Search NASA⌕ Search

SEARCH · Search NASA

Results for “3D scanning”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2021

Ferroelectric and Charge Transport Properties in Strain-Engineered Two-Dimensional Lead Iodide Perovskites

Transitions from three-dimensional (3D) hybrid organic–inorganic perovskite (HOIP) structures to lower dimensions including 2D and quasi-2D structures open a new range of functional properties in these materials systems. Yet, the performance of solar cells made from 2D HOIP is below that of the 3D compositions due to the higher band-gap energy, low carrier concentration, and anisotropy in charge transport. Controlling ferroelectricity in 2D HOIPs is considered to be a powerful strategy to drive the spatial separation of photogenerated charge carriers to improve the photovoltaic action in this class of HOIPs. Here, we explore the strain-dependent ferroelectricity and charge carrier properties in strain-engineered 2D-layered HOIPs utilizing advanced scanning probe microscopy (SPM) techniques. Band excitation-piezoresponse force microscopy (BE-PFM) and contact Kelvin probe force microscopy (cKPFM) reveal ferroelectric domains and switchable dynamics with significantly low switching voltages in the low-strain film. Photoluminescence (PL) spectroscopy shows strain-dependent optical properties and a noncentrosymmetric structure with strong second-harmonic generation (SHG) peaks in the low-strain film. In addition, KPFM results demonstrate a higher surface potential in the low-strain films, while the photovoltage and local current are the highest on the high-strain film. Furthermore, our study demonstrates the critical role of strain engineering on the electromechanical and charge carrier dynamics in 2D HOIPs, which is important for development of 2D HOIP devices.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography *

Abstract Neutron computed tomography (NCT), a 3D non-destructive characterization technique, is carried out at nuclear reactor or spallation neutron source-based user facilities. Because neutrons are not severely attenuated by heavy elements and are sensitive to light elements like hydrogen, neutron radiography and computed tomography offer a complementary contrast to x-ray CT conducted at a synchrotron user facility. However, compared to synchrotron x-ray CT, the acquisition time for an NCT scan can be orders of magnitude higher due to lower source flux, low detector efficiency and the need to collect a large number of projection images for a high-quality reconstruction when using conventional algorithms. As a result of the long scan times for NCT, the number and type of experiments that can be conducted at a user facility is severely restricted. Recently, several deep convolutional neural network (DCNN) based algorithms have been introduced in the context of accelerating CT scans that can enable high quality reconstructions from sparse-view data. In this paper, we introduce DCNN algorithms to obtain high-quality reconstructions from sparse-view and low signal-to-noise ratio NCT data-sets thereby enabling accelerated scans. Our method is based on the supervised learning strategy of training a DCNN to map a low-quality reconstruction from sparse-view data to a higher quality reconstruction. Specifically, we evaluate the performance of two popular DCNN architectures—one based on using patches for training and the other on using the full images for training. We observe that both the DCNN architectures offer improvements in performance over classical multi-layer perceptron as well as conventional CT reconstruction algorithms. Our results illustrate that the DCNN can be a powerful tool to obtain high-quality NCT reconstructions from sparse-view data thereby enabling accelerated NCT scans for increasing user-facility throughput or enabling high-resolution time-resolved NCT scans.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2023

BOLT II Forebody Surface Measurement Analysis

Surface impression molds of the nosetip/forebody interface, discrete tripping elements, and other surface imperfections were formed on the BOLT II flight vehicle prior to its launch in March 2022. The silicon-rubber molds cast on the interface between the nickel nosetip and the stainless steel forebody were measured using a 3D optical measurement system and a stylus profilometer and were evaluated for step direction and height. The step around the nosetip/forebody interface was found to vary in both magnitude and direction, but values were generally modest with most samples below 60 μm across the span and only two samples, both cast in the gutter, with step values larger than 100 μm. Impressions of the secondary-side tripping elements were strictly scanned with the optical device and similar methods were used to calculate trip height as step height. The measured centerline trip height was 2623 ±12 μm, while the leading-edge, quadrant 2 and 3, trip heights were 897 ±5 μm and 891 ±18 μm, respectively. Surface imperfections on the nose and forebody were also evaluated. Surface roughness values and spectra were estimated, and profiles were extracted across the imperfections to quantify the magnitude of those features.

Trip height↗
DOE OSTI2021

Embedding Sensors in 3D Printed Metal Structures

The Transformational Challenge Reactor (TCR) program is leveraging recent advances in modeling and simulation, materials, and additive manufacturing (AM) technologies to design a modern nuclear reactor. Some of the main TCR technologies include in situ monitoring and the integration of sensors during the manufacturing of quality-significant nuclear reactor components. This report describes the general procedure and process optimization for embedding sensors within generic stainless steel 316 (SS316) components using laser powder bed fusion (LPBF). A more detailed, quality-significant test plan and supporting procedures are available upon request (ORNL/TM-2021/2127). LPBF involves the use of a scanning laser to selectively melt regions of a powder bed, additively building a part layer by layer. This report describes the LPBF processing technique and discusses the effects of LPBF processing parameters on the success of the sensor embedding process. Experiments used machined cavities in the form of channels in an SS316 base for the sensors to lay in while material is additively built over the top, thereby embedding them in an SS316 matrix. A preliminary investigation involved using empty SS316 sheaths as surrogates to explore the effects of various LPBF processing parameters and the dimensional requirements of the machined channels. Microstructural investigations showed that a smaller channel width/depth combination closer to the sensor’s diameter was best for the embedding process. After the desired parameters were selected, Type-K thermocouples were embedded and evaluated post-embedding using nondestructive thermal testing, as well as destructive sectioning and microscopy. Post-build characterization showed that the thermocouples were well-bonded to the SS316 matrix and were fully functional after embedding. During thermal testing to temperatures up to 500 °C, the embedded thermocouples read consistently with one another and deviated only slightly from the readings of a nonembedded thermocouple located within the furnace. This slight discrepancy was most likely due to differences in the thermal time constants for a nonembedded thermocouple vs. a thermocouple embedded in a solid SS316 block. The results presented in this report will serve as the foundation for future work that will focus on embedding sensors in relevant TCR reactor components and eventually testing those components under neutron irradiation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Clathrate XI K 58 Zn 122 Sb 207 : A New Branch on the Clathrate Family Tree

Abstract The compositional screening of K‐Zn‐Sb ternary system aided by machine learning, rapid exploratory synthesis using KH salt‐like precursor and in situ powder X‐ray diffraction yielded a novel clathrate type XI K 58 Zn 122 Sb 207 . This clathrate consists of a 3D Zn‐Sb framework hosting K + ions inside polyhedral cages, some of which are reminiscent of known clathrate types while others are unique to this structure type. The complex non‐centrosymmetric structure in the tetragonal space group was solved by means of single crystal X‐ray diffraction as a 6‐component twin due to pseudocubic symmetry and further confirmed by high‐resolution synchrotron powder X‐ray diffraction and state‐of‐the‐art scanning transmission electron microscopy. The electron‐precise composition of this clathrate yields narrow‐gap p ‐type semiconductor with extraordinarily low thermal conductivity due to displacement or “rattling” of K cations inside oversized cages and as well as to twinning, stacking faults and antiphase boundary defects.

Cox, Tori↗
DOE OSTI2020

Clathrate XI K 58 Zn 122 Sb 207 : A New Branch on the Clathrate Family Tree

Abstract The compositional screening of K‐Zn‐Sb ternary system aided by machine learning, rapid exploratory synthesis using KH salt‐like precursor and in situ powder X‐ray diffraction yielded a novel clathrate type XI K 58 Zn 122 Sb 207 . This clathrate consists of a 3D Zn‐Sb framework hosting K + ions inside polyhedral cages, some of which are reminiscent of known clathrate types while others are unique to this structure type. The complex non‐centrosymmetric structure in the tetragonal space group was solved by means of single crystal X‐ray diffraction as a 6‐component twin due to pseudocubic symmetry and further confirmed by high‐resolution synchrotron powder X‐ray diffraction and state‐of‐the‐art scanning transmission electron microscopy. The electron‐precise composition of this clathrate yields narrow‐gap p ‐type semiconductor with extraordinarily low thermal conductivity due to displacement or “rattling” of K cations inside oversized cages and as well as to twinning, stacking faults and antiphase boundary defects.

Cox, Tori↗
DOE OSTI2020

Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images

Purpose In clinical practice, invasiveness is an important reference indicator for differentiating the malignant degree of subsolid pulmonary nodules. These nodules can be classified as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). The automatic determination of a nodule's invasiveness based on chest CT scans can guide treatment planning. However, it is challenging, owing to the insufficiency of training data and their interclass similarity and intraclass variation. To address these challenges, we propose a two‐stage deep learning strategy for this task: prior‐feature learning followed by adaptive‐boost deep learning. Methods The adaptive‐boost deep learning is proposed to train a strong classifier for invasiveness classification of subsolid nodules in chest CT images, using multiple 3D convolutional neural network (CNN)‐based weak classifiers. Because ensembles of multiple deep 3D CNN models have a huge number of parameters and require large computing resources along with more training and testing time, the prior‐feature learning is proposed to reduce the computations by sharing the CNN layers between all weak classifiers. Using this strategy, all weak classifiers can be integrated into a single network. Results Tenfold cross validation of binary classification was conducted on a total of 1357 nodules, including 765 noninvasive (AAH and AIS) and 592 invasive nodules (MIA and IAC). Ablation experimental results indicated that the proposed binary classifier achieved an accuracy of with an AUC of 81.3 . These results are superior compared to those achieved by three experienced chest imaging specialists who achieved an accuracy of , , and , respectively. About 200 additional nodules were also collected. These nodules covered 50 cases for each category (AAH, AIS, MIA, and IAC, respectively). Both binary and multiple classifications were performed on these data and the results demonstrated that the proposed method definitely achieves better performance than the performance achieved by nonensemble deep learning methods. Conclusions It can be concluded that the proposed adaptive‐boost deep learning can significantly improve the performance of invasiveness classification of pulmonary subsolid nodules in CT images, while the prior‐feature learning can significantly reduce the total size of deep models. The promising results on clinical data show that the trained models can be used as an effective lung cancer screening tool in hospitals. Moreover, the proposed strategy can be easily extended to other similar classification tasks in 3D medical images.

Wang, Jun↗
DOE OSTI2020

Applying Configurational Complexity to the 2D Ruddlesden–Popper Crystal Structure

The layered Ruddlesden–Popper crystal structure can host a broad range of functionally important behaviors. In this work, we establish extraordinary configurational disorder in a layered Ruddlesden–Popper (RP) structure using entropy stabilization assisted synthesis. A protype A 2 CuO 4 RP cuprate oxide with five cations on the A-site sublattice is designed and fabricated into epitaxial single crystal films using pulsed laser deposition. When grown on a near lattice matched substrate, the (La 0.2 Pr 0.2 Nd 0.2 Sm 0.2 Eu 0.2 ) 2 CuO 4 film features a T'-type RP structure with uniform A-site cation mixing and square-planar CuO4 units. These observations are made with a range of combined characterizations using X-ray diffraction, atomic-resolution scanning transmission electron microscopy, energy-dispersive X-ray spectroscopy, and X-ray absorption spectroscopy measurements. It is further found that heteroepitaxial strain plays an important role in crystal phase formation during synthesis. Compressive strain over ~1.5% results in the formation of a non-RP cubic phase consistent with a Cu X 2O 4 spinel structure. The ability to manipulate configurational complexity and move between 2D layered RP and 3D cubic crystal structures in cuprate and related materials promises to enable flexible design strategies for a range of functionalities, such as magnetoresistance, unconventional superconductivity, ferroelectricity, catalysis, and ion transport.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Designing a User Interface for Real-Time Magnetometer Data Acquisition

The Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) is a next-generation quantum sensor designed to search for ultralight dark matter and explore new frontiers in quantum mechanics. Due to the experiment s sensitivity to magnetic interference, a magnetometer trolley system was developed to scan magnetic fields along a vacuum tube. Interacting with the system required command-line inputs, creating usability challenges. To improve accessibility and streamline data acquisition, I developed a graphical user interface (GUI) using Python and the customtkinter library. The GUI supports real-time data display, state/mode switching, command execution, and CSV file management. I collaborated with another intern to integrate data visualization features into the GUI, allowing users to generate 3D plots of post-acquisition magnetic field data. In the future, I aim to fix the real-time plotting feature as it results in an unresponsive GUI.

Mendez, Milagros [DuPage Coll.]↗
DOE OSTI2025

Designing a User Interface for Real-Time Magnetometer Data Acquisition

The Matter-wave Atomic Gradiometer Interferometric Sensor (MAGIS-100) is a next-generation quantum sensor designed to search for ultralight dark matter and explore new frontiers in quantum mechanics. Due to the experiment’s sensitivity to magnetic interference, a magnetometer trolley system was developed to scan magnetic fields along a vacuum tube. Interacting with the system required command-line inputs, creating usability challenges. To improve accessibility and streamline data acquisition, I developed a graphical user interface (GUI) using Python and the customtkinter library. The GUI supports real-time data display, state/mode switching, command execution, and CSV file management. I collaborated with another intern to integrate data visualization features into the GUI, allowing users to generate 3D plots of post-acquisition magnetic field data. In the future, I aim to fix the real-time plotting feature as it results in an unresponsive GUI.

Mendez, Milagros [DuPage Coll.]↗
DOE OSTI2024

Quantification and assessment of the atmospheric boundary layer height measured during the AWAKEN experiment by a scanning LiDAR

The atmospheric boundary layer (ABL) height plays a key role in many atmospheric processes as one of the dominant flow length scales. However, a systematic quantification of the ABL height over the entire range of scales (i.e., with periods ranging from one minute to one year) is still lacking in literature. In this work, the ABL height is quantified based on high-resolution measurements collected by a scanning pulsed Doppler LiDAR during the recent American WAKE experimeNt (AWAKEN) campaign. The high availability of ABL height estimates (≈2200 collected over one year and each of them based on 10-min averaged statistics) allows to robustly assess five different ABL height models, i.e., one for convective thermal conditions and four for stable conditions. Thermal condition is quantified by a stability parameter spanning three orders of magnitude and probed by near-ground 3D sonic anemometry. The free-atmosphere stability, quantified by the Brunt–Väisälä frequency, is both calculated from simultaneous radiosonde measurements and obtained from the best fit of two of the chosen ABL height models. Good agreement is found between the data and three of the chosen models, quantified by mean absolute errors on the ABL height between 281 and 585 m. Furthermore, the seasonal variability of the convective ABL height model parameters (−15% to +23% with respect to the year baseline) agrees with the variability of buoyancy-generated turbulence caused by the variation in solar radiation throughout the year.

17 WIND ENERGY↗
DOE OSTI2024

Three-dimensional structure of buried heterointerfaces revealed by multislice ptychography

Here, we report on the three-dimensional (3D) structure determination of a twisted hexagonal boron nitride (h-BN) heterointerface from a single-view dataset using multislice ptychography. We identify the buried heterointerface between two twisted h-BN flakes with a lateral resolution of 0.57 Å and a depth resolution of 2.5 nm. The latter represents a significant improvement (∼2.7 times) over the aperture-limited depth resolution of incoherent imaging modes, such as annular-dark-field scanning transmission electron microscopy. This improvement is attributed to the diffraction signal extending beyond the aperture edge, with the depth resolution set by the curvature of the Ewald sphere. Future advancements in this approach could enhance the depth resolution to the subnanometer level and enable the identification of individual dopants, defects, and color centers in twisted heterointerfaces and other materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

2.5D Super-Resolution Approaches for X-Ray Computed Tomography-Based Inspection of Additively Manufactured Parts

X-ray computed tomography (XCT) is a key tool in non-destructive evaluation of additively manufactured (AM) parts, allowing for internal inspection and defect detection. Despite its widespread use, obtaining high-resolution CT scans can be extremely time consuming. This issue can be mitigated by performing scans at lower resolutions; however, reducing the resolution compromises spatial detail, limiting the accuracy of defect detection. Super-resolution algorithms offer a promising solution for overcoming resolution limitations in XCT reconstructions of AM parts, enabling more accurate detection of defects. While 2D super-resolution methods have demonstrated state-of-the-art performance on natural images, they tend to under-perform when directly applied to XCT slices. On the other hand, 3D super-resolution methods are computationally expensive, making them infeasible for large-scale applications. To address these challenges, we propose a 2.5D super-resolution approach tailored for XCT of AM parts. Our method enhances the resolution of individual slices by leveraging multi-slice information from neighboring 2D slices without the significant computational overhead of full 3D methods. Specifically, we use neighboring low-resolution slices to super-resolve the center slice, exploiting inter-slice spatial context while maintaining computational efficiency. This approach bridges the gap between 2D and 3D methods, offering a practical solution for high-throughput defect detection in AM parts.

Sullivan, Haley↗
DOE OSTI2023

Revealing the Bacterial Quorum-Sensing Effect on the Biofilm Formation of Diatom Cylindrotheca sp. Using Multimodal Imaging

Diatoms contribute to carbon fixation in the oceans by photosynthesis and always form biofouling organized by extracellular polymeric substances (EPS) in the marine environment. Bacteria-produced quorum-sensing signal molecules N-acyl homoserine lactones (AHLs) were found to play an important role in the development of Cylindrotheca sp. in previous studies, but the EPS composition change was unclear. This study used the technology of alcian blue staining and scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), and time-of-flight secondary ion mass spectrometry (ToF-SIMS) to directly observe the biofilm formation process. The results showed that AHLs promote the growth rates of diatoms and the EPS secretion of biofilm components. AHLs facilitated the diatom-biofilm formation by a forming process dependent on the length of carbon chains. AHLs increased the biofilm thickness and the fluorescence intensity and then altered the three-dimensional (3D) structures of the diatom-biofilm. In addition, the enhanced EPS content in the diatom-biofilm testified that AHLs aided biofilm formation. This study provides a collection of new experimental evidence of the interaction between bacteria and microalgae in fouling biofilms.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

High–Performance NiCo 2 O 4 /Graphene Quantum Dots for Asymmetric and Symmetric Supercapacitors with Enhanced Energy Efficiency

For the sustainable growth of future generations, energy storage technologies like supercapacitors and batteries are becoming more and more common. However, reliable and high-performance materials’ design and development is the key for the widespread adoption of batteries and supercapacitors. Quantum dots with fascinating and unusual properties are expected to revolutionize future technologies. However, while the recent discovery of quantum dots honored with a Nobel prize in Chemistry, their benefits for the tenacious problem of energy are not realized yet. In this context, herein, chemical-composition tuning enabled exceptional performance of NiCo 2 O 4 (NCO)/graphene quantum dots (GQDs) is reported, which outperform the existing similar materials, in supercapacitors. A comprehensive study is performed on the synthesis, characterization, and electrochemical performance evaluation of highly functional NCO/GQDs in supercapacitors delivering enhanced energy efficiency. The high-performance, functional NCO/GQDs electrode materials are synthesized by the incorporation of GQDs into NCO. The effect of variable amount of GQDs on the energy performance characteristics of NCO/GQDs in supercapacitors is studied systematically. In-depth structural and chemical bonding analyses using X-ray diffraction (XRD) and Raman spectroscopic studies indicate that all the NCO/GQDs composites crystallize in the spinel cubic phase of NiCo 2 O 4 while graphene integration evident in all the NCO/GQDs. The scanning electron microscopy imaging analysis reveals homogeneously distributed spherical particles with a size distribution of 5–9 nm validating the formation of QDs. The high-resolution transmission electron microscopy analyses reveal that the NCOQDs are anchored on graphene sheets, which provide a high surface area of 42.27 m 2 g –1 and high mesoporosity for the composition of NCO/GQDs-10%. In addition to establishing reliable electrical connection to graphene sheets, the NCOQDs provide reliable 3D-conductive channels for rapid transport throughout the electrode as well as synergistic effects. Chemical-composition tuning, and optimization yields NCO/GQDs-10% to deliver the best specific capacitance of 3940 Fg –1 at 0.5 Ag –1 , where the electrodes retain ≈98% capacitance after 5000 cycles. The NCO/GQD-10%//AC asymmetric supercapacitor device demonstrates outstanding energy density and power density values of 118.04 Wh kg –1 and 798.76 W kg –1 , respectively. The NCO/GQDs-10%//NCO/GQDs-10% symmetric supercapacitor device delivers excellent energy and power density of 24.30 Wh kg –1 and 500 W kg –1 , respectively. These results demonstrate and conclude that NCO/GQDs are exceptional and prospective candidates for developing next-generation high-performance and sustainable energy storage devices.

25 ENERGY STORAGE↗
DOE OSTI2023

Elucidating a dissolution–deposition reaction mechanism by multimodal synchrotron X-ray characterization in aqueous Zn/MnO 2 batteries

Aqueous Zn/MnO 2 batteries with their environmental sustainability and competitive cost, are becoming a promising, safe alternative for grid-scale electrochemical energy storage. Here, presented as a promising design principle to deliver a higher theoretical capacity, this work offers fundamental understanding of the dissolution–deposition mechanism of Zn/β-MnO 2 . A multimodal synchrotron characterization approach including three operando X-ray techniques (powder diffraction, absorption spectroscopy, and fluorescence microscopy) is coupled with elementally resolved synchrotron X-ray nano-tomography. Together they provide a direct correlation between structural evolution, reaction chemistry, and 3D morphological changes. Operando synchrotron X-ray diffraction and spectroscopy show a crystalline-to-amorphous phase transition. Quantitative modeling of the operando data by Rietveld refinement for X-ray diffraction and multivariate curve resolution (MCR) for X-ray absorption spectroscopy are used in a complementary fashion to track the structural and chemical transitions of both the long-range (crystalline phases) and short-range (including amorphous phases) ordering upon cycling. Scanning X-ray microscopy and full-field nano-tomography visualizes the morphology of electrodes at different electrochemical states with elemental sensitivity to spatially resolve the formation of the Zn- and Mn-containing phases. Overall, this work critically indicates that for Zn/MnO 2 aqueous batteries, the reaction pathways involving Zn–Mn complex formation upon cycling become independent of the polymorphs of the initial electrode and sheds light on the interplay among structural, chemical, and morphological evolution for electrochemically driven phase transitions.

36 MATERIALS SCIENCE↗