bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ohmic contact mechanism and charge redistribution of MoS2/Mn+
1XnO2 heterostructures
Authors: Jiang Xinting , Hou Pengfei , Zhu Gong , Tian Yumiao , Xie Yu , Du Fei , Meng Xing 
Published: November 2024

Abstract
Several M
n+1
X
n
O2 compounds exhibit work functions higher than those of three-dimensional metals, enabling the formation of Ohmic contact heterostructures with MoS2, which enhances the catalytic activity of MoS2 for the hydrogen evolution reaction. However, the Schottky barrier height (SBH) in these Ohmic contact heterostructures does not adhere to the Schottky-Mott limit, leaving the Ohmic contact mechanism between MoS2 and M
n+

1X
n
O2 unclear and hindering further investigations into these heterostructures. In this study, we investigate 22 MoS2/M
n+

1X
n
O2 heterostructures using the unfolding method. Among these, the eight M
n+

1X
n
O2 compounds—Cr2CO2, Mo2CO2, V2CO2, W2CO2, Cr2NO2, V2NO2, Ti3C2O2 and V4C3O2—form p-type Ohmic contacts with MoS2. In contrast, the twelve compounds—Hf2CO2, Nb2CO2, Ta2CO2, Ti2CO2, Zr2CO2, Mo2NO2, Ti2NO2, Ti3N2O2, Zr3C2O2, Nb4C3O2, Ta4C3O2 and Ti4N3O2—create p-type Schottky contacts, while Hf2NO2 and Zr2NO2 form n-type Schottky contacts with MoS2. In the Ohmic contact heterostructures, out-of-plane orbital states hybridize to form a splitting band, allowing the highest valence band of MoS2 to cross the Fermi level and achieve hole doping. This splitting band not only results in a SBH that does not conform to the Schottky–Mott limit but also redistributes charge density. Notably, the heterostructures formed by Cr2CO2, Mo2CO2, V2CO2, W2CO2, Cr2NO2, V2NO2, V4C3O2, and MoS2 exhibit charge polarity distribution, whereas MoS2/Ti3C2O2 does not demonstrate charge polarity distribution.
Electron emission characteristics of cathode micro-protrusion and the comparison with the MG theory analysis
Authors: Li Yuanzhao , Ding Jiangang , Liu Zhiyuan , Geng Yingsan , Wang Jianhua 
Published: November 2024

Abstract
Electron emission from micro-protrusions on the surface of metal electrodes is crucial for vacuum breakdown. The emission characteristics of cathode micro-protrusions and the impact of the space charge effect are investigated in this study, with a comparison to the Murphy–Good (MG) theory analysis. Precise calculations of the emission current density and the Nottingham effect are achieved. A thermal-field emission model for copper micro-protrusion is established to analyze emission characteristics and clarify the mismatches between the results of simulations and MG theory. The results indicate that, for copper micro-protrusions, before breakdown, the emission current is primarily field emission, with thermal emission constituting only 7% of the total. The Nottingham effect results in consistent heating during this phase. The field enhancement factor obtained through the MG theory (γ
MG) is slightly smaller than that derived from the geometric model (γ
geom). This is because γ
geom reflects the characteristics of the apex of the protrusion, while γ
MG reflects the characteristics of the entire electron emission region. The space charge effect bends the MG plot curve and decreases its slope, making it impossible to analyze the emission current using MG theory at this point, which is consistent with the theoretical derivations recorded in the current literature.
CPT-Odd effects on the electromagnetic properties of charged leptons in the standard model extension
Authors: Hurtado-Silva J S , Toscano J J , Vázquez-Hern O 
Published: November 2024

Abstract
The impact of the CPT-Odd electroweak gauge sector of the Standard Model Extension on the electromagnetic properties of charged leptons is studied. This gauge sector is characterized by the

k

1

μ

and

k

2

μ

Lorentz violation (LV) coefficients, which have positive mass dimension because they are associated with a U

Y
(1)-invariant and with an SU

L
(2)-invariant dimension-three operators, respectively. They belong to the category of relevant interactions, which can have strong effects on low-energy observables. We present a comprehensive study on the impact of this sector on the magnetic dipole moment (MDM) and the electric dipole moment (EDM) of charged leptons, up to second order in these LV coefficients, both at the tree and one-loop levels. We find that the O(k

i
) contributions at the tree and one-loop levels depend on energy, while

O
(

k

i

2

)

ones at the tree-level do not. As for

O
(

k

i

2

)

one-loop effects, there are both energy-dependent and energy-independent contributions, but we have focused only on those of the latter type. We find that the EDM only is generated at O(k

i
) up to one-loop level, whereas the MDM receives contributions from both O(k

i
) and

O
(

k

i

2

)

at both tree and one-loop levels. The contributions of O(k

i
) to the MDM are found to be suppressed relative to the corresponding contributions to the EDM by approximately three orders of magnitude. Using a recent experimental limit on the electron EDM the

∣

k
2

0

−
∣

k
2

∣
cos

θ
γ

∣
<
0.86

m
e

bound was obtained. As far as the contributions of

O
(

k

i

2

)

are concerned, we find that the tree-level contributions are suppressed with respect to the one-loop ones by at least a factor of

m

l

2

/

m

Z

2

. We find that the contribution to the electron MDM is by far the dominant one, as it can be up to four and seven orders of magnitude greater than those of the muon and tau, respectively. The Lorentz coefficient

k

AF

μ

of the Carroll–Field–Jackiw’s QED is given by a linear combination of the

k

1

μ

and

k

2

μ

vectors. Assuming that

∣

k

1

2

∣
,
∣

k

2

2

∣
≫
∣

k

AF

2

∣

and taking

k

AF

μ

=
0

, which implies that

k

1

μ

and

k

2

μ

are collinear, we obtain an upper bound of

k

2

2

m

e

2

<
4.36
×

10

−
10

. The fact that

k

2

2

is an observer Lorentz invariant allows us to introduce a new-physics scale through

k
2
2

=

Λ
CPT

, for which we obtain the upper limit ΛCPT < 2.08 × 10−5
m

e
. The physical implications derived from the fact that the LV coefficients have positive mass units are discussed.
Ultrafast optical induction of magnetic order at a quantum critical point
Authors: Fauseweh Benedikt , Zhu Jian-Xin 
Published: November 2024

Abstract
Time-resolved ultrafast spectroscopy has emerged as a promising tool to dynamically induce and manipulate non-trivial electronic states of matter out-of-equilibrium. Here we theoretically investigate light pulse driven dynamics in a Kondo lattice system close to quantum criticality. Based on a time-dependent auxiliary fermion mean-field calculation we show that light can dehybridize the local Kondo screening and induce oscillating magnetic order out of a previously paramagnetic state. Depending on the laser pulse field amplitude and frequency the Kondo singlet can be completely deconfined, inducing a dynamic Lifshitz transition that changes the Fermi surface topology. These phenomena can be identified in harmonic generation and time-resolved angle-resolved photoemission spectroscopy spectra. Our results shed new light on non-equilibrium states in heavy fermion systems.
Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems
Authors: Li Zongjie , Shi Jiakui , Li Menghui , Fan Shuangshuang , Yao Kun , Wan Jie 
Published: November 2024

Abstract
In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.
Multi-source domain generalization tool wear prediction based on wide convolution weighted antagonism
Authors: Fu Honghao , Li Zisheng , Xiao Xiaoping , Zhou Wenjun , Zhang Kai 
Published: November 2024

Abstract
While traditional deep learning models achieve high accuracy in predicting tool wear under consistent working conditions, actual production processes frequently involve varying conditions due to different processing methods. The wear data of different working conditions have a large difference in distribution, so that the wear signal of milling cutter trained in one working condition can only predict the wear value of the working condition, which will cause a large waste of material and manpower for actual production. Therefore, a domain generalization method utilizing wide deep convolutional neural networks and weighted antagonistic multi-source domain generalization is introduced for tool wear prediction, and multi-source domain wide depth convolutional weighted adversarial domain generalization model (WDCNNWAL) model is constructed. After filtering and de-noising the original data, it is input into WDCNN for automatic feature learning, and the resulting multi-source feature signals are input into the prediction model and discriminator respectively to obtain corresponding loss values. Furthermore, Wasserstein distance is used to measure the probability distribution distance of multi-source feature signals as a weighted value combined with the discriminator loss function, and the final loss value is obtained by adding together. Finally, the tool wear prediction model is set up using a backpropagation neural network. Various milling wear experimental data along with the NASA tool wear public dataset were utilized to assess the predictive performance of the trained model. The model achieves a high level of generalization in tool wear prediction, with average root mean squared error (RMSE) of 0.0599 and 0.2075, as well as average R
2 of 0.9037 and 0.9196 on the NASA and self-collecting datasets respectively. To validate its exceptional precision in generalization performance, comparative experiments were conducted with other data-driven methods resulting in over a 50% reduction in average RMSE and more than a 40% increase in average R
2.
Simultaneous calibration method for rotary axis geometric errors in five-axis dispensing equipment based on monocular vision
Authors: Tang Xianxing , Xiao Linjiao , Zhou Haibo , Wang Ziyang 
Published: November 2024

Abstract
Geometric error calibration of the rotary axis is an important and effective method for improving the five-axis dispensing equipment’s motion and processing accuracy. In most previous studies, simultaneously identifying the geometric errors of multiple rotation axes using traditional precision measuring instruments is often challenging. This paper proposes a simultaneous calibration model based on the AprilTag target visual framework. The model can synchronously identify the geometric errors of the rotation axis in the case of multi-axis linkage. The proposed kinematic calibration model avoids the singularity problem, and the calibration scheme is more economical and capable of unmanned and online calibration. Moreover, the proposed calibration model is compared against the traditional POE error model. In addition, simulation experiments are carried out using different regularization methods regarding the ill-posed problem in the recognition process under visual measurement conditions. Finally, the accuracy and feasibility of the proposed method are verified via experimental results on a five-axis dispensing equipment.
Understanding the competing growth of 2D and 3D transition metal dichalcogenides in a chemical vapor deposition (CVD) reactor
Authors: Ullah Farman , Kazemian Sina , Fanchini Giovanni 
Published: November 2024

Abstract
The competing growth of two-dimensional (2D) and three-dimensional (3D) crystals of layered transition metal dichalcogenides (TMDCs) has been reproducibly observed in a large variety of chemical vapor deposition (CVD) reactors and demands a comprehensive understanding in terms of involved energetics. 2D and 3D growth is fundamentally different due to the large difference in the in-plane and out-of-plane binding energies in TMDC materials. Here, an analytical model describing TMDC growth via CVD is developed. The two most common TMDC structures produced via CVD growth (2D triangular flakes and 3D tetrahedra) are considered, and their formation energies are determined as a function of their growth parameters. By calculating the associated energies of 2D triangular or 3D tetrahedral flakes, we predict the minimum sizes of the critical nuclei of 2D triangular and 3D morphologies, and thereby determine the minimum realizable dimensions of TMDC, in the form of quantum dots. Analysis of growth rates shows that CVD favors 2D growth of MoS2 between 820 K and 900 K and 3D growth over 900 K. Our model also suggests that the flow rates of TMDC precursors (metal oxide and sulfur) in a long, cylindrical CVD reactor are important parameters for attaining uniform growth. Our model provides a compressive analysis of TMDC growth via CVD. Therefore, it is a critical tool for helping to achieve reproducible growth of 2D and 3D TMDCs for a variety of applications.
Quantitative theoretical analysis of the electrostatic force between a metallic tip and semiconductor surface in Kelvin probe force microscopy
Authors: Ishida Nobuyuki , Mano Takaaki 
Published: November 2024

Abstract
Theoretical analysis of the electrostatic force between a metallic tip and semiconductor surface in Kelvin probe force microscopy (KPFM) measurements has been challenging due to the complexity introduced by tip-induced band bending (TIBB). In this study, we present a method for numerically computing the electrostatic forces in a fully three-dimensional (3D) configuration. Our calculations on a system composed of a metallic tip and GaAs(110) surface revealed deviations from parabolic behavior in the bias dependence of the electrostatic force, which is consistent with previously reported experimental results. In addition, we show that the tip radii estimated from curve fitting of the theory to experimental data provide reasonable values, consistent with the shapes of tip apex observed using scanning electron microscopy. The 3D simulation, which accounted for the influence of TIBB, enables a detailed analysis of the physics involved in KPFM measurements of semiconductor samples, thereby contributing to the development of more accurate measurement and analytical methods.
First principles electron transport in magnetoelectric SrRuO3/BaTiO3/SrTiO3/SrRuO3 interfaces
Authors: Filipoiu Nicolae , Plugaru Neculai , Sandu Titus , Plugaru Rodica , Alexandru Nemnes George 
Published: November 2024

Abstract
Recently, all-oxide ferroelectric tunnel junctions, with single or composite potential barriers based on SrRuO3/BaTiO3/SrTiO3 (SRO/BTO/STO) perovskites, have drawn a particular interest for high density low power applications, due to their highly tunable transport properties and device scaling down possibility to atomic size. Here, using first principles calculations and the non-equilibrium Green’s functions formalism, we explore the electronic structure and tunneling transport properties in magnetoelectric SRO/BTO/mSTO/SRO interfaces, (m = 0, 2, or 4 unit cells), considering both the RuO6 octahedra tilts and magnetic SRO electrodes. Our main results may be summarized as follows: (i) the band alignment schemes predict that polarization direction may determine both Schottky barrier or Ohmic contacts for m(STO) = 0, but only Schottky contacts for m(STO) = 2 and 4 junctions; (ii) the tunnel electroresistance and tunnel magnetoresistance ratios are evaluated at 0 and 300 K; (iii) the most magnetoelectric responsive interfaces are obtained for the m(STO) = 2 heterostructure, this system also showing co-existent giant tunnel electroresistance and tunnel magnetoresistance effects; (iv) the interfacial magnetoelectric coupling is not strong enough to control the tunnel magnetoresistance by polarization switching, in spite of significant SRO ferromagnetism.
Using Excel and VLC Player to examine the takeoff of a Boeing 747-400 ERF
Authors: Bloom Steven D 
Published: November 2024

Abstract
We discuss an in-class activity for introductory mechanics students to examine the forces on an airplane and the resulting path at takeoff using Excel and VLC Player. We use a Euler-based integration method within Excel to predict the takeoff run for a Boeing 747-400 ERF considering forces such as thrust, lift, drag, and friction. We then used VLC Player to analyse a YouTube video of a Boeing 747-400 ERF at takeoff to measure take-off times, distances, velocities, and accelerations. We then compare these data with the predictions. Particularly when the effects of friction are included, the data match the predicted values well for distances and velocities, but the discrepancies are significantly larger for the accelerations, suggesting that a more complex model is required (such as velocity dependence of thrust). We include a video, the detailed instructions for this exercise, and the Excel code as supplementary materials.
Estimation of experimental uncertainties. A simplified proposal for indirect measurements in the laboratory
Authors: Gianino Concetto 
Published: November 2024

Abstract
In physics education, understanding error theory is essential for both epistemological and practical reasons, particularly when students encounter experimental uncertainties in laboratory settings. For direct measurements, when random uncertainties were negligible, the experimental error was estimated using instrumental resolution. However, when significant random uncertainties are present, they are often estimated using a half-range formula. For indirect measurements, the traditional approach involves the error propagation theory, which can be challenging for first-year high school students because of its complexity. An alternative approach involves estimating the experimental uncertainty a posteriori using a half-range formula. This method simplifies error estimation and is more accessible to students, potentially enhancing their comprehension and the application of error analysis in experimental physics.
BD-StableNet: a deep stable learning model with an automatic lesion area detection function for predicting malignancy in BI-RADS category 3–4A lesions
Authors: Qu Hui , Chen Guanglei , Li Tong , Zou Mingchen , Liu Jiaxi , Dong Canwei , Tian Ye , Liu Caigang , Cui Xiaoyu 
Published: November 2024

Abstract
The latest developments combining deep learning technology and medical image data have attracted wide attention and provide efficient noninvasive methods for the early diagnosis of breast cancer. The success of this task often depends on a large amount of data annotated by medical experts, which is time-consuming and may not always be feasible in the biomedical field. The lack of interpretability has greatly hindered the application of deep learning in the medical field. Currently, deep stable learning, including causal inference, make deep learning models more predictive and interpretable. In this study, to distinguish malignant tumors in Breast Imaging-Reporting and Data System (BI-RADS) category 3–4A breast lesions, we propose BD-StableNet, a deep stable learning model for the automatic detection of lesion areas. In this retrospective study, we collected 3103 breast ultrasound images (1418 benign and 1685 malignant lesions) from 493 patients (361 benign and 132 malignant lesion patients) for model training and testing. Compared with other mainstream deep learning models, BD-StableNet has better prediction performance (accuracy = 0.952, area under the curve = 0.982, precision = 0.970, recall = 0.941, F1-score = 0.955 and specificity = 0.965). The lesion area prediction and class activation map results both verify that our proposed model is highly interpretable. The results indicate that BD-StableNet significantly enhances diagnostic accuracy and interpretability, offering a promising noninvasive approach for the diagnosis of BI-RADS category 3–4A breast lesions. Clinically, the use of BD-StableNet could reduce unnecessary biopsies, improve diagnostic efficiency, and ultimately enhance patient outcomes by providing more precise and reliable assessments of breast lesions.
Tooth point cloud resampling method based on divergence index and improved euclidean clustering rule
Authors: Qiu Zhixian , Jiang Jingang , Wu Dianhao , Wang Jingchao , Zhou Shan 
Published: November 2024

Abstract

Objective. In endodontic therapy, 3D cone-beam computerized tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data. Approach. Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean clustering rule (IECR) downsamples each point using its weight and joins the two halves using Iterative nearest neighbor to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth. Main results. Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas. Significance. Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.