bluuebunny/crossref_metadata_2025_split huggingface dataset:
First-principles molten salt phase diagrams through thermodynamic integration
Authors: Shah Tanooj , Fazel Kamron , Lian Jie , Huang Liping , Shi Yunfeng , Sundararaman Ravishankar 
Published: September 2023

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. We show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid–liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange–correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid–liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.
Meta-generalized gradient approximations in time dependent generalized Kohn–Sham theory: Importance of the current density correction
Authors: Richter Rian , Aschebrock Thilo , Schelter Ingo , Kümmel Stephan 
Published: September 2023

We revisit the use of Meta-Generalized Gradient Approximations (mGGAs) in time-dependent density functional theory, reviewing conceptual questions and solving the generalized Kohn–Sham equations by real-time propagation. After discussing the technical aspects of using mGGAs in combination with pseudopotentials and comparing real-space and basis set results, we focus on investigating the importance of the current-density based gauge invariance correction. For the two modern mGGAs that we investigate in this work, TASK and r2SCAN, we observe that for some systems, the current density correction leads to negligible changes, but for others, it changes excitation energies by up to 40% and more than 0.8 eV. In the cases that we study, the agreement with the reference data is improved by the current density correction.
Black hole horizons from the double copy
Authors: Chawla Samarth , Keeler Cynthia 
Published: September 2023

Abstract
We describe a procedure for locating black hole horizons in ‘Kerr–Schild’ spacetimes in the double copy paradigm. Using only single- and zeroth-copy data on flat spacetime, our procedure predicts the existence of trapped surfaces in the double-copy gravitational solution. We show explicitly how this procedure locates the horizon of the Schwarzschild black hole and the general Myers–Perry black hole.
The effects of high temperature thermal treatments on β-Ga2O3 films grown on c-sapphire by low-pressure CVD
Authors: Wu Songhao , Liu Zichun , Yang Han , Wang Yeliang 
Published: September 2023

Abstract
As a simple and effective method for improving the crystalline quality of epitaxial Ga2O3 film, post-thermal treatment has been identified as a competitive process involving crystal reconstruction accompanied by defect formation. In this study, β-Ga2O3 films grown on a c-sapphire substrate using low-pressure chemical vapor deposition were subjected to thermal treatment at 1000 °C in air for various duration to investigate the effects of treatment time on the films. The full width at half maximum (FWHM) of x-ray rocking curves initially decreased from 1.62° to 0.98° with increasing treatment time up to 5 h, indicating improved crystallinity. This improvement is likely a result of the reduced angle between Ga2O3 grains and the reconstructed Ga2O3 lattice, oriented towards the (−201) plane due to the thermal treatment, as observed in the transmission electron microscope and electron back-scattering diffraction results. However, under 7 h of treatment, the crystallinity of Ga2O3 degraded, as evidenced by an increased FWHM, as well as by x-ray photoelectron spectroscopy, photoluminescence, and time-of-flight secondary ion mass spectrometry results. This degradation can be attributed to the presence of massive oxygen vacancies and the substitutional incorporation of nitrogen into oxygen sites (NO), resulting in defects.
Thermal conductivities of ultra-lightweight
C12
-based type-VII clathrates: an ab initio and comparative study
Authors: Zheng Guohui 
Published: September 2023

Abstract
Caged carbon clathrates may have profound applications in thermoelectrics due to their inherent host–guest relations. Using ab initio techniques, we investigate the electronic and phononic properties of type-VII

C

12

clathrate, which is a prototype of the widely studied ternary X–B–C clathrate, and tune its properties using lithium atom filling and boron atom substitution. Upon the introduction of pairwise B substition and a Li filler, the half-filled and fully filled structures retain their semiconducting characteristics due to balanced electron counts; however, their indirect band gaps are reduced. By comparing the phonon and thermal properties of filled and empty structures, we show that B substitution and Li fillers lift the phonon degeneracies, increase the three-phonon scattering phase spaces and eventually cause tenfold reductions in the relaxation time of phonons. The Li filler-induced rattling of phonon modes and resonant scattering channels, which are validated by the peak features of phonon density of states and scattering rates, efficiently reduce the room temperature thermal conductivity for fully filled structures. Our findings indicate the efficiency of heteroatomic substitution and fillers to engineer the phonon and thermal properties of carbon-based clathrates.
Optimization of convolutional neural networks for background suppression in the PandaX-III experiment
Authors: Xia Shangning , Huang Suizhi , Xu Kexin , Li Tao , Chen Xun , Han Ke , Wang Shaobo 
Published: September 2023

Abstract
The tracks recorded by a gaseous detector provide a possibility for charged particle identification. For searching the neutrinoless double beta decay events of 136Xe in the PandaX-III experiment, we optimized the convolutional neural network based on the Monte Carlo simulation data to improve the signal-background discrimination power. EfficientNet is chosen as the baseline model and the optimization is performed by tuning the hyperparameters. In particular, the maximum discrimination power is achieved by optimizing the channel number of the top convolutional layer. In comparison with our previous work, the significance of discrimination has been improved by ∼70%.
Geometric and electronic structures of medium-sized boron clusters doped with plutonium
Authors: Zuo Jingning , Zhang Lili , Chen Bole , He Kaihua , Dai Wei , Ding Kewei , Lu Cheng 
Published: September 2023

Abstract
Doping metal heteroatoms is an effective strategy to regulate the geometric and electronic structure of boron based nanoclusters. However, the exploration of the ground state structures of metal-boron-based nanoclusters is still a challenge duo to the complexity of the bonding interactions between heterogeneous atoms and boron cluster and the number of isomers on the potential energy surface increases exponentially with cluster size. Here, we use the CALYPSO cluster structural search method in combination with density functional theory calculations to study the geometries and electronic properties of anionic boron clusters doped with plutonium (PuB

n

−

, n = 10–20). Our results show that the medium-sized PuB

14

−

cluster exhibits excellent stability with highest occupied molecular orbital–lowest unoccupied molecular orbital energy gap of 2.30 eV. The remarkable stability of the anionic PuB

14

−

cluster is due to the robust interactions between the Pu metal and the B14 skeleton, along with the strong covalent interactions between the B atoms. These findings enrich the geometric structure database of metal doped clusters and provide valuable insights for the future synthesis of boron based nanomaterials.
Multi-head spatio-temporal attention based parallel GRU architecture: a novel multi-sensor fusion method for mechanical fault diagnosis
Authors: Li Yaozong , Luo Xiong , Xie Yuhao , Zhao Wenbing 
Published: September 2023

Abstract
The sensor signals with multiple measuring points and data types not only bring sufficient information, but also put forward more stringent requirements for multi-sensor fusion efficiency and fault feature extraction capability. The redundancy and conflicts in the information of multi-sensor signals often hinder the accurate extraction of crucial fault features. To address this problem, our study proposes an intelligent mechanical fault diagnosis method, which is based on a multi-head spatio-temporal attention mechanism and parallel gated recurrent units (GRUs) architecture. This method utilizes multiple attention heads to model the correlation information in spatial and temporal dimensions, and employs a parallel GRU network for targeted feature extraction. Finally, it combines local features from different attention heads to achieve flexible scheduling of various spatio-temporal attention modes. This novel application and fusion approach of multi-head attention enables accurate identification of the spatio-temporal value differences in the collected multi-sensor signals from multiple perspectives. Experimental results on multiple mechanical fault datasets show that the proposed method performs well in multi-sensor signals based mechanical fault diagnosis tasks and can maintain effectiveness under small samples and imbalanced data conditions.
A hybrid framework for robust dynamic 3D point clouds removal
Authors: Zhu Hongwei , Zhang Guobao , Liang Quncong , Ye Zhiqi 
Published: September 2023

Abstract
When robot creates a map, dynamic objects can change the space and render the map unusable for navigation. Additionally, the vertical resolution of a VLP-16 LiDAR may be insufficient, making dynamic point removal challenging. To address these challenges, we propose a novel method for dynamic point detection and removal consisting of four components. Firstly, we introduce a multi-resolution heightmap to enhance the efficiency and precision of dynamic point recognition by segmenting ground points. Secondly, we address the issue of limited vertical resolution by fusing multiple scans to simulate additional scan lines and leveraging a multi-resolution range image for precise dynamic point elimination. Thirdly, we apply clustering and principal component analysis-based techniques to compute eigenvectors, facilitating the correction of misclassified static points. Lastly, we propose the utilization of a three-dimensional bounding box strategy to reinforce the monitoring of small static clusters with elevated probabilities of misclassification. These four components complement each other and are executed sequentially. We evaluated our method for both dynamic point removal and ground segmentation on the KITTI dataset and real-world environments. The results demonstrate that our method outperforms baseline methods and generates clean maps.
Enabling real-time detection of photocatalytic reactions by a re-useable micro-reactor
Authors: Hochfilzer Degenhart , Aletsee Clara , Krempl Kevin , Pedersen Thomas , Krabbe Alexander , Tschurl Martin , Hansen Ole , Vesborg Peter C K , Kibsgaard Jakob , Heiz Ulrich , Chorkendorff Ib 
Published: September 2023

Abstract
We present a highly sensitive and versatile Si-based μ-reactor for photocatalytic experiments. The μ-reactor can be operated in front side (through a borosilicate window) as well as backside (through the catalyst support) illumination. The μ-reactor is sealed by compressing the Si-based microchip, a parafilm gasket and the supported catalyst by four screws. This design allows for fast and reliable assembly of the μ-reactor and the microchip is re-usable for several independent experiments, making the presented μ-reactor ideal for catalyst screening studies. We analyze volatile reactants on-line by connecting the μ-reactor to a mass-spectrometer. The limiting capillary of the μ-reactor allows for nearly 100% collection efficiency (i.e. every produced molecule will be collected) by limiting the gas flow to the mass-spectrometer—enabling highly sensitive measurements and an absolute calibration. Finally, the small height of the reactor volume results in very fast response times, which make the reactor suitable for studies of transient activity phenomena—including catalyst activation and deactivation. We demonstrate these capabilities of the μ-reactor by performing CO oxidation experiments on a TiO2 thin film with subsequent calibration of the chip. Based on a mathematical model of the μ-reactor we additionally present a method for determining the residence time, the reaction volume and the working distance.
Singular component decomposition and its application in rolling bearing fault diagnosis
Authors: Yang Miaorui , Xu Yonggang , Zhang Kun , Zhang Xiangfeng 
Published: September 2023

Abstract
In the rolling bearing fault diagnosis based on vibration signal, the feature information always exists in the specific sideband of the signal. Singular value, as a mathematical quantity can represent the characteristic information of data, provides the theoretical support for extracting fault information from signals. Based on this, this paper proposes a new method called Singular Component Decomposition (SCD). The signal is divided into components by the signal spectrum trend. Through linear transformation of Hankel matrix constructed by each component, the corresponding singular values are calculated. Combined with the amplitude filtering characteristic and the time domain negative entropy index, the effective singular values are screened out. The reconstructed components retain complete fault information for fault diagnosis of rolling bearings. The simulation and experimental results show that the SCD can extract the effective information to the maximum extent to realize the fault diagnosis of the signal.
Deep transfer learning rolling bearing fault diagnosis method based on convolutional neural network feature fusion
Authors: Yu Di , Fu Haiyue , Song Yanchen , Xie Wenjian , Xie Zhijie 
Published: September 2023

Abstract
Current deep-learning methods are often based on significantly large quantities of labeled fault data for supervised training. In practice, it is difficult to obtain samples of rolling bearing failures. In this paper, a transfer learning-based feature fusion convolutional neural network approach for bearing fault diagnosis is proposed. Specifically, the raw vibration signal features and the corresponding time-frequency image features of the input data are extracted by a one-dimensional convolutional neural network and a pre-trained ConvNeXt, respectively, and connected by a feature fusion strategy. Then, the fine-tuning method based on transfer learning can effectively reduce the reliance on labeled samples in the target domain. A wide convolution kernel is introduced in the time-domain signal feature extraction to increase the receptive field, which is combined with the channel attention mechanism to further optimize the feature quality. Finally, two common bearing datasets are utilized for fault diagnosis experiments. The experimental results show that the proposed model achieves an average accuracy of more than 98.63% in both cross-working conditions and cross-device diagnosis tasks. Meanwhile, anti-noise experiments and ablation experiments further validate the accuracy and robustness of the proposed method.
Enhancement and quenching of plasmon-enhanced spectroscopy of single molecule confined in metallic nanoparticle dimers
Authors: Pei Huan , Zhao Jiaxin , Peng Weifeng , Dai Qiyuan , Wei Yong 
Published: September 2023

Abstract
We present a theoretical analysis of plasmon-enhanced fluorescence (PEF) and Raman scattering (PERS) spectroscopy of a single molecule confined in the laser-irradiated metallic nanoparticles (NPs) dimer, focusing on the origin of the spectral enhancement and quenching effects. The theoretical method of D-parameters has been used to calculate the dimer distance-dependent nonlocal dielectric effect in Ag and Au NPs. Meanwhile, other damping rates and electric field enhancements are quantitatively computed by finite element method. Moreover, PEF and PERS spectra of rhodamine 6G are obtained within the density-functional theory. Our calculated results show that the PERS mainly depend on the excitation and emission field enhancements, and thus it occurs at the narrower dimer gap due to the stronger localized plasmon coupling. The PEF is related to fluorescence rate caused by the competition between excitation electric field and quantum efficiency, and the increase of former may enhance the fluorescence intensity while the lower latter lead to reduce the intensity as decreasing the dimer distance. The contribution of nonlocal dielectric effect can significantly reduce the quantum efficiency at smaller distance so that it overcomes the excitation field enhancement, leading to the fluorescence quenching for Au NPs dimer. Furthermore, by optimizing the dimer distance and NPs size, the maximum PERS and PEF cross sections reach 10−14 and 10−15 under 2.45 eV laser excitation for Ag NPs dimer, and 10−18 for Au NPs. Our study finely explains the experiment results showed either fluorescence enhancement or quenching with the change of molecule-NPs distance, and better guidance for optimizing the experiments.
Black silicon spacing effect on bactericidal efficacy against gram-positive bacteria
Authors: Kayes Md Imrul , Zarei Mehdi , Feng Fanbo , Leu Paul W 
Published: September 2023

Abstract
The morphology of regular and uniform arrays of black silicon structures was evaluated for bactericidal efficacy against gram-positive, non-motile Staphylococcus
epidermidis (S.
epidermidis). In this study, uniform and regular arrays of black silicon structures were fabricated using nanosphere lithography and deep reactive ion etching. The effects of nanomorphology on bacterial killing were systematically evaluated using silicon nanostructures with pitches ranging from 300 to 1400 nm pitch on spherical cocci approximately 500 to 1000 nm in diameter. Our results show that nanostructure morphology factors such as height and roughness do not directly determine bactericidal efficacy. Instead, the spacing between nanostructures plays a crucial role in determining how bacteria are stretched and lysed. Nanostructures with smaller pitches are more effective at killing bacteria, and an 82 ± 3% enhancement in bactericidal efficacy was observed for 300 nm pitch nanoneedles surface compared to the flat control substrates.
Interplay of spin–orbit and exchange interaction in a ferromagnet/heavy-metal hybrid system: Ni on W(110)
Authors: J Grenz Pascal , Krüger Peter , Donath Markus 
Published: September 2023

Abstract
In a combined experimental and theoretical study, we investigate the interplay of spin–orbit interaction (SOI) and exchange interaction (XI) in the electronic structure of ultrathin Ni films on W(110). Using spin- and angle-resolved inverse photoemission, we observe that the size of the spin splitting of Ni-related exchange-split states differs for opposite magnetization directions. A quenched spin splitting for one of the magnetization directions reveals a contribution of SOI on an equal footing with XI. Using density-functional theory calculations, we explore the underlying mechanisms responsible for the experimentally observed coupling of SOI and XI. We find that a hybridization between adsorbate and substrate states, along with a high probability density of the respective states at the heavy W nuclei, cause the strong influence of SOI on the Ni-related exchange-split states.
Real-space renormalization-group treatment of quadratic chains
Authors: Citrin D S 
Published: September 2023

Abstract
We have recently proposed a one-dimensional nonperiodic chain with lattice positions at 02
d, 12
d, 22
d, ... with length d a constant. The spectrum is singular-continuous, and for weak potential, the states are all extended apart from a trivial set of localized states. In this study, we obtain the exact extended-state spectrum of the quadratic chain in a nearest-neighbor tight-binding model where the quadratic modulation is in the onsite matrix elements. Then, a real-space renormalization-group method (RSRG) is used by decimation to reduce the transfer matrix for the chain into self-similar matrix products. The RSRG decimation scheme is used here to organize the calculation and facilitate numerical computation. The extended-state spectrum appears as minibands broken by numerous gaps. Previous work on quadratic chains shows that the structure factor is singular-continuous and given by a dense set of states with wavevectors with scaling exponent γ(k) = 2 as in periodic and quasi-periodic chains. The origin of extended states in this nonperiodic lattice appears to arise from a type of mechanism not yet identified in deterministic nonperiodic lattices, and is based on a hidden symmetry giving rise to an energy-dependent translational invariance of the transfer matrix.
A wide measurement range plasmonic refractive index sensor based on side-polished photonic crystal fiber
Authors: Zhang Sa , Wu Biao , Gao Zhigang , Chen Hailiang 
Published: September 2023

Abstract
A wide measurement range plasmonic refractive index (RI) sensor based on side-polished photonic crystal fiber (PCF) was proposed and studied in this paper. A composite film containing an Au layer and a TiO2 layer was coated on the polished surface of PCF to excite and modulate the surface plasmon resonance (SPR) effect. The sensing characteristics were analyzed using the finite element method (FEM). Numerical results showed that the sensing performances were significantly improved through introducing the unique optoelectronic material of TiO2 film. The designed RI sensor showed an ultra-wide measurement range of 1.20–1.38 with a highest sensitivity of 14600 nm RIU−1. The best resolution (R) and figure of merit (FOM) reached 6.85 × 10−6 RIU and 224.62 RIU−1, respectively. The designed side-polished PCF which is coated with an optimized TiO2/Au composite membranes has a promising performance in the field of RI sensing requiring high sensitivity and wide range monitoring.
On QSPR study of energies and thermodynamic aspects of anti-malaria medicines
Authors: Huang Lei , Mahboob Abid , Siddiqui Muhammad Kamran , Imran Muhammad , Waheed Rasheed Muhammad , Waqas Muhammad 
Published: September 2023

Abstract
Malaria is a serious infection that is transmitted by mosquitoes. It is transmitted when a person is bitten by an infected Anopheles mosquito. It effects the red blood cells and also the spleen and liver size increase as the infection deepens. Anemia or jaundice could also results from malaria. In extreme circumstances, it can target the brain and cause neurological issues. 3.3 billion peoples, or over half of the world’s population, are in danger from malaria. The frequency, signs, and causes of malaria, as well as the possible treatments, are all carefully discussed in this article. Nine medications that are used to treat malaria are employed in the quantitative structure-property relationship (QSPR) analysis. For the molecular structures of the drugs under consideration in this study, which are shown as molecular graphs, five energies are determined. There is also a statistical modeling for physio-chemical attributes with energies.