Coupled Experimental-Theoretical Characterization of a Carbon Electrode in Vanadium Redox Flow Batteries using X-ray Absorption Spectroscopy.

Vanadium redox flow batteries (VRFBs) have emerged as promising solutions for stationary grid energy storage due to their high efficiency, scalability, safety, near room-temperature operation conditions, and the ability to independently size power and energy capacities. The performance of VRFBs heavily relies on the redox couple reactions of V2+/V3+ and VO2+/VO2+ on carbon electrodes. Therefore, a thorough understanding of the surface functionality of carbon electrodes and their propensity for degradation during electrochemical cycles is crucial for designing VRFBs with extended lifespans. In this study, we present a coupled experimental-theoretical approach based on carbon K edge X-ray absorption spectroscopy (XAS) to characterize carbon electrodes prepared under different conditions and identify relevant functional groups that contribute to unique spectroscopic features. Atomic models were created to represent functional groups, such as hydroxyl, carboxyl, methyl, and aldehyde, bonded to carbon atoms in either sp2 or sp3 environments. The interactions between functionalized carbon and various solvated vanadium complexes were modeled using density functional theory. A library of carbon K-edge XAS spectra was generated for distinct carbon atoms in different functional groups, both before and after interacting with solvated vanadium complexes. We demonstrate how these simulated spectra can be used to deconvolve ex situ experimental spectra measured from carbon electrodes and to track changes in the electrode composition following immersion in different electrolytes or extended cycling within a functional VRFB. By doing so, we identify the active species present on the carbon electrodes, which play a crucial role in determining their electrochemical performance.

Learning CT-free attenuation-corrected total-body PET images through deep learning.

OBJECTIVES: Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Therefore, we attempted to generate CT-free attenuation-corrected (CTF-AC) total-body PET images through deep learning. METHODS: Based on total-body PET data from 122 subjects (29 females and 93 males), a well-established cycle-consistent generative adversarial network (Cycle-GAN) was employed to generate CTF-AC total-body PET images directly while introducing site structures as prior information. Statistical analyses, including Pearson correlation coefficient (PCC) and t-tests, were utilized for the correlation measurements. RESULTS: The generated CTF-AC total-body PET images closely resembled real AC PET images, showing reduced noise and good contrast in different tissue structures. The obtained peak signal-to-noise ratio and structural similarity index measure values were 36.92 ± 5.49 dB (p < 0.01) and 0.980 ± 0.041 (p < 0.01), respectively. Furthermore, the standardized uptake value (SUV) distribution was consistent with that of real AC PET images. CONCLUSION: Our approach could directly generate CTF-AC total-body PET images, greatly reducing the radiation risk to patients from redundant anatomical examinations. Moreover, the model was validated based on a multidose-level NAC-AC PET dataset, demonstrating the potential of our method for low-dose PET attenuation correction. In future work, we will attempt to validate the proposed method with total-body PET/CT systems in more clinical practices. CLINICAL RELEVANCE STATEMENT: The ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radiopharmaceutical doses in total-body PET/CT. Our CT-free PET attenuation correction method would be beneficial for a wide range of patient populations, especially for pediatric examinations and patients who need multiple scans or who require long-term follow-up. KEY POINTS: • CT is the main source of radiation in PET/CT imaging, especially for total-body PET/CT devices, and reduced radiopharmaceutical doses make the radiation burden from CT more obvious. • The CT-free PET attenuation correction method would be beneficial for patients who need multiple scans or long-term follow-up by reducing additional radiation from redundant anatomical examinations. • The proposed method could directly generate CT-free attenuation-corrected (CTF-AC) total-body PET images, which is beneficial for PET/MRI or PET-only devices lacking CT image poses.

Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images.

BACKGROUND: Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images. METHODS: In this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image. RESULTS: A quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min. CONCLUSIONS: The proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

Feasibility of a deep learning algorithm to achieve the low-dose 68Ga-FAPI/the fast-scan PET images: a multicenter study.

OBJECTIVES: Our work aims to study the feasibility of a deep learning algorithm to reduce the 68Ga-FAPI radiotracer injected activity and/or shorten the scanning time and to investigate its effects on image quality and lesion detection ability. METHODS: The data of 130 patients who underwent 68Ga-FAPI positron emission tomography (PET)/CT in two centers were studied. Predicted full-dose images (DL-22%, DL-28% and DL-33%) were obtained from three groups of low-dose images using a deep learning method and compared with the standard-dose images (raw data). Injection activity for full-dose images was 2.16 ± 0.61 MBq/kg. The quality of the predicted full-dose PET images was subjectively evaluated by two nuclear physicians using a 5-point Likert scale, and objectively evaluated by the peak signal-to-noise ratio, structural similarity index and root mean square error. The maximum standardized uptake value and the mean standardized uptake value (SUVmean) were used to quantitatively analyze the four volumes of interest (the brain, liver, left lung and right lung) and all lesions, and the lesion detection rate was calculated. RESULTS: Data showed that the DL-33% images of the two test data sets met the clinical diagnosis requirements, and the overall lesion detection rate of the two centers reached 95.9%. CONCLUSION: Through deep learning, we demonstrated that reducing the 68Ga-FAPI injected activity and/or shortening the scanning time in PET/CT imaging was feasible. In addition, 68Ga-FAPI dose as low as 33% of the standard dose maintained acceptable image quality. ADVANCES IN KNOWLEDGE: This is the first study of low-dose 68Ga-FAPI PET images from two centers using a deep learning algorithm.

A Novel Three-Channel Endorectal Coil for Prostate Magnetic Resonance Imaging at 3T.

OBJECTIVE: The purpose of this work is to develop a 3-channel endorectal coil (ERC-3C) structure to obtain higher signal-to-noise (SNR) and better parallel imaging performance for prostate magnetic resonance imaging (MRI) at 3T. METHODS: The coil performance was validated by in vivo studies and the SNR, g-factor, and diffusion-weighted imaging (DWI) were compared. A 2-channel endorectal coil (ERC-2C) with two orthogonal loops and a 12-channel external surface coil were employed for comparison. RESULTS: Compared with the ERC-2C with a quadrature configuration and the external 12-channel coil array, the proposed ERC-3C improved SNR performance by 23.9% and 428.9%, respectively. The improved SNR enables the ERC-3C to produce spatial high-resolution images of 0.24 mm × 0.24 mm × 2 mm (0.1152 µL) in the prostate area within 9 minutes. CONCLUSION: We developed an ERC-3C and validated its performance through in vivo MR imaging experiments. SIGNIFICANCE: The results demonstrated the feasibility of an ERC with more than two channels and that a higher SNR can be achieved using the ERC-3C compared with an orthogonal ERC-2C of the same coverage.

Automated morphologic analysis of intracranial and extracranial arteries using convolutional neural networks.

OBJECTIVE: To develop an automated method for 3D magnetic resonance (MR) vessel wall image analysis to facilitate morphologic quantification of intra- and extracranial arteries, including vessel centerline tracking, vessel straightening and reformation, vessel wall segmentation based on convoluted neural networks (CNNs), and morphological measurement. METHODS: MR vessel wall images acquired using DANTE-SPACE sequences and corresponding time-of-flight-MRA images of 67 subjects (including 47 healthy volunteers and 20 patients with atherosclerosis) were included in this study. The centerline of the vessel was firstly extracted from the MRA images and copyed to the 3D MR vessel wall images through the registration relationship between the MRA images and the MR vessel wall images to extract, straighten, and reconstruct interested vessel segments into 2D slices. Then a complete CNN-based Unet-like method was used to automatically segment the vessel wall to obtain quantitative morphological measurements such as maximum wall thicknesses and normalized wall index (NWI). RESULTS: The proposed automatic segmentation network was trained and validated with 11,735 slices and tested on 2517 slices. The method showed satisfactory agreement with manual segmentation method. The Dice coefficients of intracranial arteries were 0.90 for lumen and 0.78 for vessel wall, while the Dice coefficients of extracranial arteries were 0.90 for lumen and 0.82 for vessel wall. The maximum wall thickness and NWI obtained from the proposed automatic method were slightly larger than those obtained from the manual method for both intra- and extracranial arteries. However, there was no significant difference of the quantitative measurements between the two methods (p > 0.05). In addition, the automatically measured NWI of plaque slice was significantly larger than that of normal slice. CONCLUSION: We propose an automatic analysis method of MR vessel wall images, which can realize automatic segmentation of intra- and extracranial vessel wall. It is expected to facilitate large-scale arterial vessel wall morphological quantification. ADVANCES IN KNOWLEDGE: We have proposed an automatic method for analysis of intra- and extracranial MR vessel wall images simultaneously based on CNN, which can facilitate large-scale quantitative analyses of vessel walls.

Flexible machine-learning interatomic potential for simulating structural disordering behavior of Li7La3Zr2O12 solid electrolytes.

Batteries based on solid-state electrolytes, including Li7La3Zr2O12 (LLZO), promise improved safety and increased energy density; however, atomic disorder at grain boundaries and phase boundaries can severely deteriorate their performance. Machine-learning (ML) interatomic potentials offer a uniquely compelling solution for simulating chemical processes, rare events, and phase transitions associated with these complex interfaces by mixing high scalability with quantum-level accuracy, provided that they can be trained to properly address atomic disorder. To this end, we report the construction and validation of an ML potential that is specifically designed to simulate crystalline, disordered, and amorphous LLZO systems across a wide range of conditions. The ML model is based on a neural network algorithm and is trained using ab initio data. Performance tests prove that the developed ML potential can predict accurate structural and vibrational characteristics, elastic properties, and Li diffusivity of LLZO comparable to ab initio simulations. As a demonstration of its applicability to larger systems, we show that the potential can correctly capture grain boundary effects on diffusivity, as well as the thermal transition behavior of LLZO. These examples show that the ML potential enables simulations of transitions between well-defined and disordered structures with quantum-level accuracy at speeds thousands of times faster than ab initio methods.

Deep Learning-Based Automated Detection of Arterial Vessel Wall and Plaque on Magnetic Resonance Vessel Wall Images.

Purpose: To develop and evaluate an automatic segmentation method of arterial vessel walls and plaques, which is beneficial for facilitating the arterial morphological quantification in magnetic resonance vessel wall imaging (MRVWI). Methods: MRVWI images acquired from 124 patients with atherosclerotic plaques were included. A convolutional neural network-based deep learning model, namely VWISegNet, was used to extract the features from MRVWI images and calculate the category of each pixel to facilitate the segmentation of vessel wall. Two-dimensional (2D) cross-sectional slices reconstructed from all plaques and 7 main arterial segments of 115 patients were used to build and optimize the deep learning model. The model performance was evaluated on the remaining nine-patient test set using the Dice similarity coefficient (DSC) and average surface distance (ASD). Results: The proposed automatic segmentation method demonstrated satisfactory agreement with the manual method, with DSCs of 93.8% for lumen contours and 86.0% for outer wall contours, which were higher than those obtained from the traditional U-Net, Attention U-Net, and Inception U-Net on the same nine-subject test set. And all the ASD values were less than 0.198 mm. The Bland-Altman plots and scatter plots also showed that there was a good agreement between the methods. All intraclass correlation coefficient values between the automatic method and manual method were greater than 0.780, and greater than that between two manual reads. Conclusion: The proposed deep learning-based automatic segmentation method achieved good consistency with the manual methods in the segmentation of arterial vessel wall and plaque and is even more accurate than manual results, hence improved the convenience of arterial morphological quantification.

Understanding Hydrogenation Chemistry at MgB2 Reactive Edges from Ab Initio Molecular Dynamics.

Solid-state hydrogen storage materials often operate via transient, multistep chemical reactions at complex interfaces that are difficult to capture. Here, we use direct ab initio molecular dynamics simulations at accelerated temperatures and hydrogen pressures to probe the hydrogenation chemistry of the candidate material MgB2 without a priori assumption of reaction pathways. Focusing on highly reactive (101̅0) edge planes where initial hydrogen attack is likely to occur, we track mechanistic steps toward the formation of hydrogen-saturated BH4- units and key chemical intermediates, involving H2 dissociation, generation of functionalities and molecular complexes containing BH2 and BH3 motifs, and B-B bond breaking. The genesis of higher-order boron clustering is also observed. Different charge states and chemical environments at the B-rich and Mg-rich edge planes are found to produce different chemical pathways and preferred speciation, with implications for overall hydrogenation kinetics. The reaction processes rely on B-H bond polarization and fluctuations between ionic and covalent character, which are critically enabled by the presence of Mg2+ cations in the nearby interphase region. Our results provide guidance for devising kinetic improvement strategies for MgB2-based hydrogen storage materials, while also providing a template for exploring chemical pathways in other solid-state energy storage reactions.

First-Principles Elucidation of Initial Dehydrogenation Pathways in Mg(BH4)2.

Complex borohydrides such as Mg(BH4)2 offer one of highest capacities to chemically store hydrogen for onboard applications; however, it suffers greatly from kinetic constraints that prevent realization of full capacity and reversibility. Understanding these kinetic limitations solely from experiments is extremely challenging due to the unusual complexity of various competing elemental reaction steps involved during the de/rehydrogenation reaction. This work aims to map out the energetics associated with initial dehydrogenation of Mg(BH4)2 from first-principles simulations and to identify the preferred reaction pathways. Our calculations suggest the rate-limiting step during BH4--B3H8- conversion is the formation of the B2H7- intermediate. We further emphasize and clarify that the B3H8- and H- intermediates, formed during initial Mg(BH4)2 decomposition, appear as molecular species that are embedded in the Mg-BH4-Mg matrix as evidenced in the nuclear magnetic resonance measurements and not as bulk MgH2 and Mg(B3H8)2 as previously assumed in theoretical predictions of the thermodynamics.

Spontaneous dynamical disordering of borophenes in MgB2 and related metal borides.

Layered boron compounds have attracted significant interest in applications from energy storage to electronic materials to device applications, owing in part to a diversity of surface properties tied to specific arrangements of boron atoms. Here we report the energy landscape for surface atomic configurations of MgB2 by combining first-principles calculations, global optimization, material synthesis and characterization. We demonstrate that contrary to previous assumptions, multiple disordered reconstructions are thermodynamically preferred and kinetically accessible within exposed B surfaces in MgB2 and other layered metal diborides at low boron chemical potentials. Such a dynamic environment and intrinsic disordering of the B surface atoms present new opportunities to realize a diverse set of 2D boron structures. We validated the predicted surface disorder by characterizing exfoliated boron-terminated MgB2 nanosheets. We further discuss application-relevant implications, with a particular view towards understanding the impact of boron surface heterogeneity on hydrogen storage performance.

Controlled Experiments and Optimized Theory of Absorption Spectra of Li Metal and Salts.

Investigation of Li metal and ionic compounds through experimental and theoretical spectroscopy has been of tremendous interest due to their prospective applications in Li-metal and Li-ion batteries. Li K-edge soft X-ray absorption spectroscopy (sXAS) provides the most direct spectroscopic characterization; unfortunately, due to the low core-level energy and the highly reactive surface, Li-K sXAS of Li metal has been extremely challenging, as evidenced by many controversial reports. Here, through controlled and ultra-high energy resolution experiments of two kinds of in situ prepared samples, we report the intrinsic Li-K sXAS of Li-metal that displays a prominent leading peak that has not been revealed before. Furthermore, theoretical simulations show that, due to the low number of valence electrons in Li, the Li-K sXAS is strongly affected by the response of the valence electrons to the core hole. We successfully reproduce the Li-K sXAS by state-of-the-art calculations with considerations of a number of relevant parameters such as temperature, energy resolution, and, especially, contributions from transitions which are forbidden in the single-particle treatment. Such a comparative experimental and theoretical investigation is further extended to a series of Li ionic compounds, which highlight the importance of considering the total and single-particle energies for obtaining an accurate alignment of the spectra. Our work provides the first reliable Li-K sXAS of the Li metal surface with advanced theoretical calculations. The experimental and theoretical results provide a critical benchmark for studying Li chemistry in both metallic and ionic states.

T2-Weighted Whole-Brain Intracranial Vessel Wall Imaging at 3 Tesla With Cerebrospinal Fluid Suppression.

BACKGROUND: T2-weighted (T2w) intracranial vessel wall imaging (IVWI) provides good contrast to differentiate intracranial vasculopathies and discriminate various important plaque components. However, the strong cerebrospinal fluid (CSF) signal in T2w images interferes with depicting the intracranial vessel wall. In this study, we propose a T2-prepared sequence for whole-brain IVWI at 3T with CSF suppression. METHODS: A preparation module that combines T2 preparation and inversion recovery (T2IR) was used to suppress the CSF signal and was incorporated into the commercial three-dimensional (3D) turbo spin echo sequence-Sampling Perfection with Application optimized Contrast using different flip angle Evolution (SPACE). This new technique (hereafter called T2IR-SPACE) was evaluated on nine healthy volunteers and compared with two other commonly used 3D T2-weighted sequences: T2w-SPACE and FLAIR-SPACE (FLAIR: fluid-attenuated inversion recovery). The signal-to-noise ratios (SNRs) of the vessel wall (VW) and CSF and contrast-to-noise ratios (CNRs) between them were measured and compared among these three T2-weighted sequences. Subjective wall visualization of the three T2-weighted sequences was scored blindly and independently by two radiologists using a four-point scale followed by inter-rater reproducibility analysis. A pilot study of four stroke patients was performed to preliminarily evaluate the diagnostic value of this new sequence, which was compared with two conventional T2-weighted sequences. RESULTS: T2IR-SPACE had the highest CNR (11.01 ± 6.75) compared with FLAIR-SPACE (4.49 ± 3.15; p < 0.001) and T2w-SPACE (-56.16 ± 18.58; p < 0.001). The subjective wall visualization score of T2IR-SPACE was higher than those of FLAIR-SPACE and T2w-SPACE (T2IR-SPACE: 2.35 ± 0.59; FLAIR-SPACE: 0.52 ± 0.54; T2w-SPACE: 1.67 ± 0.58); the two radiologists' scores showed excellent agreement (ICC = 0.883). CONCLUSION: The T2IR preparation module markedly suppressed the CSF signal without much SNR loss of the other tissues (i.e., vessel wall, white matter, and gray matter) compared with the IR pulse. Our results suggest that T2IR-SPACE is a potential alternative T2-weighted sequence for assessing intracranial vascular diseases.

Reversing the Irreversible: Thermodynamic Stabilization of LiAlH4 Nanoconfined Within a Nitrogen-Doped Carbon Host.

A general problem when designing functional nanomaterials for energy storage is the lack of control over the stability and reactivity of metastable phases. Using the high-capacity hydrogen storage candidate LiAlH4 as an exemplar, we demonstrate an alternative approach to the thermodynamic stabilization of metastable metal hydrides by coordination to nitrogen binding sites within the nanopores of N-doped CMK-3 carbon (NCMK-3). The resulting LiAlH4@NCMK-3 material releases H2 at temperatures as low as 126 °C with full decomposition below 240 °C, bypassing the usual Li3AlH6 intermediate observed in bulk. Moreover, >80% of LiAlH4 can be regenerated under 100 MPa H2, a feat previously thought to be impossible. Nitrogen sites are critical to these improvements, as no reversibility is observed with undoped CMK-3. Density functional theory predicts a drastically reduced Al-H bond dissociation energy and supports the observed change in the reaction pathway. The calculations also provide a rationale for the solid-state reversibility, which derives from the combined effects of nanoconfinement, Li adatom formation, and charge redistribution between the metal hydride and the host.

Comparison of plaque characteristics of small and large subcortical infarctions in the middle cerebral artery territory using high-resolution magnetic resonance vessel wall imaging.

BACKGROUND: The characteristics of plaque that ultimately lead to different subcortical infarctions remain unclear. We explored the differences in plaque characteristics between patients with small subcortical infarction (SSI) and large subcortical infarction (LSI) of the middle cerebral artery (MCA) using high-resolution magnetic resonance vessel wall imaging (HR-MRVWI). METHODS: The study group comprised 71 patients (mean age, 47.49±11.5 years; 55 male) with MCA territory ischemic stroke. Whole-brain HR-MRVWI was performed using a three-dimensional T1-weighted variable-flip-angle turbo spin echo (SPACE) sequence. Patients were divided into SSI and LSI groups based on routine MRI images. Plaque distribution was classified as the superior, inferior, ventral, or dorsal wall of the MCA. The number of quadrants with plaque formation, location of plaque, plaque burden (PB), arterial remodeling pattern (positive or negative), and degree of stenosis were analyzed and compared between groups. RESULTS: Of the 71 patients, 43 (60.6%) and 28 (39.4%) were identified as the SSI and LSI groups, respectively. The proportion of plaques involving only one quadrant was significantly higher in the SSI group, and these plaques were located in the superior or dorsal MCA vessel wall. There was no significant difference between groups in the proportion of plaques involving two or more quadrants, plaque distribution, or PB. Most plaques in both groups showed positive remodeling, and the percentage of remodeling pattern was similar. A significantly higher incidence of low-grade stenosis (<50%) was observed in the SSI group. CONCLUSIONS: Both SSI and LSI may be associated with major intracranial artery atherosclerosis, but patients with SSI showed relatively fewer quadrants with plaque formation and a lesser degree of stenosis.

Accelerated 3D bSSFP Using a Modified Wave-CAIPI Technique With Truncated Wave Gradients.

The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady-state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bSSFP obtains up to 12× retrospective acceleration at 0.8 mm isotropic resolution in in vivo 3D brain experiments and is superior to the state-of-the-art Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) technique, according to both visual validation and quantitative analysis. Moreover, in vivo 3D spine and abdomen imaging demonstrate the potential clinical applications of Wave-bSSFP with fast acquisition speed, improved isotropic resolution and fine image quality.

Arterial culprit plaque characteristics revealed by magnetic resonance Vessel Wall imaging in patients with single or multiple infarcts.

PURPOSE: To investigate characteristics of intra- and extracranial arterial culprit plaques between patients with single infarct and multiple-infarcts by a head-neck combined high resolution magnetic resonance vessel wall imaging (HR-MRVWI). MATERIALS AND METHODS: Forty-three patients with recent ischemic stroke due to large artery atherosclerosis were enrolled. The head-neck combined HR-MRVWI was performed in all patients both pre- and post-contrast administration. Based on diffusion weighted imaging findings, patients were divided into single-infarction and multiple-infarction groups. For patients with anterior circulation ischemic stroke, they were also divided into perforating artery infarction (PAI) and non-PAI groups. Patient demographics, number and location of culprit plaques, artery stenosis percentage, intraplaque hemorrhage, and plaque enhancement were evaluated and compared between single-infarction and multiple-infarction groups, as well as between PAI and non-PAI groups. RESULTS: A total of 83 culprit plaques were identified. The artery stenosis degree was more severe and plaque enhancement more prominent in multiple-infarction group than in single-infarction group. Patients with multiple infarcts also had more culprit plaques per patient than those with single infarct, which contributed to the occurrence of multiple infarcts. For comparison of PAI and non-PAI groups, a higher artery stenosis percentage was observed in non-PAI group, and patients with non-PAI had more culprit plaques per patient, which contributed to a variety of infarct manifestations. CONCLUSION: A higher stenosis grade and higher number of culprit plaques seem to be associated with a higher number of cerebral infarcts in patients with large artery atherosclerosis.

Effects of artery input function on dynamic contrast-enhanced MRI for determining grades of gliomas.

OBJECTIVE: To evaluate the effect of artery input function (AIF) derived from different arteries for pharmacokinetic modeling on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in the grading of gliomas. METHODS: 49 patients with pathologically confirmed gliomas were recruited and underwent DCE-MRI. A modified Tofts model with different AIFs derived from anterior cerebral artery (ACA), ipsilateral and contralateral middle cerebral artery (MCA) and posterior cerebral artery (PCA) was used to estimate quantitative parameters such as Ktrans (volume transfer constant) and Ve (fractional extracellular-extravascular space volume) for distinguishing the low grade glioma from high grade glioma. The Ktrans and Ve were compared between different arteries using Two Related Samples Tests (TRST) (i.e. Wilcoxon Signed Ranks Test). In addition, these parameters were compared between the low and high grades as well as between the grade II and III using the Mann-Whitney U-test. A p-value of less than 0.05 was regarded as statistically significant. RESULTS: All the patients completed the DCE-MRI successfully. Sharp wash-in and wash-out phases were observed in all AIFs derived from the different arteries. The quantitative parameters (Ktrans and Ve) calculated from PCA were significant higher than those from ACA and MCA for low and high grades, respectively (p < 0.05). Despite the differences of quantitative parameters derived from ACA, MCA and PCA, the Ktrans and Ve from any AIFs could distinguish between low and high grade, however, only Ktrans from any AIFs could distinguish grades II and III. There was no significant correlation between parameters and the distance from the artery, which the AIF was extracted, to the tumor. CONCLUSION: Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas, however, only Ktrans can distinguish grades II and III. ADVANCES IN KNOWLEDGE: We sought to assess the effect of AIF on DCE-MRI for determining grades of gliomas. Both quantitative parameters Ktrans and Ve calculated using any AIF of ACA, MCA, and PCA can be used for distinguishing the low- from high-grade gliomas.

Nanoconfinement of Molecular Magnesium Borohydride Captured in a Bipyridine-Functionalized Metal-Organic Framework.

The lower limit of metal hydride nanoconfinement is demonstrated through the coordination of a molecular hydride species to binding sites inside the pores of a metal-organic framework (MOF). Magnesium borohydride, which has a high hydrogen capacity, is incorporated into the pores of UiO-67bpy (Zr6O4(OH)4(bpydc)6 with bpydc2- = 2,2'-bipyridine-5,5'-dicarboxylate) by solvent impregnation. The MOF retained its long-range order, and transmission electron microscopy and elemental mapping confirmed the retention of the crystal morphology and revealed a homogeneous distribution of the hydride within the MOF host. Notably, the B-, N-, and Mg-edge XAS data confirm the coordination of Mg(II) to the N atoms of the chelating bipyridine groups. In situ 11B MAS NMR studies helped elucidate the reaction mechanism and revealed that complete hydrogen release from Mg(BH4)2 occurs as low as 200 °C. Sieverts and thermogravimetric measurements indicate an increase in the rate of hydrogen release, with the onset of hydrogen desorption as low as 120 °C, which is approximately 150 °C lower than that of the bulk material. Furthermore, density functional theory calculations support the improved dehydrogenation properties and confirm the drastically lower activation energy for B-H bond dissociation.

A Mechanistic Analysis of Phase Evolution and Hydrogen Storage Behavior in Nanocrystalline Mg(BH4)2 within Reduced Graphene Oxide.

Magnesium borohydride (Mg(BH4)2, abbreviated here MBH) has received tremendous attention as a promising onboard hydrogen storage medium due to its excellent gravimetric and volumetric hydrogen storage capacities. While the polymorphs of MBH-alpha (α), beta (ß), and gamma (γ)-have distinct properties, their synthetic homogeneity can be difficult to control, mainly due to their structural complexity and similar thermodynamic properties. Here, we describe an effective approach for obtaining pure polymorphic phases of MBH nanomaterials within a reduced graphene oxide support (abbreviated MBHg) under mild conditions (60-190 °C under mild vacuum, 2 Torr), starting from two distinct samples initially dried under Ar and vacuum. Specifically, we selectively synthesize the thermodynamically stable α phase and metastable ß phase from the γ-phase within the temperature range of 150-180 °C. The relevant underlying phase evolution mechanism is elucidated by theoretical thermodynamics and kinetic nucleation modeling. The resulting MBHg composites exhibit structural stability, resistance to oxidation, and partially reversible formation of diverse [BH4]- species during de- and rehydrogenation processes, rendering them intriguing candidates for further optimization toward hydrogen storage applications.