cost, and sequence analysis, has shed new insights on the biology of advanced disease. In addition, studies on imaging of advanced urothelial carcinoma have widened the knowledge on disease presentation and on pattern of metastatic spread and their correlation with the underlying biology of urothelial carcinoma. The wide range of treatments for advanced urothelial cancer, including combined chemotherapy regimens and immune checkpoint inhibitors, each result in treatment class-specific patterns of response and adverse events. Results of studies point to the need for a reliable biomarker, perhaps with imaging, that predicts prognosis and treatment response to systemic treatment, and can be used to select the most effective treatment while minimizing toxicity. This review of advanced urothelial cancer introduces the latest advances in genetic profiling, the current role of imaging, the radiographic appearance of treatment response and their toxicities, and details potential future areas of imaging research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2020

Reducing host DNA contamination in 16S rRNA gene surveys of anthozoan microbiomes using PNA clamps

Efforts to study the microbial communities associated with corals can be limited by inefficiencies in the sequencing process due to high levels of host amplification by universal bacterial 16S rRNA gene primers. Here, we develop an inexpensive peptide nucleic acid (PNA) clamp that binds to a target sequence of host DNA during PCR and blocks amplification. We then test the ability of this PNA clamp to mitigate host contamination and increase overall microbial sequence coverage on samples from three coral species: the gorgonians Eunicea flexuosa and Gorgonia ventalina, and the scleractinian Porites panamensis. The 20-bp PNA clamp was designed using DNA from E. flexuosa. Adding the PNA clamp during PCR increased the percentage of microbial reads in E. flexuosa samples more than 11-fold. Microbial community diversity was similar without- and with-PNA clamps, as were the relative frequencies of the ten most abundant ASVs (amplicon sequence variants), indicating that the clamps successfully blocked host DNA amplification while simultaneously increasing microbial DNA amplification proportionally across the most abundant taxa. The reduction of E. flexuosa DNA correlated with an increase in the abundance of rarer taxa. The clamp also increased the average percentage of microbial reads in another gorgonian, G. ventalina, by 8.6-fold without altering the microbial community beta diversity, and in a distantly related scleractinian coral, P. panamensis, by nearly double. The reduction of host contamination correlated with the number of nucleotide mismatches between the host amplicon and the PNA clamp. The PNA clamp costs as little as $0.48 per sample, making it an efficient and cost-effective solution to increase microbial sequence coverage for high-throughput sequencing of coral microbial communities.

16S rRNA gene sequencing↗

DOE OSTI2022

A Square Pulse Thermoreflectance Technique for the Measurement of Thermal Properties

We report on a laser-based square pulse thermoreflectance (SPTR) technique for the measurement of thermal properties for a wide range of materials. SPTR adopts the pump-probe thermoreflectance principle to monitor the evolution of local temperature after square pulse excitation. The technique features a compact setup, high spatial resolution, and fast data collection. By comparing the acquired SPTR signals with a continuum heat transfer model, material thermal properties can be obtained. Taking advantage of various spot sizes and modulation frequencies, SPTR can measure both the thermal diffusivity and thermal conductivity of poorly to moderately conductive materials and the thermal conductivity of conductive materials with satisfactory accuracy, with potential to be applied to more conductive materials. The technique was validated on three materials: fused silica, single crystal CaF2 and single crystal nickel (with conductivities ranging from 1 W·m -1 ·K -1 to 100 W·m -1 ·K -1 ) with typical measurement errors of 5 % to 20 %. The leading sources of error have been identified by Monte Carlo simulations, and the primary limitations of SPTR are discussed. The compact, fiberized platform we describe here will allow instruments based on this methodology to be deployed in complex, multi-analytical environments for the type of high-throughput correlative analyses that are key to materials design and discovery.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Chemomechanical weakening of muscovite quantified with in situ liquid nanoindentation

Abstract Chemomechanical weakening of layered phyllosilicate muscovite mica was studied as a function of chemical environment via in situ liquid-nanoindentation under four conditions (dry, deionized water, and two NaCl solutions of different pH). While traditional indentation analyses of layered materials with extreme mechanical anisotropy have been limited due to displacement bursts (pop-ins), here the bursts were used as proxies for delamination, fracture, and spalling events. Since displacement bursts during an indent represent a stochastic process, 120 indents were conducted for each condition to generate statistically significant amounts of data. In total, over 9000 bursts were assessed using a load–displacement threshold criterion, classifying this as a high-throughput nanoscale fracture technique. For each burst, initiation load, initiation displacement, plastic zone volume at initiation, and energy dissipation were analyzed. A power-law relationship between the burst load and burst energy was noted which separated the bursts into two continuous distributions: (1) bursts due only to the mechanics of the indent and (2) bursts due to both the mechanics of the indent and the environment. By using a cumulative probability distribution, it was found that the NaCl solutions decreased the minimum plastic zone volume necessary to initiate a displacement burst by an order of magnitude relative to the dry condition. Finally, the underlying mechanisms explaining the trends in initiation volume as a function of environment were discussed, with a focus on the chemomechanical degradation processes via chemical attack and cation exchange.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Hyperspectral X-ray Imaging with TES Detectors for Nanoscale Chemical Speciation Mapping

We are developing an imaging capability (“Hyperspectral X-ray Imaging”) for mapping chemical information (molecular formula, phase, oxidation state, hydration) that is based on ultra-high-resolution X-ray emission spectroscopy with large transition-edge sensor microcalorimeter arrays in the scanning electron microscope. By combining microcalorimeter arrays with hundreds of pixels, high-bandwidth microwave frequency-division multiplexing, and fast digital electronics for near real-time data processing, our goal is to enable measurements using laboratory-scale instrumentation rather than synchrotron beamlines. Our application focus here is on mapping the chemical form of uranium compounds on the nanoscale. Furthermore, we will present our approach to developing the Hyperspectral X-ray Imaging capability, progress toward a 128-pixel microwave multiplexed X-ray fluorescence instrument at LANL, and the path to high-throughput nanoscale chemical mapping.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Recent Developments of Commercially Fabricated Horn Antenna-Coupled Transition-Edge Sensor Bolometer Detectors for Next-Generation Cosmic Microwave Background Polarimetry Experiments

Here we report on the successful fabrication of orthomode transducer-coupled transition-edge sensor (TES) bolometer arrays for cosmic microwave background (CMB) polarimetry experiments with the superconductor electronics fabrication facility at SEEQC Inc. Commercial microfabrication foundry could provide increase in detector fabrication throughput for a next-generation CMB experiment, CMB-S4, that would deploy approximately one order of magnitude more detectors than current CMB experiments. We also developed TES bolometers with two TESs in series that have two different superconducting temperatures (Tc) using a superconducting proximity effect between niobium (Nb) and aluminum-manganese (AlMn) alloy. We will discuss the motivation, design considerations, fabrication processes, test results, and how industrial detector fabrication could be a path to fabricate hundreds of detector wafers for future CMB polarimetry experiments and other experiments that require TESs and superconducting RF circuits.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗