bluuebunny/crossref_metadata_2025_split huggingface dataset:
A laboratory-based multifunctional near ambient pressure X-ray photoelectron spectroscopy system for electrochemical, catalytic, and cryogenic studies
Authors: Haug Leander , Griesser Christoph , Thurner Christoph W. , Winkler Daniel , Moser Toni , Thaler Marco , Bartl Pit , Rainer Manuel , Portenkirchner Engelbert , Schumacher David , Dierschke Karsten , Köpfle Norbert , Penner Simon , Beyer Martin K. , Loerting Thomas , Kunze-Liebhäuser Julia , Klötzer Bernhard 
Published: June 2023

A versatile multifunctional laboratory-based near ambient pressure x-ray photoelectron spectroscopy (XPS) instrument is presented. The entire device is highly customized regarding geometry, exchangeable manipulators and sample stages for liquid- and solid-state electrochemistry, cryochemistry, and heterogeneous catalysis. It therefore delivers novel and unique access to a variety of experimental approaches toward a broad choice of functional materials and their specific surface processes. The high-temperature (electro)catalysis manipulator is designed for probing solid state/gas phase interactions for heterogeneous catalysts including solid electrolyzer/fuel cell electrocatalysts at pressures up to 15 mbar and temperatures from room temperature to 1000 °C. The liquid electrochemistry manipulator is specifically designed for in situ spectroscopic investigations of polarized solid/liquid interfaces using aqueous electrolytes and the third one for experiments for ice and ice-like materials at cryogenic temperatures to approximately −190 °C. The flexible and modular combination of these setups provides the opportunity to address a broad spectrum of in situ and operando XPS experiments on a laboratory-based system, circumventing the limited accessibility of experiments at synchrotron facilities.
A novel approach to determining the hydrodynamic resistance of droplets in microchannels using active control and grey-box system identification
Authors: Hébert Marie , Huissoon Jan P , Ren Carolyn L 
Published: June 2023

Abstract
Inaccurate prediction of droplet hydrodynamic resistance has a profound impact on droplet chip performance and lengthens the iterative design process. Previous studies measuring droplet resistance use various approaches such as interface comparison to quantify flow rate, and pressure taps; all these methods are classified as passive. Although each study supports well their own findings, the wide variety of conditions such as channel geometry and use of surfactant in combination with the difficulty in quantifying the droplet resistance leads to poor consensus across the different studies. Overall guidelines would be broadly beneficial to the community, but are currently fairly crude, with a rule of thumb of 2 to 5 times resistance increase. The active droplet control platform previously developed enables a novel approach that is herein confirmed as promising. This proof-of-concept study focuses on verifying this approach that employs a system identification method to determine the hydrodynamic resistance of a channel containing a single droplet, from which the droplet resistance is retrieved. This method has the potential to be further applied to a large variety of conditions, and most importantly, to non-Newtonian fluids once key limitations are overcome to improve measurement resolution. The current results qualitatively agree with the literature and demonstrate the promising future for this novel active approach to quantifying droplet resistance.
Investigating capacitive flexible tactile sensor with a wide measuring range using PVDF and conductive sponge
Authors: Dong Yukun , Yao Tao , Lu Haiyang , Wang Zhihua 
Published: June 2023

Abstract
Flexible tactile sensors have important application value in the field of artificial intelligence and internet of things. At present, it is a great challenge for tactile sensors to maintain high sensitivity and stability in a large pressure range. This article proposes a flexible capacitive tactile sensor with high sensitivity and large measuring range, which has a sandwich structure. The purpose of this paper is to determine the relationship between the thickness, surface microstructure, sensitivity, and sensing range of poly(vinylidene fluoride) (PVDF) films. The sensor uses conductive sponge with a microstructure surface and an ultra-thin PVDF film as the electrode and dielectric layer, respectively, and provides a comprehensive study in terms of sensor design, theoretical calculation, computer simulation and applications. The minimum detection limit of the sensor are tested by sound vibration, needle blowing, and human physiological signals. It has been shown that the thicker the PVDF film, the lower the sensitivity and the wider the sensing range. The proposed sensor can accurately identify physiological signals such as respiration, pulse and swallowing, and can perceive the softness, hardness and weight of the object as well as identifying the grasping posture. Moreover, the sensor has the characteristics of short response time, good recoverability and stability.
A spin–orbit configuration interaction study of the low-lying electronic states of the diatomic lithium antimonide cation
Authors: Hu Zhuo-Jin , Wu Jin-Peng , Jiang Rong-Yao , Chen Shan-Jun , Li Song 
Published: June 2023

Abstract
High-level ab initio calculations were performed to determine the structural features, electronic characteristics and transitional properties of LiSb+, which is a hitherto experimentally unknown diatomic cation. We acquired and evaluated the potential energy curves, spectroscopic constants and vibrational energy levels for low-lying Λ-S electronic states and their related Ω states. The spin–orbit coupling effect has a slight impact on these states. Transitional properties, such as transition dipole moments, Einstein coefficients, Franck–Condon factors and vibrational branching ratios, as well as the radiative lifetimes of transitions from excited Ω states to the ground state, have been identified and discussed. We anticipate that these prognostic results will act as guidelines for future research.
Influence of compressive uniaxial strain on the piezoelectric response of wurtzite crystals
Authors: Benbedra Abdesamed , Meskine Said , Boukortt Abdelkader , Hayn Roland , Abbassa Hamza 
Published: June 2023

Abstract
We present a computational study of the crystal structure and electric polarization of strained wurtzite III–V nitrides and II–VI oxides, performed in the context of density functional theory and the Berry phase method. The main goal is to investigate the degree to which the lattice parameters, piezoelectric polarization, and piezoelectric constant can be affected by compressive uniaxial strain along the hexagonal c-axis. We show that imposing such strain enhances the piezoelectric response, with both polarization and piezoelectric coefficient increasing from their equilibrium values. The internal parameter of the wurtzite structure also increases with uniaxial strain and eventually becomes equal to 0.5, resulting in a phase transition into the layered hexagonal structure. Furthermore, we discuss the physical origin behind the enhanced piezoelectricity, showing that the enhancement is caused by a strong increase in the response of the internal parameter to strain.
Harmonic generation in magnetized plasma for electromagnetic wave propagating parallel to external magnetic field
Authors: Dhalia Trishul , Juneja Rohit , Prasad Goswami Laxman , Maity Srimanta , Das Amita 
Published: June 2023

Abstract
Harmonic generation has always been of fundamental interest in the study of the nonlinear nature of any physical system. In the present study, particle-in-cell simulations have been carried out to explore the harmonic generation of electromagnetic (EM) waves in a magnetized plasma. EM wave propagation is chosen to be parallel to the applied external magnetic field. The simulations show the excitation of odd higher harmonics of right circularly polarized waves and left circularly polarized waves when the incident wave is linearly polarized. The harmonic generation is maximum when the incident EM wave frequency matches the electron cyclotron frequency. When the incident EM wave has a circular polarization, no harmonics get excited. A theoretical understanding of these observations has also been provided. The studies thus show that by appropriately tailoring plasma parameters, EM waves of higher frequencies and the desired nature of circular polarization can be generated.
A DFT study of electron–phonon interactions for the
C2CN
and
VNNB
defects in hexagonal boron nitride: investigating the role of the transition dipole direction
Authors: Sharman K , Golami O , Wein S C , Zadeh-Haghighi H , Rocha C G , Kubanek A , Simon C 
Published: June 2023

Abstract
Quantum emitters in two-dimensional hexagonal boron nitride (h-BN) have generated significant interest due to observations of ultra-bright emission made at room temperature. The expectation that solid-state emitters exhibit broad zero-phonon lines at elevated temperatures has been put in question by recent observations of Fourier transform (FT) limited photons emitted from h-BN flakes at room temperature. All decoupled emitters produce photons that are directed in-plane, suggesting that the dipoles are perpendicular to the h-BN plane. Motivated by the promise of an efficient and scalable source of indistinguishable photons that can operate at room temperature, we have developed an approach using density functional theory (DFT) to determine the electron-phonon coupling for defects that have in- and out-of-plane transition dipole moments. Our DFT calculations reveal that the transition dipole for the

C
2

C
N

defect is parallel to the h-BN plane, and for the

V
N

N
B

defect is perpendicular to the plane. We calculate both the phonon density of states and the electron–phonon matrix elements associated with the h-BN defective structures. We find no indication that an out-of-plane transition dipole by itself will result in the low electron–phonon coupling that is expected to produce FT-limited photons at room temperature. Our work provides direction to future DFT software developments and adds to the growing list of calculations relevant to researchers in the field of solid-state quantum information processing.
Prior knowledge-based self-supervised learning for intelligent bearing fault diagnosis with few fault samples
Authors: Wu Ke , Nie Yukang , Wu Jun , Wang Yuanhang 
Published: June 2023

AbstractDeep learning-based bearing fault diagnosis methods have been developed to learn fault knowledge from massive data. Owing to the deficiency of fault samples and the variability of working conditions, these deep learning-based methods are limited in industrial applications. To address this problem, this study proposes a prior knowledge-based self-supervised learning (PKSSL) method for bearing fault diagnosis. In the PKSSL method, prior diagnostic knowledge is extracted by meta-learning from a few samples. Prior diagnostic knowledge is then utilized to guide the self-supervised learning (SSL) process to reduce reliance on training data. Furthermore, a graph convolutional network is introduced to fuse the information obtained by meta-learning and SSL, which makes the model fully utilize the learned information and improves the accuracy of the fault diagnosis. The effectiveness of the proposed method was validated using two datasets. The results demonstrate that compared to other existing approaches, the proposed method exhibits a strong generalization ability to transfer diagnostic knowledge from artificial damage data to real damage data under varied operating conditions.
Automated defect recognition (ADR) for monitoring industrial components using neural networks with phased array ultrasonic images
Authors: Gantala Thulsiram , P L Sudharsan , Balasubramaniam Krishnan 
Published: June 2023

Abstract
In this paper, we propose a framework to automate the process of defect characterizing for industrial structural component health monitoring by implementing automatic defect recognition (ADR) system. The ADR system consists of a convolutional neural network (CNN) and an edge detection algorithm medial axis transform (MAT). The CNN learns the defect feature space from the training dataset to detect and classify the defect. The MAT algorithm is used upon post-validation of the ADR, and the predicted feature’s edges are extracted to size them. The ADR is trained using the simulation-assisted finite element (FE) simulation datasets consisting of side drilled holes (SDH) and crack defects images. The training datasets are generated by introducing virtual array source aperture (VASA), which is a full matrix capture (FMC) scanning strategy by activating the group of elements in an active aperture with predefined focal laws to form a focused beam at a virtual source in the material. The VASA technique uses multiple virtual sources and active aperture positions in a given transducer, which are determined using the Poisson point process. The ultrasound beam is excited in sequence on each virtual source, and the reflected wave is recoded using all the transducers in the array to create FMC A-scans signals. The total focusing method (TFM) technique is a postprocessing algorithm implemented on the FMC signal to generate an image. A large quantity of training datasets is created for each defect by modeling various FE models with varying defect morphology. To create nearly close to experimental images, the experimental noise is introduced in the simulated images. The three separate ADR systems are trained with individual defects class and combined defects. The effectiveness of the trained ADR system is validated by conducting experiments on the plates with laboratory-made SDH and crack defects, the casting components, and weldments with unknown defect types and sizes. The mAP of ADR training is 82%, and the F1-score on testing image classification is 89%. The ADR system could detect and size the smallest defect is 0.219 mm, which is λ

L
/5.
Arresting the surface oxidation kinetics of bilayer 1T′-MoTe2 by sulphur passivation
Authors: Chandran Yadu , Thakur Deepa , Raju Naik B , Balakrishnan Viswanath 
Published: June 2023

Abstract
MoTe2 garnered much attention among 2D materials due to stable polymorphs with distinctive structural and electronic properties. Among the polymorphs, 1T′-MoTe2 in bulk form is type—II Weyl semimetal while, in monolayer form is a quantum spin Hall insulator. Thus, it is suitable for a wide variety of applications. Nevertheless, 1T′-MoTe2 degrades within a few hours when exposed to the atmosphere and causes hindrances in device fabrication. Here the degradation kinetics of CVD-synthesized 1T′-MoTe2 was investigated using Raman spectroscopy, XPS, and microscopic characterizations. The degradation rate of as-grown 1T′-MoTe2 obtained was 9.2 × 10−3 min−1. Further, we prevented the degradation of 1T′-MoTe2 by introducing a thin coating of S that encapsulates the flakes. 1T′-MoTe2 flakes showed stability for several days when covered using sulphur, indicating 25 times enhanced structural stability.
Inverse design of plasmonic nanoantenna using generative adversarial network
Authors: Bao Qiwen , Zhang Dasen , Liu Xianjin , Wang Licheng , Xiao Junjun 
Published: June 2023

Abstract
The local surface plasmon resonance (LSPR) effect has been widely used in various nanophotonic applications. However, because the LSPR effect is highly sensitive to the structure and geometry, it is desirable to efficiently search viable geometries for predefined local field enhancement spectrum. Herein we present a generative adversarial network-based LSPR nanoantenna design scheme. By encoding the antenna structure information into an red-green-blue (RGB) color image, the corresponding nanoantenna structure can be inverse-designed to achieve the required enhancement spectrum of the local field. The proposed scheme can accurately offer the multiple geometry layout for the customized specific spectrum in seconds, which could be beneficial for fast design and fabrication of plasmonic nanoantenna.
Dynamic low-count PET image reconstruction using spatio-temporal primal dual network
Authors: Hu Rui , Cui Jianan , Li Chenxu , Yu Chengjin , Chen Yunmei , Liu Huafeng 
Published: June 2023

AbstractObjective.Dynamic positron emission tomography (PET) imaging, which can provide information on dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited counts received in individual frame, especially in ultra short frames. Recently, the unrolled model-based deep learning methods have shown inspiring results for low-count PET image reconstruction with good interpretability. Nevertheless, the existing model-based deep learning methods mainly focus on the spatial correlations while ignore the temporal domain.Approach.In this paper, inspired by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network (STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability.Main results.The experiments of both simulation data and real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization, spatio-temporal kernel method, LPD and FBPnet.Significance.Experimental results show STPDnet better reconstruction performance in the low count situation, which makes the proposed method particularly suitable in whole-body dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer from high level of noise.
Linearity of minimally superintegrable systems in a static electromagnetic field
Authors: Bertrand S , Nucci M C 
Published: June 2023

Abstract
Fifteen three-dimensional classical minimally superintegrable systems in a static electromagnetic field are shown to possess hidden symmetries leading to their linearization, and consequently the corresponding subsets of maximally superintegrable subcases are also linearizable. These results are strengthening the conjecture that all three-dimensional minimally superintegrable systems are linearizable by means of hidden symmetries, even in the presence of a magnetic field.
Photovoltaic performance of lattice-matched gallium indium arsenide/germanium stannide dual-junction cell
Authors: Yu Tianjing , Cui Min , Li Qianying , Deng Jinxiang , Gao Hongli , Yuan Anjuan 
Published: June 2023

Abstract
Based on the photovoltaic properties and tandem solar cells theory, Gallium Indium Arsenide/Germanium Stannide (GaInAs/GeSn)-based double-junction (DJ) solar cells have been numerically simulated for the first time. In this study, we explore the band gap combination under lattice matching and obtain the content of In/Sn at optimal efficiency, which is expressed as Ga0.84In0.16As/Ge0.93Sn0.07 DJ solar cell (1.20/0.58 eV). Afterward, it is optimized in terms of variation in the doping contents and active layer thickness. To take full advantage of the electron mobility of the material, the optimal ‘inverted doping profile’ concentration N
a(d) is 1.5(5)/5(20) × 1018 cm−3. In addition, the reasonable p(n) layer thickness could be comprised of 0.2–0.8(0.2–1)/0.5–3(1–4) μm of the DJ solar cells with less material consumption. When the p(n) layer thickness is 0.30(0.25)/0.9(1.35) μm, the tandem device can achieve an optimal efficiency of 31.00% with 28.98 mA cm−2 (J
sc), 1.25 V (V
oc) and 85% (FF). This study highlights that GeSn materials have the potential to combine with III–V materials to form low-cost and high-efficiency tandem devices.
Preparation of a beam emission spectroscopy diagnostic based on a Lyman-alpha line to diagnose core and edge-plasma density fluctuation on the HL-2A tokamak
Authors: YU Yi , ZHOU Yixuan , KE Rui , NIE Lin , XIAO Chijie , LI Jiquan , HAO Guangzhou , XU Min , WANG Zhibin , CHEN Jian 
Published: June 2023

Abstract
In this article, the design of a Lyman-alpha-based beam emission spectroscopy (LAB) diagnostic on the HL-2A tokamak has been proposed for the first time. The purpose of this novel diagnostic is to measure density fluctuations of tokamak plasma. The light-collection system of LAB, which consists of the first mirror and two groups of coaxial double-mirror telescopes, can realize a two-segmented viewing field of ρ = 0‒0.2 and ρ = 0.75‒1, which is optimized to measure plasma density fluctuation, not only in the edge transport barrier region but also in the internal transport barrier region, to investigate the underlying physics of turbulence in tokamaks. Spectrometers are developed to separate out the Doppler-shifted target line (122.03 and 122.17 nm) from the background Lyman-alpha line (121.53 nm). Here, 30 Core-LAB channels and 30 Edge-LAB channels are under development on the HL-2A tokamak. It has high radial spatial resolutions of about 2.7 mm and 3.3 mm for the core and edge channels, respectively. Taking the high light intensity of this Lyman-alpha line into account, temporal resolution of 200 kHz can be ensured by broad bandwidth amplifiers. This high spatio-temporal resolution makes LAB a potential keen tool to experimentally investigate tokamak plasma physics.
Evaluation of in vitro immunostimulatory and cytotoxic effects of recombinant survivin protein in combination with doxorubicin and breast cancer antigen-loaded polycaprolactone nanoparticles
Authors: Dinparvar Sahar , Abamor Emrah Sefik , Oztav Sedanur , Inci Tugba Gul , Ihlamur Murat , Baghirova Malahat , Turgut-Balik Dilek , Allahverdiyev Adil M 
Published: June 2023

Abstract
In this study, the immunostimulatory and anticancer activities of the doxorubicin (DOX), MCF-7 and MDA-MB-231 breast cancer antigen-loaded polycaprolactone (PCL) nanoparticles (NPs) in combination with survivin recombinant protein (RP) and an alum adjuvant are evaluated in vitro on J774 macrophage, MCF-7 and MDA-MB-231 breast cancer cell lines. A double-emulsion solvent evaporation method was used for encapsulation of DOX and antigens into PCL NPs. The physicochemical characterization of NPs included size, morphology, zeta potential, release profiles and encapsulation efficiencies, analyzed using scanning electron microscopy, a zeta-sizer and UV–vis spectrometry. The cytotoxic and inhibitory effects of NPs were determined using a methyl thiazolyl tetrazolium assay. Immunostimulatory effects of the NPs were detected by Griess reaction and ELISA tests to determine nitric oxide and cytokine levels, respectively. According to the results, DOX and antigen-loaded PCL NPs ranged between 240 nm and 290 nm. Antigen and drug-loaded NPs appear less toxic over macrophage cells in comparison with non-capsulated free agents. In addition, considerable inhibitory effects of antigen and drug-loaded NPs were observed at non-toxic concentrations, such as 25 and 50 μg ml−1, on human mammary cancer cell lines (p⩽ 0.0001). The amount of nitrite released from macrophages that were treated with antigen and DOX-encapsulated PCL NPs, in combination with alum and survivin RP, after 96 h incubation was significantly higher than the control, especially at 50 and 100 μg ml−1, and triggered macrophages to produce high quantities of IL-4 and IL-12 cytokines in contrast to the control. As a result, DOX and antigen-loaded PCL NPs in combination with survivin and alum adjuvant revealed significant immunostimulatory and inhibiting influence on macrophage and breast cancer cells, respectively. The outcomes revealed that antigen and drug-loaded PCL NPs supplemented with survivin RP and an alum adjuvant created an effective platform for the development of nanotechnology-based immunotherapeutic tools to inhibit breast cancer cells. However, these outputs should be supported by further in vivo studies.