Limited Resolution Research Articles

Fast electron initiated electron–hole pair creation in semiconductors

Through Monte Carlo modeling, it is shown that the statistics of electron–hole pair creation in semiconductors (and by extension, presumably, ion-pair creation in gas proportional counters) are substantially different for fast electrons (and by extension, presumably, alpha particles, ions, etc.) cf. x-ray/γ-ray photons. New variables are introduced to quantify the differences in the statistics: the loss parameter, ζ(E′), which acts on the average e−–h+ pair creation energy; and the broadening factor, B(E′), which acts on the Fano factor. E′ is the initial energy of the fast electron. ζ(E′) and B(E′) are computed for a variety of semiconductor materials. A new equation for the statistically limited energy resolution of a particle counting fast electron spectrometer is established. This new equation supersedes and replaces that for the Fano-limited energy resolution of a particle counting fast electron spectrometer. The implications impact a wide variety of fields wherever fast electrons (or alpha particles, ions, etc.) and/or Fano statistics are used; this includes, inter alia, quantum computing, x-ray excitonics, space science, optoelectronics, nuclear engineering, particle physics, photovoltaics, and even neural response variability in the brain.

Three-dimensional isotropic imaging of live suspension cells enabled by droplet microvortices

Fast, nondestructive three-dimensional (3D) imaging of live suspension cells remains challenging without substrate treatment or fixation, precluding scalable single-cell morphometry with minimal alterations. While optical sectioning techniques achieve 3D live cell imaging, lateral versus depth resolution differences further complicate analysis. We present a scalable microfluidic method capable of 3D fluorescent isotropic imaging of live, nonadherent cells suspended inside picoliter droplets with high-speed single-cell volumetric readout (800 to 1,200 slices in 5 to 8 s) and near-diffraction limit resolution (~216 nm). The platform features a droplet trap array that leverages flow-induced droplet interfacial shear to generate intradroplet microvortices, which rotate single cells on their axis to enable optical projection tomography (OPT)-based imaging. This allows gentle (~1 mPa shear stress) observation of cells encapsulated inside nontoxic isotonic buffer droplets, facilitating scalable OPT acquisition by simultaneous spinning of hundreds of cells. We demonstrate 3D imaging of live myeloid and lymphoid cells in suspension, including K562 cells, as well as naive and activated T cells-small cells prone to movement in their suspended phenotype. Our fully suspended, orientation-independent cell morphometry, driven by isotropic imaging and spherical harmonic analysis, enabled the study of primary T cells across various immunological activation states. This approach unveiled six distinct nuclear content distributions, contrasting with conventional 2D images that typically portray spheroid and bean-like nuclear shapes associated with lymphocytes. Our arrayed-droplet OPT technology is capable of isotropic, single live-cell 3D imaging, with the potential to perform large-scale morphometry of immune cell effector function states while providing compatibility with microfluidic droplet operations.

How Two‐Dimensional Heterocovariance Spectroscopy and Data Fusion Level out the Inadequacies of Compact Spectrometers

ABSTRACTOver the past decade, sensors and compact spectrometers have emerged as a powerful means for real‐time monitoring of chemical and biochemical processes in the field of process analytical technologies (PATs). This is largely attributed to cost‐efficiency and their robustness in near‐line applications. In comparison to more sophisticated laboratory instruments, these devices exhibit limited sensitivity and resolution. In this study, a combination of near‐infrared, low‐field 1H nuclear magnetic resonance and compact Raman spectrometers were employed for the process monitoring of the acid‐catalyzed esterification of isoamyl alcohol and acetic acid to isoamyl acetate. The resulting real‐time data were transformed and visualized for interpretation using two‐dimensional heterocovariance spectroscopy. The data were also subjected to pretreatment, concatenation, and multivariate analysis in accordance with low‐ and mid‐level data fusion. The spectral interpretation derived from heterocovariance spectroscopy provided support for the data pretreatment during data fusion. By applying these computational techniques, the inherent limitations of sensitivity and resolution associated with compact spectroscopic instruments could be overcome, thereby facilitating the interpretation of data and yielding further insights into the process under study. A comparison of the process parameters resulting from the applied methods indicated that consistent data could be obtained. This study demonstrates that heterocovariance spectroscopy and data fusion allow to enhance compact, less expensive analytical instruments for process monitoring and to acquire process knowledge. This, in turn, enables material, financial, and energy resources to be conserved.

Atomic-scale visualization of defect-induced localized vibrations in GaN.

Phonon engineering is crucial for thermal management in GaN-based power devices, where phonon-defect interactions limit performance. However, detecting nanoscale phonon transport constrained by III-nitride defects is challenging due to limited spatial resolution. Here, we used advanced scanning transmission electron microscopy and electron energy loss spectroscopy to examine vibrational modes in a prismatic stacking fault in GaN. By comparing experimental results with ab initio calculations, we identified three types of defect-derived modes: localized defect modes, a confined bulk mode, and a fully extended mode. Additionally, the PSF exhibits a smaller phonon energy gap and lower acoustic sound speeds than defect-free GaN, suggesting reduced thermal conductivity. Our study elucidates the vibrational behavior of a GaN defect via advanced characterization methods and highlights properties that may affect thermal behavior.

Highly Amplified Broadband Ultrasound in Antiresonant Hollow Core Fibers

High‐frequency broadband ultrasound in nested antiresonant hollow core fibers (NANFs) is investigated for the first time. NANFs have remarkable features enabling high‐resolution microscale optoacoustic imaging sensors and neurostimulators. Solid optical fibers have been successfully employed to measure and generate ultrasonic signals, however, they face issues concerning attenuation, limited frequency range, bandwidth, and spatial resolution. Herein, highly efficient ultrasonic propagation in NANFs from 10 to 100 MHz is numerically demonstrated. The induced pressures and sensing responsivity are evaluated in detail, and important parameters for the development of ultrasonic devices are reviewed. High pressures (up to 234 MPa) and sensing responsivities (up to −207 dB) are tuned over 90 MHz range by changing the diameters of two distinct NANF geometries. To the best of knowledge, this is the widest bandwidth reported using similar diameter fibers. The results are a significant advance for fiber‐based ultrasonic sensors and transmitters, contributing to improve their efficiency and microscale spatial resolution for the detection, diagnosis, and treatment of diseases in biomedical applications.

Laser Scan Compression for Rail Inspection

The automation of rail track inspection addresses key issues in railway transportation, notably reducing maintenance costs and improving safety. However, it presents numerous technical challenges, including sensor selection, calibration, data acquisition, defect detection, and storage. This paper introduces a compression method tailored for laser triangulation scanners, which are crucial for scanning the entire rail track, including the rails, rail fasteners, sleepers, and ballast, and capturing rail profiles for geometry measurement. The compression technique capitalizes on the regularity of rail track data and the sensors’ limited measurement range and resolution. By transforming scans, they can be stored using widely available image compression formats, such as PNG. This method achieved a compression ratio of 7.5 for rail scans used in the rail geometry computation and maintained rail gauge reproducibility. For the scans employed in defect detection, a compression ratio of 5.6 was attained without visibly compromising the scan quality. Lossless compression resulted in compression ratios of 5.1 for the rail geometry computation scans and 3.8 for the rail track inspection scans.

Lamb Wave TDTE Super-Resolution Imaging Assisted by Deep Learning

Abstract Ultrasonic Lamb waves undergo complex mode conversion and diffraction at non-penetrating defects, such as plate corrosion and cracks. Lamb wave imaging has a resolution limit due to the guided wave dispersion characteristics and Rayleigh criterion limitations. In this paper, a full convolutional network is designed to segment and reconstruct the received signals, enabling the automatic identification of target modalities. This approach eliminates clutter and mode conversion interference when calculating direct and accompanying acoustic fields in time-domain topological energy imaging. Subsequently, the measured accompanying acoustic field is reversed for adaptive focusing on defects and enhance the imaging quality. To circumvent the limitations of the Rayleigh criterion, the direct acoustic field and the accompanying acoustic field were fused to characterize the pixel distribution in the imaging region, achieving Lamb wave super-resolution imaging. Experimental results indicate that compared to the sign coherence factor-total focusing method (SCF-TFM), the proposed method achieves a 31.41% improvement in lateral resolution and a 29.53% increase in signal-to-noise ratio for single-blind-hole defects. In the case of multiple-blind-hole defects with spacings greater than the Rayleigh criterion resolution limit, it exhibits a 27.23% enhancement in signal-to-noise ratio. On the contrary, when the defect spacings are relatively smaller than the limit, this method has a higher resolution limit than SCF-TFM in super-resolution imaging.

Development of a city-level surface ozone forecasting system using deep learning techniques and air quality model: Application in eastern China

Utilizing regional air quality models to accurately forecast surface ozone (O3) concentrations, particularly high concentrations, is essential for protecting public health. However, forecasts of air quality model often deviate from site observations due to the limitation of grid resolution and uncertainties from emission sources, meteorological conditions, and chemical reaction mechanisms. Especially, the underestimation is significant under condition of high O3 concentrations. Moreover, such deviations tend to accumulate as forecast lead time increases, compounding the challenges associated with reliable air quality forecast. In this study, we employed AlexNet architecture, a classical convolutional neural network, combined with multiple variables related to meteorology, chemistry, emission and geography to establish a non-linear relationship between grid-scale input variables and site-scale hourly O3 forecast biases in Eastern China, aiming to realize accurate city-level ozone forecast based on a regional air quality prediction model (i.e., Nested Air Quality Prediction Model System, NAQPMS). By assigning weights to high-bias samples and high-concentration samples within the loss function, the proposed Weighted AlexNet model (W_AlexNet) effectively reduced forecast biases and enhanced its capability to predict O3 pollution levels. Compared to NAQPMS, W_AlexNet model demonstrated a 25.71% improvement in RMSE and a 7.17% increase in IOA averagely for hourly O3 (O3-1h) forecasts across four different lead times (24-h, 48-h, 72-h, and 96-h). Notably, W_AlexNet model alleviated the tendency of NAQPMS to underestimate high concentrations and showed a superior performance in improving O3-1h pollution level forecasts, particularly for the 72-h and 96-h lead times. W_AlexNet model can effectively mitigate the bias accumulation effect over increasing lead times, thereby enhancing the reliability of longer-term forecasts. Thus, the W_AlexNet model serves as a post-processing model that can calibrate forecast biases in air quality prediction models, significantly improving the accuracy of O3 high concentration forecasts and providing more precise early warnings of O3 pollution. This underscores its utility in air quality management.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

Scanless Spectral Imaging of Terahertz Vortex Beams Generated by High‐Resolution 3D‐Printed Spiral Phase Plates

Terahertz technology has experienced significant advances in the past years, leading to new applications in the fields of spectroscopy, imaging, and communications. This progress requires the development of dedicated optics to effectively direct, control and manipulate terahertz radiation. In this regard, 3D printing technologies have shown great potential, offering fast prototyping, high design flexibility, and good reproducibility. While traditional 3D printing techniques allow for the preparation of terahertz optical components operating at relatively low frequencies (<0.4 THz) due to their limited resolution, two‐photon polymerization lithography (TPL) exhibits high detail resolution and low surface roughness and can thus potentially enable the fabrication of high‐frequency terahertz devices. Here, as a proof of principle, spiral phase plates operating at 1 THz are designed and fabricated by means of TPL. Moreover, these samples are characterized via a rapid and scanless terahertz imaging technique customized to obtain a coherent hyperspectral analysis of the generated vortex beams at varying distances along propagation. Numerical simulations are also conducted for comparison with experiments, revealing a good agreement. Current limitations of the technique are found to be mainly related with terahertz loss in TPL polymers, and possible solutions are discussed.

Short-time adaptive compact kernel distribution for fault diagnosis under variable working condition bearing

Time-frequency distribution (TFD) methods are extensively employed in the diagnosis of bearings operating under variable working conditions. However, these methods often face high computational complexity, limited time-frequency resolution, and cross-terms. To address these issues, this paper presents a new approach called Short-Time Adaptive Compact Kernel Distribution (STACKD). Based on the characteristics of the vibration signals, we designed Chirp-modulated Gaussian Window and used them to segment the overall signal into small segments. Then, we used a two-step Bayesian optimization method to obtain adaptive compact kernel distributions (ACKD) for these small segments of the signal. Finally, we recombined the ACKD of these small segments into a global STACKD. Results from simulations and experiments demonstrate that STACKD offers improved robustness against interference signals, lower computational complexity, and higher time-frequency resolution without cross-term effects. This method is tailored for diagnosing rolling bearings under variable working conditions, providing enhanced visualization of diagnostic processes with superior time-frequency resolution.

Electromagnetic Landau Resonance: Nonlinear Dynamics and Harmonic Generation

AbstractIn situ particle observations provide useful information for determining the significance of nonlinearity in wave‐particle interactions. Here, we examine an electromagnetic Landau resonance event observed by the Magnetospheric Multiscale mission near the dayside magnetopause along this line of thought. During this event, protons with energy 748 eV and pitch angle are observed to resonate with electromagnetic‐ion‐cyclotron waves. Detailed data analysis reveals the existence of a second harmonic signal in proton modulation. Using an observation‐based test particle simulation, this signal is explained as a low‐resolution manifestation of phase space rolled‐up structures, which themselves are not observable due to limited instrument resolution. The presence of these rolled‐up structures suggests that the waves induce significant displacement of the protons in phase space, consequently confirming the nonlinear nature of the observed interaction. Based on the observations, we further discuss the possibility of the nonlinear electromagnetic Landau resonance generating second harmonic waves.

Wood trade regions of Russia: clustering approach development

Network analysis of the Russian timber industry was based on the information about timber transactions between Russian companies in 2020. The data were collected from the Unified State Automated Information System Accounting Timber and Transactions with It. The method of graph clustering was developed. The first step is clustering of the hole graph by Leiden algorithm. The second step is drawing of each Leiden created cluster in abstract space using Fruchterman–Reingold layout and extraction of clusters from layout using meanshift algorithm. Such approach helped us to divide many Leiden-created clusters into components. This algorithm has a problem of resolution limit. It tends to combine some medium and small clusters into large ones. Also, a special method was developed to limit the uncertainty of clustering – both Leiden algorithm and Fruchterman–Reingold layout are non-deterministic algorithms. Wood trade clusters vary in size and content. They are rather autonomous. The average share of intra-cluster trade is 89%. The modularity score of our partition is 0.863. Each cluster has been described using open sources from the Internet. The content of the clusters does not contradict the logic of the wood industry processes. The clusters create rather compact areas on the map – wood trade regions. Their borders often overlap with existing administrative boundaries. Five types of network structure of clusters were defined: vertical monocentric, vertical polycentric, horizontal, dendritic, simple. There are three factors of clustering in wood industry of Russia: production chains (4 types – pulp, plywood, lumber, chipboard chains), demand chains (redistribution of raw material between manufacturers within the cluster, accumulation of wood volumes for some enterprises outside the cluster, wood trade between traders within the cluster), common holder. We have shown that existing approaches to cluster detection based on location quotients often misrepresent economic networks.

Neodymium-Facilitated Visualization of Extreme Phosphate Accumulation in Fibroblast Filopodia: Implications for Intercellular and Cell–Matrix Interactions

A comprehensive understanding of intercellular and cell–matrix interactions is essential for advancing our knowledge of cell biology. Existing techniques, such as fluorescence microscopy and electron microscopy, face limitations in resolution and sample preparation. Supravital lanthanoid staining provides new opportunities for detailed visualization of cellular metabolism and intercellular interactions. This study aims to describe the structure, elemental chemical, and probable origin of zones of extreme lanthanoid (neodymium) accumulation that form during preparation for scanning electron microscopy (SEM) analysis in corneal fibroblasts filopodia. The results identified three morphological patterns of neodymium staining in fibroblast filopodia, each exhibiting asymmetric staining within a thin, sharp, and extremely bright barrier zone, located perpendicular to the filopodia axis. Semi-quantitative chemical analyses showed neodymium-labeled non-linear phosphorus distribution within filopodia, potentially indicating varying phosphate anion concentrations and extreme phosphate accumulation at a physical or physicochemical barrier. Phosphorus zones labeled with neodymium did not correspond to mitochondrial clusters. During apoptosis, the number of filopodia with extreme and asymmetric phosphorus accumulation increases. Supravital lanthanoid staining coupled with SEM allows detailed visualization of intercellular and cell–matrix interactions with high contrast and resolution. These results enhance our understanding of phosphate anion accumulation and transfer mechanisms in cells under normal conditions and during apoptosis.

An Adaptive SCG-ECG Multimodal Gating Framework for Cardiac CTA.

Cardiovascular disease (CVD) is the leading cause of death worldwide. Coronary artery disease (CAD), a prevalent form of CVD, is typically assessed using catheter coronary angiography (CCA), an invasive, costly procedure with associated risks. While cardiac computed tomography angiography (CTA) presents a less invasive alternative, it suffers from limited temporal resolution, often resulting in motion artifacts that degrade diagnostic quality. Traditional ECG-based gating methods for CTA inadequately capture cardiac mechanical motion. To address this, we propose a novel multimodal approach that enhances CTA imaging by predicting cardiac quiescent periods using seismocardiogram (SCG) and ECG data, integrated through a weighted fusion (WF) approach and artificial neural networks (ANNs). We developed a regression-based ANN framework (r-ANN WF) designed to improve prediction accuracy and reduce computational complexity, which was compared with a classification-based framework (c-ANN WF), ECG gating, and US data. Our results demonstrate that the r-ANN WF approach improved overall diastolic and systolic cardiac quiescence prediction accuracy by 52.6% compared to ECG-based predictions, using ultrasound (US) as the ground truth, with an average prediction time of 4.83 ms. Comparative evaluations based on reconstructed CTA images show that both r-ANN WF and c-ANN WF offer diagnostic quality comparable to US-based gating, underscoring their clinical potential. Additionally, the lower computational complexity of r-ANN WF makes it suitable for real-time applications. This approach could enhance CTA's diagnostic quality, offering a more accurate and efficient method for CVD diagnosis and management.

Spatial resolution limit for a solid immersion lens

The solid immersion (SI) effect is widely used to increase the spatial resolution of optical focusing systems and even overcome the Abbe diffraction limit. Resolution enhancement offered by a SI lens is mostly a function of its geometry and refractive index nSI. While SI lenses are relatively well understood, the scaling of the resolution enhancement by such lenses is still a subject of debate, with some works reporting ≃nSI and ≃nSI2 dependencies for the hemispherical and hyperhemispherical SI lens configurations, respectively. In this paper, we offer a general argument for a resolution limit for SI optics and, then, verify it via the numerical analysis of the hemispherical and hyperhemispherical silicon SI lenses designed for the terahertz (THz) range. In fact, we find that there is no contradiction in the reported resolution enhancements ≃nSI and ≃nSI2; however, they happen in different operation regimes. We then demonstrate that the resolution values reported for the different SI lens arrangements in the visible (VIS), near-, and middle-infrared (NIR and MIR), as well as THz bands obey the derived limit. Our findings will be useful for the further design and applications of SI optics.

Wavelet‐Forward Family Enabling Stitching‐Free Full‐Field Fourier Ptychographic Microscopy

AbstractFourier ptychographic microscopy (FPM) breaks through the resolution limitations of conventional optical systems, which offer a full‐field view and high resolution without additional mechanical scanning. However, conventional image‐domain optimizations require trade‐offs between correction efficacy, data redundancy, and reconstruction accuracy. Furthermore, the existing linear time‐invariant model for actual nonlinear, time‐varying optical systems leads to forward model mismatch, complicating the corrections of the vignetting effect. To overcome these challenges and achieve stitching‐free FPM, a family of forward wavelet‐transform models (WL‐FPM) is proposed. WL‐FPM employs the reversibility of the wavelet transform for high‐fidelity reconstruction in the multiscale feature domain. The wavelet loss function is updated in each iteration, and non‐convex optimization is solved by complex back diffraction. WL‐FPM offers stitching‐free, high‐resolution, and robust reconstruction under various challenging conditions, including vignetting effects, LED position mismatch, intensity fluctuations, and high‐level noise environments, which outperform conventional FPM methods. Under a 4X objective with NA 0.1, WL‐FPM achieves a 435‐nm resolution and stitching‐free full‐field reconstruction of a 3.328 × 3.328 mm2 pathological section with distinct subcellular organelles. In live cell imaging, it provides a full‐field observation with distinct lipids in a single cell. A large number of simulation and experimental results demonstrate its potential for biomedical applications.

The influence of major faults and fractures on the development of non-matrix porosity system in a pre-salt carbonate reservoir, Santos Basin – Brazil

Faults and fractures are central for characterizing the permeability distribution in carbonate reservoirs since they act as pathways for diagenetic fluids that often favor intense rock dissolution and permeability. Usually, high permeability zones and fractures are not easily recognized in seismic data due to limited resolution and they are often associated with higher concentrations of hydrocarbons or even significant fluid losses during drilling, thus creating a challenge for hydrocarbon exploration. In the Santos Basin, southeast Brazil, the pre-salt carbonate reservoirs from the Barra Velha Formation (BVE) are the main hydrocarbon producers in Brazilian Atlantic margin and well-known for being extremely heterogeneous, exhibiting complex dual-porosity systems. In this study, we built a conceptual model of these fracture zones and non-matrix porosity formation that helped narrowing the understanding of these complex systems. The characterization of faults and fractures was carried out using seismic, well-logs, and borehole image data to understand the influence of these structures in the porosity formation along the Barra Velha Formation. In the study area, three fault sets were defined (F1, F2, and F3) from seismic data. F1 represents to the larger faults, while the F3 fault set represents the smaller faults related to the reactivation of F1; both sets being oriented NE-SW. The F2 fault set is associated with the rift formation and is oriented to NNE-SSW. These three fault sets compartmentalized the studied area into different domains, each exhibiting distinct fracture sets. The natural open fractures were formed during the reactivation of rift faults and are oriented mainly NW, NNE-NNW, NE, and ENE and were identified across the entire study area, but with different intensity values. The fracture intensity closely relates to the distance from major faults where the wells with the highest fracture intensity occurs located 150–590 m from the larger F1 fault set. Scan-lines were conducted throughout the area to determine the fault width, and an average value of 1.2 km was established. Seven non-matrix porosity classes were characterized and classified into stratigraphically concordant and discordant non-matrix pore types at well scale through borehole image interpretation. The Barra Velha Formation exhibit higher occurrence of stratigraphically discordant non-matrix porosity related to fractured zones while stratigraphically concordant non-matrix porosity is mainly controlled by the paleotopography of the study area. Overall, non-matrix porosity formation tends to follow an orientation that suggests a preferential dissolution flow towards NE and ENE directions. Intervals with higher silica content shows a positive correlation with both fracture intensity and stratigraphically discordant non-matrix porosities. This work provides a conceptual model about the fractures and non-matrix porosity distribution in pre-salt carbonate rocks that can help address some of associated structural and stratigraphic uncertainties during field appraisal and development.

Dynamic sparse x-ray nanotomography reveals ionomer hydration mechanism in polymer electrolyte fuel-cell catalyst.

Tomographic imaging of time-evolving samples is a challenging yet important task for various research fields. At the nanoscale, current approaches face limitations of measurement speed or resolution due to lengthy acquisitions. We developed a dynamic nanotomography technique based on sparse dynamic imaging and 4D tomography modeling. We demonstrated the technique, using ptychographic x-ray computed tomography as its imaging modality, on resolving the in situ hydration process of polymer electrolyte fuel cell (PEFC) catalyst. The technique provides a 40-time increase in temporal resolution compared to conventional approaches, yielding 28 nm half-period spatial and 12 min temporal resolution. The results allow a quantitative characterization of the water intake process inside PEFC catalysts with nanoscale resolution, which is crucial for understanding their electrochemical mechanisms and optimizing their performance. Our technique enables high-speed operando nanotomography studies and paves the way for wider application of dynamic tomography at the nanoscale.

Full-Waveform Inversion Imaging of Cortical Bone Using Phased Array Tomography.

Classic ultrasound bone imaging modalities usually demand either a prior knowledge or an advanced estimation on speed of sound (SoS), which not only renders to a burdensome imaging process but also supplies a limited resolution. To overcome these drawbacks, this article proposed a frequency-domain full-waveform inversion (FDFWI) modality using phased array tomography for high-accuracy cortical bone imaging. A transmission scenario of ultrasound wave in 2-D space was presented in the frequency domain to simulate the forward wavefield propagation. Iterations in the inversion process were performed by matching the simulation wavefield to the experimental one from low to high discrete frequency points. Moreover, the association between the maximum initial frequency and the initial SoS model was explored to prevent the occurrence of cycle-skipping phenomenon, which could lead to the outcomes being trapped in local minima. The feasibility and effectiveness of the proposed imaging scheme were testified by simulation, phantom, and ex-vivo studies, with mean relative errors of cortical part being 3.18%, 8.71%, and 9.36%, respectively. It is verified that the proposed FDFWI method is an effective way for parametric imaging of cortical bone without any prior knowledge of sound speed.