bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementation of binder-free SnO2NWs@C electrode and LiTFSI-based electrolyte for high-performance lithium-ion battery
Authors: Hakimi Mahdieh , Habibi Alireza , Sanaee Zeinab , Ghasemi Shahnaz , Mohajerzadeh Shams 
Published: November 2022

AbstractThis study has investigated the effect of carbon coating on the electrochemical performance of SnO2Nanowires (NWs) as an electrode along with a bis (trifluoromethane) sulfonimide lithium (LiTFSI)-based electrolyte in a lithium-ion battery (LIB). The vapor–liquid–solid approach has been used to grow SnO2NWs on the stainless steel mesh current collector. The obtained results have demonstrated that the utilization of the LiTFSI-based electrolyte improved the battery performance with the SnO2NWs electrode over the LiPF6-based electrolyte. This may be due to the formation of a stable and thin solid electrolyte interphase layer. Since bare SnO2NWs exhibit inferior cycling stability due to their high volumetric expansion and poor conductivity, incorporating carbon coating could improve performance. Although the initial discharge specific capacity obtained for the carbon-coated SnO2NWs (SnO2NWs@C) electrode (1328.4 mAh g−1) was in the order of the first discharge capacity for the SnO2NWs, the SnO2NWs@C electrode possessed capacity retention with six times improvement as compared to the pristine SnO2NWs after 120 cycles. The Coulombic efficiency of 99% has been achieved for the SnO2NWs@C electrode over 120 cycles. Field emission scanning electron microscopy images revealed that the carbon coating could keep the overall structure of the electrode upon cycling. It has been evidenced that the SnO2NWs@C electrode with LiTFSI-based electrolyte has an excellent potential to implement in high-performance LIBs.
Elucidating the photovoltaic effect of monoclinic K2SnBr6 by mixed-cation mixed-halide substitution from first-principles calculations
Authors: Sreedevi P D , Ravindran P 
Published: November 2022

Abstract
Vacancy-ordered double perovskites (A2BX6) have recently received impressive attention for photovoltaic applications, and the compound K2SnBr6 has desirable features for use in solar cells in such a way that it exhibits direct bandgap behaviour with dispersed band edges. However, the large bandgap value limits its use in higher-efficiency solar cells. Therefore, we have carried out exploratory research by analysing the photovoltaic effect of K2SnBr6 by tuning its bandgap with cation and anion substitutions. We studied the properties of K(2−y)RbySnBr(6−x)I
x
and K2Sn(1−z)Ti
z
Br(6−x)I
x
compounds to explore the photovoltaic effect by gradual substitution of Rb+, Ti4+ and I−1 for K+, Sn4+ and Br−1, respectively. Our density functional calculations in the monoclinic ground state crystal structure with the space group P121
/n revealed that the K(2−y)Rb
y
SnBr(6−x)I
x
compounds considered in this study exhibit direct bandgap behaviour with well-dispersed band edges. Moreover, the bandgap value decreases as a function of the Rb and I concentrations. In addition to the low charge carrier effective mass, low excitonic binding energy values and low recombination rate, these compounds exhibit comparatively greater absorption coefficients in the visible range. The charge carrier transport properties, such as carrier mobility, carrier relaxation time, carrier diffusion coefficient and carrier diffusion length, are also seen in higher ranges for these Rb- and I-substituted compounds when compared to the parent compound. In addition, we have calculated the open-circuit voltage, fill factor, short-circuit current and power conversion efficiency for each compound. From the calculations and analysis, we observed that Rb and I substitution in K2SnBr6 increases the photovoltaic effect, and thus K(2−y)Rb
y
SnBr(6−x)I
x
compounds may be employed as absorbing layers in higher-efficiency solar cells.
In-plane magnetotransport phenomena in tilted Weyl semimetals
Authors: Shao Jianmei , Yan Lijuan 
Published: November 2022

Abstract
The nontrivial Berry curvature and chiral anomaly result in plenty of interesting magnetotransport and magneto-thermoelectric transport phenomena in Weyl semimetals, two of which are the planar Hall effect and planar Nernst effect. Based on the semi-classical Boltzmann theory, we theoretically study the in-plane magnetotransport coefficients (including electric conductivity, thermoelectric conductivity and Nernst thermopower) for both type-I and type-II Weyl semimetals using a linearized low-energy Hamiltonian. We find that for tilt-coplanar setup where the electric field (or temperature gradient) is applied along the tilted direction of the Weyl nodes, the planar Hall conductivity (or planar Nernst thermopower) shows a magnetic field linear dependence. And these linear responses do no obey the reported

cos
θ
sin
θ

dependence on the angle θ between the magnetic and electric field for planar Hall effect. For tilt-perpendicular setup where the applied field (E or

∇
T

) and magnetic field are perpendicular to the tilted direction, the planar Hall conductivity (or planar Nernst thermopower) mainly follows a magnetic field quadratic dependence, and it satisfies the

cos
θ
sin
θ

characteristic. There are also anomalous Hall effect and anomalous Nernst effect in the plane perpendicular to the tilted direction of the two oppositely tilted Weyl nodes. These findings can be verified experimentally by changing the direction and magnitude of the in-plane magnetic field.
Dynamical instability of 3D stationary and traveling planar dark solitons
Authors: Mithun T , Fritsch A R , Spielman I B , Kevrekidis P G 
Published: November 2022

Abstract
Here we revisit the topic of stationary and propagating solitonic excitations in self-repulsive three-dimensional (3D) Bose–Einstein condensates by quantitatively comparing theoretical analysis and associated numerical computations with our experimental results. Motivated by numerous experimental efforts, including our own herein, we use fully 3D numerical simulations to explore the existence, stability, and evolution dynamics of planar dark solitons. This also allows us to examine their instability-induced decay products including solitonic vortices and vortex rings. In the trapped case and with no adjustable parameters, our numerical findings are in correspondence with experimentally observed coherent structures. Without a longitudinal trap, we identify numerically exact traveling solutions and quantify how their transverse destabilization threshold changes as a function of the solitary wave speed.
Static and dynamic magnetic properties of the spin-

52

triangle lattice antiferromagnet Na3Fe(PO4)2 studied by 31P NMR
Authors: Ambika Devi V , Ding Qing-Ping , Sebastian Sebin J , Nath Ramesh , Furukawa Yuji 
Published: November 2022

Abstract

31P nuclear magnetic resonance (NMR) measurements have been carried out to investigate the magnetic properties and spin dynamics of Fe3+ (S = 5/2) spins in the two-dimensional triangular lattice (TL) compound Na3Fe(PO4)2. The temperature (T) dependence of nuclear spin-lattice relaxation rates (

1

/

T
1

) shows a clear peak around Néel temperature,

T

N

=
10.9

K, corresponding to an antiferromagnetic (AFM) transition. From the temperature dependence of NMR shift (K) above

T

N

, an exchange coupling between Fe3+ spins was estimated to be

J

/

k

B

≃
1.9

K using the spin-5/2 Heisenberg isotropic-TL model. The temperature dependence of

1

/

T
1

T

divided by the magnetic susceptibility (χ),

1

/

T
1

T
χ

, above

T

N

proves the AFM nature of spin fluctuations below

∼

50

K

in the paramagnetic state. In the magnetically ordered state below

T

N

, the characteristic rectangular shape of the NMR spectra is observed, indicative of a commensurate AFM state in its ground state. The strong temperature dependence of 1/T
1 in the AFM state is well explained by the two-magnon (Raman) process of the spin waves in a 3D antiferromagnet with a spin-anisotropy energy gap of 5.7 K. The temperature dependence of sublattice magnetization is also well reproduced by the spin waves. Those results indicate that the magnetically ordered state of Na3Fe(PO4)2 is a conventional 3D AFM state, and no obvious spin frustration effects were detected in its ground state.
Two-temperature molecular dynamics simulations of crystal growth in a tungsten supercooled melt
Authors: Grossi Joás , Pisarev Vasily 
Published: November 2022

Abstract
In this work we use the two-temperature model (TTM) coupled to molecular dynamics (MD) with sinks at the boundaries of the electronic subsystem to study crystal-growth rate in a quasi-one-dimensional tungsten crystal into a supercooled melt. The possibility of varying the extension of the electronic grid along with the sinks allows a more realistic description of the electronic thermal transport away from the system, providing a considerable heat dissipation from the crystallization front. Based on this approach, our results regarding crystal-growth rates are not affected even if the size of the system is changed. Moreover, comparisons are established with respect to MD and standard TTM simulations. For these comparisons between models, something remarkable is found, and it is that the temperature and the value of the maximum growth rate are the same. In contrast, the inclusion of sinks has a great impact with respect to the standard approaches specially reflected at low temperatures, where a frustration of the liquid–crystal interface dynamics is seen until a state of zero crystal growth is reached, which is not possible to characterize quantitatively since a kind of stochastic behavior is present.
Topological states of generalized dissipative Majorana wires
Authors: Hosseinifar Farokhnaz , Moghaddam Ali G 
Published: November 2022

Abstract
We study the generalized one-dimensional (1D) quantum dissipative models corresponding to a Majorana wire which can possess more than one Majorana bound state at each end. The system consists of a 1D fermionic open quantum system whose dynamics is governed by a quadratic Lindblad equation. Using the adjoint Lindblad equation for the fermionic two-point correlations, we find the gaps in the damping and purity spectra of a generic 1D model. Then, using the symmetry-based classification, we show that a winding number as the topological invariant can be defined which distinguishes different steady states of the system in the presence of damping and purity gaps. Then we focus on certain models with different Lindblad quantum jump terms and explore their phase diagrams by calculating the damping and the purity gaps as well as the winding number. In particular, we show that by inclusion of quantum jumps between next-nearest-neighbor sites, higher winding numbers and equivalently more Majorana bound states can be achieved. Also, by introducing imbalanced couplings we can switch between states with negative and positive winding numbers. Finally, we should mention that since our formulation is based on the fermionic correlations rather than the Majorana operators, it can be easily extended to the dissipative topological phases belonging to other symmetry classes.
Fusion of binocular vision, 2D lidar and IMU for outdoor localization and indoor planar mapping
Authors: Liu Zhenbin , Li Zengke , Liu Ao , Sun Yaowen , Jing Shiyi 
Published: November 2022

Abstract
Emergent fields such as Internet of Things applications, driverless cars, and indoor mobile robots have brought about an increasing demand for simultaneous localization and mapping (SLAM) technology. In this study, we design a SLAM scheme called BVLI-SLAM based on binocular vision, 2D lidar, and an inertial measurement unit (IMU) sensor. The pose estimation provided by vision and the IMU can provide better initial values for the 2D lidar mapping algorithm and improve the mapping effect. Lidar can also assist vision to provide better plane and yaw angle constraints in weak texture areas and obtain higher precision 6-degree of freedom pose. BVLI-SLAM uses graph optimization to fuse the data of the IMU, binocular camera, and laser. The IMU pre-integration combines the visual reprojection error and the laser matching error to form an error equation, which is processed by a sliding window-based bundle adjustment optimization to calculate the pose in real time. Outdoor experiments based on KITTI datasets and indoor experiments based on the trolley mobile measurement platform show that BVLI-SLAM has different degrees of improvement in mapping effect, positioning accuracy, and robustness compared with VINS-Fusion and Cartographer, and can solve the problem of positioning and plane mapping in indoor complex scenes.
Subnanometer accuracy of surface characterization by reflected-light differential interference microscopy
Authors: Chan Ka Hung , Du Shengwang , Chen Xian 
Published: November 2022

Abstract
We theorize the surface step characterization by reflected incoherent-light differential interference microscopy with consideration of the optical diffraction effect. With the integration of localization analysis, we develop a quantitative differential interference optical system, by which we demonstrate that the axial resolution of measuring surface height variation is sensitive to the shear distance between the two spatially differentiated beams. We fabricate three nanometer-size steps by photolithography, and successfully characterize their 1D height variations with 0.13 nm

/

H
z

axial precision. Our result suggests that the optical differential interference microscopy can be used for real-time characterization of surface structure with a subnanometer accuracy and a large field of view, which is greatly beneficial to the surface characterization of micro/nano-electromechanical systems.
A class imbalanced wafer defect classification framework based on variational autoencoder generative adversarial network
Authors: Wang Yitian , Wei Yuxiang , Wang Huan 
Published: November 2022

Abstract
Wafer defect classification (WDC) can be crucial to the wafer fabrication process. Engineers can quickly respond to improve the technological process, averting further defects through WDC. However, due to the complex fabrication steps, wafer defects are different in various types. This causes a severe data imbalance problem in WDC. To effectively solve the problem, this study introduces a class imbalanced WDC based on Variational Autoencoder Generative Adversarial Network (VAE-GAN). This framework consists of VAE-GAN and wafer defect classifier. Among them, VAE-GAN is responsible for creating new samples to solve the imbalance problem while the classifier is responsible for classifying wafer defect patterns. Specifically, VAE-GAN combines the advantage of a Variational Autoencoder (VAE) and generative adversarial network. VAE networks can produce subtle differences that do not affect the properties of the data when generating new images. At the same time, the proposed discriminator can help us constrain the generated images to be close to real samples and avoid irrational, feature-missing, and ambiguous samples. WM-811 K dataset is utilized to verify the above method. The experimental results validate that the samples generated by VAE-GAN have a significant improvement in the performance of the WDC system.
Steep-slope transistors enabled with 2D quantum coupling stacks
Authors: Raju Parameswari , Zhu Hao , Yang Yafen , Zhang Kai , Ioannou Dimitris , Li Qiliang 
Published: November 2022

Abstract
As down scaling of transistors continues, there is a growing interest in developing steep-slope transistors with reduced subthreshold slope (SS) below the Boltzmann limit. In this work, we successfully fabricated steep-slope MoS2 transistors by incorporating a graphene layer, inserted in the gate stack. For our comprehensive study, we have applied density functional theory to simulate and calculate the change of SS effected by different 2D quantum materials, including graphene, germanene and 2D topological insulators, inserted within the gate dielectric. This theoretical study showed that graphene/MoS2 devices had steep SS (27.2 mV/decade), validating our experimental approach (49.2 mV/decade). Furthermore, the simulations demonstrated very steep SS (8.6 mV/decade) in WTe2/MoS2 devices. We conclude that appropriate combination of various 2D quantum materials for the gate-channel stacks, leads to steep SS and is an effective method to extend the scaling of transistors with exceptional performance.
Quantitative reducibility of Gevrey quasi-periodic cocycles and its applications
Authors: Li Xianzhe 
Published: November 2022

Abstract
We establish a quantitative version of strong almost reducibility result for

S
L

(

2
,
R

)

quasi-periodic cocycle close to a constant in the Gevrey class. We prove that, if the frequency is Diophantine, the long range operator has pure point spectrum with sub-exponentially decaying eigenfunctions for almost all phases; for the one dimensional quasi-periodic Schrödinger operators with small Gevrey potentials, the length of spectral gaps decays sub-exponentially with respect to its labelling; and the spectrum is an interval for discrete Schrödinger operators acting on

Z

d

with small separable potentials.
Global dynamics of a nonlocal non-uniformly parabolic equation arising from the curvature flow*
Authors: Bai Xueli , Li Fang , Wang Xiaoliu 
Published: November 2022

Abstract
This paper studies a type of non-uniformly parabolic problem with nonlocal term

u

t

=

u

p

(

u

x
x

+
u
−

u
¯

)

0
<
t
<

T

m
a
x

,

0
<
x
<
a
,

u

x

(
t
,
0
)
=

u

x

(
t
,
a
)
=
0

0
<
t
<

T

m
a
x

,

u
(
0
,
x
)
=

u

0

(
x
)

0
<
x
<
a
,

where p > 1, a > 0. First the classification of the finite-time blow-up/global existence phenomena based on the associated energy functional and explicit expression of all nonnegative steady states are demonstrated. More importantly, we derive that any bounded solution converges to some steady state as t → +∞. The difficulties in proving this convergence result lie in the existence of a continuum of steady states and the lack of the comparison principle due to the introduction of nonlocal term. To conquer these difficulties, we combine the applications of Lojasiewicz–Simon inequality and energy estimates.
Uncertainty-aware self-supervised neural network for liver T1ρmapping with relaxation constraint
Authors: Huang Chaoxing , Qian Yurui , Yu Simon Chun-Ho , Hou Jian , Jiang Baiyan , Chan Queenie , Wong Vincent Wai-Sun , Chu Winnie Chiu-Wing , Chen Weitian 
Published: November 2022

AbstractObjective.T1ρmapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can mapT1ρfrom a reduced number ofT1ρweighted images but requires significant amounts of high-quality training data. Moreover, existing methods do not provide the confidence level of theT1ρestimation. We aim to develop a learning-based liverT1ρmapping approach that can mapT1ρwith a reduced number of images and provide uncertainty estimation.Approach. We proposed a self-supervised neural network that learns aT1ρmapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for theT1ρquantification network to provide a Bayesian confidence estimation of theT1ρmapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data. Main results. We conducted experiments onT1ρdata collected from 52 patients with non-alcoholic fatty liver disease. The results showed that when only collecting twoT1ρ-weighted images, our method outperformed the existing methods forT1ρquantification of the liver. Our uncertainty estimation can further regularize the model to improve the performance of the model and it is consistent with the confidence level of liverT1ρvalues.Significance. Our method demonstrates the potential for accelerating theT1ρmapping of the liver by using a reduced number of images. It simultaneously provides uncertainty ofT1ρquantification which is desirable in clinical applications.
Multi-scale contextual semantic enhancement network for 3D medical image segmentation
Authors: Xia Tingjian , Huang Guoheng , Pun Chi-Man , Zhang Weiwen , Li Jiajian , Ling Wing-Kuen , Lin Chao , Yang Qi 
Published: November 2022

Abstract

Objective. Accurate and automatic segmentation of medical images is crucial for improving the efficiency of disease diagnosis and making treatment plans. Although methods based on convolutional neural networks have achieved excellent results in numerous segmentation tasks of medical images, they still suffer from challenges including drastic scale variations of lesions, blurred boundaries of lesions and class imbalance. Our objective is to design a segmentation framework named multi-scale contextual semantic enhancement network (3D MCSE-Net) to address the above problems. Approach. The 3D MCSE-Net mainly consists of a multi-scale context pyramid fusion module (MCPFM), a triple feature adaptive enhancement module (TFAEM), and an asymmetric class correction loss (ACCL) function. Specifically, the MCPFM resolves the problem of unreliable predictions due to variable morphology and drastic scale variations of lesions by capturing the multi-scale global context of feature maps. Subsequently, the TFAEM overcomes the problem of blurred boundaries of lesions caused by the infiltrating growth and complex context of lesions by adaptively recalibrating and enhancing the multi-dimensional feature representation of suspicious regions. Moreover, the ACCL alleviates class imbalances by adjusting asy mmetric correction coefficient and weighting factor. Main results. Our method is evaluated on the nasopharyngeal cancer tumor segmentation (NPCTS) dataset, the public dataset of the MICCAI 2017 liver tumor segmentation (LiTS) challenge and the 3D image reconstruction for comparison of algorithm and DataBase (3Dircadb) dataset to verify its effectiveness and generalizability. The experimental results show the proposed components all have unique strengths and exhibit mutually reinforcing properties. More importantly, the proposed 3D MCSE-Net outperforms previous state-of-the-art methods for tumor segmentation on the NPCTS, LiTS and 3Dircadb dataset. Significance. Our method addresses the effects of drastic scale variations of lesions, blurred boundaries of lesions and class imbalance, and improves tumors segmentation accuracy, which facilitates clinical medical diagnosis and treatment planning.