bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design and testing of an (inert) gas cell for in situ polarization modulation infrared reflection absorption spectroscopic (PM-IRRAS) measurements under specific atmospheres
Authors: Clausen Kai Uwe , Suren Ralf , Schlimm Alexander , Tuczek Felix 
Published: November 2024

Infrared reflection absorption spectroscopy (IRRAS) is a powerful surface-sensitive analytical technique to characterize the adsorbed molecules on metal surfaces down to (sub)monolayer coverage. In this paper, a new (inert) gas cell is presented that expands the scope of the commercially available Bruker PMA50 module. The cell is designed as a sample holder to measure thin films of molecules adsorbed on a metal substrate under a specific gaseous atmosphere. The dimensions of the cell are chosen in such a way that it can be transferred into a glovebox via the standard entrance port (Ø150 mm), allowing the investigation of air-sensitive molecules under an inert-gas atmosphere. The cell has two hose connections through which the gas atmosphere can be varied as desired. This also allows for studying the reactivity of the adsorbed structures toward the surrounding gas in situ and in a (potentially) time-dependent fashion. Furthermore, the metal substrate can be irradiated via an exposure window to investigate the influence of light on the adsorbed molecules and/or their reactivity. Using the polarization-modulation (PM-) IRRAS technique along with the described gas cell, an air-sensitive molybdenum(0) tricarbonyl complex adsorbed on an Au(111) surface is investigated. This complex reacts with molecular oxygen to the molybdenum(VI) trioxo analog, and this conversion is accelerated by irradiation with light of 365 and 440 nm.
Consistency between the distributions for the two phases in aeolian sand transport: Impact of sand on wind
Authors: Yang Bin , Liu Yuxin , Qu Jianjun 
Published: November 2024

A field observation was conducted to collect the gust signal at different heights above the bed surface, and the normalized probability distribution function of gust strength was obtained. The result shows that below the height of 800 mm, the closer to the bed, the greater the rise in the left half of the distribution map, indicating that the reduction in gust strength mainly occurs in the low velocity range. The distribution of particle size at bed surface and the normalized gust strength belong to the same distribution. Specifically, the particle size distribution is quantitatively in accordance with the gust distribution at the height of 300 mm, which denotes the thickness of the saltation layer. In accordance with the two characteristic heights (300 and 800 mm), the region near bed surface is divided into three parts, namely, the active zone (within 300 mm), the subactive zone (300–800 mm), and the inert zone (above 800 mm). Accordingly, a point-to-point equivalent momentum exchange theory between the wind and the sand is proposed, which is convenient for predicting the local wind conditions with the information of sand particle size.
From certainty to uncertainty
Authors: Habicht Külli , Prillop Külli , Simmul Carl Eric 
Published: November 2024

Abstract
In this article, we examine diachronically the use of two central and frequent epistemic particles,
vist ‘perhaps, probably’ and kindlasti ‘definitely, certainly’, in written texts in Estonian
from the 16th century to the present day. Currently, these particles convey various degrees of the speaker’s epistemic assessment:
vist expresses uncertainty regarding the truthfulness of the information conveyed, while
kindlasti indicates high certainty. The meaning of vist has diminished in terms of certainty
to become an uncertainty marker. The meaning of the particle kindlasti has stayed the same over time, but a
downtoning function has developed. Thus, we observe a weakening of epistemic functions for both particles over time within the
framework of pragmaticalisation. We highlight a regularity where, during the pragmaticalisation of particles within the domain of
probability, a unidirectional functional-semantic shift occurs in the chain of certainty > assumption >
uncertainty.
Recovery of a coefficient in a diffusion equation from large time data*
Authors: Rundell William 
Published: November 2024

Abstract
This paper considers the determination of a spatially varying coefficient in a parabolic equation from time trace data. There are many uniqueness theorems known for such problems but the reconstruction step is severally ill-posed: essentially the problem comes down to trying to reconstruct an analytic function from values on a strip. However, we look at an even more restricted data where the measurements are not made on the whole time axis but only for large values adding further to the ill-conditioning situation. In addition, we do not assume the initial state is known. Uniqueness is restored by making changes to the boundary condition, in particular, to the impedance parameter, for each of a series of measurements. We show that an undefined implementation of the above paradigm leads to both uniqueness and an effective reconstruction algorithm. Extension is also made to the case of fractional model and to replacing the parabolic equation with a damped wave equation.
Layered nanoarchitectonics for condensed hard matter, soft matter, and living matter
Authors: Ariga Katsuhiko 
Published: November 2024

Abstract
Nanotechnology has elucidated scientific phenomena of various materials at the nano-level. The next step in materials developments is to build up materials, especially condensed matter, based on such nanotechnology-based knowledge. Nanoarchitectonics can be regarded as a post-nanotechnology concept. In nanoarchitectonics, functional material systems are architected from nanounits. Here, this review would like to focus on layered structures in terms of structure formation. The unit structures of layered structures are mostly two-dimensional materials or thin-film materials. They are attractive materials that have attracted much attention in modern condensed matter science. By organizing them into layered structures, we can expect to develop functions based on communication between the layers. Building up layered functional structures by assembling nano-layers of units is a typical approach in nanoarchitectonics. The discussion will be divided into the following categories: hard matter, hybrid, soft matter, and living object. For each target, several recent research examples will be given to illustrate the discussion. This paper will extract what aspects are considered important in the creation of the layered structure of each component. Layering strategies need to be adapted to the characteristics of the components. The type of structural precision and functionality required is highly dependent on the flexibility and mobility of the component. Furthermore, what is needed to develop the nanoarchitectonics of layered structures is discussedas future perspectives.
Emerging new phases in correlated Mott insulator Ca2RuO4
Authors: Cuono Giuseppe , Forte Filomena , Romano Alfonso , Noce Canio 
Published: November 2024

Abstract
The Mott insulator Ca2RuO4 is a paradigmatic example among transition metal oxides, where the interplay of charge, spin, orbital, and lattice degrees of freedom leads to competing quantum phases. In this paper, we focus on and review some key aspects, from the underlying physical framework and its basic properties, to recent theoretical efforts that aim to trigger unconventional quantum ground states, using several external parameters and stimuli. Using first-principle calculations, we demonstrate that Ca2RuO4 shows a spin splitting in the reciprocal space, and identify it as an altermagnetic candidate material. The non relativistic spin-splitting has an orbital selective nature, dictated by the local crystallographic symmetry. Next, we consider two routes that may trigger exotic quantum states. The first one corresponds to transition metal substitution of the 4d
4 Ru with isovalent 3d
3 ions. This substitutional doping may alter the spin–orbital correlations favoring the emergence of negative thermal expansion. The second route explores fledgling states arising in a non-equilibrium steady state under the influence of an applied electric field. We show that the electric field can directly affect the orbital density, eventually leading to strong orbital fluctuations and the suppression of orbital imbalance, which may, in turn, reduce antiferromagnetism. These aspects suggest possible practical applications, as its unique properties may open up possibilities for augmenting existing technologies, surpassing the limitations of conventional materials.
Pressure induced semiconductor-like to metal transition and linear magnetoresistance in Cr2S2.88 single crystal
Authors: Sun Xiaodong , Zhou Xuebo , Sun Hao , Wu Feng , Li Yuanzhe , He Wanli , Ye Pengda , Li Xiang , Luo Jianlin , Jin Meiling , Wu Wei 
Published: November 2024

Abstract
The transition metal chalcogenide Cr2S3−x
has unique properties, such as a lower antiferromagnetic transition temperature, semiconducting behavior, and thermoelectric properties. We focus on the effects of high pressure on the properties of electrical transport and structure in the single crystal Cr2S2.88. It is observed that the resistance drops abruptly by approximately two orders of magnitude and the temperature derivative of the resistance changes from negative to positive after 15.7 GPa. The Cr2S2.88 crystal has undergone transitions from a semiconductor-like phase to a metal I phase and then to another metal II phase. Simultaneously, a structural phase transition after 16.1 GPa is confirmed by synchrotron angle dispersive x-ray diffraction. After the structural phase transition, the negative magnetoresistance becomes positive with increasing pressure and shows a linear relationship in the metal II phase. Electron-type carriers dominate in the semiconductor-like phase, but hole-type carriers dominate after the structural phase transition. Our work provides an example of the effective modulation of semiconductor-like properties by pressure, which is meaningful for the innovation and development of semiconductor technology.
Phase behavior and atomic dynamics in RbxNa1–x: insights from machine learning interatomic potentials based on ab initio molecular dynamics
Authors: Irie A , Koura A , Shimamura K , Shimojo F 
Published: November 2024

Abstract
Liquid alkali metal alloys have garnered significant attention because of their potential applications in coolant systems and batteries, driven by the need for environmental conservation and technological development. However, research on these complex systems is limited, necessitating a deeper understanding to ensure their safe and effective utilization. This study presents a comprehensive investigation of the factors that determine the phase diagram of Rb
x
Na

1
−
x

. By reproducing the experimental results using the thermodynamic integration method and machine learning interatomic potentials based on ab initio molecular dynamics simulations, we uncovered the delicate balance between the energy and entropy contributions that influence the phase stability of these liquid metal alloys. Further analysis of the liquid phase revealed the crucial roles of volume and atomic mass. Additionally, the coordination numbers of the atoms revealed distinct clustering behaviors, where Na atoms tended to avoid proximity to other Na atoms, whereas Rb atoms exhibited a strong tendency to cluster together. Moreover, the diffusion dynamics further illustrated the asymmetry in the behavior of Rb and Na, with Rb showing increased diffusion at higher concentrations and Na exhibiting higher diffusion at lower concentrations. These findings offer significant insights into the phase stability and the dynamic and structural properties of these complex liquid metal alloys.
Research on the sensing properties and vibration reduction performance of self-sensing self-tuning magnetic fluid damper
Authors: Zhang Mingmin , Yang Wenrong 
Published: November 2024

Abstract
The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper (SSMFD) is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper’s natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit’s output induced electromotive force model is created, followed by an expression of the damper’s natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage. Finally, an experimental platform was developed to assess the damper’s self-sensing and self-tuning vibration reduction performance. The experimental results show that the proposed SSMFD performs well, making it a feasible solution for achieving self-sensing and self-tuning vibration reduction.
An adaptive method for predicting bearing remaining useful life across various degradation stages
Authors: Sun Aoyun , Miao Xiaodong , Xu Kun , Jia Chao 
Published: November 2024

Abstract
Bearing degradation is a multi-stage, multi-trend and highly complex process, significant information discrepancies and extreme imbalances exist in degradation data across different stages. These complexities hinder the accuracy of predictive model in predicting the remaining useful life (RUL) throughout all stages of the bearing’s degradation. In this paper, a novel prediction model based on adaptive convolutional neural network (ACNN)-multiple kernel convolutional long short-term memory (MKConvLSTM) is proposed, which utilizes adaptive feature extraction and multi-scale dynamic selection to solve the problem of multi-stage, multi trend and highly complex information in bearing degradation. First, the ACNN is used to perform convolutional feature extraction and adaptive mapping on input samples, effectively distinguishing the degradation stages. Then, the MKConvLSTM generates features at different time scales and dynamically selects these features to capture temporal information during the degradation process, enriching the model’s capability to represent complex information and improving its predictive performance. To validate the effectiveness of the proposed model, experiments were conducted on the PHM2012 datasets and XJTU datasets. The MAE and RMSE of ACNN-MKConvLSTM reaches 0.078 and 0.099 on the first dataset, 0.086 and 0.107 on the second dataset, respectively. Approximately 20% improvement Compared to the baseline model, respectively. Experimental results demonstrate that the model exhibits high accuracy and robustness in bearing RUL prediction, effectively addressing the impact of feature variations across different degradation stages on prediction performance.
A transfer learning model for rolling bearing fault diagnosis based on texture loss strategy and nuclear norm regularization
Authors: Zhou Yuan , Yue Xiaofeng , Liu Zeyuan 
Published: November 2024

Abstract
In recent years, transfer learning (TL) approaches have seen extensive application in diagnosing bearing faults due to their exceptional performance. However, mechanical noise, equipment aging, and wear lead to notable disparities and differences in the multi-level feature distributions across the source and target domain signals. The issue is addressed by proposing a TL model based on a texture loss strategy and nuclear norm regularization method. First, a feature-enhanced network is designed, which significantly improves the ability to capture local details and long-range dependencies by combining a multi-scale feature extraction module with a dilated residual module. Next, a texture loss strategy is proposed to align multi-scale features across domains by minimizing the Gram matrix of signal features. Finally, a nuclear norm regularization method is proposed to perform low-rank approximation on the signal matrix, facilitating the extraction of more robust feature data and mitigating the risk of overfitting. The experimental results demonstrate that the proposed method achieved an average accuracy of 98.58% on the University of Ottawa bearing fault dataset and 98.11% on the Jiangnan University bearing dataset, surpassing eight other algorithms in bearing fault diagnosis.
Development and optimization of metal silicide EUV pellicle for 400W EUV lithography
Authors: Choi Munsu , Park Chulkyun , Hong Juhee 
Published: November 2024

Abstract
In the extreme ultraviolet lithography (EUVL) process, extreme ultraviolet (EUV) pellicles serve as thin, transparent membranes that shield the photomask (reticle) from particle contamination, thereby preserving photomask pattern integrity, reducing chip failure risks, and enhancing production yields. The production of EUV pellicles is highly challenging due to their mechanical fragility at nanometer-scale thicknesses and the need to endure the rigorous conditions of the EUVL environment, which include high temperatures and hydrogen radicals. Consequently, extensive research has been conducted on a variety of materials, such as carbon-based and silicon-based substances, for the development of EUV pellicles. This study explores the feasibility of implementing metal silicide (MeSi
x
) pellicles for high-power EUVL applications. We successfully fabricated MeSi
x
pellicles in two dimensions: a 10 mm × 10 mm sample and a full-size 110 mm × 144 mm pellicle. We then evaluated their optical, mechanical, thermal, and chemical properties, as well as their lifespan. The pellicles demonstrated over 90% transmittance and less than 0.04% reflectance. The films exhibited a deflection of 300 μm under a 2 Pa differential pressure and an ultimate tensile strength exceeding 2 GPa. The thermal emissivity was measured at 0.3. Additionally, the durability of the pellicles was validated through exposure to 20,000 wafers using a 400 W EUV power (offline test: 20 W cm−2). The transmittance variations of the pellicles were evaluated by comparing the measurements obtained before and after exposure to 400 W EUV power.
A unified framework for the analysis of accuracy and stability of a class of approximate Gaussian filters for the Navier–Stokes equations
Authors: Biswas Animikh , Branicki Michał 
Published: November 2024

Abstract
Bayesian state estimation of a dynamical system utilising a stream of noisy measurements is important in many geophysical and engineering applications. In these cases, nonlinearities, high (or infinite) dimensionality of the state space, and sparse observations pose key challenges for deriving efficient and accurate data assimilation (DA) techniques. A number of DA algorithms used commonly in practice, such as the Ensemble Kalman Filter (EnKF) or Ensemble Square Root Kalman Filter (EnSRKF), suffer from serious drawbacks such as catastrophic filter divergence and filter instability. The analysis of stability and accuracy of these DA schemes has thus far focused either on finite-dimensional dynamics, or on complete observations in case of infinite-dimensional dissipative systems. We develop a unified framework for the analysis of several well-known and empirically efficient DA techniques derived from various Gaussian approximations of the Bayesian filtering schemes for geophysical-type dissipative dynamics with quadratic nonlinearities. We establish rigorous results on (time-asymptotic) accuracy and stability of these algorithms with general covariance and observation operators. The accuracy and stability results for EnKF and EnSRKF for dissipative PDEs are, to the best of our knowledge, completely new in this general setting. It turns out that a hitherto unexploited cancellation property involving the ensemble covariance and observation operators and the concept of covariance localization in conjunction with covariance inflation play a pivotal role in the accuracy and stability for EnKF and EnSRKF. Our approach also elucidates the links, via determining functionals, between the approximate-Bayesian and control-theoretic approaches to DA. We consider the ‘model’ dynamics governed by the two-dimensional incompressible Navier–Stokes equations (NSEs) and observations given by noisy measurements of averaged volume elements or spectral/modal observations of the velocity field. In this setup, several continuous-time DA techniques, namely the so-called 3DVar, EnKF and EnSRKF reduce to a stochastically forced NSEs. For the first time, we derive conditions for accuracy and stability of EnKF and EnSRKF. The derived bounds are given for the limit supremum of the expected value of the L
2 norm and of the

H

1

Sobolev norm of the difference between the approximating solution and the actual solution as the time tends to infinity. Moreover, our analysis reveals an interplay between the resolution of the observations (roughly, the ‘richness’ of the observation space) associated with the observation operator underlying the DA algorithms and covariance inflation and localization which are employed in practice for improved filter performance.
On the area rate of perturbed Siegel disks
Authors: Qiao Jianyong , Qu Hongyu 
Published: November 2024

Abstract
In the famous work by Buff and Chéritat constructing quadratic Julia sets with positive area, the control of the shape of perturbed Siegel disks is a key technique. To do it, Buff and Chéritat added a restrictive condition on rotation numbers. In this paper, we show that this restrictive condition is not necessary. As a consequence, more quadratic Julia sets with positive area can be constructed.
Experimental investigation of the electromagnetic acceleration mechanisms in an applied-field magnetoplasmadynamic thruster
Authors: Sun Yuzhe , Wang Yibai , Wang Yimeng , Wu Peng , Zhang Guangchuan , Chen Zhiyuan , Gao Yongcheng , Ren Junxue , Tang Haibin 
Published: November 2024

Abstract
The high specific impulse and efficiency of the applied-field magnetoplasmadynamic thruster (AF-MPDT) make it one of the most promising electric thrusters in deep space exploration. However, the crucial electromagnetic acceleration mechanisms are still not clearly investigated, which limits performance improvement. The electromagnetic acceleration mechanism is closely related to the current path and magnetic field distribution in the plume. Experiments are conducted using a water-cooled Hall probe in the steady-state AF-MPDT plume. The radial, azimuthal and axial magnetic fields are measured, then the current density and Lorentz force density are calculated. The results show that the outflow current accounts for 63% to 82% of the thruster discharge current depending on the strength of the applied magnetic field. Moreover, the outflow current can extend the range of electromagnetic force action, which in turn increases the effect of electromagnetic acceleration. The radial Lorentz force is numerically dominant, and the combined effect of the radial Lorentz force and axial Lorentz force is to compress the plasma toward the axis. In electromagnetic acceleration, Self-field contributions are less than 5%, while E× B acceleration constitutes −12.2%–21.2%, and diamagnetic acceleration dominates at approximately 76.7%–90.5%. Finally, a method for evaluating the rotational velocity was presented based on the MHD equations. The centrifugal force was then calculated by combining this with the plasma density. At the thruster outlet, the centrifugal force is significant and cannot be ignored in comparison to the radial Lorentz force.