Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “3D cell mapping”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Print-and-Plate Architected Electrodes for Electrochemical Transformations Under Flow

Flow cell electrodes are typically composed of porous carbon materials, such as papers, felts, and cloths. However, their random architecture hinders the fundamental characterization of electrode structure-performance relationships during in situ operation of porous electrochemical flow systems. This work describes a “print-and-plate” method that combines direct ink writing of micro-periodic lattices with a two-step metal plating process that converts them into highly conductive (sheet resistance 40 mΩ sq -1 ) electrodes. Their operando performance is assessed in an anthraquinone disulfonic acid half-cell using widefield electrochemical fluorescence microscopy, where output current and fluorescence intensity are in excellent agreement. The pressure drop associated with flow through three electrode designs is determined via simulations from which the most efficient design is identified and manufactured via print-and-plate. Confocal fluorescence microscopy is then used to create a 3D map of the state of charge (SOC) inside this print-and-plate electrode. The experimental state of the charge map is in good agreement with computational predictions. The rapid design, simulation, and fabrication of print-and-plate electrodes enable fundamental investigations of how architected porosity affects electrochemical performance under flow.

3D-printing↗
DOE OSTI2026

Multimodal Nanoscale Mapping of Local Structure and CO 2 Adsorption in Metal–Organic Frameworks

Diamine functionalization of the metal−organic framework Mg 2 (dobpdc) (dobpdc 4− = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate) significantly enhances its selectivity for CO 2 capture from flue gases and air. The structure and CO 2 capacity of such materials are typically assessed using bulk techniques that rely on averaging signal over large ensembles of unit cells, obscuring local heterogeneities, such as variations in CO 2 occupancy across individual nanocrystals. To resolve this limitation, we demonstrate a multimodal, nanoscale characterization of Mg 2 (dobpdc) appended with 1,3-diaminopropane. By employing recently developed characterization techniques at progressively smaller length scales, we uncover insights from correspondingly smaller populations of unit cells. First, we use parallel-beam 3D electron diffraction (3D ED) to identify a prominent expansion in lattice parameters upon desorption of CO 2 , as observed at the level of single nanocrystals. Second, we use convergent-probe 4D scanning transmission electron microscopy (4D-STEM) to quantify associated differences in lattice strain as a function of gas loading and diamine appending. These measurements sample small subvolumes within individual nanocrystals. Finally, we apply infrared scattering scanning near-field optical microscopy (IR s- SNOM) to confirm variable CO 2 chemisorption across adsorption sites at the surface of single nanocrystals. This multimodal, multiscale approach allows us to map heterogeneity within individual nanocrystals. Collectively, these findings emphasize the importance of local, nanoscale characterization of metal−organic frameworks in revealing previously unresolvable features that impact their performance.

Karstens, Sarah L. [University of California, Berk↗
DOE OSTI2021

Multiwavelength anomalous X-ray diffraction for combined imaging of atomic displacement and strain

The X-ray Bragg coherent diffractive imaging (CDI) technique assumes that the structure factor holds constant over the measured crystal. This approximation breaks down for materials exhibiting variations in the unit-cell configuration, such as piezo- and ferroelectrics. In that case, the strain field cannot be reliably determined from the reconstruction because the lattice deformation and the structure factor contribute concomitantly. Proposed here is a solution to this problem achieved by combining Bragg CDI and the multiwavelength anomalous diffraction approach that measures a Friedel pair of reflections at two different photon energies near an absorption edge. Comparing the obtained reconstructions with a parametric model that includes calculating the scattering amplitude as a function of wavelength and the unit-cell configuration, the contributions of the lattice deformation and the structure factor are separated. Simulations of the ferroelectric material BaTiO 3 demonstrate the possibility of simultaneous probing of the strain and displacement of the Ti atoms. The proposed method opens up an opportunity to apply coherent X-ray diffraction for nanoscale-resolved 3D mapping of polarization domains in micro- and nanocrystals.

lattice deformation↗
DOE OSTI2025

Multiscale Modeling Framework for Lithium Nucleation in 3D Porous Carbon Anodes

Porous carbon scaffolds offer a promising route for mitigating non-uniform lithium (Li) plating to enhance the safety and longevity of Li metal batteries. However, the influence of microstructural morphology on Li nucleation is not well understood. Here, we present a multiscale modeling framework to investigate how the porous microstructure of carbon materials affects Li nucleation behavior. Ab initio molecular dynamics simulations quantify the nucleation energy barriers of Li on graphene as a function of Li content, surface curvature, and applied potential, providing key parameters for a classical nucleation theory (CNT) model. From macroscale half-cell simulations, we obtained Li concentration and electrical potential profiles to define boundary conditions for mesoscopic simulations. At the mesoscale, three distinct synthetic 3D microstructures with different porosities and characteristic feature sizes are generated to resolve local distributions of Li flux, current density, and mechanical stress. These outputs are integrated into the CNT model to map spatial variation in nucleation rates. Our findings reveal trade-offs between suppressing nucleation rates and achieving spatial uniformity, offering design guidelines for optimizing porous carbon anodes to balance nucleation control and mechanical integrity.

Materials science↗
DOE OSTI2024

Linac_Gen: Integrating Machine Learning and Particle-in-Cell Methods for Enhanced Beam Dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in Linacs over traditional methods, through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of Linac systems, as evidenced in its application to Fermilab's PIP-II Linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Linac_Gen: integrating machine learning and particle-in-cell methods for enhanced beam dynamics at Fermilab

Here, we introduce Linac_Gen, a tool developed at Fermilab, which combines machine learning algorithms with Particle-in-Cell methods to advance beam dynamics in linacs. Linac_Gen employs techniques such as Random Forest, Genetic Algorithms, Support Vector Machines, and Neural Networks, achieving a tenfold increase in speed for phase-space matching in linacs over traditional methods through the use of genetic algorithms. Crucially, Linac_Gen's adept handling of 3D field maps elevates the precision and realism in simulating beam instabilities and resonances, marking a key advancement in the field. Benchmarked against established codes, Linac_Gen demonstrates not only improved efficiency and precision in beam dynamics studies but also in the design and optimization of linac systems, as evidenced in its application to Fermilab's PIP-II linac project. This work represents a notable advancement in accelerator physics, marrying ML with PIC methods to set new standards for efficiency and accuracy in accelerator design and research. Linac_Gen exemplifies a novel approach in accelerator technology, offering substantial improvements in both theoretical and practical aspects of beam dynamics.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Small, solubilized platinum nanocrystals consist of an ordered core surrounded by mobile surface atoms

In situ structures of Platinum (Pt) nanoparticles (NPs) can be determined with graphene liquid cell transmission electron microscopy. Atomic-scale three-dimensional structural information about their physiochemical properties in solution is critical for understanding their chemical function. We here analyze eight atomic-resolution maps of small (<3 nm) colloidal Pt NPs. Their structures are composed of an ordered crystalline core surrounded by surface atoms with comparatively high mobility. 3D reconstructions calculated from cumulative doses of 8500 and 17,000 electrons/pixel, respectively, are characterized in terms of loss of atomic densities and atomic displacements. Less than 5% of the total number of atoms are lost due to dissolution or knock-on damage in five of the structures analyzed, whereas 10–16% are lost in the remaining three. Less than 5% of the atomic positions are displaced due to the increased electron irradiation in all structures. The surface dynamics will play a critical role in the diverse catalytic function of Pt NPs and must be considered in efforts to model Pt NP function computationally.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Evolving data standards for cryo-EM structures

Electron cryo-microscopy (cryo-EM) is increasingly being used to determine 3D structures of a broad spectrum of biological specimens from molecules to cells. Anticipating this progress in the early 2000s, an international collaboration of scientists with expertise in both cryo-EM and structure data archiving was established (EMDataResource, previously known as EMDataBank). The major goals of the collaboration have been twofold: to develop the necessary infrastructure for archiving cryo-EM-derived density maps and models, and to promote development of cryo-EM structure validation standards. We describe how cryo-EM data archiving and validation have been developed and jointly coordinated for the Electron Microscopy Data Bank and Protein Data Bank archives over the past two decades, as well as the impact of evolving technology on data standards. Just as for X-ray crystallography and nuclear magnetic resonance, engaging the scientific community via workshops and challenging activities has played a central role in developing recommendations and requirements for the cryo-EM structure data archives.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Microscopic analysis of copper current collectors and mechanisms of fragmentation under compressive forces

Extensive fragmentation of copper current collectors was observed after spherical indentation on prismatic and large-format pouch Li-ion cells by 3D X-ray computed tomography (XCT). Microscopic analysis including scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM) and x-ray photoelectron microscopy (XPS) was carried out on copper current collectors from used commercial cells and pristine anodes. The copper-graphite cross-section images showed rough interface areas affected by reactions and diffusion in the used cell. Electron probe micro-analyzer (EPMA) element mapping showed the interface area was rich in oxygen and phosphorus. A detectable amount of phosphorus was also uniformly distributed inside the current collector. The same oxygen and phosphorus distributions were confirmed by STEM/EDS analysis. XPS depth profiles on multiple elements revealed the interface area of the aged anode was rich in Li, F, P, O and C and diffused at least 50 nm into the copper. In comparison, the pristine anode showed a very smooth C/Cu interface. No other elements were detected. For commercial cells, the reactions in the interface area and diffusion of multiple elements into the lattice and grain boundaries were responsible for the embrittlement of the copper current collectors. Finally, permanent cell capacity loss was observed in electrochemical performance of the indented cells.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A three-dimensional laser ray-tracing methodology for radiation-hydrodynamics simulations

We report on a methodology for performing laser ray-tracing in three spatial dimensions for radiation-hydrodynamics simulation codes. Our method, which is an extension of that developed in Haines et al., Comput. Fluids 201, 104478 (2020), utilizes an automatically generated separate mesh for the laser ray-tracing from the radiation-hydrodynamics mesh. This enables the laser mesh to be tailored to minimize ray noise with significantly fewer rays than would be required when the ray-tracing is performed on the radiation-hydrodynamics mesh, primarily by allowing the use of high-aspect-ratio cells that are not suitable for hydrodynamics solvers. For a planar target, we show that our method provides a ≈ 100× reduction in computational expense to achieve a fixed level of ray noise relative to ray-tracing directly on the radiation-hydrodynamics mesh. The relatively low ray requirement also enables efficient computation of cross-beam energy transfer. Each cell in the logically cubic laser mesh is a non-convex dodecahedron with triangular sides, and numerical integration of the ray trajectories and inverse bremsstrahlung is performed by mapping each cell to the unit cube. We will describe our methodology in detail as well as its implementation in the xRAGE radiation-hydrodynamics code, discuss performance, and present the results from applying the methodology to test problems with analytic solutions for laser ray-tracing through a quadratic density gradient with an analytic solution as well as for a laser-driven heat front. In 3D radiation-hydrodynamics simulations of laser-driven experiments performed on the National Ignition Facility, laser ray-tracing with our methodology uses less than 1% of total computational time while introducing acceptably low levels of ray noise.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Securing 3D NAND Without Density Loss via In-Situ Encryption Using a Single Transistor XOR Cell

In this article, we push lightweight XOR-based in-situ encryption to extreme density by proposing a singletransistor XOR memory cell and applying it to 3D NAND, enabling secure data storage without density loss. Using a ferroelectric field-effect transistor (FeFET) as an example technology, we demonstrate that: i) a single-transistor memory can realize the XOR function by exploiting the ability to charge the source and drain separately and control current flow direction, eliminating the need for conventional encrypted cells that rely on complementary devices; ii) with a XOR-based cipher, encryption and decryption can be mapped to in-situ array operations, where ciphertext is stored as the threshold voltage (VTH) states of FeFETs in a NAND string, and decryption is achieved through read operations using key-dependent complementary source/drain bias; iii) the proposed technique is scalable to multi-level cell (MLC) storage by encrypting and decrypting data bit by bit; iv) using an integrated NAND FeFET array, we experimentally demonstrate encryption and decryption operations for both single-level cell (SLC) and MLC storage; v) systemlevel benchmarking shows that the proposed technique achieves 48× and 278× improvements in encryption and decryption throughput, respectively, compared to AES.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Lightning and Radar Measures of Mixed-Phase Updraft Variability in Tracked Storms during the TRACER Field Campaign in Houston, Texas

Properties of 7488 thunderstorms are summarized for June–September 2022 during the Tracking Aerosol Convection Interactions Experiment (TRACER) field campaign Houston, Texas, using polarimetric weather radar and VHF 3D Lightning Mapping Array data. Automated tracking of storms linked each instrument’s measurements to a data-defined, time-evolving storm footprint. Within each storm, the depth and magnitude of episodic columns of radar differential reflectivity and specific differential phase quantified the prevalence of updrafts that activated mixed-phase precipitation pathways. Lightning measurements further distinguished the degree of rimed precipitation formation: the fraction of tracks with lightning varied from day to day and cells with lightning had stronger polarimetric columns. Track-level correlation of the lightning flash rate with radar polarimetric measures had substantial spread, showing that lightning provides an additional signal of mixed-phase precipitation processes that can complement future studies of thermodynamic and aerosol controls on cloud microphysics in the Houston region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2024

Conservative discontinuous Galerkin interpolation: Sheared boundary conditions

Local studies of accretion disks and laboratory magnetized plasmas employ analytical coordinate mappings that introduce sheared boundary conditions (BCs). Here, we present a discontinuous Galerkin (DG) algorithm to apply such BCs based on projections and quadrature-free integration. The procedure is high-order accurate, preserves moments exactly and works in multiple dimensions. Tests of increasing complexity are provided, beginning with translations of one and two dimensional fields, followed by 3D and 5D simulations with sheared (twist-shift) BCs. Results show that the algorithm is (p + 1)-order accurate in the DG representation and (p + 2) -order accurate in the cell averages, with p being the order of the polynomial basis. Quantification of the algorithm's hyperdiffusion and discussion of aliasing errors are given. This technique enables conservative local simulations of plasma turbulence with DG, not possible until now.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Local Lattice Deformation of Tellurene Grain Boundaries by Four-Dimensional Electron Microscopy

Two-dimensional (2D) tellurene is a promising competitor for the fabrication of ultrathin optoelectronic devices belonging to a new family of monoelemental 2D materials. Furthermore, the precise fabrication and characterization of tellurene and its lattice defects is of utmost importance to determine device reliability and predict functionality, yet it remains experimentally challenging. The rapid growth of the four-dimensional scanning transmission electron microscopy (4D-STEM) technique as well as postprocessing tools now allows for structural analysis with nanometer-scale resolution over a broad range of length scales. Here, we use 4D-STEM to reveal the three-dimensional (3D) atomic structure of the characteristic grain boundary in 2D tellurium formed through a new microwave-enabled chemical self-assembly. Strain and lattice parameter maps permit the reconstruction of the roughness of the grain boundary and suggest that its formation is promoted by a wedge of helical atomic chains along the [0001] crystallographic direction. The observation of wrinkles at the grain boundary is found to be the dominant relaxation mechanism. Insights into the formation mechanism are elucidated by mapping the lattice parameters as the first demonstration of local in-plane and out-of-plane unit cell variation in a nanometer-by-nanometer real-space array.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Nramp: Deprive and conquer?

Solute carriers 11 (Slc11) evolved from bacterial permease (MntH) to eukaryotic antibacterial defense (Nramp) while continuously mediating proton (H+)-dependent manganese (Mn2+) import. Also, Nramp horizontal gene transfer (HGT) toward bacteria led to mntH polyphyly. Prior demonstration that evolutionary rate-shifts distinguishing Slc11 from outgroup carriers dictate catalytic specificity suggested that resolving Slc11 family tree may provide a function-aware phylogenetic framework. Hence, MntH C (MC) subgroups resulted from HGTs of prototype Nramp (pNs) parologs while archetype Nramp (aNs) correlated with phagocytosis. PHI-Blast based taxonomic profiling confirmed MntH B phylogroup is confined to anaerobic bacteria vs. MntH A (MA)’s broad distribution; suggested niche-related spread of MC subgroups; established that MA-variant MH, which carries ‘eukaryotic signature’ marks, predominates in archaea. Slc11 phylogeny shows MH is sister to Nramp. Site-specific analysis of Slc11 charge network known to interact with the protonmotive force demonstrates sequential rate-shifts that recapitulate Slc11 evolution. 3D mapping of similarly coevolved sites across Slc11 hydrophobic core revealed successive targeting of discrete areas. The data imply that pN HGT could advantage recipient bacteria for H+-dependent Mn2+ acquisition and Alphafold 3D models suggest conformational divergence among MC subgroups. It is proposed that Slc11 originated as a bacterial stress resistance function allowing Mn2+-dependent persistence in conditions adverse for growth, and that archaeal MH could contribute to eukaryogenesis as a Mn2+ sequestering defense perhaps favoring intracellular growth-competent bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Shotgun Immunoproteomic Approach for the Discovery of Linear B-Cell Epitopes in Biothreat Agents Francisella tularensis and Burkholderia pseudomallei

Peptide-based subunit vaccines are coming to the forefront of current vaccine approaches, with safety and cost-effective production among their top advantages. Peptide vaccine formulations consist of multiple synthetic linear epitopes that together trigger desired immune responses that can result in robust immune memory. The advantages of linear compared to conformational epitopes are their simple structure, ease of synthesis, and ability to stimulate immune responses by means that do not require complex 3D conformation. Prediction of linear epitopes through use of computational tools is fast and cost-effective, but typically of low accuracy, necessitating extensive experimentation to verify results. On the other hand, identification of linear epitopes through experimental screening has been an inefficient process that requires thorough characterization of previously identified full-length protein antigens, or laborious techniques involving genetic manipulation of organisms. In this study, we apply a newly developed generalizable screening method that enables efficient identification of B-cell epitopes in the proteomes of pathogenic bacteria. As a test case, we used this method to identify epitopes in the proteome of Francisella tularensis (Ft), a Select Agent with a well-characterized immunoproteome. Our screen identified many peptides that map to known antigens, including verified and predicted outer membrane proteins and extracellular proteins, validating the utility of this approach. We then used the method to identify seroreactive peptides in the less characterized immunoproteome of Select Agent Burkholderia pseudomallei (Bp). This screen revealed known Bp antigens as well as proteins that have not been previously identified as antigens. Although B-cell epitope prediction tools Bepipred 2.0 and iBCE-EL classified many of our seroreactive peptides as epitopes, they did not score them significantly higher than the non-reactive tryptic peptides in our study, nor did they assign higher scores to seroreactive peptides from known Ft or Bp antigens, highlighting the need for experimental data instead of relying on computational epitope predictions alone. The present workflow is easily adaptable to detecting peptide targets relevant to the immune systems of other mammalian species, including humans (depending upon the availability of convalescent sera from patients), and could aid in accelerating the discovery of B-cell epitopes and development of vaccines to counter emerging biological threats.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Differentiating Double-Layer, Psuedocapacitance, and Battery-like Mechanisms by Analyzing Impedance Measurements in Three Dimensions

Electrochemical energy storage arises from processes that are broadly categorized as capacitive, pseudocapacitive, or battery-like. Advanced charge-storing materials that are designed to deliver high capacity at a high rate often exhibit a multiplicity of such mechanisms, which complicates the understanding of their charge-storage behavior. In this paper, we apply a “3D Bode analysis” technique to identify key descriptors for fast Li-ion storage processes, where AC impedance data, such as the real capacitance ($C'$) or phase angle ($Φ$), are represented versus the frequency ($f$) and a third independent variable, the applied DC cell voltage. For double-layer processes, a near-constant $C'$ or $Φ$ is supported across the entire voltage range, and the decrease in these values shows a near-linear decrease at higher $f$. For pseudocapacitance, an increase in $C'$ is delivered, accompanied by high $C'$ retention at higher $f$ compared to double-layer processes. Interestingly, the lower $Φ$ values, where $C'$ is highest, suggest that this is a key descriptor for pseudocapacitance, where high-rate charge storage is still facilitated within a kinetically limited regime. For battery-like processes, a high $C'$ is only observed at the voltage at which the material stores charge, while outside that voltage, $C'$ is negligible. The three-dimensional (3D) Bode analysis allows charge-storage dynamics to be mapped out in great detail with more delineation between mechanisms compared to the more frequently deployed kinetic analyses derived from cyclic voltammetry.

25 ENERGY STORAGE↗