The fast charging capability of lithium (Li)-ion batteries (LIBs) is heavily dependent on the way Li-ion diffusion occurs within the electrode. The current status of using compacted cathodes for high volumetric energy density of LIBs, however, brings about the difficulty of high-rate operation due to insufficient ion diffusion originating from poor electrolyte infiltration in limited porosity. Here, we propose a strategy of constructing a solid-phase Li-conducting network by using Li6.4La3Zr1.4Ta0.6O12 additive to offset the inability of electrolyte infiltration into the compacted cathode. Insights into the way ion transport is carried out in the compacted cathode are further provided by X-ray computed tomography technology. Even at a 5 C rate, the modified cathode still exhibits a specific capacity of 97.9 mAh g-1, in contrast to the full electrochemical failure of the routine cathode. Besides, the practicality of this strategy has been confirmed in Li-ion pouch cells using a compacted cathode (120 μm thickness, 24 mg cm-2). This work may prove the potential of tuning Li transport pathways for the fast charging operation of compacted cathodes.

Infrared-visible image fusion (IVIF) has no ideal fused reference, so algorithms are ranked by scalar objective metrics that formalize proxies for information transfer, structure, or source similarity. These proxies often disagree with the judgment that ultimately matters: given the same sources, which of two fused results does a human prefer? Direct pairwise comparison is an established protocol for relative subjective assessment, but its cost grows quadratically with the number of algorithms. We present the Learned Perceptual Image Fusion Measure (LPIFM), a source-conditioned model that operationalizes the human A/B/Tie comparison protocol as a repeatable, scalable surrogate. LPIFM jointly observes the two sources and two fused candidates and predicts whether A is better, B is better, or the two are perceptually equivalent. Supervision comes from a new dense preference corpus covering all 6,300 unordered comparisons among 25 fusion methods on the 21 scenes of the VIFB benchmark, labeled under a blinded, randomized, two-stage protocol with expert adjudication. Across scene- and method-generalization settings, LPIFM attains pairwise accuracy of 0.792-0.840 and Spearman correlation of 0.941-0.977 with human-derived tie-aware Bradley-Terry rankings; on full 25-method pools it exceeds the strongest conventional metric by 0.163-0.211 in accuracy. Its verdicts are also antisymmetric under candidate swap, free of preference cycles, and fully transitive, matching or exceeding the internal consistency of the human panel. We publicly release the dataset, model weights, and code. LPIFM offers a practical instrument for human-aligned comparison and ranking of IVIF methods at scale.

Image dehazing is a critical challenge in computer vision, essential for enhancing image clarity in hazy conditions. Traditional methods often rely on atmospheric scattering models, while recent deep learning techniques, specifically Convolutional Neural Networks (CNNs) and Transformers, have improved performance by effectively analyzing image features. However, CNNs struggle with long-range dependencies, and Transformers demand significant computational resources. To address these limitations, we propose DehazeSNN, an innovative architecture that integrates a U-Net-like design with Spiking Neural Networks (SNNs). DehazeSNN captures multi-scale image features while efficiently managing local and long-range dependencies. The introduction of the Orthogonal Leaky-Integrate-and-Fire Block (OLIFBlock) enhances cross-channel communication, resulting in superior dehazing performance with reduced computational burden. Our extensive experiments show that DehazeSNN is highly competitive to state-of-the-art methods on benchmark datasets, delivering high-quality haze-free images with a smaller model size and less multiply-accumulate operations. The proposed dehazing method is publicly available at https://github.com/HaoranLiu507/DehazeSNN.

Rechargeable batteries using electrodes based on intercalation chemistry exhibit notable cyclability, yet their performance still suffers from chemomechanical degradation. In this study, by combining a suite of operando microscopy methods, we explored electrode strain evolution and observed intricate particle cluster rearrangement under electrochemical stimuli. We show that early-stage strain accumulation in intercalation cathodes occurs during the period of interparticle charge transfer and redox reactions stemming from asynchronous coupling and decoupling between chemical (de)intercalation and physical grain motion. This interplay drives heterogeneous redox activity, localized charge equilibration, and multiscale strain cascades that propagate through an asynchronous network of chemical-mechanical interactions. Together, these findings reveal how collective particle dynamics and hierarchical strain transmission dictate electrode deformation and degradation in intercalation cathodes.

Lithium-ion batteries are indispensable power sources for a wide range of modern electronic devices. However, battery lifespan remains a critical limitation, directly affecting the sustainability and user experience. Conventional battery failure analysis in controlled lab settings may not capture the complex interactions and environmental factors encountered in real-world, in-device operating conditions. This study analyzes the failure of commercial wireless earbud batteries as a model system within their intended usage context. Through multiscale and multimodal characterizations, the degradations from the material level to the device level are correlated, elucidating a failure pattern that is closely tied to the specific device configuration and operating conditions. The findings indicate that the ultimate failure mode is determined by the interplay of battery materials, cell structural design, and the in-device microenvironment, such as temperature gradients and their fluctuations. This holistic, in-device perspective on environmental influences provides critical insights for battery integration design, enhancing the reliability of modern electronics.

X-ray Talbot-Lau interferometer, offering multi-contrast for the imaged objects, has shown powerful capacity in imaging biological soft tissues, low density materials, etc. However, the length of the system is constant, which limits its applications. Here, we establish a new X-ray Talbot-Lau interferometer theory to achieve a flexible phase contrast imaging system. This interferometer has a tunable length-scale scattering sensitivity. The experiments show that we can regulate the scattering signal individually. Furthermore, we construct a G2-less phase contrast imaging based on this flexible interferometer to validate the possibility of highly compact X-ray Talbot-Lau interferometer.

Since the mid-1990s, X-ray phase contrast imaging (XPCI) has attracted increasing interest in the industrial and bioimaging fields due to its high sensitivity to weakly absorbing materials and has gained widespread acceptance. XPCI can simultaneously provide three imaging modalities with complementary information, offering enriched details and data. This study proposes an image fusion method that simultaneously retrieves the three complementary channels of XPCI. It integrates block features, non-subsampled contourlet transform (NSCT), and a spiking cortical model (SCM), comprising three steps: (I) Image denoising, (II) Block-based feature-level NSCT-SCM fusion, and (III) Image quality enhancement. Compared with other methods in the XPCI image fusion field, the fusion results of the proposed algorithm demonstrated significant advantages, particularly with an impressive increase in the standard deviation by over 50% compared to traditional NSCT-SCM. The results revealed that the proposed algorithm exhibits high contrast, clear contours, and a short operation time. Experimental outcomes also demonstrated that the block-based feature extraction procedure performs better in retaining edge strength and texture information, with released computational resource consumption, thus, offering new possibilities for the industrial application of XPCI technology.

High-nickel LiNixMnyCo1-x-yO2 (NMC) cathodes have demonstrated superior energy density, yet their stability is compromised under high voltage conditions. To address this, we propose a strategy of heterogeneous doping with a concentration gradient, specifically through Sr–Zr co-modification. We synthesized Ni-rich NMC particles featuring several micron-sized secondary particles composed of micron-sized primary grains. This design aims to harness the structural robustness of single-crystalline grains and the favorable diffusion kinetics of polycrystalline secondary particles. Systematic characterization using a combination of electrochemical measurements and synchrotron analytics reveals an intriguing pattern of hierarchically heterogeneous Sr–Zr co-doping. It demonstrates a depth-dependent concentration gradient at the secondary particle level and competing dopant segregation over the buried grain boundaries. This unique characteristic creates opportunities for enhancing battery performance, particularly by optimizing precursors and implementing advanced modulation techniques. We also investigate the dissolution and precipitation of the cathode's transition metal cations upon high-voltage cycling. These insights suggest that a tailored compositional variation can be a viable approach to effectively design the next-generation high-Ni NMC cathode materials for high-voltage lithium batteries.

We introduce an ultrahigh-resolution (50

We introduce an ultrahigh-resolution (50\mu m\) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse and a micro-CT phantom scan, both producing sharper images with finer details than that prior correction.

Resolving morphological chemical phase transformations at the nanoscale is of vital importance to many scientific and industrial applications across various disciplines. The TXM-XANES imaging technique, by combining full field transmission X-ray microscopy (TXM) and X-ray absorption near edge structure (XANES), has been an emerging tool which operates by acquiring a series of microscopy images with multi-energy X-rays and fitting to obtain the chemical map. Its capability, however, is limited by the poor signal-to-noise ratios due to the system errors and low exposure illuminations for fast acquisition. In this work, by exploiting the intrinsic properties and subspace modeling of the TXM-XANES imaging data, we introduce a simple and robust denoising approach to improve the image quality, which enables fast and high-sensitivity chemical imaging. Extensive experiments on both synthetic and real datasets demonstrate the superior performance of the proposed method.

X-ray grating interferometry (XGI) can provide multiple image modalities. It does so by utilizing three different contrast mechanisms—attenuation, refraction (differential phase-shift), and scattering (dark-field)—in a single dataset. Combining all three imaging modalities could create new opportunities for the characterization of material structure features that conventional attenuation-based methods are unable probe. In this study, we proposed an image fusion scheme based on the non-subsampled contourlet transform and spiking cortical model (NSCT-SCM) to combine the tri-contrast images retrieved from XGI. It incorporated three main steps: (i) image denoising based on Wiener filtering, (ii) the NSCT-SCM tri-contrast fusion algorithm, and (iii) image enhancement using contrast-limited adaptive histogram equalization, adaptive sharpening, and gamma correction. The tri-contrast images of the frog toes were used to validate the proposed approach. Moreover, the proposed method was compared with three other image fusion methods by several figures of merit. The experimental evaluation results highlighted the efficiency and robustness of the proposed scheme, with less noise, higher contrast, more information, and better details.

Objective. High energy and large field of view (FOV) phase contrast imaging is crucial for biological and even medical applications. Although some works have devoted to achieving a large FOV at high energy through bending gratings and so on, which would be extremely challenging in medical high energy imaging.Approach.We analyze the angular shadowing effect of planar gratings in high-energy x-ray Talbot-Lau interferometer (XTLI). Then we design and develop an inverse XTLI coupled with a microarray anode-structured target source to extend the FOV at high energy.Main results.Our experimental results demonstrate the benefit of the source in the inverse XTLI and a large FOV of 106.6 mm in the horizontal direction is achieved at 40 keV. Based on this system, experiments of a mouse demonstrate the potential advantage of phase contrast mode in imaging lung tissue.Significance.We extend the FOV in a compact XTLI using a microarray anode-structured target source coupled with an inverse geometry, which eliminates grating G0 and relaxes the fabrication difficulty of G2. We believe the established design idea and imaging system would facilitate the wide applications of XTLI in high energy phase contrast imaging.

Rechargeable battery research often involves improving electrodes to electrolyte materials with new chemistry with the end goals to lower cost, extend cycle life, higher energy densities and better safety. Advancements in battery and fuel cell research require the understanding of the complex interplay of several components and factors in a battery ecosystem. It calls for an integrated and multimodality approach involving several new analytical techniques which have to be capable of probing the batteries electrochemistry, structures and composition at different length and time scales, several of which have to be performed non destructively (ex-situ, in situ and in operando). Because of the need to study these in operando or at higher resolution or sensitivity, conventional lab based x-ray techniques are often inadequate. Most of this research is currently performed through synchrotron X-ray techniques. These include: X-ray Absorption Spectroscopy (XAS) to probe changes in oxidation states, bond lengths and coordination numbers of electrochemistry during charge-discharge cycles. XAS comprise XANES (X-ray absorption near edge spectroscopy) & EXAFS (Extended X-ray Absorption Fine Structure). They provide information on element-specific changes in oxidation state and local atomic structure. Such microscopic descriptors are crucial for elucidating charge transfer and structural changes associated with bonding or site mixing, two key factors in evaluating state of charge and modes of cell failure or catalytic efficiency. Another major technique is synchrotron X-ray Imaging at multiple lengthscales, from micrometers to 10s of nanometers through 3D X-ray Microscopy (XRM) to determine structural changes and degradation over time of the complex system, from the electrodes, separators, current collectors to binders. Trace level elemental composition at the ppm or sub-ppm level can be studied through high sensitivity synchrotron X-ray fluorescence spectroscopy (s-XRF)- to track the migration of metallic ions from cathode to anode during charge-discharge cycle or to investigate cross contamination during manufacturing. Unfortunately, many of these X-ray techniques such as XAS has to be performed almost exclusively at synchrotron X-ray light sources, where beamtime is infrequent and experiment time-frames are limited. As a consequence, high level battery, fuel cell or catalyst research in many research institutions have been largely curtailed. In this talk, we will discuss the advancements made in high flux, tunable lab x-ray sources and high efficiency optics for enabling novel synchrotron-equivalent XAS, XRM and XRF techniques in the laboratory. Breakthrough correlative applications through these suite of tools in the field of battery research and catalysts are now feasible in your own laboratory 24/7, without the constraint of limited access nor the research continuity challenges at synchrotron beamlines. Measurements results (including in operando) will be illustrated for conventional NMC batteries to novel solid-state lithium air batteries and next generation battery materials.

X-ray imaging (2D & 3D) has been one of the primary non-destructive analysis methods for electronic packages and printed circuit boards (PCB) for over three decades. The continually shrinking features and growth of heterogeneous packaging and wafer-level packaging drive urgent demand for even higher resolution but on larger samples, including larger packages and on wafers. Currently, gaps in non-destructive 2D and 3D imaging in failure analysis exist due to lack of resolution to resolve sub-micron defects typically found in most cracks or voids in microbumps less than 30 microns in diameter and on low contrast materials such as defects in organic substrates. The larger form factors of samples including modern heterogeneous packages, PCB, wafers adds yet another layer of difficulty, and submicron lengthscale defects on such samples are far beyond the resolution power of most existing 2D or 3D X-ray tools. Conventional, high resolution 3D X-ray tools are designed to inspect small packages, but as sample size increases, the time to detect small defects in large packages or PCB may run into several hours or days, rendering this application impractical. We describe a novel 3D X-ray tool that overcome the sample size and speed limitation of traditional X-ray imaging systems. Time to obtain a sub-micron resolution imaging on a region of interest in a package, pcb or 300 mm can be completed within a few minutes. The rapid multiresolution capabilities are also well suited for construction analysis or reverse engineering from packages to pcbs.

Metal dendrite is one of the most common issues in a variety of rechargeable batteries. It deteriorates cell capacity, increases interphase adverse reactions, and causes safety concerns. X-ray computed tomography facilitates an operando/in situ visualization of the three-dimensional (3D) morphology of the dendrites and their dynamic evolutions during battery operation. In this Perspective, we discuss the important technical developments and challenges when utilizing X-ray computed tomography for investigating the dendrite formation and growth in several different battery systems. In addition, we provide our perspective for the future directions and challenges in the field.

The high interfacial resistance and lithium (Li) dendrite growth are two major challenges for solid-state Li batteries (SSLBs). The lack of understanding on the correlations between electronic conductivity and Li dendrite formation limits the success of SSLBs. Here, by diluting the electronic conductor from the interphase to bulk Li during annealing of the aluminium nitride (AlN) interlayer, we changed the interphase from mixed ionic/electronic conductive to solely ionic conductive, and from lithiophilic to lithiophobic to fundamentally understand the correlation among electronic conductivity, Li dendrite, and interfacial resistance. During the conversion-alloy reaction between AlN and Li, the lithiophilic and electronic conductive Li x Al diffused into Li, forming a compact lithiophobic and ionic conductive Li 3 N, which achieved an ultrahigh critical current density of 2.6/14.0 mA/cm 2 in the time/capacity-constant mode, respectively. The fundamental understanding on the effect of interphase nature on interfacial resistance and Li dendrite suppression will provide guidelines for designing high-performance SSLBs.

Aqueous Zn-ion battery is a promising technology for electrochemical energy storage. The formation of Zn dendrites, however, can jeopardize the cell cycle life and thus, hinders the industrial adoption of this technology. A fundamental understanding of the kinetic mechanisms is crucial for improving the Zn-ion battery. Here, in situ and operando X-ray microscopy methods are utilized to visualize the Zn plating and stripping behaviors under different electrochemical conditions. It is demonstrated that the substrate curvature, local morphology, electrochemical protocols, and the surface chemistry can collectively affect the Zn plating behavior. These results provide new insights for developing the next-generation dendrite-free and long-span aqueous Zn-ion battery.

Lithium-ion battery (LIB) is a broadly adopted technology for energy storage. With increasing demands to improve the rate capability, cyclability, energy density, safety, and cost efficiency, it is crucial to establish an in-depth understanding of the detailed structural evolution and cell-degradation mechanisms during battery operation. Here, we present a laboratory-based high-resolution and high-throughput X-ray micro-computed laminography approach, which is capable of in situ visualizing of an industry-relevant lithium-ion (Li-ion) pouch cell with superior detection fidelity, resolution, and reliability. This technique enables imaging of the pouch cell at a spatial resolution of 0.5 μm in a laboratory system and permits the identification of submicron features within cathode and anode electrodes. We also demonstrate direct visualization of the lithium plating in the imaged pouch cell, which is an important phenomenon relevant to battery fast charging and low-temperature cycling. Our development presents an avenue toward a thorough understanding of the correlation among multiscale structures, chemomechanical degradation, and electrochemical behavior of industry-scale battery pouch cells.

Transition metal dissolution in layered cathodes is one of the most intractable issues that deteriorate the battery performance and lifetime. It not only aggravates the structure degradation in cathode but also damages the solid electrolyte interphase in anode and even induces the formation of lithium dendrites. In this work, we investigate the dissolution behaviors of polycrystalline and single-crystalline layered cathode via operando X-ray imaging techniques. The cathode particle morphology appears to have a significant impact on the evolution of the dissolution dynamics. As a mitigation strategy, we reveal that doping with a trace amount of Zr in the layered cathode could improve its robustness against the transition metal dissolution. Our finding provides valuable insights for designing the next-generation highly stable layered battery cathodes.