Three-dimensional diffusion MRI using simultaneous multislab with blipped-CAIPI in a 4D k-space framework.

PURPOSE: To develop an efficient simultaneous multislab imaging method with blipped-controlled aliasing in parallel imaging (blipped-SMSlab) in a 4D k-space framework, and to demonstrate its efficacy in high-resolution diffusion MRI (dMRI). THEORY AND METHODS: First, the SMSlab 4D k-space signal expression is formulated, and the phase interferences from intraslab and interslab encodings on the same physical z-axis are analyzed. Then, the blipped-SMSlab dMRI sequence is designed, with blipped-controlled aliasing in parallel imaging (blipped-CAIPI) gradients for interslab encoding, and a 2D multiband accelerated navigator for inter-kz-shot phase correction. Third, strategies are developed to remove the phase interferences, by RF phase modulation and/or phase correction during reconstruction, thus decoupling intraslab and interslab encodings that are otherwise entangled. In vivo experiments are performed to validate the blipped-SMSlab method and preliminarily evaluate its performance in high-resolution dMRI compared with traditional 2D imaging. RESULTS: In the 4D k-space framework, interslab and intraslab phase interferences of blipped-SMSlab are successfully removed using the proposed strategies. Compared with non-CAIPI sampling, the blipped-SMSlab acquisition reduces the g-factor and g-factor-related SNR penalty by about 12%. In addition, in vivo experiments show the SNR advantage of blipped-SMSlab dMRI over traditional 2D dMRI for 1.3-mm and 1.0-mm isotropic resolution imaging with matched acquisition time. CONCLUSION: Removing interslab and intraslab phase interferences enables SMSlab dMRI with blipped-CAIPI in a 4D k-space framework. The proposed blipped-SMSlab dMRI is demonstrated to be more SNR-efficient than 2D dMRI and thus capable of high-quality, high-resolution fiber orientation detection.

AisNet: A Universal Interatomic Potential Neural Network with Encoded Local Environment Features.

This paper proposes a new interatomic potential energy neural network, AisNet, which can efficiently predict atomic energies and forces covering different molecular and crystalline materials by encoding universal local environment features, such as elements and atomic positions. Inspired by the framework of SchNet, AisNet consists of an encoding module combining autoencoder with embedding, the triplet loss function and an atomic central symmetry function (ACSF), an interaction module with a periodic boundary condition (PBC), and a prediction module. In molecules, the prediction accuracy of AisNet is comparabel with SchNet on the MD17 dataset, mainly attributed to the effective capture of chemical functional groups through the interaction module. In selected metal and ceramic material datasets, the introduction of ACSF improves the overall accuracy of AisNet by an average of 16.8% for energy and 28.6% for force. Furthermore, a close relationship is found between the feature ratio (i.e., ACSF and embedding) and the force prediction errors, exhibiting similar spoon-shaped curves in the datasets of Cu and HfO2. AisNet produces highly accurate predictions in single-commponent alloys with little data, suggesting the encoding process reduces dependence on the number and richness of datasets. Especially for force prediction, AisNet exceeds SchNet by 19.8% for Al and even 81.2% higher than DeepMD on a ternary FeCrAl alloy. Capable of processing multivariate features, our model is likely to be applied to a wider range of material systems by incorporating more atomic descriptions.

Scanning the latent phases and superconductivity in the Nb-Pb system at high pressure.

So far, few literature studies have been reported on niobium-lead binary intermetallic compounds, which are expected to have very different properties compared to existing niobium-carbon binary compounds, due to the distinct electronic properties of lead when compared to other carbon-group elements. Herein, we carry out a global structure search for the Nb-Pb system based on the evolutionary algorithm and density functional theory. Based on the dynamical and mechanical stability analyses, we unveiled five new phases, P4/m-Nb9Pb, Cmcm-Nb3Pb, I4/mmm-Nb2Pb, Pmm2-Nb5Pb3, and I4/mmm-NbPb2, that are promising candidates for experimental synthesis. Moreover, the superconducting transitions of all Nb-Pb binary intermetallic compounds are performed with electron-phonon calculations. As Nb9Pb exhibited the maximum Tc in the Nb-Pb intermetallics, greater than 3.0 K at 20 GPa, the phonon band structures, partial phonon density of states (PHDOS), the corresponding Eliashberg spectral functions α2F(ω), and integral electron-phonon coupling (EPC) parameters λ as a function of frequency of Nb9Pb were also studied. This work filled the gap in the pressure-tuned Nb-Pb phase transitions from a systematic first principles study for the first time.

Load-induced dynamical transitions at graphene interfaces.

The structural superlubricity (SSL), a state of near-zero friction between two contacted solid surfaces, has been attracting rapidly increasing research interest since it was realized in microscale graphite in 2012. An obvious question concerns the implications of SSL for micro- and nanoscale devices such as actuators. The simplest actuators are based on the application of a normal load; here we show that this leads to remarkable dynamical phenomena in microscale graphite mesas. Under an increasing normal load, we observe mechanical instabilities leading to dynamical states, the first where the loaded mesa suddenly ejects a thin flake and the second characterized by peculiar oscillations, during which a flake repeatedly pops out of the mesa and retracts back. The measured ejection speeds are extraordinarily high (maximum of 294 m/s), and correspond to ultrahigh accelerations (maximum of 1.1×1010 m/s2). These observations are rationalized using a simple model, which takes into account SSL of graphite contacts and sample microstructure and considers a competition between the elastic and interfacial energies that defines the dynamical phase diagram of the system. Analyzing the observed flake ejection and oscillations, we conclude that our system exhibits a high speed in SSL, a low friction coefficient of 3.6×10-6, and a high quality factor of 1.3×107 compared with what has been reported in literature. Our experimental discoveries and theoretical findings suggest a route for development of SSL-based devices such as high-frequency oscillators with ultrahigh quality factors and optomechanical switches, where retractable or oscillating mirrors are required.

General orientation transform for the estimation of fiber orientations in white matter tissues.

PURPOSE: The orientation distribution function (ODF), which is obtained from the radial integral of the probability density function weighted by rn$$ {r}^n $$ ( r$$ r $$ is the radial length), has been used to estimate fiber orientations of white matter tissues. Currently, there is no general expression of the ODF that is suitable for any n value in the HARDI methods. THEORY AND METHODS: A novel methodology is proposed to calculate the ODF for any n>-1$$ n>-1 $$ through the Taylor series expansion and a generalized expression for -1

Slab boundary artifact correction in multislab imaging using convolutional-neural-network-enabled inversion for slab profile encoding.

PURPOSE: This study aims to propose a novel algorithm for slab boundary artifact correction in both single-band multislab imaging and simultaneous multislab (SMSlab) imaging. THEORY AND METHODS: In image domain, the formation of slab boundary artifacts can be regarded as modulating the artifact-free images using the slab profiles and introducing aliasing along the slice direction. Slab boundary artifact correction is the inverse problem of this process. An iterative algorithm based on convolutional neural networks (CNNs) is proposed to solve the problem, termed CNN-enabled inversion for slab profile encoding (CPEN). Diffusion-weighted SMSlab images and reference images without slab boundary artifacts were acquired in 7 healthy subjects for training. Images of 5 healthy subjects were acquired for testing, including single-band multislab and SMSlab images with 1.3-mm or 1-mm isotropic resolution. CNN-enabled inversion for slab profile encoding was compared with a previously reported method (i.e., nonlinear inversion for slab profile encoding [NPEN]). RESULTS: CNN-enabled inversion for slab profile encoding reduces the slab boundary artifacts in both single-band multislab and SMSlab images. It also suppresses the slab boundary artifacts in the diffusion metric maps. Compared with NPEN, CPEN shows fewer residual artifacts in different acquisition protocols and more significant improvements in quantitative assessment, and it also accelerates the computation by more than 35 times. CONCLUSION: CNN-enabled inversion for slab profile encoding can reduce the slab boundary artifacts in multislab acquisitions. It shows better slab boundary artifact correction capacity, higher robustness, and computation efficiency when compared with NPEN. It has the potential to improve the accuracy of multislab acquisitions in high-resolution DWI and functional MRI.

Anisotropic Fracture of Graphene Revealed by Surface Steps on Graphite.

The anisotropic fracture toughness G(θ) is an intrinsic feature of graphene and is fundamental for fabrication, functioning, and robustness of graphene-based devices. However, existing results show significant discrepancies on the anisotropic factor, i.e., the ratio between zigzag (ZZ) and armchair (AC) directions, G_{ZZ}/G_{AC}, both qualitatively and quantitatively. Here, we investigate the anisotropic fracture of graphene by atomic steps on cleaved graphite surfaces. Depending on the relation between the peeling direction and local lattice orientation, two categories of steps with different structures and behaviors are observed. In one category are straight steps well aligned with local ZZ directions, while in the other are steps consisting of nanoscale ZZ and AC segments. Combined with an analysis on fracture mechanics, the microscale morphology of steps and statistics of their directions provides a measurement on the anisotropic factor of G_{ZZ}/G_{AC}=0.971, suggesting that the ZZ direction has a slightly lower fracture toughness. The results provide an experimental benchmark for the widely scattered existing results, and offer constraints on future models of graphene fracture.

Deep Learning Enabled Comprehensive Evaluation of Jumping-Droplet Condensation and Frosting.

Superhydrophobicity-enabled jumping-droplet condensation and frosting have great potential in various engineering applications, ranging from heat transfer processes to antifog/frost techniques. However, monitoring such droplets is challenging due to the high frequency of droplet behaviors, cross-scale distribution of droplet sizes, and diversity of surface morphologies. Leveraging deep learning, we develop a semisupervised framework that monitors the optical observable process of condensation and frosting. This system is adept at identifying transient droplet distributions and dynamic activities, such as droplet coalescence, jumping, and frosting, on a variety of superhydrophobic surfaces. Utilizing this transient and dynamic information, various physical properties, such as heat flux, jumping characteristics, and frosting rate, can be further quantified, conveying the heat transfer and antifrost performances of each surface perceptually and comprehensively. Furthermore, this framework relies on only a small amount of annotated data and can efficiently adapt to new condensation conditions with varying surface morphologies and illumination techniques. This adaptability is beneficial for optimizing surface designs to enhance condensation heat transfer and antifrosting performance.

Rational Design of Dynamic Interface Water Evolution on Turing Electrocatalyst toward the Industrial Hydrogen Production.

Manipulating the structural and kinetic dissociation processes of water at the catalyst-electrolyte interface is vital for alkaline hydrogen evolution reactions (HER) at industrial current density. This is seldom actualized due to the intricacies of the electrochemical reaction interface. Herein, this work introduces a rapid, nonequilibrium cooling technique for synthesizing ternary Turing catalysts with short-range ordered structures (denoted as FeNiRu/C). These advanced structures empower the FeNiRu/C to exhibit excellent HER performance in 1 m KOH with an ultralow overpotential of 6.5 and 166.2 mV at 10 and 1000 mA cm-2, respectively, and a specific activity 7.3 times higher than that of Pt/C. Comprehensive mechanistic analyses reveal that abundant atomic species form asymmetric atomic electric fields on the catalyst surface inducing a directed evolution and the dissociation process of interfacial H2O molecules. In addition, the locally topologized structure effectively mitigates the high hydrogen coverage of the active site induced by the high current density. The establishment of the relationship between free water population and HER activity provides a new paradigm for the design of industrially relevant high performance alkaline HER catalysts.

The electronic and mechanical properties of (U, Th)O2 compounds: a first-principles study.

Alloying is widely acknowledged as an effective strategy for enhancing the performance of UO2 nuclear fuel. Herein, the thermodynamic stability and kinetic stability of U-Th-O ternary compounds are used to clarify the hidden stable structures. The calculation results of the total and the partial density of states indicated that there is significant orbital hybridization between the added Th and O atoms at -5 eV. Furthermore, the mechanical anisotropy was evaluated by means of the three-dimensional Young's modulus, revealing that the U-Th-O ternary compound exhibits a high degree of isotropy, with the Young's modulus reaching approximately 200 GPa in all three directions. In our upcoming work, our focus will be on studying the changes in properties, such as thermal conductivity of the U-Th-O ternary compound, which may provide a data basis for the application of ternary U-Th-O fuel in reactors.

Fully automatic transfer and measurement system for structural superlubric materials.

Structural superlubricity, a state of nearly zero friction and no wear between two contact surfaces under relative sliding, holds immense potential for research and application prospects in micro-electro-mechanical systems devices, mechanical engineering, and energy resources. A critical step towards the practical application of structural superlubricity is the mass transfer and high throughput performance evaluation. Limited by the yield rate of material preparation, existing automated systems, such as roll printing or massive stamping, are inadequate for this task. In this paper, a machine learning-assisted system is proposed to realize fully automated selective transfer and tribological performance measurement for structural superlubricity materials. Specifically, the system has a judgment accuracy of over 98% for the selection of micro-scale graphite flakes with structural superlubricity properties and complete the 100 graphite flakes assembly array to form various pre-designed patterns within 100 mins, which is 15 times faster than manual operation. Besides, the system is capable of automatically measuring the tribological performance of over 100 selected flakes on Si3N4, delivering statistical results for new interface which is beyond the reach of traditional methods. With its high accuracy, efficiency, and robustness, this machine learning-assisted system promotes the fundamental research and practical application of structural superlubricity.

Constructing built-in electric field via ruthenium/cerium dioxide Mott-Schottky heterojunction for highly efficient electrocatalytic hydrogen production.

Ensuring the consumption rate of noble metals while guaranteeing satisfactory hydrogen evolution reaction (HER) performance at different pH values is imperative to the development of Ru-based catalysts. Herein, we design a Mott-Schottky electrocatalyst (Ru/CeO2) with a built-in electric field (BEF) based on density functional theory (DFT). The Ru/CeO2 achieves the criterion current density of 10 mA cm-2 at overpotentials of 55 mV, 80 mV, and 120 mV in alkaline, acidic and neutral media, respectively. Both theoretical calculations and experimental analysis confirm that the improved HER activity in the Ru/CeO2 catalyst could be due to the successful construction of BEF at the interface between the prepared Ru clusters and CeO2. Under the action of BEF, the electron-deficient Ru atoms can optimize the adsorption energy of H* and H2O and thus promote HER kinetics. Furthermore, the Ru/CeO2 catalyst delivers a power density of approximately 94.5 mW cm-2 in alkaline-acidic Zn-H2O cell applications while maintaining good H2 production stability. In this work, we optimize the electrocatalytic performance of the Ru/CeO2 catalyst through examination of the interfacial BEF electrical charge, which combines hydrogen production with power generation and provides a promising method for sustainable energy conversion.

Nickel-decorated RuO2 nanocrystals with rich oxygen vacancies for high-efficiency overall water splitting.

Designing transition metal-oxide-based bifunctional electrocatalysts with excellent activity and stability for OER/HER to achieve efficient water splitting is of great importance for renewable energy technologies. Herein, a highly efficient bifunctional catalysts with oxygen-rich vacancies of nickel-decorated RuO2 (NiRuO2-x) prepared by a unique one-pot glucose-blowing approach were investigated. Remarkably, the NiRuO2-x catalysts exhibited excellent HER and OER activity at 10 mA cm-2 in alkaline solution with only a minimum overpotential of 51 mV and 245 mV, respectively. Furthermore, the NiRuO2-x overall water splitting exhibited an ultra-low voltage of 1.6 V to obtain 10 mA cm-2 and stability for more than 10 h. XPS measurement and theoretical calculations demonstrated that the introduction of Ni-dopant and oxygen vacancies make the d-band center to lie close to the Fermi energy level, the chemical bonds between the active site and the adsorbed oxygen intermediate state are enhanced, thereby lowering the reaction activation barriers of HER and OER. The assembly of solar-driven alkaline electrolyzers facilitate the application of the NiRuO2-x bifunctional catalysts.

Diffusion coefficient orientation distribution function for diffusion magnetic resonance imaging.

BACKGROUND: Diffusion magnetic resonance imaging (dMRI) is a popular non-invasive imaging technique applied for the study of nerve fibers in vivo, with diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) as the commonly used dMRI methods. However, DTI cannot resolve complex fiber orientations in a local area and HARDI lacks a solid physical basis. NEW METHOD: We introduce a diffusion coefficient orientation distribution function (DCODF). It has a clear physical meaning to represent the orientation distribution of diffusion coefficients for Gaussian and non-Gaussian diffusion. Based on DCODF, we then propose a new HARDI method, termed as diffusion coefficient orientation distribution transform (DCODT), to estimate the orientation distribution of nerve fibers in voxels. RESULTS: The method is verified on the simulated data, ISMRM-2015-Tracto-challenge data, and HCP datasets. The results show the superior capability of DCODT in resolving the complex distribution of multiple fiber bundles effectively. COMPARISON WITH EXISTING METHOD(S): The method is compared to other common model-free HARDI estimators. In the numerical simulations, DCODT achieves a better trade-off between the resolution and accuracy than the counterparts for high b-values. In the comparisons based on the challenge data, the improvement of DCODT is significant in scoring. The results on the HCP datasets show that DCODT provides fewer spurious lobes in the glyphs, resulting in more coherent fiber orientations. CONCLUSIONS: We conclude that DCODT may be a reliable method to extract accurate information about fiber orientations from dMRI data and promising for the study of neural architecture.

First-principles investigations on the anisotropic elasticity and thermodynamic properties of U3Si2-Al.

U3Si2 has been tested as a new type of nuclear fuel, and Al has been proven to improve its oxidation resistance. However, there is no research on its anisotropic mechanical and thermal properties. The mechanical and thermal properties of Al-alloyed U3Si2 nuclear fuel are calculated on the basis of first principles. Through the phonon dispersion curves, two kinetic stable structures sub-U3Si1.5Al0.5 and sub-U2.5Si2Al0.5(I) are screened out. It is found that the toughness of these two compounds after alloying are significantly improved compared to U3Si2. The three-dimensional Young's modulus shows that, the sub-U3Si1.5Al0.5 formed by Al alloying in U3Si2 maintains a higher mechanical isotropy, while sub-U2.5Si2Al0.5(I) shows higher mechanical anisotropy, which is consistent with the value of A u. The calculation result shows that the lattice thermal conductivity of sub-U3Si1.5Al0.5 and sub-U2.5Si2Al0.5(I) after alloying exhibits high isotropy as the temperature increases.