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DOE OSTI2020

Computing water flow through complex landscapes – Part 2: Finding hierarchies in depressions and morphological segmentations

Depressions – inwardly draining regions of digital elevation models – present difficulties for terrain analysis and hydrological modeling. Analogous “depressions” also arise in image processing and morphological segmentation, where they may represent noise, features of interest, or both. Here we provide a new data structure – the depression hierarchy – that captures the full topologic and topographic complexity of depressions in a region. We treat depressions as networks in a way that is analogous to surface-water flow paths, in which individual sub-depressions merge together to form meta-depressions in a process that continues until they begin to drain externally. This hierarchy can be used to selectively fill or breach depressions or to accelerate dynamic models of hydrological flow. Complete, well-commented, open-source code and correctness tests are available on GitHub and Zenodo.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Unveiling mesoscopic structures in distorted lamellar phases through deep learning-based small angle neutron scattering analysis

Hypothesis: The formation of distorted lamellar phases, distinguished by their arrangement of crumpled, stacked layers, is frequently accompanied by the disruption of long-range order, leading to the formation of interconnected network structures commonly observed in the sponge phase. Nevertheless, traditional scattering functions grounded in deterministic modeling fall short of fully representing these intricate structural characteristics. Our hypothesis posits that a deep learning method, in conjunction with the generalized leveled wave approach used for describing structural features of distorted lamellar phases, can quantitatively unveil the inherent spatial correlations within these phases. Experiments and Simulations: This report outlines a novel strategy that integrates convolutional neural networks and variational autoencoders, supported by stochastically generated density fluctuations, into a regression analysis framework for extracting structural features of distorted lamellar phases from small angle neutron scattering data. To evaluate the efficacy of our proposed approach, we conducted computational accuracy assessments and applied it to the analysis of experimentally measured small angle neutron scattering spectra of AOT surfactant solutions, a frequently studied lamellar system. Findings: The findings unambiguously demonstrate that deep learning provides a dependable and quantitative approach for investigating the morphology of wide variations of distorted lamellar phases. It is adaptable for deciphering structures from the lamellar to sponge phase including intermediate structures exhibiting fused topological features. In conclusion, this research highlights the effectiveness of deep learning methods in tackling complex issues in the field of soft matter structural analysis and beyond.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Exploring Responses of Contact Kelvin Probe Force Microscopy in Triple-Cation Double-Halide Perovskites

Hybrid organic–inorganic perovskite (HOIP) solar cells have undergone unprecedentedly rapid improvement in power conversion efficiencies during the last decade, but controversy remains regarding the role of local phenomena and heterogeneity in grain by grain. These challenges in turn necessitate understanding the fundamental mechanisms operating at individual grains and exploring how these mechanisms give rise to the collective behaviors of the films. Although numerous efforts have targeted local phenomena in these materials using classical scanning probe microscopy modalities, the measurements remain difficult to interpret due to intrinsic coupling between possible ferroelectric, ion migration, bulk and surface electrochemical phenomena, and the effects of light on these behaviors. Here, we explore the electromechanical responses in (Cs 0.05 FA 0.85 MA 0.15 )Pb(I 0.85 Br 0.15 ) 3 perovskite thin film in the dark and under illumination using band excitation-piezoresponse force microscopy (BE-PFM). To identify and separate ferroic, electrostatic, and electrochemical responses, we utilize contact-Kelvin probe force microscopy (cKPFM). The cKPFM responses show weak variability with morphological features in the dark, while such variability significantly increases under illumination. Deconvolution of the cKPFM responses via principal component analysis (PCA) shows heterogeneity in grain to grain possibly due to variation in migration and segregation of halide ions in mixed perovskites at specific grains. Further, we propose an electrostatic mechanism based on the physics of semiconductors, which can directly link the observed cKPFM behaviors with the ionic and electronic density of states (DOS) and their time dynamics. Here, these findings suggest that cKPFM is a powerful probe of local ionic behaviors on the nanoscale, both in HOIPs and other materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A robust approach to 3D neuron shape representation for quantification and classification

We consider the problem of finding an accurate representation of neuron shapes, extracting sub-cellular features, and classifying neurons based on neuron shapes. In neuroscience research, the skeleton representation is often used as a compact and abstract representation of neuron shapes. However, existing methods are limited to getting and analyzing “curve” skeletons which can only be applied for tubular shapes. This paper presents a 3D neuron morphology analysis method for more general and complex neuron shapes. First, we introduce the concept of skeleton mesh to represent general neuron shapes and propose a novel method for computing mesh representations from 3D surface point clouds. A skeleton graph is then obtained from skeleton mesh and is used to extract sub-cellular features. Finally, an unsupervised learning method is used to embed the skeleton graph for neuron classification. Extensive experiment results are provided and demonstrate the robustness of our method to analyze neuron morphology.

3D neuron morphology↗

DOE OSTI2022

Critical length scales for chemical heterogeneity at Cu/Nb 3D interfaces by atom probe tomography

Cu/Nb nanocomposites containing sharp, two-dimensional (2D) interfaces have outstanding strength but limited deformability. In contrast, Cu/Nb with three dimensional (3D) biphase interfaces exhibiting crystallographic, topological, and chemical variations in all spatial dimensions overcomes this limitation by simultaneously enhancing material strength and deformability. Here, while structural characterization of 3D interfaces has been performed to understand their mechanical behavior, three dimensional chemical characterization of such interfaces is lacking. In this work we quantify the local chemistry of 3D interfaces in Cu/Nb nanocomposites using atom probe tomography (APT). Our analysis demonstrates chemical heterogeneities along all spatial dimensions in 3D interfaces, establishes the length scale of such features, and quantifies the morphology of 3D interfaces. 3D interface heterogeneities form by surface diffusion during physical vapor deposition (PVD), suggesting that deposition parameters can be used to control interface structure and provide unique ways to explore processing-structure-property relationships in interface-dominated nanocomposites.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Description and Genome Characterization of Three Novel Fungal Strains Isolated from Mars 2020 Mission-Associated Spacecraft Assembly Facility Surfaces—Recommendations for Two New Genera and One Species

National Aeronautics and Space Administration’s (NASA) spacecraft assembly facilities are monitored for the presence of any bacteria or fungi that might conceivably survive a transfer to an extraterrestrial environment. Fungi present a broad and diverse range of phenotypic and functional traits to adapt to extreme conditions, hence the detection of fungi and subsequent eradication of them are needed to prevent forward contamination for future NASA missions. During the construction and assembly for the Mars 2020 mission, three fungal strains with unique morphological and phylogenetic properties were isolated from spacecraft assembly facilities. The reconstruction of phylogenetic trees based on several gene loci (ITS, LSU, SSU, RPB, TUB, TEF1) using multi-locus sequence typing (MLST) and whole genome sequencing (WGS) analyses supported the hypothesis that these were novel species. Here we report the genus or species-level classification of these three novel strains via a polyphasic approach using phylogenetic analysis, colony and cell morphology, and comparative analysis of WGS. The strain FJI-L9-BK-P1 isolated from the Jet Propulsion Laboratory Spacecraft Assembly Facility (JPL-SAF) exhibited a putative phylogenetic relationship with the strain Aaosphaeria arxii CBS175.79 but showed distinct morphology and microscopic features. Another JPL-SAF strain, FJII-L3-CM-DR1, was phylogenetically distinct from members of the family Trichomeriaceae and exhibited morphologically different features from the genera Lithohypha and Strelitziana. The strain FKI-L1-BK-DR1 isolated from the Kennedy Space Center facility was identified as a member of Dothideomycetes incertae sedis and is closely related to the family Kirschsteiniotheliaceae according to a phylogenetic analysis. The polyphasic taxonomic approach supported the recommendation for establishing two novel genera and one novel species. The names Aaosphaeria pasadenensis (FJI-L9-BK-P1 = NRRL 64424 = DSM 114621), Pasadenomyces melaninifex (FJII-L3-CM-DR1 = NRRL 64433 = DSM 114623), and Floridaphiala radiotolerans (FKI-L1-BK-DR1 = NRRL 64434 = DSM 114624) are proposed as type species. Furthermore, resistance to ultraviolet-C and presence of specific biosynthetic gene cluster(s) coding for metabolically active compounds are unique to these strains.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Biases associated with a particular image volume metric

Image analysis area and volume metrics A 80 and V 80 , used by others, are studied as a function of image morphology, a 3D feature, and Monte Carlo noise in simulated images. The A 80 area metric is found to be stable, with consistent results (and meaning) obtainable without a detailed understanding of 3D features and/or simulation noise levels. However, the V 80 volume metric varies significantly with image morphology, 3D features, and simulation noise. If an experimental image set gives A 80 area inferences that are consistent with 2D image simulations, but V 80 volume inferences that are significantly larger than those corresponding to simulated images, then issues related to 3D features and simulation noise should be explored before conclusions are drawn related to a possible mismatch in the real physical size of experimental and simulated source volumes.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Exemplary Merging Clusters: Weak-lensing and X-Ray Analysis of the Double Radio Relic, Merging Galaxy Clusters MACS J1752.0+4440 and ZWCL 1856.8+6616

We report the investigation of merging galaxy clusters that exhibit radio relics is strengthening our understanding of the formation and evolution of galaxy clusters, the nature of dark matter, the intracluster medium, and astrophysical particle acceleration. Each merging cluster provides only a single view of the cluster formation process, and the variety of merging clusters is vast. Clusters hosting double radio relics are rare and extremely important because they allow tight constraints on the merger scenario. We present a weak-lensing and X-ray analysis of MACS J1752.0+4440 (z = 0.365) and ZWCL 1856.8+6616 (z = 0.304), two double radio relic clusters. Our weak-lensing mass estimates show that each cluster is a major merger with approximately 1:1 mass ratio. The total mass of MACS J1752.0+4440 (ZWCL 1856.8+6616) is M 200 =14.7 $_{-3.3}+{+3.8}$ x 10 14 M ⊙ M 200 = 2.4 $_{-0.7}^{+0.9}$ 10 14 M ⊙ ). We find that these two clusters have comparable features in their weak-lensing and gas distributions, even though the systems have vastly different total masses. From the likeness of the X-ray morphologies and the remarkable symmetry of the radio relics, we propose that both systems underwent nearly head-on collisions. However, revelations from the hot-gas features and our multiwavelength data analysis suggest that ZWCL 1856.8+6618 is likely at a later merger phase than MACS J1752.0+4440. We postulate that the SW radio relic in MACS J1752.0+4440 is a result of particle reacceleration.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Growth, total lipid, and omega-3 fatty acid production by Nannochloropsis spp. cultivated with raw plant substrate

Improving productivity and lipid concentration in microalgae is important for the economic success of both biofuel and microalgae coproducts production. Nannochloropsis spp. are marine microalgae currently being grown at large-scale for the production of biofuel and lipid coproducts. In this work, we demonstrate improvements of growth and omega-3 production in Nannochloropsis gaditana CCMP526 and Nannochloropsis oceanica CCAP849/10 with plant substrate addition, a potentially economical option for increasing microalgal productivity. Furthermore, we examine growth in the presence of corn stover, switchgrass, sugarcane bagasse, and yard waste. By examining the microbial ecology of N. gaditana cultures with and without plant substrate and with and without antibiotics, we discovered a potential bacterial interaction in these cultures, but its presence is not necessary for algal growth improvements in the presence of plant substrate. Analysis of plant substrate morphology by scanning electron microscopy (SEM) after cultivation in media with and without N. gaditana shows a degradation of specific plant structural features and a colonization of the plant phloem by N. gaditana. An examination of N. gaditana in a Congo red plate assay indicates potential cellulolytic activity, but a preliminary examination of the potential cellulase transcripts does not reveal differential expression of a candidate in the presence of plant. This evidence demonstrates potential raw plant utilization by a marine microalga for increased productivity and provides a potentially economical option for increasing coproduct concentration at industrial scale without genetic engineering.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Compression Analysis of Materials via in situ X-ray Computed Tomography

X-ray computed tomography (X-ray CT) is an analytical technique used in materials science to non– destructively characterize features in a variety of materials like polymers, metals, composites, and explosives. The non-destructive imaging allows for the analysis of features (voids and cracks), which give a fundamental understanding of material characteristics.This is more effective when combinedwith a load cell to expose the material to realworld stimuli to understand material characteristics and morphological behavior in situ. This study focused on using in situ X-ray CT to understand the material characteristics and morphological changes of sugar prills and a lattice under a compressive load. Sugar prills are used as a surrogate to analyze explosives in a safer and less expensiveway. The sugar prills were found to be brittle and shattered when compressed via the CT renderings and the stress/strain information. The lattices are used as both the basis and ground truth comparison for the development of models to predict the morphological and material characteristic changes that occur as a result of loading. The lattice was found to have weak regions due to low polymer interlayer fusion in these regions during the printing process. It was also found that under the initial compression the lattice demonstrated elastic behavior as its cellular structure was intact until the cellular structure started to collapse causing yielding in the lattice. Additionally, the analysis of the sugar prills and lattice demonstrate the information that can be gleaned from in situ X-ray CT experiments that would be lost in traditional pre- and post-mortem X-rayCT analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

A Comprehensive Comparison of Building Height Products at a Global Level

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

42 ENGINEERING↗

DOE OSTI2023

Colloidal Nafion Particles: Are Cylinders Ubiquitous?

Colloidal Nafion morphology plays a critical role in determining the performance of fuel cells and electrolyzers. While small-angle neutron scattering (SANS) studies previously described Nafion in liquid media as dispersed cylinders, the analysis remains nonunique with multiple possible morphological descriptions of the data. Here, using SANS and all-atomistic molecular dynamics, we confirm that Nafion morphology in liquid media differs substantially depending on dispersing agent and dispersion method. H + Nafion dispersed in N-methyl pyrrolidone forms swollen cluster particles with physically cross-linked ionic groups. Scattering profiles from dispersed Nafion membrane have a large structure factor feature not observed for redispersed Nafion D-521. H + Nafion dispersed in water has a highly elongated cylindrical morphology (radius = 10 ± 1.5 Å, height = 358 ± 4.7 Å) with fully dissociated and solvated sulfonic acid groups on the particle wall. In conclusion, these results highlight an important discrepancy between the methods of preparing Nafion dispersions and the use of simplified analysis techniques to describe Nafion morphology.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Jet classification using high-level features from anatomy of top jets

Recent advancements in deep learning models have significantly enhanced jet classification performance by analyzing low-level features (LLFs). However, this approach often leads to less interpretable models, emphasizing the need to understand the decision-making process and to identify the high-level features (HLFs) crucial for explaining jet classification. To address this, we consider the top jet tagging problems and introduce an analysis model (AM) that analyzes selected HLFs designed to capture important features of top jets. Our AM mainly consists of the following three modules: a relation network analyzing two-point energy correlations, mathematical morphology and Minkowski functionals for generalizing jet constituent multiplicities, and a recursive neural network analyzing subjet constituent multiplicity to enhance sensitivity to subjet color charges. We demonstrate that our AM achieves performance comparable to the Particle Transformer (ParT) while requiring fewer computational resources in a comparison of top jet tagging using jets simulated at the hadronic calorimeter angular resolution scale. Furthermore, as a more constrained architecture than ParT, the AM exhibits smaller training uncertainties because of the bias-variance tradeoff. We also compare the information content of AM and ParT by decorrelating the features already learned by AM. Lastly, we briefly comment on the results of AM with finer angular resolution inputs.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Sex Differences in Vagus Nerve Stimulation Effects on Rat Cardiovascular and Immune Systems

Background: Investigations into the benefits of vagus nerve stimulation (VNS) through pre-clinical and clinical research have led to promising findings for treating several disorders. Despite proven effectiveness of VNS on conditions such as epilepsy and depression, understanding of off-target effects and contributing factors such as sex differences can be beneficial to optimize therapy design. New Methods: In this article, we assessed longitudinal effects of VNS on cardiovascular and immune systems, and studied potential sex differences using a rat model of longterm VNS. Rats were implanted with cuff electrodes around the left cervical vagus nerve for VNS, and wireless physiological monitoring devices for continuous monitoring of cardiovascular system using electrocardiogram (ECG) signals. ECG morphology and heart rate variability (HRV) features were extracted to assess cardiovascular changes resulting from VNS in short-term and long-term timescales. We also assessed VNS effects on expression of inflammatory cytokines in blood during the course of the experiment. Statistical analysis was performed to compare results between Treatment and Sham groups, and between male and female animals from Treatment and Sham groups. Results: Considerable differences between male and female rats in cardiovascular effects of VNS were observed in multiple cardiovascular features. However, the effects seemed to be transient with approximately 1-h recovery after VNS. While short-term cardiovascular effects were mainly observed in male rats, females in general showed more significant long-term effects even after VNS stopped. We did not observe notable changes or sex differences in systemic cytokine levels resulting from VNS. Comparison With Existing Methods: Compared to existing methods, our study design incorporated wireless physiological monitoring and systemic blood cytokine level analysis, along with long-term VNS experiments in unanesthetized rats to study sex differences. Conclusion: The contribution of sex differences for long-term VNS off-target effects on cardiovascular and immune systems was assessed using awake behaving rats. Although VNS did not change the concentration of inflammatory biomarkers in systemic circulation for male and female rats, we observed significant differences in cardiovascular effects of VNS characterized using ECG morphology and HRV analyses.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Physics-informed machine learning analysis for nanoscale grain mapping by synchrotron Laue microdiffraction

Understanding the grain morphology, orientation distribution and crystal structure of nanocrystals is essential for optimizing the mechanical and physical properties of functional materials. Synchrotron X-ray Laue microdiffraction is a powerful technique for characterizing crystal structures and orientation mapping using focused X-rays. However, when the grain sizes are smaller than the beam size, mixed peaks in the Laue pattern from neighboring grains limit the resolution of grain morphology mapping. We propose a physics-informed machine learning (PIML) approach that combines a convolutional neural network feature extractor with a physics-informed filtering algorithm to overcome the spatial resolution limits of X-rays, achieving nanoscale resolution for grain mapping. Our PIML method successfully resolves the grain size, orientation distribution and morphology of Au nanocrystals through synchrotron microdiffraction scans, showing good agreement with electron backscatter diffraction results. This PIML-assisted synchrotron microdiffraction analysis can be generalized to other diffraction-based probes, enabling the characterization of nanosized structures with micrometre-sized probes.

X-ray crystallography↗

DOE OSTI2025

From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems

Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.

AI/ML↗

DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗